Masaryk University Faculty of Economics and Administration Department of Finance and Institute for Financial Market European Financial Systems 2017 Proceedings of the 14th International Scientific Conference Part 1 June 26-27, 2017 Brno, Czech Republic Suggested citation: AUTHOR, A. Title of the paper. In: European Financial Systems 2017. Proceedings of the 14th International Scientific Conference, Brno: Masaryk University, 2017, part 1, pp. xxxx. ISBN 978-80-210-8609-8, ISBN 978-80-210-8610-4 (online : pdf). Editors: Josef Nešleha Tomáš Plíhal Karel Urbanovský © 2017 Masarykova univerzita ISBN 978-80-210-8609-8 ISBN 978-80-210-8610-4 (online : pdf) All published papers have been reviewed before publishing. The proceedings have not been amended or proofread. Editors are not responsible for the language used in the papers. Program Committee of the Conference Chair: Ing. Petr Valouch, Ph.D. (Masaryk University) Members: Prof. Dr. Ing. Dana Dluhošová (VŠB – Technical University of Ostrava) Prof. Ing. Eva Horvátová, CSc. (University of Economics in Bratislava) Prof. Ing. Juraj Němec, CSc. (Matej Bel University) Prof. Ing. Danuše Nerudová, Ph.D. (Mendel University Brno) Prof. Ing. Erika Pastoráková, PhD. (University of Economics in Bratislava) Prof. Dr. Svend Reuse (FOM Hochschule für Oekonomie und Management, Republic of Germany) Assoc. Prof. Ing. Petr Dvořák, Ph.D. (University of Economics, Prague) Assoc. Prof. Mag. Dr. Peter R. Haiss, MBA (Vienna University of Economics and Business) Assoc. Prof. Dr. Peter Krištofík (Matej Bel University, Slovak Republic) Assoc. Prof. Antonin Rusek, Ph.D. (Susquehanna University, USA) Assoc. Prof. Ing. Jaroslav Sedláček, CSc. (Masaryk University) Assoc. Prof. Dr. Jitka Veselá (University of Economics, Prague) Assoc. Prof. Ing. Eva Vávrová, PhD. (Mendel University Brno) Assoc. Prof. Dr. Jana Vodáková (University of Defense Brno) Dr. Piotr Tworek (University of Economics in Katowice) Organizing Committee of the Conference Chair: Ing. Jan Krajíček, Ph.D. Members: Ms. Bohuslava Doláková Ms. Hana Florianová Mr. Josef Nešleha Mr. Tomáš Plíhal Ms. Martina Sponerová Mr. Karel Urbanovský Conference website: www.efs.econ.muni.cz PREFACE Dear readers, It is my pleasure to introduce you a collection of papers from the 14th annual international scientific conference The European Financial Systems 2017 organized annually by Department of Finance of the Faculty of Economics and Administration, Masaryk University in Brno, Czech Republic. This year's conference was focused especially on the current issues related to accounting, banking sector, insurance, new regulations of financial markets, different tax systems, corporate finance, public finance, financing of non-profit organizations and financial literacy. Because the collection of papers presents the latest scientific knowledge in this area, I believe you will get a number of new insights usable both for your scientific, and educational or practical activities. I would also like to express my conviction that we meet each other in occasion of the 15th year of this conference held in 2018. I wish you pleasant reading Petr Valouch Chairman of the Program Committee CONTENTS Katarína Belanová THE IMPACT OF FINANCIAL MARKET IMPERFECTIONS ON THE INVESTMENT – CASH FLOW SENSITIVITY IN SLOVAK COMPANIES ............................................................11 Karina Benetti THE DEVELOPMENT OF GROSS PREMIUMS WRITTEN IN LIFE INSURANCE IN THE CZECH REPUBLIC............................................................................................................................15 Miloš Bikár, Miroslav Kmeťko, Katarína Vavrová, Peter Badura VOLATILITY AND CORRELATIONS IN STOCK MARKETS: THE CASE OF US S&P 500, JAPAN NIKKEI 225 AND DAX INDICES ....................................................................................23 Mária Bohdalová, Michal Greguš LINKAGES BETWEEN BREXIT AND EUROPEAN EQUITY MARKETS EVIDENCE FROM QUANTILE REGRESSION APPROACH.......................................................................................33 Mária Bohdalová, Natália Klempaiová BANKRUPTCY MODEL IN05 AND PRIVATE SLOVAK CIVIL ENGINEERING COMPANIES......................................................................................................................................41 Roman Brauner, Sylvia Plottová FACTORS THAT AFFECT THE MARKET PRICES OF FLAT RENTING..............................50 Zuzana Brokešová, Erika Pastoráková, Tomáš Ondruška, Miroslav Szabo THE EFFECT OF PREMIUM FRAMING ON LIFE INSURANCE DEMAND..........................60 Dana Dluhošová, Barbora Ptáčková, Dagmar Richtarová APPLICATION OF SELECTED FINANCIAL PERFORMANCE METHODS TO CHOSEN INDUSTRY IN THE CZECH REPUBLIC.......................................................................................67 Bohuslava Doláková CAUSES OF THE FORECLOSURE CRISIS – IRRATIONAL OR RATIONAL DECISIONS?76 Małgorzata Doman, Ryszard Doman THE DYNAMICS OF LINKAGES BETWEEN EUROPEAN CURRENCIES: HOW DOES IT CHANGE ACCORDING TO THE TIME OF DAY?.......................................................................82 Tomáš Dráb CLOSET INDEXING IN CZECHIA: AN EXTENDED ANALYSIS..............................................91 Jan Drbola, Miroslav Krč IMPLEMENTATION OF COST MODEL FOR EFFECTIVE PLANNING OF FOREIGN MILITARY OPERATIONS ........................................................................................................... 101 Justyna Dyduch, Katarzyna Stabryła-Chudzio ALLOCATION OF ENVIRONMENTAL TAXES IN THE CONTEXT OF GENERAL GOVERNMENT EXPENDITURES ON ENVIRONMENTAL PROTECTION IN THE EUROPEAN UNION MEMBER STATES................................................................................... 109 Michal Fabuš, Viktória Čejková REGULATION OF INSURANCE MARKET IN SLOVAK REPUBLIC................................... 118 Maria Forlicz, Tomasz Rólczyński TO GAMBLE OR NOT TO GAMBLE – COMPARISON OF DECISIONS MADE UNDER RISK AND UNDER UNCERTAINTY.......................................................................................... 127 Katarína Gašová, Katarína Repková Štofková COMPARISON OF THE TAX BURDEN ON NATURAL PERSONS IN THE SLOVAK REPUBLIC AND SPAIN................................................................................................................ 135 Beata Gavurova, Eva Huculova COMPARISON OF SELECTED ASPECTS OF FINANCIAL LITERACY AND THEIR DIFFERENCES IN THE CONDITIONS OF UNIVERSITY EDUCATION IN SLOVAKIA.. 143 Martin Geško TOBACCO TAX AND TOBACCO CONSUMPTION IN SLOVAKIA...................................... 152 Ján Gogola, Lucie Kopecká MULTIPLE STATE MODELS FOR CRITICAL ILLNESS POLICY........................................ 159 Grabińska Barbara, Grabiński Konrad IMPACT OF R&D INVESTMENTS ON EARNINGS PREDICTABILITY............................. 165 Ewa Gubernat FINANCIAL RESOURCES OF LOCAL GOVERNMENT UNITS IN RESPECT OF THE FINANCIAL PERSPECTIVE OF THE EUROPEAN UNION................................................... 174 Dagmar Halabrinová, Karel Brychta TAXONOMY OF EU MEMBER STATES FROM THE VIEW OF VAT IMPOSED ON IMMOVABLE PROPERTY (YEAR 2017)................................................................................ 179 Eva Hamplová, Jaroslav Kovárník THE IMPACT OF E-SALES ON BIRTH AND DEATH RATE OF BUSINESSES IN THE CZECH REPUBLIC......................................................................................................................... 187 Martin Hodula, Stanislav Poloucek LOAN-TO-DEPOSIT RATIO AND FINANCIAL STABILITY: MACROPRUDENTIAL POLICY PERSPECTIVE................................................................................................................ 195 Martin Hodula, Martin Machacek and Ales Melecky MACROECONOMIC DETERMINANTS OF SHADOW BANKING: EVIDENCE FROM SPAIN............................................................................................................................................... 204 Sabina Hodzic, Jana Kockovicova A COMPARATIVE ANALYSIS OF SHADOW ECONOMY IN CROATIA AND SLOVAKIA ........................................................................................................................................................... 213 Günter Hofbauer, Monika Klimontowicz, Aleksandra Nocoń THE CHANGES IN CAPITAL STRUCTURE OF SELECTED BANKING MARKETS AS A RESULT OF NEW REGULATIONS ............................................................................................ 222 Irena Honková TWO VARIABLES AFFECTING THE ECONOMIC VALUE ADDED (EVA)....................... 230 Jan Horvat, Eva Horvatova THE STRUCTURE OF BANKS' ASSETS IN TERMS OF PORTFOLIO THEORY AND BANK CAPITAL REGULATION................................................................................................. 236 Jarmila Horváthová, Martina Mokrišová ANALYSIS OF THE IMPACT OF CAPITAL STRUCTURE ON BUSINESS PERFORMANCE ........................................................................................................................................................... 243 Eva Horvátová TECHNICAL EFFICIENCY OF BANKS SELECTED COUNTRIES OF EASTERN EUROPE ........................................................................................................................................................... 251 Eliška Hrabalová, Eva Vávrová, Lenka Přečková ASSESSING THE IMPACT OF THE FINANCIAL CRISIS ON GLOBAL INSURANCE REGULATION................................................................................................................................ 258 Juraj Hruška IMPACT OF HIGH FREQUENCY TRADING ON VOLATILITY IN SHORT RUN AND LONG RUN.................................................................................................................................................. 266 Jana Hvozdenska THE APPLICATION OF SOVEREIGN BOND SPREADS AND THE DEVELOPMENT OF THE STOCK MARKET ON GDP PREDICTION: THE CASE OF VISEGRAD GROUP...... 274 Monika Kaczała EMPIRICAL ANALYSIS OF FARMERS’ WINTERKILL RISK PERCEPTION ................... 281 František Kalouda NEGATIVE INTEREST RATES – CONSEQUENCE STAYING IN ERROR? (EMPIRICAL EVIDENCE)..................................................................................................................................... 290 Özcan Karahan, Olcay Çolak FINANCIAL CAPITAL INFLOWS, CURRENT ACCOUNT DEFICIT AND ECONOMIC GROWTH IN TURKEY................................................................................................................. 300 Özcan Karahan, Metehan Yılgör THE CAUSAL RELATIONSHIP BETWEEN INFLATION AND INTEREST RATE IN TURKEY.......................................................................................................................................... 309 Mariusz Karwowski THE ROLE OF ACCOUNTING IN REGARD TO INFORMATION CONCERNING THE CONVERGENCE OF AIRLINE BUSINESS MODELS............................................................... 317 Magdalena Kludacz-Alessandri IMPACT OF COST CALCULATION QUALITY ON HOSPITAL PERFORMANCE............ 326 Kristína Kočišová MARKET CONCENTRATION AND STABILITY IN EUROPEAN BANKING .................... 333 Irina V. Kolupaieva THE DEVELOPMENT OF INSTRUMENT IMPACT SCENARIOS WITH RESPECT TO THE REGULATORY POLICY MODEL................................................................................................ 342 Tomasz Kopyściański,Tomasz Rólczyński, Marzena Franków CORRELATION ANALYSIS BETWEEN MACROECONOMIC INDICATORS AND LEVEL OF RATINGS IN EU COUNTRIES .............................................................................................. 350 Jaroslav Korečko, Ján Vravec SELECTED ASPECTS OF FINANCIAL LITERACY OF SENIORS IN SLOVAKIA.............. 357 Dmitrii Kornilov, Nadezhda Yashina, Nataliya Pronchatova-Rubtsova, Oksana Kashina RESEARCH OF PUBLIC FINANCING EFFICIENCY OF SOCIAL WELL-BEING IN THE RUSSIAN FEDERATIONS’ REGIONS........................................................................................ 366 Anna Korzeniowska, Wojciech Misterek THE IMPACT OF BARRIERS IN THE ACCESS TO FINANCIAL PRODUCTS AND SERVICES ON THE FINANCIAL EXCLUSION OF THE GENERATION 50+ IN POLAND ........................................................................................................................................................... 375 Jaroslav Kovárník, Eva Hamplová THE BRIEF ANALYSIS OF THE FOREIGN TRADE OF THE CZECH REPUBLIC IN INTERNATIONAL COMPARISON............................................................................................. 384 Patrycja Kowalczyk-Rólczyńska, Tomasz Rólczyński DETERMINANTS FOR THE DEVELOPMENT OF SUPPLEMENTARY PENSION SCHEMES........................................................................................................................................ 393 Jan Krajíček NEW TRENDS IN BANKING SECTOR...................................................................................... 402 Miroslav Krč, Vladimír Golik, Aleš Olejníček, Vladan Holcner POSSIBILITY OF ACCRUAL ACCOUNTING APPLICATION IN CASE OF ACTIVE RESERVE FORCES OF THE CZECH ARMY ............................................................................. 408 Michaela Krejčová, Jana Gláserová, Milena Otavová, Sabina Mašová MERGERS OF AGRICULTURAL ENTERPRISES IN THE CZECH REPUBLIC.................. 417 Martina Krügerová ANALYSIS OF INSURANCE DISTRIBUTION ON THE CZECH INSURANCE MARKET…………………………………………………………………………………………………………….432 Petro Krush, Dmytro Mastiuk, Petr Valouch THE GOVERNMENTAL POLICY OF BUDGET BALANCING IN UKRAINE...................... 441 Anetta Kuna-Marszałek, Jakub Marszałek SOME CONSIDERATIONS ON THE GREEN BONDS MARKET DEVELOPMENT.......... 458 Oleksandra Lemeshko THE DYNAMIC RELATIONSHIP BETWEEN AGGREGATE FUND FLOWS AND SHARE MARKET RETURNS: EMPIRICAL EVIDENCE FROM BRIC ............................................... 466 Malwina Lemkowska ENVIRONMENTAL INSURANCE IN POLAND – THE NOTION, SCOPE AND LEGAL DETERMINANTS.......................................................................................................................... 475 Matěj Liberda WHAT DRIVES AGRICULTURAL COMMODITIES PRICES? MIXED-FREQUENCY ANALYSIS OF THE AGRICULTURAL MARKET DRIVERS ................................................. 484 Dagmar Linnertová, Veronika Kajurová EFFECT OF ZERO LOWER BOUND ON LARGE FIRMS FINANCING IN THE CZECH REPUBLIC....................................................................................................................................... 492 Karolina Lisztwanová, Iveta Ratmanová ASSESSMENT OF IMPACT OF ITEMS REDUCING TAX BASE AND TAX ON TOTAL AMOUNT OF CORPORATE INCOME TAX IN THE CZECH REPUBLIC IN SELECTED PERIODS...………………………………………………………………………………………………………….498 Dušan Litva BREXIT IMPLICATIONS ON THE CZECH REPUBLIC VIA GNI BASED CONTRIBUTIONS TO THE EU BUDGET ................................................................................................................... 506 11 The impact of financial market imperfections on the investment – cash flow sensitivity in Slovak companies Katarína Belanová 1 University of Economics Faculty of National Economy Dolnozemska Street 1, 852 35 Bratislava, Slovakia E-mail: katarina.belanova@euba.sk Abstract: A company usually has get at least part of financing for investment projects from financial markets. If financial markets are perfect, the choice of the sources of finance does not influence investment decisions. Since financial markets are imperfect, companies find that external finance is costly or rationed. Especially small and medium – sized companies (SMEs) have difficulties in getting external financial sources. As a result, corporate investment is sensitive to the amount of internal funds. The aim of the article is to survey the impact of financial market imperfections on firm investment on the sample of 53 automotive companies in the SR. The survey was carried out during the year 2011. Using augmented accelerator model, we find supportive evidence for the fact that companies which are supposed to be more financially constrained exhibit greater investment – cash flow sensitivity. Our findings support the results of Fazzari et al. (1988) who also find that investment spending of firms with high levels of financial constraints is more sensitive to the availability of cash flow. Keywords: investment – cash flow sensitivity, capital market imperfections, financial constraint JEL codes: G14, G31 1 Introduction Companies need finance to operate and undertake investment projects. It is obvious that if finance is inadequate, they will not invest. A company can finance its investment projects by the use of internal and/or external funds. It usually has to get at least part of its financing from financial markets. When firms find that external finance is costly or rationed, they face financing constraints in their investment decisions. The accessibility to external finance of companies has become more interesting after the financial crisis from 2007. The body of empirical research which explores the connection between investment and finance has developed with the theme that financial structure is relevant to a firm`s investment decisions when capital markets are imperfect. This is in contrast to Modigliani and Miller`s (1958) irrelevance theorem. Modigliani and Miller have argued that in a perfect capital market, a firm`s investment decisions are independent of its financing decisions because the financial structure would not affect the costs of investing. Under such assumption, they conclude that a firm`s financial structure is irrelevant to its value. For example, Myers and Majluf (1984), Greenwald et al. (1984), and Myers (1984) provide strong support of the fact that external funds are not a perfect substitute for internal capital. As a result, the cost of external finance may differ substantially from internal capital. According to this view, investment expenditures may depend on financial factors such as the availability of internal capital (Fazzari et al., 1988). According to them, firm`s internal cash flow may impact investment because of a financing hierarchy (Pecking Order Theory), in which internal capital has a cost advantage over external capital. A large number of empirical studies have provided strong support for the financing hierarchy hypothesis. The standard approach of this research is to categorize firms according to a variety of firm – level financial variables (dividend payout, size, leverage, etc.) before measuring the investment – cash flow sensitivity. The main results of these papers suggest that investment is more sensitive to cash flow for firms with high 12 levels of financial constraints. For instance, Fazzari et al. (1988) consider firms with high dividend payout ratios as unconstrained and firms with low ratios as financially constrained. They show that investment is less sensitive to internal funds for firms with high dividend payout ratio. Kaplan and Zingales (1977) challenged the seminal study of Fazzari et al. (1988) extensively. They questioned the validity of the measure of financial constraints. Based on the same database complemented with firms’ annual reports, Kaplan and Zingales (1997) proved that the investment – cash flow sensitivity is the highest for firms which seem to be the least financially constrained. As we can see, researchers devoted much attention to the influence of internal finance on investment. However, literature is ambiguous whether this influence has a positive or a negative effect on the relationship. Studies comparable with Fazzari et al. (1988) – for example Chirinko and Schaller (1995); Hubbard et al. (1995); Bond, Harhof and Van Reenen (1999); Carpenter, Fazzari and Petersen (1994); Nickel and Nicolitsas (1999); Marhfor et al. (2012) conclude that investment – cash flow sensitivity for financially constrained firms is higher compared to lower financially constrained firms. However, studies comparable with Kaplan and Zingales (1997) – for example Chang, Tan, Wong and Zhang (2007); Erickson and Whited (2000) and Cleary (1999) conclude the contrary, lower constrained firms displayed a higher sensitivity of cash flow to investment than higher constrained firms. According to Clearly et al. (2007), the cause of these contradictory conclusions is the lack of a precise empirical proxy for financial constraints. The aim of the article is to survey the impact of financial market imperfections on firm investment on the sample of 53 automotive companies in the SR. From the text we can conclude that the recognition of the effect of financial market imperfections on firm investment suggests that internal funds should be accounted for when estimating firm investment. 2 Methodology and Data In the year 2011, we carried out a survey among automotive companies in the SR. All information on the variables used in our study was obtained through the financial statements of the companies. The data received from the survey allow us to investigate the sensitivity of corporate investments to internal funds. The dataset used in the empirical analysis contains 53 companies. There are two main approaches in the empirical literature regarding the issue of finance constraint. The most popular and widely use dis the reduced form regression. It employs Tobin`s Q, defined as the stock market valusation of firms vis – à – vis its replacement cost (capital stock at historical prices adjusted for inflation and depreciation). The other approach is structural model estimation, using the Euler equation. There are also some modified versions of the reduced form approach. By adding an internal – funds variable to the standard accelerator model, we use the augmented accelerator model for testing the argument that if financial markets are imperfect, firm investment may be sensitive to internal funds. Although there are some criticisms regarding this model, its advantage is that it consists of variables that are observable. Model Specification We use the following model specification: where: iiii PROSALI εααα +++= 2010,32010,21 13 Ii is total planned (gross) investment divided by total fixed assets in 2010; SAL2010 is total sales in 2010 divided by total fixed assets in 2010 and reflects the accelerator model of investment, we expect α2 to be positive; PRO2010 is total profit in 2010 divided by total fixed assets in 2010 and measures the existence of financial constraints with which automotive companies may be confronted; we expect α3 to be positive reflecting the importance of the availabilityof internal funds in determining investment decisions. i the individual firm index; and ε is an error term. As stated, when coefficient α3 is positive and statistically significant, investment is positively sensitive to internal funds. If financial markets are perfect or financial constraints are absent, the coefficient should be zero. Kaplan and Zingales (1997) also derive an equation that helps to explain this argument. In addition, since the degree of financial constraints may vary across firms of different characteristics, coefficient α3 may also be found to vary across different groups of a priori classified firms according to the degree of financial constraints they face. 3 Results and Discussion This section presents the results of the empirical test of the link between financial market imperfections and firm investment using the data on automotive companies in the SR. Table 1 shows the outcomes of the OLS estimations of equation. Table 1 Determinants of investment of automotive companies: entire sample Constant 0.033 (1.1338) PROt-1 0.166* (0.7181) SALt 0.49** (2.4045) N 53 R2 0.132 Source: own survey Note ⃰ significant at the 10 per cent level; ⃰ ⃰ significant at the 5 per cent level The positive sign of PRO2010 indicates the existence of financing constraints for the automotive companies. Yet, it is possible that the degree of financing constraints differs across companies that have different characteristics. Unfortunately, the data we have gathered for this study do not permit analysis of these issues. 4 Conclusions The paper is devoted to an empirical study on the effect of financial market imperfections on investment of automotive companies in the SR. Despite the theoretical plausibility of a relationship between capital market imperfections and real investments, the empirical literature has found it difficult to identify this channel. Overall, more research is needed to identify a method that will not be subject to criticisms related to the use of cash-flow in the investment equation and will be based on the data that are relatively available across countries and over time. The empirical study lends support to the view that companies surveyed have faced financing constraints. More specifically, we estimate an augmented investment equation using the data obtained from the questionnaire on automotive companies in the SR. The 14 results show that the availability of internal funds has a positive and statistically significant effect on investment of the entire sample, suggesting that automotive companies face financing constraints. Our findings support the results of Fazzari et al. (1988) who also find that investment spending of firms with high levels of financial constraints is more sensitive to the availability of cash flow. Acknowledgments This contribution is a partial result of the VEGA project nr. 1/0776/16 Taxation of the financial sector and harmonizing tendencies in the European Union. References Bond, S. R. (1999). Investment, R & D and financial constraints in Britain and Germany. Institute for Fiscal Studies Working Paper, no. 99/5. Carpenter, R. E. (1994). Inventory, (dis)investment, internal finance fluctuations and the business cycle. Econ Working Paper. Cleary, S. (1999). The relationship between firm investment and financial status. The Journal of Finance, vol. 54(2), pp. 673 – 692. Erickson, T., Whited, T. M. (2000). Measurement error and the relationship between investment and q. Journal of Political Economy, vol. 108(5), pp. 1027 – 1057. Fazzari, S., Hubbard, R. G., Petersen, B. (1988). Financing constraints and corporate investment. Brooking Papers on Economic Activity, 1. Greenwald, B., Stiglitz, J., Weiss, A. (1984). Informational imperfections in the capital market and macroeconomic fluctuations. American Economic Review, vol. 74(2), pp. 194 – 199. Hubbard, R. G. (1995). Internal finance and firm investment. Journal of Money, Credit and Banking, vol. 27(3), pp. 683 – 701. Chang, X. (2007). Effects of financial constraints on corporate policies in Australia. Accounting and Finance, vol, 47(1), pp. 85 – 108. Chririnko, B., Schaller, H. (1995). Why does liquidity matter in investment equations? Journal of Money, Credit and Banking, vol. 27(2), pp. 527 - 48. Kaplan, S. N., Zingales, L. (1997). Do investment – cash flow sensitivities provide useful measures of financing constraints? The Quarterly Journal of Economics, vol. 112(1), pp. 169 – 215. Marhfor, A. (2012). Firm`s financing constraints and investment cash - flow sensitivity. Journal of Finance and Risk Perspectives, vol. 1, pp. 50 - 66. Modigliani, F., Miller, M. H. (1958). The cost of capital, corporation finance and the theory of investment. American Economic Review, vol. 48(3), pp. 261 – 297. Myers, S. C. (1984). Capital structure puzzle. NBER Working Paper, no. 1393. Myers, S. C., Majluf, N. S. (1984). Corporate financing and investment decisions. NBER Working paper, no. 1396. Nickel, D., Nickolitsas, D. (1997). What makes firms perform well? European Economic Review, vol. 41, pp. 783 – 796. 15 The Development of Gross Premiums Written in Life Insurance in the Czech Republic Karina Benetti Technical University of Liberec Faculty of Economics, Department of Economic Statistics Studentska 1402/2, 461 17 Liberec, Czech Republic E-mail: karina.benetti@tul.cz Abstract: Life insurance industry over the last decade has changed greatly, not only in the offer of products. Originally this type of insurance resulted to cover the consequences of the risk of death and survival. Products of life insurance covering the risk of death should provide family of in case of death of the breadwinner, products aimed at the risk of life expectancy were mainly-saving products which should primarily secure the individual in old age. It was also possible to arrange a product called mixed life insurance. It is worth mentioning that there are various modern variations and possibilities of arranging the different kinds of insurance within the life insurance of people. The product development is escalated in investment life insurance product, which has its strengths, but also weaknesses. The main objective of this paper is to capture the main trends of gross life insurance premium, focusing on changes in gross premiums written by individual types of life insurance products including the analysis of selected parameters (number of insurance contracts, insurance penetration and other selected indicators). The software STATGRAPHICS Centurion will be used for this analysis. Keywords: life insurance, development, gross premium written JEL codes: G18, G22 1 Introduction The main roles of life insurance in the financial services system are gradually changing. According Ducháčková (2016) the life insurance is considered to be an instrument to cover the needs of people, on the one hand, a tool of covering the consequences of the risk (death and other risks - accident, invalidity, illness etc.), and on the other hand, a tool for savings to cover the needs of people in post-productive age. At present, many factors affect the development of life insurance and especially its efficiency. In the use of life insurance as a means of addressing the needs of people in old age is in the last period on the Czech insurance market a number of problems. The problems, according Ducháčková (2016) arise from the form of life insurance products, from regulatory approaches in life insurance, from approaches to selling life insurance contracts. However, life insurance is a standard tool of the insurance (financial) market (Ducháčková, 2015). Its role, significance and form are changing throughout its development in relation to changing conditions of life insurance. It is characteristic for the recent period that life insurance has been going through modifications, in particular the typical increase in the share of unit linked life insurance which is representing in particular the product of the investment life insurance. Life insurance and especially unit linked life insurance have been recently facing a few challenges. Some of them resulted from changing financial markets and others were connected with the conclusion of life insurance policies. The aim of the paper is to analyze the development of the gross premium written of life insurance in the Czech Republic and the basic groups of life insurance products by analyzing selected indicators. In the following part will be characterized research methods and data for analysis. 16 2 Methodology and Data In the research were particular used scientific methods: induction, comparative analysis, synthesis of partial knowledge, elementary statistical analysis and dependence analysis. For elementary statistical analysis was used the following selected indicators (Hindls, et al, 2000): • the first difference (1) 1 (1) −∆ = ∆ − ∆t tt . (1) • the second difference (2) 1 (2) (1) (1) − ∆ = ∆ − ∆ t t t (2) • the growth coefficient (3) 1 t t t y k y − = (3) • the growth rate (4) 100t ty yTδ = − (4) • the increase rate (5) 100ty tT k ⋅= (5) • the average absolute gain (6) and 1 2 1 1 , 1 1 n t t ny y n n = ∆ − ∆ = = − − ∑ (6) • the average growth coefficient (7) 1 1 n n y k y −= (7) where n is the number of values (in this paper n = 22). For regression analysis was used software STATGRAPHICS Centurion XVI. For the analysis was used secondary data from Czech National Bank (2017). According to the results of an elementary statistical analysis will be chosen a suitable model for trend analysis. The results of trend analysis will be evaluated based on their individual indices: • RMSE (root mean squared error); • modified index of determination; • p-value (of parameters and model) of significance, according to which the robustness of a particular model is evaluated at the 5% significance level; • t-test, • F-ratio of model. Other indicators that will assess the development of life insurance in the Czech Republic will include, in particular: development of number of insurance contracts, insurance penetration, gross premium written in life insurance per capita and the ratio of the individual life insurance groups (insurance relate to an investment fund, wedding insurance or child-care insurance, accident insurance or sickness insurance, retirement insurance, death and survival insurance) to the total prescribed life insurance in the Czech Republic. 17 3 Results and Discussion The results of elementary statistical analysis, by selected characteristics, of development of gross premium written in life insurance are given below. The development of the percentage ratio of gross premium written in life insurance versus non-life insurance is illustrated in Figure 1. This figure shows the importance of life insurance on the Czech insurance market. It should be added that this ratio is reversed on average across the EU (see Eurostat data). The basic development of gross premium written in life insurance of its first difference illustrated Figure 2 and Table 1. Figure 1 Development of The Percentage Ratio of Gross Premium Written in Life Insurance Versus Non-life Insurance Source: own from Czech National Bank (2017) 27,64% 26,60% 26,45% 27,12% 31,53% 32,26% 35,03% 37,54% 38,82% 39,26% 38,40% 38,68% 40,73% 40,69% 41,76% 46,00% 46,43% 46,91% 45,71% 45,12% 40,67% 40,86% 72,36% 73,40% 73,55% 72,88% 68,47% 67,74% 64,97% 62,46% 61,18% 60,74% 61,60% 61,32% 59,27% 59,31% 58,24% 54,00% 53,57% 53,09% 54,29% 54,88% 59,33% 59,14% 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5 2 0 0 6 2 0 0 7 2 0 0 8 2 0 0 9 2 0 1 0 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 5 2 0 1 6 Percentage share of gross premium written of life insurance in total gross premium written Percentage share of gross premium written of non-life insurance in total gross premium written 18 Figure 2 Development of Premium Written in Life Insurance with Development of its First Difference Source: own from Czech National Bank (2017) According to the development of the values specified in Figure 1 can be deduced that the observed characteristics recorded growth till 2014. For this reason, does not make sense to describe the examined values other statistical characteristics (such as e.g. coefficient growth, growth rate and increase rate). For a basic overview of the development of the examined values sufficient to indicate the results of absolute average gain and average growth coefficient. The result of average absolute gain is for gross premium written in life insurance CZK 2 392 810 520. The result of average growth coefficient is for gross premium written in life insurance 1.092248 (which corresponds to 9.23 %). Table 1 Elementary Characteristic Development of Gross Premium Written in Life Insurance Years (t) Gross premium written of non-life insurance (in thousands CZK (yt)) ∆t (1) ∆t (2) kt Tyt δyt 1995 9 341 715 × × × × × 1996 10 937 216 1 595 501 × 1.170793157 0.170793157 17.08% 1997 12 692 286 1 755 070 159 569 1.16046771 0.16046771 16.05% 1998 15 089 372 2 397 086 642 016 1.188861644 0.188861644 18.89% 1999 19 793 331 4 703 959 2 306 873 1.311739879 0.311739879 31.17% 2000 22 770 132 2 976 801 -1 727 158 1.15039414 0.15039414 15.04% 2001 28 281 966 5 511 834 2 535 033 1.242064209 0.242064209 24.21% 2002 34 036 346 5 754 380 242 546 1.203464639 0.203464639 20.35% 2003 41 128 802 7 092 456 1 338 076 1.20837889 0.20837889 20.84% 2004 44 201 009 3 072 207 -4 020 249 1.074697216 0.074697216 7.47% -20 000 000 -10 000 000 0 10 000 000 20 000 000 30 000 000 40 000 000 50 000 000 60 000 000 70 000 000 80 000 000 Years Gross premium written of non-life insurance (in thousands CZK (yt)) ∆t(1) 19 2005 44 954 269 753 260 -2 318 947 1.017041692 0.017041692 1.70% 2006 47 233 389 2 279 120 1 525 860 1.050698633 0.050698633 5.07% 2007 54 128 225 6 894 836 4 615 716 1.145973773 0.145973773 14.60% 2008 56 909 094 2 780 869 -4 113 967 1.051375581 0.051375581 5.14% 2009 60 209 323 3 300 229 519 360 1.057991241 0.057991241 5.80% 2010 71 764 862 11 555 539 8 255 310 1.191922753 0.191922753 19.19% 2011 72 009 104 244 242 -11 311 297 1.003403365 0.003403365 0.34% 2012 72 049 292 40 188 -204 054 1.000558096 0.000558096 0.06% 2013 71 577 033 -472 259 -512 447 0.993445335 -0.006554665 -0.66% 2014 71 186 464 -390 569 81 690 0.994543375 -0.005456625 -0.55% 2015 62 415 277 -8 771 187 -8 380 618 0.876785747 -0.123214253 -12.32% 2016 59 590 736 -2 824 541 5 946 646 0.954745999 -0.045254001 -4.53% Source: own elaboration Czech National Bank (2017) From Table 1 it is clear, that the largest increase was recorded in the surveyed quantity in 2010, on the contrary, the largest decline in 2015. The question is which life insurance group this increase / decrease was caused. This will be the focus below. The results of the ratio of the individual life insurance groups (insurance relate to an investment fund, wedding insurance or child-care insurance, accident insurance or sickness insurance, retirement insurance, death and survival insurance) to the total prescribed life insurance in the Czech Republic are shown in the Figure 3. Figure 3 The Development of the Ratio of the Individual Life Insurance Groups to the Total Premium Written in Life Insurance in the Czech Republic Source: own elaboration from Czech National Bank (2017) From Figure 3 it is clear that the increase in the total life insurance premium written in 2010 was driven by an increase in subscribed life insurance premiums linked to the investment fund. On the other hand, the decline in total life insurance premiums written 0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00% 80,00% 90,00% 100,00% 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Insurance related to an investment fund Wedding insurance or child-care insurance Accident insurance or sickness insurance Retirement insurance Death and survival insurance 20 in 2015 was due to a decline in written life insurance premiums in the event of death and survival, as well as a partial decline in gross written premium written linked to the investment fund. The results of development of number of insurance contracts, insurance penetration and gross premium written in life insurance per capita see in Table 2. The number of the insurance contract has a decreasing trend over the reference period, except 2012. However, the gross premium written per insurance contract has a volatility development period. This means that with the decreasing number of contracts, the gross premiums written not decreases. Premium per capita increases in the period 2006 to 2011, and in the following year, since 2012, it has declined. In the case of this indicator, it is highly desirable for its value to have a growth tendency. The decreasing trend of this indicator indicates the low use of the product by the population of that country. Insurance penetration a growing tendency in 2006 to 2010, with a downward trend in the following period since 2006, indicating the unfavorable development of the indicator. Another research question is how to develop gross premiums written in life insurance in the future this will be used to analyze time series and software STATGRAPHICS Centurion XVI. The trend in time series can be described by trend functions unless the development of time series corresponds to a particular function of time (for example: linear, quadratic and exponential). Table 2 The Results of Selected Indicators 2006 2007 2008 2009 2010 Number of insurance contracts 10 010 546 10 119 438 10 104 445 9 349 600 8 919 070 Gross premium written per insurance contract 4 718 5 349 5 631 6 442 8 046 Premium per capita 4 601 5 243 5 456 5 741 6 824 Insurance penetration 1.35% 1.41% 1.42% 1.54% 1.82% 2011 2012 2013 2014 2015 Number of insurance contracts 8 675 566 9 357 769 8 060 735 7 740 318 7 392 770 Gross premium written per insurance contract 8 300 7 699 8 880 9 197 8 443 Premium per capita 6 860 6 856 6 810 6 764 5 920 Insurance penetration 1.79% 1.77% 1.75% 1.65% 1.37% Source: own elaboration from (Czech Statistical Office, 2017a and 2017b), (Czech National Bank, 2017) Linear trend function (line) has the following form (8), (Artl et al, 2002): (8) Quadratic trend function (parabola) has the following form (9), (Artl et al, 2002): (9) Exponential trend function has the following form (10), (Artl et al, 2002): (10) To evaluate the suitability of the trend have been identified and assessed values of trend function forecast, values of the root mean squared error (RMSE) and values of modified index of determination . RMSE (11), (Artl et al, 2002): 21 RMSE = ∑ (11) where is modeled values at time t. Determination modified index (12), (Artl et al, 2002): (12) Other indicators used for trend analysis include: p-value, t-test and F-ratio. Based on the results of these indicators, using the software STATGRAPHICS Centurion XVI was selected the best model – quadratic trend. Trend function forecast is: 4 572 620 000 6 184 630 000 126 960 000 . Forecast for next two periods is – point for year 2017 CZK 70 511 000 000 (interval CZK: 56 552 400 000 – 84 471 300 000) and point for year 2018 CZK 70 729 300 000 (interval CZK: 55 850 200 000 – 85 608 400 000). Figure 4 Time Series Equalization by Quadratic Trend and Forecast of Development for Next Two Years Source: own elaboration The predicted development of the gross premiums written in life insurance according to the selected time series model for the following two periods – 2017 and 2018 – has a growing tendency. If the model prediction would be fulfilled, it would indicate a positive development in the use of life insurance products. However, it is questionable whether declining trends in the use of traditional life insurance products, death and survival insurance, rather than suggesting a change in clients' interest in other products – such as the life insurance product linked to the investment fund. On the contrary, the increase in the use of life insurance products that are linked to the investment fund does not necessarily imply a positive development in the use of life insurance products, as the investment life insurance products are not the classic investment products but rather the investment instrument (when the reasonable amount for the risk of death not met). This could be a basic research question for further research. Acknowledgments This work was supported by specific research from the Technical University of Liberec, Faculty of Economics. References Artl, J. (2002). Analýza ekonomických časových řad s příklady. Vysoká škola ekonomická v Praze. Time Sequence Plot for Col_1 Quadratic trend = -4,57262E6 + 6,18463E6 t + -126960, t^2 0 4 8 12 16 20 24 0 2 4 6 8 10 (X 1,E7) Col_1 actual forecast 95,0% limits 22 Czech National Bank (2017), ARAD. Retrieved from: http://www.cnb.cz/docs/ARADY/HTML/index_en.htm. Czech Statistical Office (2017a), Population – annual time series. Retrieved from: https://www.czso.cz/csu/czso/population_hd. Czech Statistical Office (2017b), Gross domestic product – time series. Retrieved from: https://www.czso.cz/csu/czso/hdp_ts. Ducháčková, E. (2016). The Role of Life Insurance in the Context of Cover the Needs of the People in the Czech Republic. In: European Financial Systems 2016: Proceedings of the 13th International Scientific Conference, pp. 133–140. Ducháčková, E. (2015). LIFE INSURANCE AND THE ROLE OF FINANCIAL ARBITRATOR FOR THE RESOLUTION OF DISPUTES WITHIN LIFE INSURANCE. In: 7th International Scientific Conference on Finance and Performance of Firms in Science, Education and Practice, pp. 217–231. Hindls, R. (2000). Statistika pro ekonomy. Professional Publishing, Praha. 23 Volatility and Correlations in Stock Markets: The case of US S&P 500, Japan Nikkei 225 and DAX indices Miloš Bikár1 , Miroslav Kmeťko1 , Katarína Vavrová1 , Peter Badura1 1 University of Economics in Bratislava Faculty of Business Management, Corporate Finance Department Dolnozemská cesta 1, 852 35 Bratislava, Slovak Republic Corresponding authors e-mail: milos.bikar@gmail.com Abstract: Movements on the global financial markets are nowadays interconnected and influenced by both fundamental and behavioural or psychological effects. During the last couple of years, besides of standard corporate results and predictions, the market volatility is significantly trigger by monetary policies of central banks and policy makers. Nevertheless, many investors find stock market indices as an appropriate way for their investments, especially at time of low bond yields. The objective of this paper is to analyse the time-varying nature of selected world stock market indices by using a correlation model as well as to evaluate the influence of behavioural effects of market participants on the volatility. The output from the model confirmed the negative correlation among selected stock indices and volatility, while real effective exchange rates effect differs. The paper highlights key aspect having weight on stock markets mainly the central bank monetary policies, public debts, currency pair fluctuations, as well as inflation levels. Keywords: stock price indices, market and exchange rate volatility, price correlations, monetary policy, behavioural effects. JEL codes: F34, G15, G18. 1 Introduction The stock markets as well as financial instruments which are offered there, have recently drawn the attention of investors, policy makers and academics across the world. Several studies have focused on the correlation between the markets, the main factors influencing the trend movements, contagion between the spot and future markets, the short and mid-term volatility reasons and reactions to different shock scenarios. Such information is crucial not only for fund managers, portfolio risks analysts but for individual investors as well. It enables them to timely address and diversify their investments in order to reduce the underlying risk. Conceptual views on stock market indices correlations differ and point out striking differences in outputs and model interpretations. Morana and Beltratti (2008) analyse linkage and monthly stock market returns for the USA, the UK, Germany and Japan over the period 1973-2004. Results point to a progressive integration of the four stock markets, leading to increasing co-movements in prices, returns, volatility and correlation. Evidence of a positive and non-spurious linkage between volatility and correlation, and an increasing trend in correlation coefficients over time, were also found. Similar research studies about transmission of stock returns, volatility and international stock market correlations were published by Pan and Hsueh (1998), Forbes and Rigobon (2002), Rua a Nunes (2009) and Ranta (2013). In a more recent study of Connor and Suurlaht (2013) analyzed the relationship between macroeconomic variables and time-varying correlations between Eurozone markets. They find that Eurozone markets seem to be more correlated when recent cumulative returns are on average lower within the region. The correlation magnitude positively varies with Eurozone GDP growth measures. More specifically the correlation magnitude is higher during quarters when the cross-country average quarterly GDP growth rate is higher. Cappiello et al. (2006) proposed an asymmetric approach called Asymmetric Dynamic 24 Conditional Correlation (ADCC) model. The main reason was to analyze the behavior of international equities and government bonds. While equity returns show a strong evidence of asymmetries in conditional volatility, little is found for bond returns. However, both equities and bonds exhibit asymmetries in conditional correlations, with equities responding stronger than bonds to joint bad news. The article also found that during periods of financial turmoil, equity market volatilities show important linkage and conditional equity correlations among regional groups increase dramatically. The most significant effects on stock markets during the last couple of years occurred due to variety of standard and non-standard measures adopted by major central banks. Purchase of financial assets financed from money of central bank, increased liquidity and pushed up asset prices. Those who sold assets to the central bank rebalanced their portfolios into riskier assets. According to Gerlach-Kristen et al. (2016), announcements of large-scale bond purchases pushed down bond yields across a wide swatch of bond markets, as well as in Japan. They point to a global portfolio balance effect that reflects the global integration of many bond markets. The study of Rogers (2014) analyzed the effects of unconventional monetary policy by the Federal Reserve, Bank of England, European Central Bank and Bank of Japan on bond yields, stock prices and exchange rates. A sizeable percentage of investors are using social media to obtain information about companies. As a consequence, social media content about firms may have an impact on stock prices. Various studies utilize social media content to forecast stock market-related factors such as returns, volatility, or trading volume. Pieter de Jong et. all (2017) investigate whether a bidirectional intraday relationship between stock returns and volatility and tweets exists. Findings indicate that 87% of stock returns are influenced by lagged innovations of the tweets data, but there is little evidence to support that the direction is reciprocal, with only 7% of tweets being influenced by lagged innovations of the stock returns. Nooijen and Broda (2016) examine the predictive capabilities of online investor sentiment for the returns and volatility of MSCI U.S. Equity Sector Indices by including exogenous variables in the mean and volatility specifications of a Markov-switching model. They find that the Thomson Reuters Marketpsych Indices (TRMI) predict volatility to a greater extent than they do returns. In the two-regime setting, there is evidence supporting the hypothesis of emotions playing a more important role during stressed markets compared to calm periods. The authors also find differences in sentiment sensitivity between different industries: it is greatest for financials, whereas the energy and information technology sectors are scarcely affected by sentiment. Another economic indicator that measures the degree of optimism that consumers feel about the overall state of the economy as well as their personal financial situation represent the consumer confidence. Several authors (Shiller, 1984, Fisher and Statman, 2003, Baker and Wurgler, 2007, and Reed 2016) measure consumer sentiment via analysis of social networks and show that such sentiment affects stock prices; specifically, the S&P 500 and the Dow Jones Industrial Average. The authors implemented lexicographic analysis of Twitter data over a three-month period and found that talk intensity of economic issues not only causes shifts in the daily stock market prices, but also has a significant negative effect. The paper deals with the analyses and predictions of both fundamental and behavioural effects on stock market movements. The objective of this paper is to analyse the timevarying nature of selected world stock market indices by using a dynamic correlation model as well as to evaluate the influence of behavioural effects of market participants on the volatility. 25 2 Methodology and Data To suggest the regression model, it was required a use of methods of summary, synthesis and analogy of the knowledge and creation of a short literature review. Second, it was done a data collection. In our model there were used daily data and our time series range from 2000 to 2017 (4243 observations). While similar studies generally use quarterly data, we decided to work on a daily frequency in order to capture the more precise outputs. It were used the time series from the QUANDL DataStream. To capture the dynamics of our model, it was used as a dependant variable indices (DAX, S&P 500 and Nikkei 225), as a measure of stock price movement and as a remark of economies. Further, the real effective exchange rates (USD/EUR, USD/ JPY and EUR/PJY) were used to incorporate the currency depreciation/appreciation into our framework. Last, we incorporated into model the historical volatility of selected world stock indices to analyse the influence on the stock price movements. The selected parts of data series are plotted in a Figures 1-3. Regarding the methodology, we were used a method of multiple regression in order to explain the relationship among the independent variables to the dependent variable, according the formula (Hair et all., 2010): % & ' ( ' ( ⋯ '*(* (1) where Y is the value of the Dependent variable (Y), a (Alpha) is the Constant or intercept, and b1 is the slope (Beta coefficient) for X1 , X1 first independent variable that is explaining the variance in Y, b2 is the slope (Beta coefficient) for X2 , X2 first independent variable that is explaining the variance in Y, and so on. The computations were completed in Eviews. Figure 1 reports the data, mnemonics, descriptions, sources and specifications. Figure 1 World stock price indices movement since 2000 Source: Quandl, 2017. 3 Results and Discussion In this section, the multiple regression estimates for the selected stock price indices DAX, S&P 500 and Nikkei 225 and chosen independent variables – real effective exchange rates USD/EUR, USD/JPY and EUR/JPY and historical volatility of selected world stock indices. The output from the model confirmed the negative correlation among selected stock indices and volatility, while real effective exchange rates effect differs. The volatility significantly grows in time of negative mood on the market, higher geopolitical risks or 26 monetary policy changes. In a such situations, market tend to make corrections from previous rallies, and statistically ones a year there are a massive sell offs. This happened for example in summer 2015 (China currency devaluation) or in February 2016 (market fears over the European banking system, oil price fall). And vice versa, the volatility declines in times of bull market moods. As an example could serve the last stock rally from Dec. 2016 up to March 2017 (Trump economic effect). The last example was characteristic by extremely low volatility and simultaneously several historical stock markets records, especially in US market. The next table below summarises the model output for DAX (similar output were calculated for S&P 500 and Nikkei 225 indices, could be delivered upon request). Table 1 Model output Dependent Variable: DAX Method: Least Squares Date: 06/24/17 Time: 11:49 Sample (adjusted): 4/27/2010 3/31/2017 Included observations: 1772 after adjustments Variable Coefficient Std. Error t-Statistic Prob. EUR_JPY -16.61796 1.675051 -9.920869 0.0000 EUR_USD 0.000471 5.56E-05 8.468616 0.0000 GERMANY -34.73515 2.391942 -14.52174 0.0000 JAPAN -5.957127 1.738761 -3.426077 0.0006 NIKKEI_225 0.137846 0.010030 13.74362 0.0000 S_P_500 2.820317 0.081273 34.70162 0.0000 US -239.1736 97.42216 -2.455022 0.0142 USD_JPY 28.94847 2.861030 10.11820 0.0000 C 1887.464 165.5598 11.40050 0.0000 R-squared 0.953396 Mean dependent var 8583.351 Adjusted R-squared 0.953184 S.D. dependent var 1868.848 S.E. of regression 404.3608 Akaike info criterion 14.84756 Sum squared resid 2.88E+08 Schwarz criterion 14.87539 Log likelihood -13145.94 Hannan-Quinn crit. 14.85784 F-statistic 4508.288 Durbin-Watson stat 0.156175 Source: own calculation (Eviews), 2017 27 Figure 2 Historical Volatility of selected world stock indices Source: Quandl, 2017. The reaction of the real effective exchange rates on the stock markets differs. While EUR and USD appreciations does have a negative, but small correlation on stock price movements, JPY effect is much stronger. Generally stronger currency leads to more expensive exports, what is reflected in decline of exporter’s stock prices. This is visible in case of japan yen. The appreciation of JPY, especially in time of risk appetite mood on the markets, leads immediately to stock price decline and lower level of Nikkei 225 index. This situation happened in April 2017, an appreciation of USD/JPY from 112.5 to 108.5 lead to Nikkei 225 index decline from 19.500 to 18.350 (US health care act disapproval, Syria attack, Korea fears). Second we analyse cross correlation matrix among chosen stock indices. There are positive relations among all of them, but DAX and S&P 500 dispose of quite high correlation. This is caused by similar economic cycles of European and US economies and their interconnections. The positive investor mode from one economy is strongly reflected in another one and reverse. The positive correlation between Nikkei 225 and S&P 500 is lower compare the previous one, what is influenced by different industry structure of Japan and US economy. Japan economy is heavily dependent on row material imports and higher share of export on GDP. Those facts are reflected in NIKKEI 225 index development. In addition the exchange rate of Japanese yen play an important role in index price trends. The lowest positive correlation shows DAX and NIKKEI 225 indices. Table 3 Cross Correlation Matrix DAX S&P 500 NIKKEI 225 DAX - 0,8966 0,4815 S&P 500 0,8966 - 0,5581 NIKKEI 225 0,4815 0,5581 Source: own calculation (Eviews), 2017 Further aspect, which we analyse, was the influence of applied monetary policy to change in stock prices. An expansionary monetary policy applied by major central banks after 2001 was reflected in a massive asset reallocations, mainly from less risky assets (government bonds) toward riskier equities. These trend caused significant equity prices 28 increases, what could be seen in figure 1. The policy of law interest rates have helped governments to consolidate their debts and contributed to stabile economic environment. Since 2016, the general labour market conditions improved and inflation levels increased, what lead Federal Open Market Committee (FOMC) to raise the target for the federal funds rate by 0.25 % percentage point to a range of 0.5-0.75 %, and in March 2017 by further 0.25%. The FOMC expects that economic activity will expand at a moderate pace, labor market conditions will strengthen somewhat further, and inflation will rise to 2 % over the medium term. This change of interest rate was partially reflected in stock price movements, but the major trend came for the Trump policy actions. In September 2016, the Bank of Japan committed to expanding the monetary base until inflation exceeds 2 % in a stable manner and adopted a new policy framework aimed at controlling the yield curve by targeting short and long-term interest rates. In December, the European Central Bank announced an extension of the intended duration of its asset purchases through at least December 2017, albeit with a slight reduction in those purchases beginning in April 2017. In a mid-term view, the increase of interest rate would come in Europe in 2018, while the Japan situation is difficult to predict. Nevertheless, the positive correlation of analyzed markets would not change and the interest rate changes would have a higher impact on bond markets. Figure 3 Major exchange rate pairs development Source: Quandl, 2017. Further we try to forecast the price development of selected world indices for the next two years, while taking into account the past volatility and cross currency exchange rate movements since 2000 as an independent variables. There are negative relations among stock price indices and volatility and negative correlations among exchange rate movements and stock price indices. For example, an appreciation of the japan JPY leads to downturn trend of Nikkei 225 index, as stronger JPY means more expensive export, what is immediately expressed in lower prices of japan export companies. The results confirmed the strong correlations of analysed word stock indices. R-squared coefficient amounts 0.9533 by DAX, 0.9457 by Nikkei 225 and 0.9419 by S&P 500 index. The next figure below summarises the model output for the stock indices forecasting. 29 Figure 4 Model output – Stock indices forecasting Source: Own calculation (Eviews), 2017. The results confirmed the strong correlations of analysed word stock indices. R-squared coefficient amounts 0.9533 by DAX, 0.9457 by Nikkei 225 and 0.9419 by S&P 500 index. Aggregate real stock prices are fairly highly correlated over time with aggregate real dividends. As confirmed by the study of Shiller (1984), the simple correlation coefficient between the annual real Standard and Poor's composite stock price index and the corresponding annual real dividend series between 1926 and 1983 is 0.91. The correlation coefficient between the real stock price index and a linear time trend over the same sample is only 0.60. Thus, the price of the aggregate stock market is importantly linked to its dividends, and much of the movements of the stock market that we often regard as inexplicable can be traced to movements in dividends. Looking in a development since last year, U.S. equity markets were volatile around the Brexit vote in the United Kingdom but operated without disruptions. Broad equity price indexes have increased notably since late June, with a sizable portion of the gain occurring after the U.S. elections in November 2016. Reportedly, equity prices have been supported in part by the perception that corporate tax rates may be reduced. Moreover, market participants pointed to expectations of changes in the regulatory environment as a factor contributing to the outperformance of bank stocks. By contrast, stock prices of firms that tend to benefit from lower interest rates, such as utilities, declined moderately on net. The implied volatility of the S&P 500 index—the VIX— fell, ending the period close to the bottom of its historical range. Financial market conditions in both the advanced foreign economies (AFEs) and the emerging market economies have generally improved since June 2016. In the AFEs, increasing distance from the Brexit vote, better-than-expected economic data for Europe, and the continuation of accommodative monetary policies by advanced-economy central banks have contributed to improved risk sentiment. Except of the fundamental aspect of the economies, the financial markets are significantly influenced by behavioral, or psychological effects (Fenzl, 2016). The key 30 question is, what facts about financial decisions and what cognitive and neural processes influence people by taking financial decisions. Because of cognitive constraints and a low average level of financial literacy, many household decisions violate sound financial principles. Households typically have under diversified stock holdings and low retirement savings rates. Investors over extrapolate from past returns and trade too often. Even top corporate managers, who are typically highly educated, make decisions that are affected by overconfidence and personal history. Many of these behaviors can be explained by well-known principles from cognitive science. The literature in behavioral finance has forcefully demonstrated that these robust decision anomalies have important consequences for individual investor wealth, stock market prices, and regulatory policy (Frydman and Camerer, 2016). What is less clear, from a cognitive science perspective, is the psychology that generates the observed patterns of saving, investing, and trading behavior. There is a large number of trading patterns that are inconsistent with the rational use of information and the ideal balance of risk and return. A useful next step in organizing this set of facts is to understand the correlation structure among the various biases. Many of these seemingly distinct biases could be generated by a common neural and psychological mechanism. Some emerging evidence for this conjecture has already been found, as the same brain areas encode signals that generate the disposition effect and repurchase effect. This neural overlap fits with a strong correlation between these effects at the behavioral level. Most of the research use longstanding folk psychological constructs such as limited attention, emotion, salience, and the value of simplicity that psychological limits imply. Being able to measure the psychological influence on stock market movements, there will be needed a broader interdisciplinary study of financial decision making, as a collaboration using the ideal combination of mathematical modeling, cognitive and neural measures, and observed behavior. 4 Conclusions This paper investigated the link between the German DAX, Japan Nikkei 225 and US S&P 500 index, and chosen macroenomic variables government bond yields and exchange rate. We relied on the correlation method to establish whether the correlations between markets and macroeconomic variables has evolved over time and were affected by unconventional monetary policy pursued by central banks. The output from the model confirmed the negative correlation among selected stock indices and volatility, while real effective exchange rates effect differs. The volatility significantly grows in time of negative mood on the market, higher geopolitical risks or monetary policy changes. The quantitative monetary policy adopted by major central banks caused rapid decline of government bond prices, while stock indices went up significantly. This movement of stock market participants toward potential high yields could be seen in US, Europe and Japan markets as well. Second we analyse cross correlation matrix among chosen stock indices. There are positive relations among all of them, but DAX and S&P 500 dispose of quite high correlation. This is caused by similar economic cycles of European and US economies and their interconnections. Next we try to forecast the price development of selected world indices for the next two years, while taking into account the past volatility and cross currency exchange rate movements since 2000 as an independent variables. The model outputs predict further grow of all analysed indices. In case of Europe and Japan, this is in line with market analyst predictions. Regarding the US equities, both trends could be confirmed. Some analyst predict a 5-10 % correction, while other see further increase thanks to Trump economy policy. Further we analyse the psychological effect on stock market movements. Stock prices are likely to be among the prices that are relatively vulnerable to purely social movements because there is no accepted theory by which to understand the worth of stocks and no clearly predictable consequences to changing one's 31 investments. Ordinary investors have no model or at best a very incomplete model of the behavior of prices, dividends, or earnings of speculative assets. Since investors lack any clear sense of objective evidence regarding prices of speculative assets, the process by which their opinions are derived may be especially social. There is an extensive literature in social psychology on individual suggestibility and group pressure. Much of this literature seeks to quantify, by well-chosen experiments, how individual opinions are influenced by the opinions of others. The research shows evidence of flagrant decision errors under social pressure but not of abandonment of rational individual judgment. Behavioral Finance has identified contagion as one of the underlying mechanisms of booms and panics in financial markets, where contagious entities such as rumors, profit expectations, trading rules and others are transmitted via social interactions. Acknowledgments The presented working paper is the output of the scientific grants VEGA n. 1/0007/16 The impact of the global economic developments and trends in the direction of the euro area economy on financial management of business entities in Slovakia. References Baker, M., Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, vol. 21, pp. 129–151. Cappiello, L., Engle, R. F., Sheppard, K. (2006). Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns. Journal of Financial Econometrics, Vol. 4, Issue 4, pp. 537-572. Connor, G., Suurlaht, A. (2013). Dynamic Stock Market Covariances in the Eurozone, Journal of International Money and Finance, vol. 37, pp. 353–370. Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models. Journal of Business and Economic Statistics, vol. 20, pp. 339–350. 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J., Broda, S. A. (2016). Predicting Equity Markets with Digital Online Media Sentiment: Evidence from Markov-switching Models. Journal of Behavioral Finance, vol.17 (4), pp. 321-335. Pan, M. S., Hsueh, P. (1998). Transmission of Stock Returns and Volatility Between the U.S. and Japan: Evidence from the Stock Index Futures Markets. Asia-Pacific Financial Markets, vol. 5, pp. 211–225. Pieter de Jong, Elfayoumy, S., Schnusenberg, O. (2017). From Returns to Tweets and Back: An Investigation of the Stocks in the Dow Jones Industrial Average. Journal of Behavioral Finance, vol.18 (1), pp. 54-54. 32 Ranta, M. (2013). Contagion among major world markets: a wavelet approach. International Journal of Managerial Finance, vol. 9, pp. 133–150. Reed, M. (2016). A Study of Social Network Effects on the Stock Market. Journal of Behavioral Finance, vol.17 (4), pp. 342-351. Rogers, J. H., Scotti, C., Wright, J. H. (2014). 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Retrieved from: https://www.brookings.edu/wp- content/uploads/1984/06/1984b_bpea_shiller_fischer_friedman.pdf. 33 Linkages between Brexit and European Equity Markets Evidence from Quantile Regression Approach Mária Bohdalová1 , Michal Greguš2 1 Comenius University Faculty of Management, Department of Information Systems Odbojárov 10, 820 05 Bratislava, Slovakia E-mail: maria.bohdalova@fm.uniba.sk 2 Comenius University Faculty of Management, Department of Information Systems Odbojárov 10, 820 05 Bratislava, Slovakia E-mail: michal.gregus@fm.uniba.sk Abstract: The aim of this paper is to give a comprehensive description of the risk dependence and interdependence between selected European stock markets and Brexit equity in the period spanning from January, 7, 2000 to February, 3, 2017. We have studied behavior of extreme quantiles using quantile regression approach. This approach is robust because it is based on the use of various measures of central tendency and dispersion statistics for a detailed analysis of the relationship between variables. We have found evidence of significant interdependence/independence between financial markets and Brexit. The analysis of upper and lower quantiles allows to observe that the interdependence is positive asymmetric and higher for bear markets compared to bear or normal markets conditions. Keywords: risk, quantile regression, stock market, Brexit JEL codes: G15, G17 1 Introduction The changes of interdependence of financial markets often show asymmetric dependence of assets or markets. The literature which concentrates on analysis of changes of financial market dependencies compares financial markets during normal period and during crisis period. Many authors use a linear dependence measure such as OLS methods (Bae et al. 2003), (Baur, Schulze, 2005), or Markow-switching model (Hu, 2006) or copulas (Baur, 2012). In the recent time, policy uncertainty in Europe has intensified due to the Global Financial Crisis, serial crises in the Eurozone, etc. Economic policy uncertainty increased after the start of the recession during 2007-2009 due to businesses and household uncertainty about future tax, spending, regulatory, health-care and monetary policies. Baker et al. (Baker et al., 2015) have investigated the role of policy uncertainty, and they have developed an index of economic uncertainty (EPU) for the United States. New uncertainty is caused due to Brexit. Brexit is an abbreviation for "British exit," referring to the UK's decision in a June 23, 2016 referendum to leave the European Union (EU). The vote's result shook global markets, causing the British pound to fall to its lowest level against the dollar in 30 years. There is inconsistency of opinion about the influence of this vote on financial markets. Some authors (Ringe, 2017) argue that, in reality, the impact of Brexit for financial services will be minuscule, if not irrelevant. (Raddant, 2016) analyzed response of selected European stock markets to Brexit. He analyzed the correlation of market indices, stock volatility and the special role of stocks from the financial sector. Bloomberg gives us an opportunity to analyze economic policy uncertainty for Europe, the United Kingdom and for Brexit in index EPUCBREX. The construction of these indices is based on newspaper articles regarding policy uncertainty about economy, uncertainty 34 and information about spending, deficit, regulation, budget, tax, policy, or the Bank of England, or ECB. We have taken EPUCBREX index as risk factor for analyzing six European markets – the DAX, Germany market, CAC40, French market, WIG20, Poland market, ISEQ, Irish stock market, IBEX Spain stock market and XU100, Turkey stock market. This paper contributes to the existing literature by using a quantile regression approach to investigate how Brexit uncertainty expressed in EPUCBREX index affects returns of the mentioned market indices. We have analyzed the dynamics of selected stocks indices across selected quantiles. Our findings could provide a new insight into the behavior of European stock markets, thus leading to meaningful implications for policy makers, investors and risk managers dealing with this market. We have used linear quantile regression approach. While OLS regression model estimates the expected value of the dependent variable, linear quantile regression approach provides essentially the same estimates as sample quantiles. Quantile regression has been used in many papers concerning the risk analysis (Engle and Manganelli, 2004), (Alexander, 2008), (Baur, 2013) (Birău, Antonescu, 2014), (Naifar, 2016), (Aymen, Mongi, 2016), etc. The aim of this paper is to find the linkage between the selected stock markets and the uncertainty expressed in EPUCBREX index. Is there an asymmetric or symmetric dependence? Which market has been influenced the most by Brexit? Which market has been stable? The paper gives answers to these questions in our empirical study. The data used in our empirical study are based on daily data for the period from January, 7, 2000 to February, 3, 2017 are expressed in EUR. Our findings may have implications for portfolio risk managers, policymakers, international investors in terms of risk management which should vary per changes in economy. The remainder of the paper is organized as follows: Next section describes the quantile regression methodology and data. Our empirical analysis with results is presented in the following section. Conclusions and discussions are presented in the last section. 2 Methodology and Data Quantile regression was developed by Koenker and Bassett in 1978 (Koenker, Bassett, 1978) as an extension of the traditional least squares estimation of the conditional mean. Quantile regression is a distribution-free technique to estimate the effect of a regressor on the quantiles of the response distribution. Let Y be a linearly dependent variable on variable X. The quantile regression expresses the conditional quantiles of dependent variable Y for given independent variable X, based on an arbitrary joint distribution. It is assumed that the errors of the quantile regression are i.i.d with the specific error distribution function Fε. We take the q-th conditional quantile function ( )yQ q X of Y specified by regression model (Mensi, 2014) ( )yQ q X = inf { ( )yb F b X q≥ } = ( )k k k q Xβ∑ , (1) where ( )yF b X is a conditional distribution function of Y for given X. Quantile regression coefficient ( )k qβ determines the dependence between vector X and the q-th conditional quantile of Y. The values of ( )k qβ for [0,1]q ∈ determine the whole dependence structure of Y. The dependence of Y based on an explanatory variable in vector X could be either constant where the values of ( )k qβ do not change for different values of q or monotonically increasing (decreasing) when ( )k qβ increases (decreases) with the value of 35 q or symmetric (asymmetric) where the value of ( )k qβ is similar (dissimilar) for low and high quantiles (Aymen and Mongi, 2016). The coefficients of ( )k qβ for a given q are estimated by minimizing the weighted absolute deviations between Y and X ( ) ( ), 1 ˆ( ) min 1 T Y X t tt t t q q Y Xα β α β β α β≤ + = = − − +∑ , (2) where 1 if 1 0 t t Y Xt t Y X otherwise α β α β ≤ + ≤ + =   . (3) Model Specification In order to investigate different effects that the conditioning variables have on the quantile function we have used the quantile regression model that explains the relationship between the stock market Y (developed or emerging markets) and Brexit uncertainty as an independent variable. This dependency can be presented by equation QY(q|X) = α(q) + β(q) EPUCBREX + ε. (4) Equation (4) enables us to examine what kind of dependence structure exists in the selected European stock markets and how the dependence structure is affected by X. Data In this paper, we have shown the effect of Brexit on selected developed and emerging European markets. Developed European markets are represented by the Germany DAX index, French CAC40, Ireland ISEQ and Spain IBEX stock market index. Emerging European stock market is represented by Poland WIG20 and Turkey XU100 stock market indices. The choice of the markets has been based on the fact that Germany is the strongest member of the EU, France, the second strongest pillar of the European Union, the development during the recent period just before the election has been quite uncertain and therefore interesting to analyze. We have chosen Turkey and Poland because these countries are comparable in size to Germany or France, and Ireland and Spain were chosen as the countries that could be in our opinion influenced the most by Brexit. The daily data were collected from January, 7 2000 to February, 3, 2017. Data provider is Bloomberg Ltd. All data are expressed in EUR. The closing prices of analyzed time series are nonstationary, therefore we have used logarithmic returns (log returns are obtained by formula: rt = ln Pt /ln Pt-1, t = 1,…, T, where Pt is the closing price in time t excluding account dividends). 3 Results and Discussion Table 1 provides the results obtained using IBM SPSS software. We have estimated quantile regressions with intercept for next nine quantiles q = {0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.90, 0.95, 0.99} according to equation (4). The table shows the quantile regression estimates for each market index return per the empirical model defined by Eq. (4). The asterisk “*” denotes statistical insignificance at the 5% level. We have found a significant positive effect of Brexit on each stock market. The strongest and symmetric dependence of the DAX log returns on Brexit is in 5% and 95% quantile, then for 25% and 99% quantile the dependence is slightly lower and finally for 50% and 90% quantiles. The lowest significant dependence was for 1% quantile. The effect of Brexit on French market is slightly higher as on Germany market. The effect is positive and significant for all quantiles. We see the strongest and the same effect for 5% and 10% quantile. For other quantiles, the influence of Brexit is lower in 25% and 1% 36 quantile. From the median to 90% quantile, the effect is stable. Brexit has the lowest impact in 99% quantile. The dependency of the Poland market on Brexit is oscillating, positive and significant. We can find a symmetric influence in several quantiles. The highest impact of Brexit has appeared in 5% and 95% quantile, and then in 90% a 99% quantile. Comparable effect can be observed also in lower and upper quartiles. Then follows the median and the lowest influence is in bear markets in 1% quantile. Brexit had the strongest influence on the Irish market, mainly in bear markets. The influence has been positive gradually decreasing from 1% to 90% quantile. In bull markets (in 95% and 99%), the influence of Brexit has again increased, but it has been lower compared to bear markets. Table 1 Quantile regression estimates of the coefficients 0.010 0.050 0.100 0.250 0.500 0.750 0.900 0.950 0.990 DAX ( )qα -0.03 -0.01 -0.01 0.00 0.00 0.00 0.01 0.01 0.03 t-stat -38.64 -34.87 -35.17 -28.88 2.77 33.66 33.69 41.10 26.37 ( )qβ 0.87 0.94 0.91 0.92 0.91 0.90 0.91 0.94 0.92 t-stat 17.06 28.76 40.63 77.16 88.83 78.88 38.43 34.95 12.18 CAC40 ( )qα -0.02 -0.01 -0.01 0.00 0.00 0.00 0.01 0.01 0.02 t-stat -29.06 -32.52 -42.34 -32.18 0.92* 35.25 42.97 40.87 28.92 ( )qβ 0.95 0.98 0.98 0.96 0.93 0.93 0.93 0.92 0.89 t-stat 16.40 32.68 61.08 94.10 106.76 99.21 58.08 39.88 14.49 WIG20 ( )qα -0.04 -0.02 -0.02 -0.01 0.00 0.01 0.02 0.02 0.04 t-stat -24.14 -34.93 -39.30 -29.96 0.85* 31.46 44.72 34.32 24.79 ( )qβ 0.66 0.79 0.73 0.72 0.68 0.72 0.78 0.79 0.78 t-stat 4.96 14.96 21.58 35.11 36.02 34.45 25.93 14.99 6.14 ISEQ ( )qα -0.03 -0.02 -0.01 0.00 0.00 0.01 0.01 0.02 0.03 t-stat -17.01 -35.64 -30.23 -30.34 2.64 34.59 43.61 35.16 24.97 ( )qβ 0.77 0.77 0.75 0.69 0.67 0.65 0.65 0.68 0.76 t-stat 5.78 21.42 27.17 55.62 60.65 54.14 34.14 20.48 9.13 IBEX ( )qα -0.03 -0.02 -0.01 -0.01 0.00 0.01 0.01 0.02 0.03 t-stat -27.24 -31.28 -47.33 -29.47 1.14 33.74 36.79 35.42 24.97 ( )qβ 0.99 0.87 0.87 0.84 0.82 0.83 0.83 0.83 0.82 t-stat 12.38 22.24 47.57 63.26 73.81 68.30 36.77 25.16 10.13 XU100 ( )qα -0.07 -0.04 -0.03 -0.01 0.00 0.01 0.02 0.04 0.07 t-stat -22.09 -42.49 -46.74 -28.40 1.27* 29.67 43.12 32.85 24.48 ( )qβ 0.79 0.83 0.82 0.77 0.74 0.82 0.80 0.73 0.64 t-stat 3.17 12.16 18.99 25.93 30.03 26.54 18.39 8.80 3.04 Source: author’s calculations based on data from Bloomberg The Spanish stock market has been relatively affected the most positively by Brexit. The dependence has been the highest for 1% quantile, gradually declining and moderately stabilized over the median. The Turkish market has been slightly oscillating influenced by Brexit, with the highest positive impact for 5% quantile. Brexit had the lowest impact in bull markets (99% quantile) (see Table 1). 37 Quantile regression estimates of the regression parameters are shown in Figure 1 – Figure 6. Note that the dashed line shows the quantile regression estimates of the regression parameters across the quantile ranging from 0.01 to 0.99. Gray band depicts 95% confidence intervals for the quantile regression parameter estimates. Figure 7 compares the impact of Brexit on individual markets. We see that in bear markets, Brexit had the greatest impact on the Spanish stock market, followed by the French and German markets, followed by the Turkish, Irish stock market. Brexit had the least impact on the Polish stock market. During the bull markets, Brexit had the greatest influence on German, French, Spanish, Polish, Irish and the lowest on the Turkish stock market. Under standard business conditions expressed by the median, the French, then German, Spanish, Turkish, Polish, and finally Irish, stock markets were most affected. Figure 1 Quantile regression estimates of the regression parameters for DAX Source: Author’s illustrations Figure 2 Quantile regression estimates of the regression parameters for CAC40 Source: Author’s illustrations 38 Figure 3 Quantile regression estimates of the regression parameters for IRELAND Source: Author’s illustrations Figure 4 Quantile regression estimates of the regression parameters for IBEX Source: Author’s illustrations Figure 5 Quantile regression estimates of the regression parameters for WIG20 Source: Author’s illustrations 39 Figure 6 Quantile regression estimates of the regression parameters for XU100 Source: Author’s illustrations Figure 7 Comparison of the Quantile regression estimates Source: Author’s illustrations 4 Conclusions Our paper contributes to the existing literature by assessing the impact of Brexit uncertainty on selected developed and emerging European stock markets across different quantiles of the return distributions. Our empirical evidence for the daily data from January, 7 2000 to February, 3, 2017 indicates positive significant dependence between the EU stock markets and Brexit. Brexit has had an asymmetric influence on individual stock markets. It has more influence during bear rather than bull markets. Our findings are similar to (Raddant, 2016) findings that wrote the Brexit vote had similar effects in Germany, France, Spain and Italy, but our results precisely describe the impact of Brexit on the analyzed stock markets. Mapping the impact of Brexit is helpful for international investors, portfolio risk managers, traders, policymakers to avoid the downside risk in their investments. Uncertainty about future agreements between the UK and the EU and changes in the future EU financial infrastructure still remains, and the two year period to finalize the process of leaving the EU can therefore bring some interesting developments. References Alexander, C. (2008). Market Risk Analysis. Chichester: John Wiley & Soons. Aymen, B. R., Mongi, A. (2016). Financial market interdependencies: A quantile regression analysis of volatility spillover. Research in International Business and Finance, 36, pp. 140-157. 40 Bae, K. H., Karolyi, G. A., Stulz, R. M. (2003). A new approach to measuring financial contagion. Review of Financial Studies, vol. 16 (3), pp. 717–763. Baker, S. R., Bloomb, N., Davis, S. J. (2015). Measuring Economic Policy Uncertainty. CEP Discussion Paper No 1379. Centre for Economic Performance. Retrieved from: http://eprints.lse.ac.uk/64986/1/dp1379.pdf. Baur, D. G. (2013). The structure and degree of dependence: A quantile regression approach. Journal of Banking & Finance, vol. 37, pp. 786–798. Hu, L. (2006). Dependence patterns across financial markets: a mixed copula approach. Applied Financial Economics, vol.16 (10), pp. 717–729. Mensi, W., Hammoudeh, S., Reboredo, J. C., Nguyen, D. K. (2014). Do global factors impact BRICS stock markets? A quantile regression approach. In: Emerging Markets Review, Vol. 19, pp. 1–17. Raddant, M. (2016). The Response of European Stock markets to the Brexit. Kiel Policy Brief, No. 100. IFW Kiel Institute for the world economy. Ringe, W. G. (2017). The Irrelevance of Brexit for the European Financial Market. Oxford Legal Studies Research Paper, No 10/2017. Retrieved from: https://ssrn.com/abstract=2902715 or http://dx.doi.org/10.2139/ssrn.2902715. 41 Bankruptcy Model IN05 and Private Slovak Civil Engineering Companies Mária Bohdalová1 , Natália Klempaiová2 1 Comenius University in Bratislava Faculty of Management, Department of Information Systems Odbojárov 10, 820 05 Bratislava, Slovakia E-mail: maria.bohdalova@fm.uniba.sk 2 Comenius University in Bratislava Faculty of Management, Department of Information Systems Odbojárov 10, 820 05 Bratislava, Slovakia E-mail: nklempai@gmail.com Abstract: The aim of the paper is to analyze bankruptcy model IN05 to determine its usability for private civil engineering companies in Slovakia. The analysis has been carried out using a sample of companies obtained from a Slovak database called FinStat. In this paper, we have analyzed data from a static point of view when we have used one year data, and from a dynamic point of view when we have analyzed samples of companies that have been bankrupt for the last 4 years. The reporting ability of the selected indicator IN05 is judged by the number of correct and incorrect predictions. We have found that the IN05 index is an appropriate model, the validity of which has been verified by logistic regression. We have found out the predictive power of the IN05 model is not reduced unless we consider the EBIT/interest expense and current assets/shortterm liabilities indicators. We have found that the index IN05 is a reliable model for civil engineering industry in Slovakia. Keywords: Index IN05, bankruptcy model, logistic regression JEL codes: G32, M10, C35 1 Introduction Civil engineering sector makes a significant contribution to Slovakia's GDP despite the declining construction production in recent years (Yearbook of Construction in SR 2016). The situation in civil engineering sector has slightly improved because of state contracts that were obtained, especially by large construction companies. However, the financial situation of the large construction companies is not completely without problems. Medium, small and micro construction businesses in the civil engineering sector can have some financial problems, they can default by failing to pay at a specified time an obligation. Default is a specific event, when a company fails to meet obligation. Bankruptcy is a legal process, where creditors work with legal authorities to oversee the finances of an entity which is in default or insolvent. Bankrupt is the process through which creditors collect the debts owed to them. The fact that Slovak civil engineering sector is in a crisis proves, compared to other sectors, a higher number of companies that are bankrupt or possibly facing the bankruptcy. These facts have led us to analyze a sample of companies from the construction sector the data from accounts database FinStat. All around the world there are known numerous bankruptcy models, but most of them were created based on surveys of economic conditions that are very different from the economic reality in Slovakia. The added value of these models can be misleading for a Slovak company. Due to similar economic conditions of the Czech and Slovak Republic, it is appropriate to use the same model developed for the conditions in the Czech Republic for Slovak companies. The advantage of the Czech model is based on the fact Slovak companies 42 have comparable or identical items of financial statements (Diheneščíková and Hičák, 2011). The best-known Czech model is Neumaier's husband model Index IN (Neumaierová and Neumaier, 2005). The prediction possibilities of the financial situation of the Slovak companies were also mentioned in (Zalai et al., 2016). An analysis of the usefulness of Altman's functions for predicting the financial development of Slovak companies was carried out by Kabát’s team (Zalai et al., 2016). Many experts try to create reliable indicators for detecting creditworthiness or bankruptcy in Slovakia. The most common used method for creating a bankruptcy model is multidimensional discriminatory analysis or logistic regression. At present, either new models arise or experts deal with the usability of the already established credit and bankruptcy models in the selected economy. The aim of the paper is to analyze and validate the IN05 model for Slovak civil engineering companies. The paper contains four sections. The first and the second section provides theoretical background for the practical processing of the topic. The third section includes a more detailed description of the objectives set, used methodology and the methods of investigation. The fourth section presents our results and findings for the bankruptcy model IN05 for Slovak civil engineering sector and model IN05 adjusted by logistics regression. 2 Methodology and Data The first Neumaier’s model originated in 1995. It was based on mathematical and statistical rating models and practical experience in analyzing the financial health of companies (Knápková et al., 2013). Insolvency was frequent in Czech companies at that time. Therefore, this model includes the ratio of the reflected insolvency of the companies expressed in relation to the liabilities over the maturity divided by incomes. Similarly, Slovak companies had similar problems, so the model can be used in Slovakia as well (Zalai et al., 2016). This model considers more creditors than ownership (Růčková, 2015). Ownership model was created in 1999. Combining both models, a new model IN01 was created in 2001, linking creditworthy and bankruptcy insight. In 2005, Neumaiers created IN05 model that upgrades the IN01 model from the point of view of enterprise's ability to create value. Compared to the model IN01, the weights in the EBIT/Total Assets and bandwidth indicators have been changed (Zalai et. al, 2016). The IN05 model is IN05=0,13(A/L)+0,04(EBIT/IE)+3,97(EBIT/TA)+0,21(TR/TA)+0,09(CA/(STL+STBL)) (1) where A/L - Assets/Liabilities or Financial Leverage, EBIT/IE – EBIT/Interest Expense or Interest Coverage, EBIT/TA – EBIT/Total Assets or Ability to Create Profits or Production force TR/TA – Total returns/Total Assets CA/(STL+STBL) – Current Assets / (Short-term Liabilities + Short-term Bank Loans and Borrowings) or 3rd grade liquidity. Enterprise rating and the assignment of the future status is based on the next bands: IN05 > 1.6 – enterprise creates worthiness with probability 67% (good financial situation of the company) 0.9 < IN05 < 1.6 – gray zone (Area of unmatched results) IN05 < 0.9 – enterprise is at risk of bankruptcy with probability 86%. To avoid problems in the indicator EBIT/IE in the case of loans approaching zero, Neumaiers recommended to reduce the value of the indicator EBIT/IE to value 9. This 43 eliminates any case when the impact indicator EBIT/IE outweighs the other effects, and the index value is approaching to infinity (Neumaierová and Neumaier, 2005). Our analysis of the IN05 model has been carried out using a sample of companies obtained from the Slovak FinStat database. Data were filtered, refilled and merged as needed. The resulting databases were continuously controlled for instance on data duplicity or randomly on the correctness of the data associated with the company's ID (company registration number). We have created two data samples. The first contains 1360 Slovak private civil engineering companies. The second sample contains information about 35 bankrupted and restructuring Slovak private civil enginnering companies during 4 years before the bankruptcy. In this paper, we have analyzed data from a static point of view when we have used one year data using crosstabs. Static analysis was carried out for 1360 private Slovak civil engineering companies. From a dynamic point of view, we have analyzed the predictive value of the IN05 model for bankrupt companies over four full consecutive calendar years immediately prior to their bankruptcy (dynamic point of view) (Klempaiová, 2017). The number of bankrupt companies was 35. In the last analysis, we verify the performance of the IN05 model using logistic regression. We have used IBM SPSS software (Verma, 2013). 3 Results and Discussion Crosstabs have been used to test goodness of the fit of the observed frequency of bankrupt and restructured companies in private civil engineering Slovak sector and IN05 model and to test the significance of association between these two attributes. Table 1 Bankruptcy and Restructuring vs IN05 category IN05 finstat TotalBankruptcy Gray zone Prosperity BankruptcyandRestructuring No Count 409 271 523 1203 % within Bankruptcy and Restructuring 34,0% 22,5% 43,5% 100,0% % within IN05 finstat 74,6% 97,8% 97,8% 88,5% % of Total 30,1% 19,9% 38,5% 88,5% Yes Count 139 6 12 157 % within Bankruptcy and Restructuring 88,5% 3,8% 7,6% 100,0% % within IN05 finstat 25,4% 2,2% 2,2% 11,5% % of Total 10,2% 0,4% 0,9% 11,5% Total Count 548 277 535 1360 % within Bankruptcy and Restructuring 40,3% 20,4% 39,3% 100,0% % within IN05 finstat 100,0% 100,0% 100,0% 100,0% % of Total 40,3% 20,4% 39,3% 100,0% Source: own processed based on data www.finstat.sk Table 1 shows results of the static analysis that was performed on 1 360 companies. 157 companies (11.5%) were bankrupt or in restructuring. IN05 model identified 548 companies as bankrupt (40.3%). 139 companies out of 548 companies i.e. 25.4% were correctly identified as nonperforming. The model correctly classified 88.5% of true nonperforming companies. Chi-square test confirmed the dependence between bankruptcy and restructuring frequency and the model IN05 at the 99% confidence level (sig=0.000). Cramer’s V and Contingency Coefficient were significant and equal to 0.335. 44 Using the simple sum of the totally rated companies or of the total error rate (error α + error β), we reached the results summarized in Table 2. The wrong type 1 (error α) represents the incorrect classification of insolvent companies as solvent (prosperous) and wrong type 2 (error β) evaluates solvent companies as insolvent. The simple sum method did not show reality in a real sample of companies. The success rate of the IN05 model in the individual categories studied (success structure) has been considered together with their location throughout the sample. In such a way of assessment, IN05 model appeared to be successful, unlike the simple sum method. Table 2 Correctness of the IN05 model Model IN05 Correct evaluation Wrong evaluation Number of companiesFrequency % frequency % Type 1 139 88.5 18 11.5 157 Type 2 794 66 409 34 1203 Total 933 68.6 427 31.4 1360 Source: own processed based on data www.finstat.sk Dynamic analysis has been performed for a sample that contained 35 bankrupt companies. Correctness of the model IN05 during four consecutive calendar years just before the known bankruptcy is given in Table 3. We can see the model IN05 has the highest success one year before bankruptcy. With an increasing number of years, the ability to accurately predict bankruptcy decreases. Table 3 Correctness of the IN05 model 1 Year 2 Years 3 Years 4 Years Bankruptcy 33 32 29 25 94% 91% 83% 71% Gray zone 1 1 2 4 3% 3% 6% 11% Prosperity 1 2 4 6 3% 6% 11% 17% Source: own processed based on data www.finstat.sk The financial situation of all 35 companies, as assessed by IN05 over four years, is shown in Figure 1. The top row is the situation four years before the bankruptcy, the lowest row is the situation one year before bankruptcy. The black square in the chart means the company is located in the border zone bankruptcy, gray square - gray zone, white square - prosperity. Figure 1: The informative value of IN05 within 35 companies bankrupt Source: Own processed based on data www.finstat.sk Verification of IN05 for Slovak civil engineering companies using logistic regression To validate the IN05 using logistic regression, we have modified the data sample of 1390 companies as follows: we ignored extreme values such as assets over EUR 1,000,000, interest expense over EUR 1,000,000, and liabilities over EUR 10,000,000. We considered EBIT values greater than EUR -1,000,000, sales over EUR 7,500,000, current assets that are less than EUR 5,000,000, short-term liabilities lower than EUR 45 10,000,000 and short-term bank loans of less than EUR 5,000,000. Missing data were omitted. Validation of the IN05 model took place on a sample of 322 companies from total 1360 companies. We will consider the following logistics model for verifying bankruptcy forecasting: Log p/(1-p) = a + b(A/L) + c(EBIT/IE) + d(EBIT/TA) + e(TR/A) + f(CA/( STL+STBL)) (2) Independent variables were the ratios (see equation (1)) that enter the IN05 model calculation and p is the probability of bankrupcty. The dependent variable indicates whether an enterprise is in bankruptcy or restructuring (value equals to 1) or not (value equals to 0). We have used the Forward: LR (likelihood-ratio) method to estimate the logistics regression model, by which we have gradually analyzed the logistic model for model IN05. Our results of the logistic regression will be discussed in two steps. In the first step, the logistic regression model will be developed using the constant without using any of the independent variables. This model can be used to compare the utility of the model developed in step 2 by using the identified independent variables. Constant model captures the fact that 88.8% of companies were not bankrupt. Table 4 shows that if nothing is known about the independent variables and one simply guesses that a company would not be bankrupt, we would be correct 88.8% of the time. The probability of bankruptcy is 0.126 (see Table 5). Significance of Wald's statistics is low (less than 1%), the estimated chance is significant with significance value 0.01. Table 4 Classification table Observed Predicted Bankruptcy and Restructuring Percentage Correct No Yes Step 0 Bankruptcy and Restructuring No 286 0 100.0 Yes 36 0 0.0 Overall Percentage 88.8 Source: own processed based on data www.finstat.sk Table 5 Variables in equation (2) B S.E. Wald df Sig. Exp(B) Step 0b Constant -2,072 0,177 137,338 1 0,000 0,126 Source: own processed based on data www.finstat.sk Table 6 shows whether each independent variable improves the model or not. We can see that the ratios A/L, and CA/(STL+STBL) may improve the model as they are significant with EBIT/IE and A/L slightly better than CA/(STL+STBL). Inclusion of these variables would add to the predictive power of the model. Table 6 Variables not in equation (2) Step 0 Score df Sig. A/L 8.467 1 .004 EBIT/IE 12.972 1 .000 EBIT/TA .427 1 .513 TR/A 2.728 1 .099 CA/( STL+STBL) 6.033 1 .014 Overall Statistics 24.748 5 .000 Source: own processed based on data www.finstat.sk 46 Table 7 shows the value of -2 log likelihood (-2LL), which is a deviance statistic between the observed and predicated values of the dependent variable. All deviance statistics are insignificant, which indicates that the model is suitable. Nagelkerke’s R2 explains 42.8% variability of the dependent variable by the independent variables in the model. Table 7 Model summary Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square 1 218.428b .022 .044 2 168.033c .164 .325 3 170.000c .159 .315 4 151.395c .206 .408 5 147.454d .215 .428 Source: own processed based on data www.finstat.sk To find whether the deviance statistic -2LL is insignificant or not, Hosmer and Lemeshow tests were executed. The results are shown in Table 8. Since the p values associated with Chi-square in Table 8 are greater than 0.05 started from the second to the fifth model, models are efficient. Table 8 Hosmer and Lemeshow Test Step Chi-square df Sig. 1 47.432 8 .000 2 1.965 8 .982 3 2.125 8 .977 4 5.914 8 .657 5 13.136 8 .107 Source: own processed based on data www.finstat.sk Table 9 is a classification table, it shows the observed and the predicted values of the dependent variable in each model, when we add some ratio into the model. When we add ratio EBIT/IE, the model correctly classifies 89.4% companies. The model with ratios EBIT/IE and A/L classifies correctly 90.1% companies. If the independent variable is the ratio A/L then the log linear model correctly classifies 89.1% companies. When model uses ratios A/L and TR/TA, its capability to correctly classify bancrupty is 92.2%. If the model uses ratios such as A/L, EBIT/TA and TR/TA, then model’s ability to properly evaluate the bancrupty is 92.5%. Table 9 Classification table Observed Predicted Bankruptcy and Restructuring Percentage CorrectNo Yes Step 1 Bankruptcy and Restructuring No 286 0 100.0 Yes 34 2 5.6 Overall Percentage 89.4 Step Bankruptcy and No 281 5 98.3 47 2 Restructuring Yes 27 9 25.0 Overall Percentage 90.1 Step 3 Bankruptcy and Restructuring No 279 7 97.6 Yes 28 8 22.2 Overall Percentage 89.1 Step 4 Bankruptcy and Restructuring No 282 4 98.6 Yes 21 15 41.7 Overall Percentage 92.2 Step 5 Bankruptcy and Restructuring No 283 3 99.0 Yes 21 15 41.7 Overall Percentage 92.5 Source: own processed based on data www.finstat.sk Table 10 shows the value of regression coefficients B, Wald statistics, its significance and odds ratio exp(B) for each variable in each model. Note that all coefficients are in logodds units. The logistics regression equation for predicting the bancrupty in the fifth model is given by equation (3). By increasing the financial leverage, the A/L ratio, the chance of bankruptcy of the company decreases in the odds by 97.6%. With increasing the EBIT/TA ratio, the chance of bankruptcy of the company grows by 31.8% and when increasing the TR/TA ratio, the chance of bankruptcy will be reduced by 40.18% when other ratios are kept and all other predictors are constant. log 2,829 3,762 / 0,276 / 0,514 / 1 p A L EBIT TA TR TA p = − + − − . (3) Table 10 Variables in the equation (Forward LR (likelihood-ratio) method) B S.E. Wald Df Sig. Exp(B) Step 1b EBIT/IE .000 .000 3.193 1 .074 1.000 Constant -2.122 .181 137.395 1 .000 .120 Step 2c A/L -2.607 .455 32.785 1 .000 .074 EBIT/IE .000 .000 .936 1 .333 1.000 Constant .770 .466 2.739 1 .098 2.161 Step 3c A/L -2.689 .453 35.169 1 .000 .068 Constant .876 .460 3.637 1 .057 2.402 Step 4d A/L -3.447 .547 39.754 1 .000 .032 TR/TA -.409 .149 7.587 1 .006 .664 Constant 2.324 .627 13.726 1 .000 10.215 Step 5e A/L -3.762 .591 40.528 1 .000 .023 EBIT/TA .276 .147 3.529 1 .060 1.318 TR/TA -.514 .158 10.591 1 .001 .598 Constant 2.829 .717 15.577 1 .000 16.927 Source: own processed based on data www.finstat.sk 48 Now we estimate IN05 log linear model using “Enter” method. Hosmer and Lemeshow test rejects the suitability of the model (sig=004) at confidence level 1%. The individual coefficients (see Table 11) of this model say: increasing the financial leverage of the A/L ratio, the chance of bankruptcy of the company will be reduced by 96.3%. The EBIT/IE indicator does not affect the bankruptcy, increasing the EBIT/TA of the company, the chance of bankruptcy increases by 38.3%, by increasing the TR/TA ratio the chance of bankruptcy will be reduced by 39.5%. Increasing the CA/( STL+STBL) indicator, the bankruptcy will be reduced by 38.2% keeping the other indicators unchanged. ( )log 2,758 3,297 / 0,0 / 0,324 / 0,503 / 0,482 1 / p A L EBI CA STL STBT IE EBIT TA TR TA p L= − + + − − − + (4) Table 11 Variables in the equation B S.E. Wald df Sig. Exp(B) Step 1b A/L -3.297 .726 20.639 1 .000 .037 EBIT/IE .000 .000 .545 1 .460 1.000 EBIT/TA .324 .168 3.736 1 .053 1.383 TR/TA -.503 .162 9.608 1 .002 .605 CA/( STL+STBL) -.482 .579 .693 1 .405 .618 Constant 2.758 .733 14.147 1 .000 15.767 Source: own processed based on data www.finstat.sk The informative value of the full model log linear IN05 model (4) to correctly predict the bankruptcy is 92.5% (Table 12). If we do not consider IN05 model indicators EBIT/IE and CA/( STL+STBL) (model (4)) its expressiveness ability would be impaired. The model (3) without these indicators, unlike the IN05, is statistically significant, with the leverage of both models being the same for Slovak private construction companies. Table 12 Classification table Observed Predicted Bankruptcy and Restructuring Percentage Correct No Yes Step 1 Bankruptcy and Restructuring No 283 3 99.0 Yes 21 15 41.7 Overall Percentage 92.5 Source: own processed based on data www.finstat.sk 4 Conclusions This paper aims to analyze the bankruptcy and creditworthiness model IN05 from a static point of view on a real sample of companies and from a dynamic perspective on a sample of real estate businesses. Subsequently, IN05 has been verified for private construction companies in Slovakia. We have found out that the predictive power of the IN05 model is not reduced unless we consider the EBIT/IE and CA/(STL+STBL) indicators. Although the model without these indicators is statistically significant, unlike the IN05 model, with the leverage of both models being the same for Slovak private companies in the construction sector. References Diheneščíková, D., Hičák, Š. (2011). Index IN05 v priemyselných podnikoch na východnom Slovensku. Trendy v podnikání, vol. 150(2), pp. 39-43. 49 Finstat s.r.o. (2017). Databáza finančných údajov. Retrieved from: https://www.finstat.sk/databaza-financnych-udajov. Klempaiová, N. (2017). Use of Statistics Methods for Predicting Bankruptcy. Diploma Thesis. Comenius University, Bratislava. Knápková, A., Pavelková, D., Šteker, K. (2013). Finanční analýza: Komplexní průvodce s příklady, 2nd ed. Praha: Grada Publishing. Neumaierová, I., Neumaier, I. (2005). Index IN05. In Evropské finanční systémy: Sborník příspěvků z mezinárodní vědecké konference. Brno: Masarykova univerzita v Brně, 2005, pp. 143-148. Růčková, P. (2015). Finanční analýza: metody, ukazatele, využití v praxi, 5th ed. Praha: Grada Publishing. Verma, J. P. (2013). Data Analysis in Management with SPSS Software. 2nd ed. Springer India. Yearbook of Construction in SR 2016. Bratislava: Štatistický úrad Slovenskej republiky, pp. 11-12. Zalai, K., Dávid, A., Šnircová, J., Moravčíková, E., Hurtošová, J., Tučníková (Sovíková), D. (2016). Finančno-ekonomická analýza podniku, 9th ed. Bratislava: Sprint 2. 50 Factors that affect the market prices of flat renting Roman Brauner, Sylvia Plottová University of Technology Faculty of Business and management, Department of Economics Kolejní 2906/4, 612 00 Brno, Czech Republic E-mail: brauner@fbm.vutbr.cz Abstract: This paper aims to investigate the factors influencing the level of market prices of flat renting in the Czech Republic, more precisely on big cities such as Prague or Brno. Firstly, it analyzes the development of market rent prices over the last 7 years and examines the specific factors which influenced this period. The subject of the research is also the relation of the difference in the development of the market prices rental according to flat size. In the paper we also focus on the influence of non-standard real estate price development behavior in cooperation with a significant decrease in interest rates on mortgage loans. An integral part of the comparison is the development of market rent prices with other markets bringing regular dividends, especially capital markets, or dependence on the development of alternative investments such as precious metals. On the basis of the achieved results, the conclusion predicts the future development of the market rentals in the Czech Republic. Keywords: financial system, interest rate, prices of flat renting, central banks, inflation JEL codes: E42, E43, E58, G15 1 INTRODUCTION In the case of a decision to save or to postpone the current consumption for later consumption, it is necessary to decide responsibly both on the amount of the deposit, so that it makes sense as well as on the way we will create the savings. That needs to take place both in case of decision making of households or businesses. In general, it is the same approach for all as they are potential investors. There exist an option and it is the easiest one to save into the box. This saving method, in addition to the risk associated with the theft has one certainty that the future value converted to the current value of the amount saved will be lower than the real current value. In other words if we do not value these savings anymore their interest rate will gradually decrease. Therefore, investors who want to keep their value of their savings or better to increase this value need to go to the financial market. Especially they look at the capital market or the precious metals market and the real estate market and search for options to secure the value of their savings. However, in the event that we go to these markets we should be aware that we must always consider the three main factors influencing our decision. It is namely risk, profitability and liquidity. However, the prices in these markets are interdependent and incidentally regulated by various interventions. These interventions can also have side effects such as when consumers are forced by various interventions to keep their assets for later consumption, not invest them into financial markets while seeking alternatives for their use. Great alternative appears in the purchase of real estate, both for the purpose of own housing and also for the depositing of free financial capital. Real estate investment is also 51 great advantage over other alternatives, and it has also the ability to be funded through a mortgage. That is actually one of the cheapest financing alternatives on the market. Therefore, in the case of so called "saving" into the real estate it is not necessary to possess initial investment because real estate can be covered by nearly 100% with usage of different combinations of loans. 2 Methods and Data Mainly secondary data from the database of the Czech Statistical Office and the Czech National Bank were used in order to analyze the problem. These data were subjected to the study of interdependencies and links. The results of the secondary data analysis determine the factors that can influence the development of rental prices and which are the output of primary research. Examined secondary data are e.g.: • property prices • the development of interest rates on mortgage loans • the development of interest rates on savings accounts • the developments in capital markets • the development of precious metal prices The results of the analysis then predict the possible future development of rental prices on the real estate market. Since the article deals with rent in the form of savings, research has focused only on the most liquid and stable position of real estate in large cities, especially Prague and Brno. From the point of view of size, focuses on the flats where their values are converted into m2 or divided according to their layout as 1 + k, 2 + k, 3 + k, 4 + k. To determine the psychological value of the investment (real estate) for the subsequent lease, the modified Gordon dividend discount model was used, namely: VH = (D · (1 + g)) / (rd - g) [1] where: VH - internal (current) property value d - expected rent g - expected annual growth rate of rent rd - required (constant) rate of return (discount rate) Data was processed in MS Excel 2007. The data was reviewed for the period from year 2009 until the beginning of year 2017. The methodology used is a combination of qualitative and quantitative analysis. The qualitative component of the research is represented by the findings of economic theory, which are then compared with the current way of managing the most important world economies. In terms of economics, the following theoretical approaches are used: "Classical Interest Theory", "Theory of Interest Rate Liquidity Preference", "Loan Capital Theory" and "Rational Expectations of Interest Rates", and represented by the monetary economy, namely Fisher Quantitative theory of money. As far as the quantitative component is concerned, it is based on economic data taken from the Eurostat database, the US Department of the Treasury and the Trading Economist. 52 3 Results and discussions In the event that we decide for investment in real estate instead of consumption of financial capital we must realize that in most cases it may be a long-term form of financial capital deposit. Since, there is a need to transfer a larger amount of financial capital to invest in real estate, bank credit is largely used. It stands for a commitment on the part of the investor that if the investment does not bear the expected income he or she must be able to cover the loan from their own resources. Additionally, their creditworthiness is affected when extra loans are needed. Many people carry out this form of investment only a few times in their lifetime so it is appropriate to consider the parameters that affect its variables. In analyzing the factors influencing the value of the investment income in the form of rents on the real estate market, we analyze the relationships between the values of the rent charged on the real estate market and the development of other factors. Variables that affect them most are: real estate prices, inflation, interbank interest rates announced by the Czech National PRIBOR. These variables affect the interest rates of commercial mortgage banks and the interest rates on savings accounts or alternatively, the development of prices on other markets such as the capital markets and the precious metals markets. Analysis of the development of rental prices in Prague and Brno As we deal with the analysis of real estate investments, for their stability, the research was carried out for flats in Prague and Brno where their value was always converted to m2. Figure 1 Rental prices of flats Source: Remax rental price index 53 As seen from chart 1, in reviewed period from year 2010, rental prices in all sized flats have grown apart from a non-standard small apartment. Furthermore, it is clear from chart 1 that the most profitable group is a group of small apartments due to the layout of the size of the flat per m2. [6] It is necessary to say that other housing costs are linked to general housing costs but not all of them are being converted per m2 but per unit. It can´t be clearly stated that the smaller the flat the more economically advantageous it is. Therefore, it is good to choose a certain average with regard to the possible use or to target onto certain groups of usability. Those might be apartments 1 + k and 2 + k with an area of 40-60 m2. In the following years there is a trend of moderate decline in 2011 to 2013 and thereafter the growth from 2014 calls with the development of other factors in order to determine possible effects on rental price growth. Thereinafter, the focus needed to be on analyzing the overall development of property prices over the period in general. In the first place, it is necessary to think about who is doing these investments in particular. Real estate investing is carried out by a wide range of subjects. On one hand it can be end users or households that buy real estate for their own housing needs, or on the other hand small to medium and large investors or businesses that store their assets for their preservation or as way of generating income from rents. Analysis of factors affecting rents Real estate investment is one of the long-term stable investments. However, even in this segment large and sometimes unexpected fluctuations can be experienced in terms of their immediate value. Figure 2 Price index of real estate 2010=100 Source: Czech national bank In chart 2, we can see that if we omit the extreme situation that has been occurring since 2014 the value of real estate is almost constant. This trend which is shown by the two charts can be determined as identical. However, in the case of rental prices, it is not as significant as in case of property prices. Therefore, by examining or following these changes we can then predict if such stable situation will or will not move into different 54 levels than it is now. Question remains whenever this wave will change downwards again or remains on the original level. [2] After a non-standard period in the years following year 2008 when the world struck the economic crisis, the real estate market stabilized slightly and gradually declined until the beginning of 2014. Since 2014, real estate prices have been steadily rising and starting to align with the situation between 2007 and 2008. Nonetheless this increase is not so significant but continues to last and has been going for three years on. What is affecting these prices on the real estate market is in particular the demand that has been growing steadily in recent times as well. As has been said, the purchase of real estate is accompanied by a significant investment so for the most part these operations are realized through loans, especially mortgages. Therefore, interest rates are one of the major factors influencing the mood in the real estate market. These interest rates, especially mortgage loans, are largely influenced by the PRIBOR interbank reference rate announced by the Czech National Bank. The interbank interest rate is one of the key factors influencing the interest rates of commercial banks providing both mortgage loans and the possibility of depositing surpluses e.g. on saving and term accounts. This is the reason why this downward trend also influenced the rates. Figure 3 Development interest rates Source: Czech national bank This trend, which began in 2010 namely the gradual reduction of interest rates on savings accounts, resulted in the consumer looking at possible alternatives of forms of saving or depositing surplus capital. These alternative instruments are investments in financial markets, in particular capital markets and precious metal markets and possibly real estate markets, both in the form of funds and direct purchase of real estate [3]. Real estate investment is largely influenced by interest rates on mortgage loans. And this can be observed in the period when interest rates have fallen to an average interest rate sometimes below 2% pa. More and more clients are going to banks for mortgage loans, where the volume only in September 2016 was 19.607 billion crowns of mortgages. This increase can be seen in comparison with the three quarters of 2016 where the total 55 amount of 81 996 mortgages was negotiated in a total amount of CZK 159.676 billion. For comparison, for the whole year 2015 CZK 190.42 billion was provided in mortgage loans. Subsequently, this border has been overcome in 2016. Table 1 Comparison of mortgages in particular years YEAR Amount in August Amount in September Volume in August in billions CZK Volume in September in billions CZK 2010 4 046 4 800 6,757 8,011 2011 5 891 6 219 9,865 10,379 2012 6 164 5 302 9,143 8,698 2013 7 661 8 730 12,424 14,221 2014 6 515 8 302 11,187 13,831 2015 8 493 8 600 15,637 15,931 2016 9 290 9 825 18,139 19,607 Source: own processing (data form NSI) Another intervention in the real estate market and possibly also in the mortgage lending market is the recommendation of the CNB that from the 1st of October 2016 banks can´t provide so-called 100% mortgages. The client must finance at least five percent of its own resources. This bank regulation did not affect the bank's offer yet, but major changes still await as there was amendment to a consumer credit act in December 2016. The regulator's concerns about the development of the real estate market are appropriate, the average amount of the mortgage loan for the third quarter increased year-on-year by 8.6%, from the beginning of the interventions by over 20%, which is the rapid growth of real estate prices. Another factor influencing the future value of both property prices and their subsequent income in the form of rent is inflation. The interbank referential interest rate PRIBOR is used as a price source for determining the interest rates and returns of various financial products such as bonds, financial derivatives, mortgage loans, etc. and also is used as a tool for regulating the inflation rate. Therefore the analyzed area focuses on the relationship between inflation and its responsive interest rate adjustments. Figure 3 Development Inflation and PRIBOR of period 2010-2017 Source: Czech national bank 56 The Czech National Bank has set the level of inflation between 2-4% as a safe inflation target. If this limit is exceeded it will begin to use the available methods to offset it. In 2008, inflation started to rise disproportionately. It surpassed all forecasts from both domestic and foreign analysts. This growth was due to several factors such as the expected increase in value added tax from 5% to 9%. Traders "included" this tax rise in the prices before its introduction. Above all, the direct and indirect effects of the previous sharp rise in world energy and food prices had its impact on this growth. In addition, growth in wage costs accelerated in recent quarters, although labor productivity growth slowed down at the same time, leading to a sharp rise in unit wage costs. [8] At the end of 2008, the Czech National Bank responded to this growth by one of the most used instruments therefore by lowering interest rates. With this regulator, the CNB wants to make it possible for all entities to increase their consumption and thus raise the level of inflation. After this intervention, the expected level was reached at the end of 2012 but the trend turned again and inflation fell to an even lower level than it was before these interventions. The Czech National Bank responded to this development by making use of its main monetary policy tool when lowered interest rates to the technical zero rate (0.05%) at the end of 2012. In addition, the CNB has committed itself to maintaining interest rates at this record low level as long as needed. However, for the regulation of such a low inflation further measures had to be taken. So on November 7, 2013, the CNB Bank Board decided to start using the exchange rate as another instrument for the release of monetary conditions. Only the announcement of the CNB that it is ready to use the exchange rate led to a weakening of the koruna's exchange rate in late 2012 and early 2013, which helped to hinder the disinflationary tendencies and helped the economy to breathe a little. The CNB expected the inflation to decline to zero at the beginning of 2014 and that even after adjusting for excise duties on cigarettes the overall price level has fallen. Prices for many consumer basket items (especially consumer goods prices) have fallen for a long time. This CNB's exchange rate commitment was only adopted unilaterally, preventing the koruna's exchange rate appreciation above set up level by intervention of selling and buying foreign currencies. In the opposite direction, weakening above this level leaves CNB to the development of supply and demand on the foreign exchange market. This decision resulted in a rapid depreciation of the koruna's exchange rate at a desired level, then slightly above it, reaching the 27 CZK / EUR border only in July 2015. Therefore, during this period, the CNB did not directly intervene in the development of the exchange rate. At its extraordinary meeting on April 6, 2017, the Bank Board of the Czech National Bank decided to end the use of the koruna exchange rate as another instrument for the release of monetary conditions. This decision was immediately applicable. With this step, the CNB has returned to the standard monetary policy regime in which the main instrument is interest rates. The exchange rate of the koruna may fluctuate in both directions depending on the development of demand and supply. Nevertheless, the CNB is prepared to mitigate potential excessive exchange rate fluctuations with its instruments. Other possibilities for consumers to keep their capital funds are capital markets, especially equity and bond funds, possibly other types of funds. 57 Figure 4 Development of the fond Source: Conseq – Active invest dynamic Above hereinafter, there are also significant fluctuations in the period under review where extreme situation occurred around year 2008. As follows, there was a gradual growth that reached the values from between 2007 until August 2014 when the stock and bond markets again began to experience significant weaknesses. These markets are, however, influenced by events from around the world. [4] There are many factors that affect the demand for real estate and hence influence the prices on the real estate market. However, the most prominent are the development of prices of capital markets and precious metals markets and the level of interest rates influenced to a large extent by the Czech National Bank by its interventions in support of the growth of the economy. [7] Internal property value All above mentioned factors are the decision-making agents of an investor who is considering how to dispose of their funds. Therefore, another and crucial factor is the art of determining the intrinsic value of the property at its own discretion. In order to determine the intrinsic value the so-called "one-stage profit model with constant growth" can be used. Such models are used to determine the intrinsic value of an enterprise's share. One-step models with constant growth are the most commonly used profit models in investment practice. This is due to the fact that the (net) profit on the rents is divided into two parts: the part from which the dividends are paid and the part which goes to the net investment. This is also related to the assumption of a constant growth rate of the company's profit. At the same time it is obvious that as the dividend payout ratio is decreasing, so the future profit growth rate is expected to be increasing and also higher growth rate of dividend is expected and vice versa. Therefore, we can use the so-called Gordon Dividend Discount Model to use the determination of the intrinsic value of the property, which represents a truncated version of a time-unbound single-rate dividend discount model with constant growth. [1]The equation for the calculation needs to be modified only in terms of parameters for the use of internal values of an investment properties. 58 VH = (D · (1 + g)) / (rd - g) [1] We calculate the internal value (VH) as a dividend, in our example, the rent (D) divided by the required rate of return (rd) from which the expected rental growth rate is deducted. Although Gordon's model of investment practice is the most widely used one-step dividend discount model, it is only applicable if the expected growth rate of rent (g) is not only constant but also below the required rate of return (rd). At the same time, it should be remembered that the growth rate of rental property valued should not be higher than the growth rate of the whole economy. For example, if we were considering buying a real estate of a size of 2 + k and 62m it would be possible to consider according to chart no. 1 that the monthly rent of such property will be around CZK 14,000 including all services. If we further consider that the operating costs are about CZK 3,500, we have remaining 10,500 CZK or 126,000 CZK per year as net profit on the real estate (without deducting the repayment of the mortgage loan) which can be considered as a dividend (D). Furthermore, we have to include the discount rate (rd), which is composed of the cost of capital expenditures (we will consider the average value of the mortgage loan which we can fix for a longer period for rate of 2.5% pa) and the liquidity of the investment (which will be determined using the so-called opportunistic costs, for example of 3.5% pa) and as a constant rate of growth in rent (g) we will use the value of the expected inflation rate at 2% pa. From these parameters we can now calculate our psychological limit, which we should not exceed when buying real estate. VH = (126000 · (1 + 0,02)) / ((0,025 + 0,035) -0,02) = CZK 3,213,000 In the above example, it would be possible to realize the investment if the possibly purchased investment property had a lower purchase value than CZK 3,213,000. However, under what specific assumptions the investor must determine by himself. Today, this value for such a large apartment and given location is real but at the limit of market opportunities. 4 Conclusions While analyzing the factors of investment in real estate, it is necessary to include the influence of emotional decision making of an investor. Because, unlike other alternatives for example in the form of investment into the funds where the investor only sees a sheet of paper signed and the money is largely allocated to the world, the property relates both to the place of storage and to the visual side and different investor taste. After analysis of the examined factors, we can say that the rental prices of investment properties are influenced to a large extent by their purchase price which is influenced by the combination of interest rates, purchasing power of businesses and the population, inflation and developments on financial markets, especially capital markets and precious metal markets. However, it should be noted that these investments can be very dangerous because such investment goes on in larger financial volumes, so the positive state during positive times may turn into a nightmare in times of crisis as interest rates can rise above the bearable rate of the investor. The value of the property may also notice a significant decrease at this time, and any sale may not cover all the obligations arising from the acquisition 59 If we consider investment property as a long-term investment, no matter how the price of the property is going to develop, it is necessary to free itself from the emotional point of view, and in the realization of such a project, to calculate "only" the economic profitability and performance of the whole investment. Therefore, if the factors of positive economic growth, low interest rates, and the declining trend of financial markets, especially equity, come together, it is very likely that demand for real estate will grow and hence its price, which will also support rental growth. It is obvious that in the surveyed areas, which are the big cities, the amount of people interested in renting is still increasing. And in the case of more expensive real estate there is also a pressure to sell own properties with a subsequent demand for rents. References ČNB. Ceny bytů [online]. Retrieved from: http://www.cnb.cz/cnb/STAT.ARADY_PKG. STROM_SESTAVY? p_strid=ACBHB&p_sestuid=&p_lang=CS. ČNB. Úrokové sazby [online]. Retrieved from: http://www.cnb.cz/cnb/STAT.ARADY_PKG.PARAMETRY_ SESTAVY?p_sestuid=12864&p_strid=AAABAA&p_lang=CS. ČSÚ. Inflace - druhy, definice, tabulky [online] (2017). Retrieved from: https://www.czso.cz/csu/czso /mira_inflace. CONSEQ, Conseq akciový dinamický fond [online]. Retrieved from: https://www.conseq.cz/prices_historical.asp. PLUMMER, Tony. Prognóza finančních trhů: psychologie úspěšného investování. 2. vyd. Překlad Romana Hegedüsová, Radomír Čížek. Brno: BizBooks, 2014. REJNUŠ, O. (2014). Finanční trhy, 4. aktualizované a rozšířené vydání, Praha: Grada, pp. 733-740. ROSE, P. S., MARQUIS, M. H. (2009). Money and capital markets: financial institutions and instruments in a global marketplace. 10th ed. Boston: McGrawHill. The McGrawHill/Irwin series in finance, insurance, and real estate. McGraw-Hill International Edition. TOPOLAK I. (2017). Prognóza ceny bytů 2017. Retrieved from: http://www.toplak.cz/ceny-najmu-bytu-leti-prudce/. TOMŠÍK, V. (2016). “Kurzový závazek a jeho vliv na ekonomiku” [online]. Retrieved from: http://www.cnb.cz/ miranda2/export/sites/www. cnb.cz/cs/verejnost/pro_media/konference_projey/vystoupeni_projevy/download/tomsik _20160520_newton_college.pdf. 60 The effect of premium framing on life insurance demand Zuzana Brokešová1 , Erika Pastoráková2 , Tomáš Ondruška3 , Miroslav Szabo4 University of Economics in Bratislava Faculty of National Economy, Department of Insurance Dolnozemská cesta 1, 852 35 Bratislava, Slovak Republic 1 E-mail: zuzana.brokesova@euba.sk 2 E-mail: erika.pastorakova@euba.sk 3 E-mail: tomas.ondruska@euba.sk 4 E-mail: miroslav.szabo@gmx.com Abstract: In behavioral economics, framing effect represents a cognitive bias, in which the choice of the decision-maker is influenced by presentation of the decision problem. The specific formulation of the decision frame can thus incite individuals to choose a certain alternative. In the paper, we report preliminary results on the effect of price framing on the life insurance demand. Using the sample of 200 respondents, we test the differences in the attractiveness and demand for life insurance under different premium framing. In the between-subject setting, we identify statistically significant difference in the rating of the life insurance under various price formulations, controlling for socioeconomics characteristics. We do not observe the effect on the life insurance demand. Keywords: framing, premium, insurance demand, survey JEL codes: G02, G22 1 Introduction In the economic theory, insurance demand is considered the purest example of economic behavior under uncertainty, where individuals act rationally with the aim to maximize their expected utility (Schlesinger, 1999). Economic models conclude that the main factors, affecting insurance purchase, include the likelihood and extent of losses, the risk aversion of the buyer and the premium (Mossin 1968; Padmanabhan and Rao, 1993; Schlesinger, 1999). The premium represents the driver that could be influenced by the insurers in attracting clients at competitive insurance market. However, in the many situations, it was observed that not only the amount but also a presentation of the premium could affect the attractiveness of the product from the client’s point of view. Even thought that according to standard economic theory, price formulation should not be included into the decision-making process of the utility maximizing economic agent, this effect was observed the market. This phenomenon is studied in behavioural economics and it is known as price framing. In the paper, we reported preliminary results of the pilot study focusing on the effect of price framing on the life insurance demand. Using the sample of 200 respondents, we test the differences in the attractiveness and demand for life insurance under different premium framing. In the between-subject setting, we identify statistically significant difference in the rating of the life insurance under various price formulations, controlling for socio-economics characteristics. We do not observe the effect on the life insurance demand. Theoretical definition of framing effect In standard microeconomic framework, economic agents are purely rational and they try to maximize their expected utility. Individuals are price takers constrained by their financial resources and the price that they face when they make their choices (Nechyba, 2015). In their decisions, the price as an absolute (or in some cases relative) value is taking into the consideration. However, economic agents do not behave strictly according to these predictions and the consumer decision-making model provided a lack of description of human behavior in particular issues. People experience deviations from 61 rationality - errors in judgments and procedures - defined patterns of judgment and behavior that differ from rational individuals. As a reaction, behavioral economics was introduced as a new stream of economics that implement elements of psychology into economic decisions. We can define them as systematic errors of economic agents in collecting, analysing and evaluating information and in making economic decisions. Framing effect represents a cognitive bias, in which the choice of the decision-maker is influenced by presentation of the problem. Therefore, formulating or presenting a problem affects the behavior of individuals. This contradicts the theory of rational choice, which assumes that preferences should not change between two choices, and one should choose, always the most beneficial alternative. Kaheman and Tversky (1981) described this phenomenon. In their seminal paper, they tested the consistency of students’ choices in the famous “disease problem”. Two groups of students were presented two problems. In each of these problems, they had to choose one of two alternatives. Presented problems and alternatives were as follows: Problem 1: Imagine the U.S. is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600. Two alternative programs to combat the disease have been proposed. Assume that the exact scientific estimate of the consequence of the programs are as follows: If Program A is adopted, 200 people will be saved. If Program B is adopted, there is a 1/3 probability that 600 people will be saved and 2/3 the probability that no people will be saved. Which of the two programs would you favour? Problem 2: Imagine the U.S. is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600. Two alternative programs to combat the disease have been proposed. Assume that the exact scientific estimate of the consequence of the programs are as follows: If Program C is adopted, 400 people will die. If Program D is adopted, there is a 1/3 probability that nobody will die and 2/3 the likelihood that 600 people will die. Kahneman and Tversky observed that in the first problem, 72% of students opted for alternative A and 28% of students for B. Students chose 200 survivors, which seemed more attractive to them than the second alternative, which assumed a chance of 1/3 to survive 600 people. In the second problem, the C option was chosen by 22% of the students and the D alternative choose 88% of the students. The expected value of program A and program C are the same, as well as the expected value in program B and D. Rational individuals should be consistent. However, changing the formulation of alternatives in these two programs - the first program talks about the living and the second about the dead people - changed student behavior from risk averse to loss averse. By this observation, authors concluded that the way in which the options are presented in the framework could influence how an individual is making their decision. The effect of the framing may occur randomly without the individual being aware of its impact on the final decision. Declared discrepancy reflects two psychological traits of individual agents: risk aversion (in the decision-making problem in terms of profit) and loss aversion (in the decision-making problem in terms of loss). This pioneer study was followed by many verifications of the results and proves of the effect of framing in the decision-making process. For example, Puto (1985) found that individuals who are less involved in the problem are more vulnerable to framing and therefore their decision is easier to be influenced. Knowledge and previous experience within the problem frame is crucial for evaluating and making decisions and it could prevent biased decisions. 62 Framing effect in insurance theory In the insurance theory, framing was studied in different aspects including the framing of the insurance policy as well as price formulation. Wiener et al. (1986) examined the impact of framing of insurance policy in flood insurance. The authors used two types of frames: asset framing and loss framing. Asset framing means presenting the problem from a profit perspective, i.e. the insurance offer was formulated in the way that something is acquired by buying insurance, e.g. protection, paid insurance, etc. Loss framing means presenting the problem from a loss perspective, i.e. the insurance offer was formulated in the way that something is losing, e.g. loss of the property if they do not buy insurance, etc. In the asset framing, the standard expected utility theory was applied while the loss framing was based on prospect theory. The subjects were presented an insurance offer with a scenario that they bought the house and they had to decide whether they are buying flood insurance. They received supplemental information about the area where they would live. The impact of framing (asset or loss) was significant over the control group, i.e. authors concluded that framing is affecting the decision to buy insurance, but their assumptions regarding the effect of asset framing over the loss framing were not observed. Brown et al. (2008) supported the effect of framing in insurance purchase decisionmaking in the annuity market. In this study, subjects were asked to choose among annuity programs framed in a consumption or investment way. If the offer was framed as consumption, 72% of respondents preferred the annuity program, while only 21% preferred the annuity program, if the alternative was presented as an investment. Framing effect in insurance was also studied by the Huber et al. (2015) who examined the effect of price bundling and price optics on insurance. Price bundling is a price strategy that combines multiple products or components at a single set price (Eppen et al., 1991). As insulation of profits and losses affects product evaluation and selection, the price bundling should result in more positive consumer ratings than price presentation across multiple folders because price information is perceived as a relative loss (Johnson et al. 1999). On the other hand, price optics is the consumer's response to the price in various forms, if the expected value of the proposal is the same. The price may be presented as one payment in advance (up-front payment), monthly payments, or percentage of the annual payment. Authors assumed that both price optics as well as price bundling have an impact on consumer behaviour, namely the decision to buy unitlinked life insurance, as paid premium are perceived as a loss rather than as a saving. Empirical analysis revealed that neither price bundling nor the price optics had any impact on consumer ratings or the intention to purchase insurance. 2 Methodology and Data Data for our analysis were obtained by the questionnaires distributed in the Slovak Republic as a part of diploma thesis in February 2017. In general, we collected 200 responses in four different treatments. In each treatment, in all questionnaires, there was following term life insurance policy offer described: Insurance company XY offer you a term life insurance for 10 years period with guaranteed insurance sum 12 000 € that will be paid conditionally in the case of client’s death or in the case of client’s survivor of the end of life insurance policy. The guaranteed interest rate is 1,68% p. a. The framing of the premium varied in treatments. In the Treatment 1 - Baseline, we formulated the bundled price as a monthly payment of 105 €, the usual formulation used in the Slovak insurance market. In the Treatment 2, we introduced the up-front payment and the premium was formulated as 684 € payed in the first month followed by the 100 € monthly payment. In the Treatment 3, we applied price optic and we isolate three components of the monthly premium: 1 € for risk coverage, 91 € for saving, 13 € for 63 administrative fee. In the Treatment 4, we added up-front payment to price optic introduced in Treatment 2 and the premium was defined as follows: 1 € for risk coverage, 91 € for saving, 13 € for administrative fee, 584 € for administrative fee payed in the first month and followed by the 8 € month administrative fee. It is necessary to emphasize that time value of the premiums in all four treatments are equal that means that rational economic agents should be indifferent between all offers. We applied between-subject design, which means that every respondent responded to only one offer and then we compared the responses across the groups of respondents. The life insurance calculations were based on Huber et al. (2015). In all questionnaires, respondents should rate their satisfaction with the life insurance offer and their willingness to recommend this insurance to their friends and family on the Likert scale with values from 1 to 5, where 1 represented total dissatisfaction/dissuade and 5 represented total satisfaction/ recommendation. In addition, respondents were asked about their interest in purchase of offered insurance policy and we also recorded sociodemographic variables (age, gender, education level, income level, previous experience with insurance). We control for these socio-demographic variables as they were identified as important drivers of life insurance demand by the previous research. From the socio-economic characteristics, 63,5% of our respondents are females, 55% of the respondents are younger than 40 years of age, 31,5% of our respondents have university education, 52% of respondents have household income lower than 1 500 €. In addition, 83% of our respondents have previous experience with insurance. Descriptive statistics of the dataset are available in the Table 1. Table 1 Descriptive statistics Variable N Minimum Maximum Mean Std. Dev. Satisfaction with the offer 200 1 5 2,71 1,2465 Recommendation to friends and family 200 1 5 2,54 1,2675 Demand for offered insurance 200 0 1 0,28 0,4501 Previous experience with insurance 200 0 1 0,83 0,3766 University education 200 0 1 0,315 0,4657 Female respondent 200 0 1 0,635 0,4826 Age category 200 1 4 2,22 0,9142 Income category 200 1 4 1,765 0,9770 Source: authors’ own calculations We formulated two hypotheses: Hypothesis 1: Price optic will increase the satisfaction with the insurance policy compare to baseline bundled price formulation. Hypothesis 2: Price optic will increase the demand for insurance policy compare to baseline bundled price formulation. Based on the categorical character of our data, we use binary logistic regression for the analysis of the insurance policy demand as the demand was defined as binary variable (1 if individuals would like to purchase such insurance and 0 otherwise). In the analysis of the satisfaction with the insurance policy and willingness to recommend this policy to family and friends, we applied ordered logistic regression as these dependent variables were measured on the Likert scale. Logistic regression models predict the probability of occurring a desired event and represent a technique applicable for categorical dependent variable analysis (Field, 2014). 64 3 Results and Discussion The simple comparison of observed data shown in Table 2, reveal that satisfaction with the offered life insurance policy and its recommendation to family and friends do not varied substantially across different treatments. Table 2 Mean values of analysed variables in different treatments Variable Treatment 1 Baseline Treatment 2 Treatment 3 Treatment 4 Satisfaction with the insurance 2,5686 2,5273 2,4694 3,3556 Recommendatio n to friends and family 2,4902 2,4364 2,2449 3,0444 Demand for offered insurance 0,2745 0,20 0,5102 0,1333 Number of observations 51 55 49 45 Source: authors’ own calculations In Treatment 1 – Baseline, where the price is bundled, the average satisfaction (M=2,57, SD=1,02) is very similar to the value in Treatment 2 (M=2,53, SD=1,37) and 3 (M=2,47, SD=1,25). Different level of satisfaction was evoked in Treatment 4 (M=3,36, SD=1,11), where the combination of price optic with up-front payment is introduced. In this price formulation, subjects rate the life insurance policy more positive compare to baseline and the other treatments as well. Recommendation to friends and family follow the same pattern and subjects are more prone recommended the life insurance policy in Treatment 4. However, while the satisfaction and recommendation are strongly correlated (r=0,8726, p<0,000), willingness to purchase offered life insurance policy do not correlate with the satisfaction (r=-0,0785, p=0,2695) and recommendation (r=- 0,0814, p=0,2519). The average value of subjects interested in purchase vary around 20% in Treatment 1 and 2, 51,02% of subjects were interested in purchase in Treatment 3 and only 13,33% in Treatment 4. Subjects were substantially more interested in the purchase of insurance in Treatment 3 where the price was decomposed to its components. These results are confirmed by logistic regression analysis reported in Table 3. Based on the character of dependent variables, models 1-2 are binary logistic regression models and models 3-6 are ordered logistic regression models. In the case of satisfaction with offered insurance policy, decomposition of the premium with up-front payment in Treatment 4 significantly increased the value of the satisfaction. This result is confirmed also with the controlling for the socio-demographic characteristics of respondents. We do not reject our first hypothesis that price optic will increase the satisfaction with the insurance policy compare to baseline bundled price formulation in the price decomposition with up-front payment. Regarding recommendation to the friends and family, the effect is significant in the model without controls but disappeared if the controls are included. The effect of price formulation on life insurance demand is not statistically significant and instead of formulation of the price the demand is driven by the income and previous experience with life insurance. Based on these outcomes, we reject our second hypothesis that Price optic will increase the demand for insurance policy compare to baseline bundled price formulation. These results are in line with the conclusions of Huber et al. (2015). 65 Table 3 Regression results Dependent variables Demand for insurance Satisfaction with the insurance Recommendation to friends and family Independent variables (1) (2) (3) (4) (5) (6) Treatment 2 -0,242 -0,391 -0,040 -0,088 -0,070 -0,147 0,269 0,363 0,207 0,269 0,207 0,269 Treatment 3 0,625** 0,319 -0,086 -0,234 -0,219 -0,40 0,259 0,339 0,211 0,259 0,213 0,261 Treatment 4 -0,512* -0,469 0,680** 0,60** 0,451** 0,340 0,301 0,352 0,217 0,244 0,216 0,243 Constant -0,599*** 0,350 - - - - 0,187 0,463 - - - N 200 200 200 200 200 200 Controls No Yes No Yes No Yes Pseudo R-squared 0,0799 0,1602 ,0265 0,0371 0,0169 0,0279 Prob > chi2 0,0003 0,0000 ,0010 0,0038 0,0165 0,0309 Note: *, ** and *** denote significance at the 10%, 5% and 1% level, respectively. Standard errors are in parentheses. Source: Authors’ own calculations 4 Conclusions The paper reports preliminary results of the pilot study focusing on the effect of price framing on the demand for life insurance. Using the sample of 200 respondents, we tested the differences in the satisfaction and demand for life insurance with different price framing. Based on our results, we conclude that the price optic influences attractiveness of the life insurance policy and economic agents are more satisfied with the decomposed price with up-front payment. Consumers in our research, on the contrary, rated the currently used price bundled model more negatively than the premium presentation by its components with up-front payment. On the other hand, price framing has no effect on the demand for life insurance. Based on our results, we conclude that price decomposition increases the rating of the life insurance policy. The inconsistency in our results represents a field for further research. Reported dataset represent a pilot study. In the next step, we would like to increase the sample to assure robustness of our results. In addition, we also would like to analyse the missing correlation between satisfaction/recommendation and demand for life insurance. The formulation of the price represents an easy and cheap way that insurers could increase the interest for their policies. Our results supported current trends in financial regulation where supervisors try to bring transparency into price in the insurance industry and push insurers to declare all fees and other invisible components to the potential clients before the insurance purchase. Currently, average client is usually not informed about the different components of the premium. 66 Acknowledgments This paper is part of a research grant VEGA No. 1/0849/15 entitled ‘Economic and social aspects of the information asymmetry in the insurance market’ supported by the Ministry of Education, Science, Research and Sport of the Slovak Republic. References Brown, J. R., Kling, J. R., Mullainathan, S., Wrobel, M. V. (2008). Why Don’t People Insure Late-Life Consumption? A Framing Explanation of the Under-Annuitization Puzzle. American Economic Review, vol. 98(2), pp. 304-09. Eppen, G. D., Hanson, W. A., Martin, K. R. (1991). Bundling—new products, new markets, low risk. Sloan Management Review, vol. 32(4), pp. 7–14. Field, A. 2014. Discovering statistics using IBM SPSS Statistics, 3rd ed. London: Sage. Huber, C., Gatzert, N., Schmeiser, H. (2015). How Does Price Presentation Influence Consumer Choice? The Case of Life Insurance Products. Journal Risk and Insurance, vol. 82 (2), pp.401–432. Johnson, M. D., Herrmann, A., Bauer, H. (1999). The effects of price bundling on consumer evaluations of product offerings. International Journal of Research in Marketing, vol. 16(2), pp. 129-142. Kahneman, D., Tversky, A. (1981). The framing of decision and the psychology of choice. Science, vol. 211, pp. 453-458. Mossin, J. (1968). Aspects of Rational Insurance Purchasing, Journal of Political Economy, vol. 76(4), pp. 553–68. Nechyba, T. J. (2015) Microeconomics: An intuitive approach, 2nd ed. Boston: Cengage learning. Padmanabhan, V., Rao, R. C. (1993). Warranty Policy and Extended Service Contracts: Theory and an Application to Automobiles, Marketing Science, vol. 12(3), pp. 230–47. Puto, C. P. (1987). The framing of buying decisions. Journal of Consumer Research, vol. 14(3), pp. 301–315. Schlesinger, H. (1999). The Theory of Insurance Demand. In: Dionne, G., ed., Handbook of Insurance. Boston: Kluwer, pp. 131–52. Wiener, J. L., Gentry, W. L., Miller, R. K. (1986). The Framing of the Insurance Purchase Decision. Advances in Consumer Research, vol. 13, pp. 251-256. 67 Application of selected financial performance methods to chosen industry in the Czech Republic Dana Dluhošová1 , Barbora Ptáčková2 , Dagmar Richtarová3 1 VSB – Technical University of Ostrava Faculty of Economics, Department of Finance Sokolská třída 33, 702 00, Ostrava 1, Czech Republic E-mail: dana.dluhosova@vsb.cz 2 VSB – Technical University of Ostrava Faculty of Economics, Department of Finance Sokolská třída 33, 702 00, Ostrava 1, Czech Republic E-mail: barbora.ptackova@vsb.cz 3 VSB – Technical University of Ostrava Faculty of Economics, Department of Finance Sokolská třída 33, 702 00, Ostrava 1, Czech Republic E-mail: dagmar.richtarova@vsb.cz Abstract: This paper is dedicated to financial performance evaluation of selected industry of the Czech Republic. Financial performance of the industry or company is a random process, which can be decomposed into the particular indicators. Very important is to find and to quantify main factors which influence financial performance of the industry the most. One of the possible ways is to apply the method of pyramidal decomposition to financial indicators. The aim of this paper is to evaluate financial performance of selected industry in the Czech Republic in the period 2007 to 2015 according to the methods of analysis of deviation and to find main value drivers of this selected industry. Annual data from the period 2007 to 2015 will be used for the analysis. The data for the analysis will be taken from the web site of Ministry of industry and trade of the Czech Republic. Firstly, financial ratios will be determined. Secondly, the method of pyramidal decomposition will be applied to selected financial indicators, such as profitability ratios, liquidity ratios and activity and debt ratios. Analysis of deviation will be then applied to particular financial ratios and dynamic analysis of selected industry financial performance will be performed. Evaluation of selected industry in the Czech Republic according to economic value added indicator will be included in the conclusion of the paper and also value drivers of this industry will be determined in the analyzed period. Keywords: financial performance, financial ratios, analysis of deviations, pyramidal decomposition, economic value added, JEL codes: C2, C5, C58, G3, G30, G32 1 Introduction Financial performance of an industry or a company is very important for the management. It is a random process, which can be decomposed into the particular indicators. Financial performance has been analyzed by traditional indicators in the past, while nowadays modern indicators are more used, such as economic value added. Methods used for financial performing of an industry or a company were presented e.g. Ehrbar (1998), Dluhošová (2010) or Mařík (2005). It is very important to find the main factors, which have the main influence of the industry´s financial performance. One way, how to find main influencing factors is pyramidal decomposition of financial performance indicators. If this method is proposed to analyze financial performance of an industry, it is possible to find relations among the component indicators, Zmeškal (2013). 68 The aim of this paper is to evaluate financial performance of automotive industry in the Czech Republic in the period 2007 to 2015 according to the methods of analysis of deviation and to find main value drivers of this selected industry. 2 Methodology of financial performance Financial performance of an industry or a company can be evaluated according to accounting indicators, economic or market indicators. In this paper for financial performance of an automotive industry will be used financial ratios analysis, pyramidal decomposition of selected financial ratio and analysis of deviation. Financial ratio analysis One of the commonly used tools for analysis of the industry´s financial performance is the usage of financial ratio analysis together with the pyramidal decomposition. Financial ratios analysis is the quantitative analysis of financial information from industry´s financial statements. Financial ratios can be divided into different categories according to the parameter, which is being measured. It is possible to distinguish profitability ratios, liquidity ratios, activity or efficiency ratios and solvency ratios, Dluhošová (2004). In Table 1 there are financial ratios, which will be used for the analysis of automotive industry in the Czech Republic. Table 1 Financial ratios Ratio Abbreviation Formula Return on Equity ROE EAT/Equity Return on Assets ROA EBIT/Assets Return on Capital Employed ROCE EBIT/Capital Employed Return on Revenues ROR EBIT/Revenues Number of Days of Receivable NDR Inventory/Receivable·360 Number of Days of Payable NDP Inventory/Payable·360 Number of Days of inventory NDI Inventory/Revenues·360 Current Liquidity CL Current Assets/Short-Term Liabilities Quick Liquidity QL (Current Assets- Inventory)/Short-Term Liabilities Cash Liquidity CASH L (Cash+Short-Term Securities)/Short-Term Liabilities Debt Ratio DR Debt/Assets Financial Leverage FL Assets/Equity Source: Richtarová, Čulík, Gurný, Ratmanová (2013) Economic value added Traditional performance measures such as NOPAT, ROI or ROE have been criticized due to their inability to incorporate full cost of capital and therefore accounting revenue is not a consistent predictor of firm value and cannot be used to measure company performance, Vernimmen (2005). One such innovation in the field of internal and external performance measurement is Economic value added. This indicator is based on the concept of the economic profit. When the economic profit is positive, it means that company earns more than the weighted average costs of capital, which also means that some wealth for the shareholders is created. 69 There are many ways how economic value added can be expressed. It can be distinguished EVA – equity, EVA – entity or relative economic value added. Financial performance of an automotive industry will be analyzed according to EVA – equity in this paper. EVA – equity is expressed as ,)( ERROEEVA e ⋅−= (1) where ROE is return on equity, E is equity and eR are costs of equity. By using building model of Ministry of industry and trade of the Czech Republic it is possible to express costs of equity as 4321 RPRPRPRPRR Fe ++++= , (2) where FR is risk free rate, 4,3,2,1RP are risk premiums which are determined according to methodology of Ministry of industry and trade of the Czech Republic, mpo.cz. If economic value is expressed in this way, the difference between ROE and Re is called spread. If this spread is positive, it means that industry or company earns more than the costs of equity are. Method of pyramidal decomposition and analysis of deviation Method of pyramidal decomposition is usually used for quantification of the impact of component ratios on the change in the base ratio. This method also allows to determine the interactions and relationships among the component ratios. The pyramidal decomposition together with the analysis of deviation helps to identify the relationships between the financial ratios and also quantify the impact of selected ratios on the base ratio, Dluhošová (2010). It is useful to apply the analysis of deviations for in-depth analysis of the impact of component ratios on the base ratio. It is possible to quantify the impact of the changes in the component ratios on the base ratio according to this analysis, Zmeškal (2013). It is possible distinguish two operations according to this analysis - additive relationship and multiplicative relationship, Zmeškal (2013). Quantification of the impact under the additive relationship is generally applicable and the total impact is divided in proportion to the changes in the component ratios, Zmeškal (2013). According the way in which the multiplicative relationship is handled, five basic methods can be distinguished: a method of gradual changes, a decomposition method with surplus, a logarithmic method or functional method or the integral method, their description including derivation can be found in Dluhošová (2004). Integral method will be used for the deeper analysis of financial performance of automotive industry. Quantification of the influences according to integral method is similar to logarithmic method or functional method. The only difference is that only the linear component of the Taylor series approximation is applied, Gurný, Richtarová, Čulík (2014). Resulted influence quantification for any component ratio is expressed as x x a a y R R x i i ∆⋅=∆ ´ (3) where 0,i i a a a R i ∆ = and ∑= ′ = N i ax i RR 1 . 70 In the Figure 1 proposed pyramidal decomposition of economic value added is possible to see. Economic value added is decomposed to component ratios. Figure 1 Pyramidal decomposition of Economic value added Source: own calculation where E is equity, ROE is return on Equity, EAT is earnings after taxes, EBT is earnings before taxes, EBIT is earnings before interests and taxes, RC is registered capital, OL is other liabilities, R is revenues, T is tax income, ITFA is intangible and tangible fixed Assets, LTFA is Long –term Financial Assets, OA is Other Assets, STFA is Shot-term financial assets, WIP is work in progress, FG is finished goods, M is merchandise, FC is financial costs, CoGSare costs of goods sold, OE is operating expenses, OOC is other operating costs, WS is wages and salaries. In the Figure 1 it is clear, that economic value added is influenced by the equity and spread, while spread is the main influencing factor. The impact of profitability, debt and determination of costs of equity is figured in other levels of decomposition. Data Financial statements should be completed to evaluate the financial performance of an automotive industry. Input data were taken from the website of Ministry of industry and trade of the Czech Republic from the period 2007 to 2015. Financial ratios analysis was provided according to a Table 1 according to completed financial statements. 3 Application Automotive industry in the Czech Republic Automotive industry of the Czech Republic was chosen for evaluation of financial performance. Automotive industry is one of the main parts of the manufacturing industry in the Czech Republic. Automotive industry of the Czech Republic significantly contributes to the gross domestic product. Czech automotive industry belongs to one of the most developed automotive industries in the European Union. According to CZ – NACE automotive industry includes: . . + + + + . . + + + + + / + + - + + + + + + + + + + + + + Equity ROE- Re ROE Re EVA R1 R2 R3 R4EAT/EBIT . EBIT/A A/E RF OL/E L/E OtherE/E Interest/R Tax/R EAT/R 1 A/R Reserves/E Payables/E Debt/E EAT/EBT EBT/EBIT EBIT/R R/A RC /E CA/R Tax/R OC/R FC/R ITFA/R LTFA/R Reseivables/R 1 Costs/R FA/R OtherA/R Inventory/R STFA/R CoGS/R OE/R WS/R OOC/R Materials/R WIP/R FG/R M/R 71 personal, light utility and freight vehicles, buses and trolleybuses, snowmobiles, golf carts, amphibious vehicles, fire trucks, trailers and semi-trailers, and the manufacture of their parts. Figure 2 Financial ratios Source: mpo.cz Companies in the automotive sector and related suppliers regularly rank in the CZECH TOP 100, but not all of them do. Škoda auto, a.s., Hyunday and TPCA can be placed among the most important companies in the automotive sector. Data In the Table 2 there are the values of chosen financial ratios. These ratios were set according to an input data from the financial statements of an automotive industry. Table 2 Financial ratios analysis Ratio/Year 2007 2008 2009 2010 2011 2012 2013 2014 2015 Return on Equity 0,176 0,148 0,070 0,142 0,173 0,144 0,125 0,175 0,219 Return on Assets 0,131 0,108 0,042 0,076 0,085 0,082 0,080 0,112 0,137 Return on Capital Empolyed 0,186 0,165 0,065 0,113 0,128 0,124 0,117 0,168 0,202 Return on Revenues 0,073 0,068 0,028 0,049 0,052 0,050 0,049 0,063 0,075 Number of Days of Receivable 61,82 8 62,23 7 62,92 8 77,59 3 57,58 4 52,35 0 56,48 1 49,90 3 49,59 5 Number of Days of Payable 63,18 5 74,62 8 83,25 2 96,20 6 97,71 5 94,70 2 89,81 6 84,25 7 73,65 1 Number of Days of inventory 19,71 3 23,66 5 21,02 2 20,41 0 20,66 3 20,80 2 21,67 8 19,59 0 20,38 4 Current Liquidity 1,570 1,262 1,423 1,535 1,575 1,471 1,682 1,707 1,844 Quick Liquidity 1,232 0,956 1,164 1,252 1,288 1,177 1,365 1,408 1,513 Cash Liquidity 0,174 0,153 0,387 0,174 0,489 0,436 0,540 0,646 0,708 Debt Ratio 0,473 0,491 0,523 0,543 0,575 0,549 0,519 0,542 0,511 Financial Leverage 1,923 1,984 2,146 2,245 2,417 2,264 2,130 2,236 2,087 Source: own calculation Profitability ratios are influenced by the amount of the profit. In the analyzed period automotive industry didn’t generate a loss. ROE was higher than ROA in all analyzed years, which means that the invested capital was valued more than the total capital. From the proposed pyramidal decomposition of Economic value added generation of profit is clarified. 72 Solvency rule was met in all years. It means that companies in the automotive industry had previously received payments from customers before paying their debts. Turnover of receivables has a positive trend. The average inventory turnover is stable (20days). There is no problem with liquidity in automotive industry in Czech Republic in the whole analyzed period. All liquidity ratios testify to solvency at all levels. Low debt ratio is typical in analyzed period for automotive industry. Foreign resources are consisted of liabilities, especially short-term liabilities, namely trade payables. Economic value added Economic value added of the automotive industry of the Czech Republic was evaluated according to a formula (1). Automotive industry of the Czech Republic is one of the main parts of the manufacturing industry in the Czech Republic. Figure 2 is illustrating the evolution of economic value added of automotive industry compared to the economic value added of the whole manufacturing industry. Figure 2 Economic value added (thousand CZK) Source: own calculation From the Figure 2 it is clear that the trend of economic value added of automotive industry corresponds to the development of economic value added of manufacturing industry. Only between the years 2010 and 2012 economic value added of automotive industry had inverse trend to the economic value added of manufacturing industry. Analysis of deviations Method of pyramidal decomposition was applied for deeper analysis of the factors affecting economic value added of automotive industry evolution. Integral method was used for influence quantification according to formula (3). In the 2007 economic value added was negative (-5 664 144 thousand CZK.), but in the 2015 economic value added of automotive industry of the Czech Republic was positive (16 960 840 thousand CZK.). It means, that economic value added increased by 24 868 096 thousand CZK in the analyzed period. Table 3 First level of decomposition of Economic value added Ratio Influence EVA 24 868 093 Equity 7 121 313 Spread (ROE – RE) 17 746 781 Source: own calculation 73 From the first level of decomposition of economic value added it is clear that the spread is the most influential factor. To determine the value drivers of Economic value added of automotive industry in the period 2007 to 2015 other levels of decomposition are applied. In the Table 4 there are shown impacts of other component ratios. Table 4 Other levels of decomposition of Economic value added Ratio Influence Equity 7 121 313 RF 8 871 236 R1 -165 781 R2 -4 630 683 R3 2 559 831 R4 734 447 EAT/EBT 4 195 886 EBT/EBIT 28 487 RC/E -2 802 288 OL/E 157 092 Interests/R 51 158 T/R -2 728 383 ITFA/R 3 031 096 LTFA/R 3 374 784 OA/R -370 529 Receivables//R 2 838 351 STFA/R -7 762 717 Materials/R -95 780 WIP/R -40 148 FG/R 138 027 M/R -157 839 Reserves/E 1 924 374 Payables/E 4 138 676 Debt/E -2 347 738 T/R 2 728 383 FC/R -51 158 CoGS/R -9 711 270 OE/R -16 810 906 OOC/R 22 688 453 OtherE/E 2 802 288 WS/R 5 159 436 Summary 24 868 093 Source: own calculation Economic value added of automotive industry increased by 24 868 093 thousand CZK during the analyzed period. For a deeper analysis of all years of the analyzed period should be done. After applying method of pyramidal decomposition to economic value added positive and negative effects of component ratios were found. Three ratios with the highest positive effect and three ratios with the highest negative effect were selected for explanation in this paper. These ratios are shown in the Table 5. 74 Table 5 Selected ratios with positive and negative effects Positive effects 1+ 2+ 3+ 2007_08 Other operating costs/Revenues Payables/Equity Interests/Revenues 2008_09 Tax/Revenues EAT/EBT Financial costs/Revenues 2009_10 Other operating costs/Revenues Wages and Salaries/Revenues R3 2010_11 Costs of goods sold/Revenues Receivables/Revenues Intangible, Tangible, Fixed assets/Revenues 2011_12 EAT/EBT Other operating costs/Revenues Tax/Revenues 2012_13 Interests/Revenues R1 Operating expenses / Revenues 2013_14 Wages and Salaries/Revenues Interests/Revenues Operating expenses / Revenues 2014_15 Other operating costs/Revenues Equity RF Negative effects 1- 2- 3- 2007_08 Wages and Salaries/Revenues Costs of goods sold/Revenues Operating expenses / revenues 2008_09 Other operating costs/revenues Operating expenses / Revenues R2 2009_10 Equity Debt/Equity Receivables/Revenues 2010_11 Operating expenses / revenues Short-term Financial Assets / Revenues Other operating costs/revenues 2011_12 EBT/EBIT Costs of goods sold/Revenues Tax/Revenues 2012_13 Other operating costs/revenues Tax/Revenues R3 2013_14 Financial costs/Revenues EBT/EBIT Tax/Revenues 2014_15 Payables/equity Interests/Revenues Tax/Revenues Source: own calculation Share of costs items on revenues is one of the ratios, which has the largest positive effect to economic value added in all analyzed years. Only between the year 2011 and 2012 EAT/EBT ratio was the most significant. The largest negative impact was observed in the proportion of selected costs on revenues (for example wages and salaries, other operating costs). In 2009 – 2010 equity was significantly reduced and this had the largest negative impact on economic value added. In the analyzed period costs of equity determined economic value added. Cost of equity we evaluated according to a methodology of Ministry of industry and trade of the Czech Republic, mpo.cz. Risk premiums had in the analyzed period as positive as negative impact. Order of impact of component indicators to economic value added had changed during the analyzed period. 75 4 Conclusions This paper was dedicated to a financial performance analysis of automotive industry of the Czech Republic in the period 2007 to 2015. Automotive industry is one of the main parts of the manufacturing industry in the Czech Republic. The aim of this paper was to evaluate financial performance of automotive industry in the Czech Republic in the period 2007 to 2015 according to the methods of analysis of deviation and to find main value drivers of financial performance of this selected industry. Selected financial ratios had positive trends. There is no problem with liquidity, profitability, solvency, activity and efficiency. Financial performance of automotive industry was analyzed according to economic value added. Economic value added had significantly increased during the analyzed period. Analysis of deviation was applied to find the main indicators influencing economic value added of automotive industry in the Czech Republic. According to integral method, indicators with positive and negative effect were found. Share of selected costs on revenues had the largest impact to economic value added of automotive industry of the Czech Republic in the analyzed period 2007 to 2015. Acknowledgments This paper was supported by the SGS Project VŠB – TU Ostrava SP2017/148 "Finanční rozhodování podniků a finančních institucí za rizika”. References Analytical material and statistics – Analysis of Czech economic development according to Ministry of industry and trade (2007 – 2015). Retrieved from: http://www.mpo.cz/ministr-a-ministerstvo/analyticke-materialy. Dluhošová, D. (2004). Přístupy k analýze finanční výkonnosti firem a odvětví na bázi metody EVA – Economic Value Added. Finance a úvěr - Czech Journal of Economics and Finance, vol. 54. Dluhošová, D. (2010). Finanční řízení a rozhodování podniku. Praha: EKOPRESS. Ehrbar, A. (1998). EVA the real key to creating wealth. New York: Wiley. Gurný, P., Richtarová, D., Čulík, M. (2014). Liquidity analysis and prediction in the processing industry by applying VG process: the case of the Czech Republic. Ekonomická revue - Central European review of economic issues, vol. 20(1), pp. 17-28. Maříková, P., Mařík, M. (2005). Moderní metody hodnocení výkonnosti a oceňování podniku. Praha: Ekopress. Panorama of manufacturing industry (2017). Retrieved from: https://www.mpo.cz/assets/cz/prumysl/zpracovatelsky-prumysl/panoramazpracovatelskeho-prumyslu/2017/1/Panorama_ENG_1.pdfPanorama of manufacturing industry.2017. Retrieved from: https://www.mpo.cz/assets/cz/prumysl/zpracovatelsky- prumysl/panorama-zpracovatelskeho-prumyslu/2017/1/Panorama_ENG_1.pdf. Richtarová, D., Čulík, M., Gurný, P., Ratmanová, I. (2013). Financial management and decision – making of company: workbook with solutions. Vernimmen, P. (2005). Corporate finance, theory and practice. England: Wiley. Zmeškal, Z. (2013). Finanční modely. Praha: Ekopress. 76 Causes of the Foreclosure Crisis – Irrational or Rational Decisions? Bohuslava Doláková1 1 Masaryk University Faculty of Economics and Administration, Department of Finance Lipová 41 a, 602 00 Brno, The Czech Republic E-mail: 175975@mail.muni.cz Abstract: The aim of this paper is to show non-traditional approach to the causes of foreclosure crisis in 2008. Most often used story is based on idea that the crisis was result of the finance market industry where market insiders betrayed uninformed mortgage borrowers and investors. But non-traditional approach argues that borrowers and investors made decisions that were rational and logical given their ex post overly optimistic beliefs about house prices. They expected situation would have been much different than it was, but they knew theoretical risks. This can show limits of our understanding of asset price bubbles and help to design policies and help us in crisis prediction system. Keywords: Financial crisis, Behaviour, Consumer behaviour, Rationality, Irrationality JEL codes: D14, D18, D53, D82, G01, G02 1 Introduction Few years after the beginning of the U.S. mortgage crisis there is still a crucial question regarding its origin – why did so many people—including homebuyers and the purchasers of mortgage-backed securities—make so many decisions that turned out to be disastrous in their consequences? The dominant explanation that can be called conventional wisdom claims that wellinformed mortgage insiders used the securitization process to take advantage of uninformed outsiders (e.g. Foote, 2015). The process is explained as a loan from a mortgage broker through a series of Wall Street intermediaries to an ultimate investor. According to this point of view, betray starts with a mortgage broker, who convinces a borrower to take out a mortgage that appeared at the beginning affordable. Process is displayed in Figure 1. Unbeknownst to the borrower, the interest rate on the mortgage will reset to a higher level after a few years, and the higher monthly payment will force the borrower into default. Figure 1 Conventional Wisdom Source: Foote (2015) 77 The broker knows that the mortgage is firmly connected to explode, but it does not matter because the securitization process means that it will pass on the mortgage to someone else. The investment bank buys a loan for inclusion in a mortgage-backed security. An investment banker knows that the investor is likely to lose money, but it does not matter because it's not his money. When the loan explodes, the borrower loses his home and the investor loses his money. Both borrowers and lenders believe home prices will grow rapidly soon, not surprising that they find borrowers who pull out to buy the largest houses they could and investors who would give them money. Rising house prices create large capital gains for home buyers. They also increase the value of mortgage pledges and thus reduce or eliminate credit losses for creditors. Higher expectations in house prices streamline the decisions of debtors, investors and intermediaries - their acceptance of high leverage in home purchase or financing of mortgage investments, their inability to demand a rigorous documentation of income or assets before lending, and their extension of loans to debtors’ history of debt repayment (e.g. Foote, 2015). The process is clearly displayed in the Figure 2. Figure 2 Bubble Theory Source: Foote (2015) Many borrowers got mortgages that they would never have bound up before. If this alternative theory is true, securitization was not the cause of the crisis. The securitization rather facilitated the transactions the borrowers and investors had yet to do. According to bubble theory is foreclosure crisis seen because of distorted beliefs rather than distorted incentives. Growing literature in economics seeks to accurately identify how financial market participants shape their beliefs and what can happen when these beliefs are distorted. Bubbles do not need securitization, government Involvement or non-traditional credit products. Bubbles in many other activities have appeared without these things (e.g. Tulips in Holland from the 17th century). Expectations of higher real estate prices have made investors more willing to use both securitized and non-traditional mortgage products. 2 Methodology and Data Theories of asymmetric information argue that mortgage originators failed to adequately screen loans and passed them on to unsuspecting investors in mortgage-backed investments. The resulting expansion in credit then drove prices higher. Some of our 78 facts have argued directly against this line of reasoning; in this section, we show that explanations based on asymmetric information fail on theoretical grounds as well. A second group of explanations claims that mortgage market developments related to financial innovation allowed credit to expand and prices to rise. We show that these explanations also have theoretical and empirical problems. Finally, we discuss the only set of theories left standing. These theories claim that the U.S. housing market was a classic asset bubble, just like previous bubbles in tulips and tech stocks. Explanations based on asymmetric information The theory of asymmetric information argues that mortgage brokers have been unable adequately to project loans and hand them over to shameless investors in mortgage investment. The resulting credit expansion then led to higher prices. The second group of explanations argues that mortgage market developments related to financial innovation have allowed credit growth and price growth. Other theories claim that the US housing market was a classic bubble of assets, just like the previous bubbles in the tulips and technological supplies. Theories based on financial innovation A second group of theories argue that the source of rising house prices was some fundamental change in mortgage market institutions. These loans were uniquely labelled as loans with those assets, so investors knew what they were getting. In particular, investors knew that the borrowers were likely to inflate their income and assets. Nevertheless, investors bought loans because they expected these loans to be profitable. Investors were willing to take advantage of their chances with risky loans because they thought higher real estate prices would mean that this risk is worth not because of the inconsistent stimulus in the securitization process. They argue that the source of real estate price growth was a major change in mortgage market institutions, although this change may not be the result of asymmetric information. E.g. Decrease in requested payments from potential domestic buyers. The lesson of financial innovation models is that it is not possible to explain the dynamics of housing prices in the US in 2000 with a dynamic forward-looking model of overall balance. Researchers should draw attention to less conventional approaches, for example, based on distorted beliefs. Theories based on bubbles and distorted beliefs Economists are fascinated by bubbles and have been for a long time. Speculative fervour captured some of the asset, which led to prices that surpassed any realistic estimate of the future income that this asset could cause. When no more buyers of this asset are available - when music stops - prices fall. The models were developed to explain why bubbles may take a long time, but as Brunnermeier (2008) states, they do not have many convincing models that explain when and why the bubbles begin. The link between financial innovations and bubbles also does not support a historical record. In the 1930s, many blamed the US bubble on securities exchanges in the 1920s for financial innovations, which allowed companies and individuals to raise leverage positions in stocks. However, this regulation did not hinder the technological bubble of the 1990s, though it probably prevented the collapse of stock prices from causing a financial crisis. The US housing market was in a bubble from the beginning to the middle of 2000, then debtors and creditors are understandable. Higher price expectations may also explain why so many mortgage loans have been allocated to low-income households and why this allocation was via securitization. Expectations of higher prices will encourage all households to increase their exposure to the housing market, but wealthy households can finance this growth by lowering their investment in bonds. Households with little or no wealth can finance growth only through increased loans. Expectations of high prices dramatically reduced the expected losses on 79 loans at risk, but had little impact on the expected losses on primary loans, which due to their much higher credit quality were minimal. High prices can explain the growth of mortgage securitization. Since the characteristics of the borrower no longer affect loss estimates, if the underlying collateral is expected to be evaluated quickly, it is not sufficient for the originator to obtain information on these characteristics or equivalent to the investor requesting. As a result, the developer will end up with less private information in an environment where the expected growth in prices is lower. Rather than depositing money in a financial institution that had a decision on where to borrow, the securitization of investors could directly focus their money on a particular market place in this case. Normally, it's a good thing. But in the middle of 2000, securitization worked the same way as Othello - not wisely, but too well. The inefficiency of a more traditional financial system could be a blessing now, as it could prevent too optimistic borrowers and investors from finding each other. 3 Results and Discussion If borrowers and investors made bad decisions because of the collective belief that housing prices would grow rapidly and could never fall, better information, simplified products and improved incentives for intermediaries would not have a big impact. But this does not mean that this policy is always ineffective. Although scientists can not predict earthquakes or prevent them, robust building laws still prevent millions of deaths. Many procurement rules are designed to make the financial system more robust. When evaluating future policy designs there should be answered two basic questions: • Can financial institutions resist severe price shock? • Can borrowers withstand a substantial decline in property prices? We can use for illustrative answer speech in Shanghai Symposium on OTC Derivatives made by Evans in spring 2017. He stated that political actions that increase public confidence in the ability of institutions to withstand financial shocks will tend to offset the destructive behavior of liquidity. The international community has taken many such measures since the crisis. Particularly noteworthy are changes in bank capital regulation, including higher capital requirements; the beginnings of a truly anti-cyclical capital regime; regular stress tests; and minimum Standards for Balance Sheet Liquidity. These developments reduce the likelihood that a large systemically important bank will be vulnerable to further shocks that have hit the financial system (Evans, 2017). Very useful in predicting of ability of down payment is the concept of stress test. Warren (2010) argued that families could practice a "financial explosion against fire" to ask how they would get if someone had won the job. A fire drill could also include a scenario in which the cost of parachuting houses would prevent a family from selling their home more than owed a mortgage. Such a drill would be like the so-called Load Tests that regulators perform in financial institutions. We can use the data from Ministry of Finance judging these abilities of the Czech Republic inhabitants. In 2010 and in 2015, the Ministry of Finance carried out a measurement of the level of financial literacy of the adult population of the Czech Republic. This survey has become part of the global measurement together with the other dozens of countries of the Organization for Economic Co-operation and Development (OECD). In Figure 3 is displayed specific part of this financial literacy measurenment - the length of the cost of Living without the main source of household income 80 Figure 3 Measuring the Level of Financial Literacy in 2010 and 2015 Source: Ministry of Finance 48% of households cover their cost of living at the loss of main income for at least three months (41% of households in 2010), 15% of households would not even cover them for one month (16% of households in 2010). This research showed Czechs are getting better and their stress tests results would be better too. Speaking back to financial crisis origin, everyone - from first class houses to the director of Wall Street - must realize that asset prices are moving in a way that we do not understand yet. Unfortunately, none of the new mortgage-backed forms proposed by the regulators contains critical information the borrowers need to know: there is a chance that the house they buy will soon be considerably lower than the remaining balance of the mortgage. If this occurs and the borrower does not have sufficient preventive savings, then the debtor is a job loser or serious illness that has not been touched. Politicians and regulators have little or no ability to identify or trigger bubbles in real time. Policy makers should not try to stop bubbles that are not easily identifiable but should clean up the damage that was left in their explosion (much like A. Greenspan). This strategy works only if the financial system is robust against unfavorable shocks. Determining the origin of the financial crisis is not just an inactivity of academic entertainment, because alternative explanations require different political responses. If borrowers and investors made bad decisions because of the collective belief that housing prices would grow rapidly and could never fall, better information, simplified products and improved incentives for intermediaries would not have a big impact. But this does not mean that this policy is always ineffective. Although scientists can not predict earthquakes or prevent them, robust building laws still prevent millions of deaths. How can we create a bubble-resistant financial system? Many new regulations are designed to make the financial system more robust. We propose two questions that may be raised in the evaluation of future policy proposals. Critics may say that dealing with bubbles, such as the earthquake, recalls the doctrine often associated with Alan Greenspan: political politicians should not try to stop the bubbles that are not easily identifiable but should instead clean up the damage that remains when they burst. To some extent, we agree with this doctrine because we believe that policy makers and regulators have little or no ability to identify or tear bubbles in real time. This strategy works only if the financial system is resistant to adverse shocks. As we have already mentioned, the reforms of the 1930s failed to prevent the emergence of a bubble in the stock market in the late 1990s. Stock market collapse, however, did not lead to the economic crisis or to the large financial problems 0% 20% 40% 60% 80% 100% 2015 (n=1000) 2010 (n=1005) The Length of the Cost of Living without the Main Source of Household Income more than 6 months 3 months longest, less than 6 months 1 month longest, less than 3 months 1 week longest, less than 1 month Less than 1 week 81 of households. Why not? One possible explanation is that the 1930s reforms have made the financial system "bubble-proof", at least for the stock. Our hope lies in the fact that in the future we can achieve a similar result to housing. However, housing policy must be based on facts. 4 Conclusions During last big financial crisis, foreclosure crisis, borrowers and investors made decisions that were rational and logical regarding ex post too optimistic beliefs about real estate prices. Reforms of the 1930s could not prevent the emergence of a bubble at the end of the 1990s. Stock market collapse, however, did not lead to the economic crisis or to the large financial problems of households. Why not? One possible explanation is that the 1930s reforms have made the financial system "bubble-proof", at least for the stock. Could we get something similar in the future in the future? Prognosis, valuation, and analysis must be modest and sincere about how little we know about the future. This will allow decision makers to seriously consider extreme and unfavorable scenarios, even though they seem unlikely. In many cases, it is the right decision about risk management and the recognition of what we do not know. Therefore, we need consider what are people´s beliefs like and what are they based on. Just knowing that we can make some relevant predictions about future crises. Acknowledgments Support of Masaryk University within the project MUNI/A/0823/2016 “Behavioural, knowledge and economic aspects of trading and pricing financial assets” is gratefully acknowledged. References Brunnermeier, M. K. (2008). Bubbles. In The New Palgrave Dictionary of Eco-nomics, eds. Steven N. Durlauf and Lawrence E. Blume, second edition. Basingstoke: Palgrave Macmillan. Retrieved from: http://www.dictionaryofeconomics.com/article?id=pde2008_S000278. Evans, Ch. (2017). Toward a More Resilient Financial System. Retrieved from: https://www.chicagofed.org/publications/speeches/2017/05-23-toward-a-more-resilient- financial-system-shanghai. Financial Crisis Inquiry Commission (2010). “Credit Ratings And the Financial Crisis.” Preliminary Staff Report. Retrieved from: http://fcic- static.law.stanford.edu/cdn_media/fcic-reports/2010-0602-Credit-Ratings.pdf. Foote, Ch. L., Gerardi, K., Willen, P. (2012). Why Did so Many People Make so Many Ex Post Bad Decisions? The Causes of the Foreclosure Crisis. FRB Atlanta Working Paper No. 2012-7. Retrieved from: https://ssrn.com/abstract=2479576 or http://dx.doi.org/10.2139/ssrn.2479576. Ministry of Finance. (2016). Retrieved from: http://www.psfv.cz/cs/pro- odborniky/mereni-urovne-financni-gramotnosti/2015/mereni-urovne-financni- gramotnosti-2601. Warren, E. (2010). Priorities for the New Consumer Financial Protection Bureau. Speech before the Consumer Federation of America Financial Services Conference. Retrieved from: http://www.treasury.gov/press-center/press-releases/ Pages/tg987.aspx. 82 The dynamics of linkages between European currencies: How does it change according to the time of day? Małgorzata Doman1 , Ryszard Doman2 1 Poznań University of Economics and Business Faculty of Informatics and Electronic Economy, Department of Applied Mathematics Al. Niepodległości 10, 61-875 Poznań, Poland E-mail: malgorzata.doman@ue.poznan.pl 2 Adam Mickiewicz University in Poznań Faculty of Mathematics and Computer Science, Laboratory of Financial Econometrics Umultowska 87, 61-614 Poznań, Poland E-mail: rydoman@amu.edu.pl Abstract: The aim of the paper is to document how the dynamics of linkages between European currencies changes during a trading day according to the activity of different groups of traders, and show the impact of important events and news on the dependence structures. We analyze conditional dependencies between the Czech koruna, Hungarian forint, Polish zloty and the major European currencies (the euro, British pound and Swiss franc). The analysis is performed for the exchange rates against the US dollar. We consider daily returns calculated using the exchange rates quoted at different times of day. The dynamics of the dependencies is modeled by means of Markov regime switching copula models, and the strength of the linkages is described using dynamic Spearman’s rho coefficients and the dynamic coefficients of tail dependence. The approach used allows to scrutinize changes in the dynamics of the conditional dependence structure according to the time of day, which can be useful in recognizing diversification possibilities. Keywords: exchange rates, European currencies, linkages, copula, Markov regime switching, tail dependence JEL codes: G15, F31, C58, C32 1 Introduction In the paper we show that the strength and dynamics of linkages between European currencies change depending on the time of day. The analysis is based on daily returns on the exchange rates of selected currencies (EUR, GBP, CHF, CZK, HUF, PLN) against the US dollar that are calculated at 4 fixed times each trading day. To describe changes in a pattern of the dependence, we apply 3-regime Markov regime switching copula models, which allows to measure the strength of the dependence by means of dynamic Spearman’s rho coefficients and some coefficients of dependence in tails. The FOREX market is open 24 hours a day during 5 days of a week, but activity of traders from different parts of the world changes according to trading hours of their regional markets. The prices observed in this market are thus heterogeneous since at different times of a day they are formed by different groups of traders. Moreover, the intensity of a FOREX trade changes during a day. In Figure 1, we show diverse daily periodic patterns in 5-minute returns on EUR/USD and PLN/USD, which depict differences in activity of trading the currencies in regional markets. The maxima visible in plots indicate the periods of the highest activity of traders. A general observation is that three global currencies, i.e. EUR, CHF and GBP, show similar behavior as they are actively traded during all the day, though with changing trade intensity. For the considered Central-European currencies, we observed an explosion of volatility at 8:05 (all times in the paper are CET), i.e. immediately after the regional markets are opened. Taking into account the common maxima, we decided to analyze the linkages between the considered exchange rates at 8:05, 9:05 (the beginning of the trading day in 83 continental Europe and the UK), 14.35 (the most significant US announcements) and at 17:00 (the end of the trading day in Europe). Figure 1 EUR/USD and PLN/USD. The daily periodic patterns in 5-minute returns (Central European Time). Based on averages of absolute returns over the period: November 11, 2011 – March 24, 2017 Source: authors We have drawn inspiration for seeking evidence of time-of-day effects in the structure of dependence in the FX market from the papers by Ranaldo (2009) and Breedon and Ranaldo (2013). These authors do not investigate the dependence structure but present evidence of time-of-day effects in foreign exchange returns showing that currencies depreciate during local trading hours and appreciate during the working hours of the foreign counterpart. This can be partly explained by the observation that there is the convention of closing or reducing open positions on exchange rates out of the liquidity clustering during the main working hours (Ranaldo, 2009). Our earlier analysis of intraday changes in the dependence structure in the currency market (Doman and Doman, 2014) we took up, however, without knowing the papers by Ranaldo and Breedon. The results of our research are as follows. First we show how the dynamics of linkages depends on the time of day and on the changes in activity of different groups of FOREX traders. The next part of our results deals with the impact of important market events and information on dynamics of the linkages, and shows differences in the ways a news impacts them depending on the time of day. The presented analysis is thus performed from the point of view of a US dollar investor owning a portfolio of European currencies and the results show that diversification of such a portfolio and the portfolio risk depend on the time of day. 2 Methodology and Data Modeling the conditional distribution of multivariate daily financial returns is not an easy task. Asymmetries in one-dimensional marginal distributions as well as in the dependence structure imply that such distributions mostly do not belong to a well-studied class of elliptical distributions, which include multivariate normal and Student’s t distributions (see e.g. McNeil et al., 2005). Moreover, the conditional dependence structure of the returns can significantly change in time. Because of non-ellipticity, modeling the returns using standard multivariate GARCH models (Bauwens et al., 2006) should be avoided. Instead, an approach employing copulas, which allow to model dependence structure separately from univariate marginal distributions, should be applied. 0 0,02 0,04 0,06 0,08 0,1 0,12 0,14 0:10 1:25 2:40 3:55 5:10 6:25 7:40 8:55 10:10 11:25 12:40 13:55 15:10 16:25 17:40 18:55 20:10 21:25 22:40 23:55 PLN/USD EUR/USD 84 A bivariate copula is a function ]1,0[]01[]1,0[: →×C from the unit square to the unit interval that is a distribution function whose marginal distributions are standard uniform. If 1X and 2X are random variables with joint distribution function F and marginal distributions 1F and 2F then, by a theorem by Sklar (1959), the following holds ))(),((),( 21 yFxFCyxF = . (1) Given that 1F and 2F are continuous, the function C is uniquely given by the formula ))(),((),( 221121 uFuFFuuC ←← = , (2) for ]1,0[, ∈vu , where })(:inf{)( uxGxuG ≥=← . In such a situation, C is called the copula of 1X and 2X or of F . The simplest copula, which corresponds to independence of marginal distributions, is defined by uvvuC =Π ),( . (3) In this paper we also apply the Gaussian (normal), Student, and Joe-Clayton copulas. They are defined by the following formulas: ))(),((),( 2 1 1 1 21 uuuuCGauss −− ΦΦΦ= ρρ , (4) ))(),((),( 2 1 1 1 ,21, ututtuuCStudent −− = νννρνρ , (5) ( ) κγγκγκ γκ /1/1 2121, )1])1(1[])1(1([11),( −−−− −−−+−−−−= uuuuC ClaytonJoe , (6) where in (4) ρΦ denotes the distribution function of a bivariate standardized Gaussian vector with the correlation coefficient ρ, and Φ stands for the distribution function of the standard normal distribution. Similarly, νρ,t in (5) denotes the bivariate Student t distribution function with ν degrees of freedom and the correlation coefficient ρ, and νt stands for the univariate Student t distribution function with ν degrees of freedom. The parameters in the Joe-Clayton copula (6) satisfy the conditions: 1≥κ , 0>γ . For 1=κ , the Joe-Clayton copula becomes the Clayton copula Clayton Cγ . In the limit case, 0=γ , the Clayton copula approaches the independence copula Π C (Nelsen, 2006). If a copula C has the density c , then 21 21 2 21 ),( ),( uu uuC uuc ∂∂ ∂ = , (7) and for any variables 1X and 2X with marginal distribution functions 1F and 2F , marginal densities 1f and 2f , joint density f , and copula C the following relation holds: )()())(),((),( 2211221121 xfxfxFxFcxxf = . (8) Spearman’s rho for variables 1X and 2X with marginal distribution functions 1F and 2F can be defined as ))(),((),( 221121 XFXFXXS ρρ = , (9) 85 where ρ denotes the usual Pearson correlation. Spearman’s rho is a dependence measure, which is a copula invariant. When C is the copula linking 1X and 2X , then 3),(12),( 2 ]1,0[ 212121 −== ∫∫ duduuuCXX CS ρρ (10) (Nelsen, 2006). From (10) it follows, in particular, that if for some copulas 1C and 2C , and 10 ≤≤α , 21 )1( CCC αα −+= , then 21 )1( CCC ρααρρ −+= . (11) In this paper, the strength of lower tail dependence of variables 1X and 2X with distribution functions 1F and 2F and copula C is measured by the function qqqCqFXqFXPqL ),())(|)(()( 1122 =≤≤= ←← λ . (12) If a limit )(lim 0 qL q L λλ + → = exists and ]1,0(∈Lλ , then C is said to show extremal dependence in the lower tail. This property is possessed by the Student and Joe-Clayton copulas, but not the Gaussian. We model the joint conditional distribution of bivariate returns assuming that there can be three regimes in each of which a fixed copula describes dependence structure, and the regime switching is driven by some Markov chain (cf. Garcia and Tsafack, 2011). Thus, in the applied Markov switching copula (MSC) model the conditional distribution of the vector ),( ,2,1 ttt rr=r has the following form )|)(),((~| 1,2,11 −− Ω⋅⋅Ω tttStt FFC t r , (13) where tΩ denotes the up to time t information set, titti Fr ,1, ~| −Ω , 2,1=i , and tS is a homogeneous Markov Chain with state space }3,2,1{ . The parameters of the MSC model (i.e. those of the univariate ARMA-GARCH models for the marginal distributions, of the copulas 1C , 2C and 3C , and the transition probabilities )|( 1 iSjSPp ttij === − ) are estimated by the maximum likelihood method. The main by-products of the estimation, which are used to construct time-varying conditional dependence measures, are the conditional probabilities )|( 1−Ω= tt jSP , )|( tt jSP Ω= and )|( Tt jSP Ω= (Hamilton, 1994). We investigate the exchange rates EUR/USD, CHF/USD, GBP/USD, CZK/USD, HUF/USD and PLN/USD. According to the established practice of FX markets, the symbol X/Y means the price of a unit of currency X in units of currency Y. The analysis is performed for the daily percentage logarithmic returns, which are separately calculated based on exchange rates quoted at 8:05, 9:05, 14:35 and 17:00. The period under scrutiny is from November 11, 2011 to March 24, 2017 (1394 daily observations for each of the selected times). The exchange rate series were obtained from the service Stooq. Prior to modeling the dynamics of dependence we calculated empirical Spearman’s rho coefficients for each pair of the considered returns. They turned out not to be very sensitive on the time of day. Their values change from 0.4674 (GBP/USD-HUF/USD at 9:05) to 0.9017 (EUR/USD-CZK/USD at 8:05). The analysis presented in the next section shows that the pattern changes when considering the conditional Spearman's rho coefficients, which take into account the information flow. 3 Results and Discussion We investigate the dynamics of dependence between the analyzed exchange rates by means of 3-regime Markov switching copula (MSC) models. This allows us to describe 86 different types of dependence and capture temporal changes in the linkages. The analysis is performed for pairs of the exchange rates, so we use bivariate copulas. As in many financial applications, we used a two-stage estimation procedure of model fitting. In the first step, a univariate ARMA-GARCH model was fitted to each of the investigated return series. Among the best fitted models, standard GARCH, GJR, and EGARCH models with Student’s t (symmetric or skewed) distribution for the innovations (Tsay, 2010) appeared. The standardized residuals from the models were tested for serial independence and consistency with the assumed distributional properties. They were then transformed by means of the theoretical cumulative distribution functions into the series of uniformly distributed pseudo-observations, based on which the copula and regime-switching parameters were estimated in the second step. We tried a variety of copulas, and in final choice of the models decisive were the results of information criteria, and the likelihood ratio tests, where applicable. The estimation was performed using G@RCH 7.0 package (Laurent, 2013) and the MATLAB software. Although the considered currencies have been subject to different exchange rate regimes, their exchange rates against the US dollar indicate the level of volatility that justifies applying the above modeling procedure. This is due to invariance of a copula under strictly increasing transformations of the marginals. Our analysis focuses on the dynamics of the conditional Spearman’s rho coefficients and the lower tail dependence coefficients defined in (12) and evaluated at 01,0=q , which is the most frequently used tolerance level in Value at Risk calculations. Presented below estimates for the dynamic Spearman’s rho coefficients were calculated by the formula ∑= Ω== 3 1 )|()( i Ttt iSPiρρ , (14) where )(iρ is Spearman’s rho for a copula prevailing in regime i, and )|( Tt iSP Ω= is the smoothed probability of regime i, defined in (16). Estimates for the dynamic left tail dependence coefficients were calculated by a similar formula. Table 1 Parameter estimates for MSC models fitted to daily returns calculated for the pair CZK/USD-HUF/USD 8:05 Regime 1 Regime 2 9:05 Regime 1 Regime 2 Regime 3 Copula Gauss Cρ Student Cρ Copula Gauss C ρ Gauss C ρ ClaytontJoe C − γκ, ρ 0.5653 (0.0418) 0.8464 (0.0152) ρ 0.4193 (0.0736) 0.8528 (0.0109) ν 8.5615 (3.1710) κ 2.0431 (0.1598) γ 1.2175 (0.1533) 14:35 Regime 1 Regime 2 Regime 3 17:35 Regime 1 Regime 2 Copula Gauss C ρ Gauss C ρ ClaytonJoe κ,γC − Copula Student Cρ ClaytonJoe κ,γC − ρ 0.2601 (0.1277) 0.8299 (0.0105) ρ 0.7193 (0.0191) κ 1.6926 (0.1426) κ 3.8467 (0.0001) γ 1.0137 (0.1306) γ 2.4799 (0.0001) Source: authors In Table 1 we show parameter estimates for the MSC models in the case of CZK/USD and HUF/USD. For returns computed at 9:05 and 14:35, the fitted models are 3-regime MSC with the Gaussian copula in regimes 1 and 2, and the Joe-Clayton copula in regime 3. In the two remaining cases, 2-regime MSC models were the best: with the Gaussian and Student copulas for returns computed at 8:05, and the Gaussian and Joe-Clayton copulas for returns computed at 17:00. Thus at each of the considered times there exists a 87 regime described by a copula exhibiting extremal dependence in the lower tail. In Table 2 we present all types of models fitted to the considered pairs of exchange rate returns. The most often occurring are 2-regime MSC models with two Gaussian copulas which differ in the value of correlation coefficient, and 2-regime MSC models with regimes described by the Gaussian and Student copulas. Table 2 The types of MSC models fitted to daily returns calculated from data quoted at the selected times (copulas: N-normal, t-Student, JC-Joe-Clayton, PI-independence) EUR-GBP EUR-CHF EUR-PLN EUR-CZK EUR-HUF GBP-CHF PLN-CZK PLN-HUF CZK-HUF 8:05 N-N N-N-PI N-t N-N N-t N-N N-t N-t N-t 9:05 N-t N-N N-N N-t N-N N-N N-t N-t N-N-JC 14:35 N-t N-N N-N N-N-PI N-t N-N N-t t-JC N-N-JC 17:00 N-N N-N-PI N-N N-t N-t N-N N-N N-t t-JC Source: authors Due to the limitation on the file size, we are able to present here only two figures showing examples of the obtained results. Figure 2 shows a comparison of the dynamic Spearman’s rho coefficients (for EUR/USD and CHF/USD) and Figure 3 depicts the dynamics of the lower tail dependence coefficients (for CZK/USD and HUF/USD) for the considered times. Figure 2 Dynamic Spearman’s rho coefficients for (EUR/USD, CHF/USD) Source: authors The unconditional Spearman’s rho for (EUR/USD, CHF/USD) is about 0.86 for all the considered times, but the plots in Figure 2 indicate that the dynamics of linkages between EUR and CHF is very strong and clearly depends on the time of day. In September 2011 the Swiss National Bank (SNB) introduced the exchange-rate peg and a minimum exchange rate of CHF 1.20 per euro was applied. On January 15, 2015 the SNB suddenly announced that it would no longer hold the Swiss franc at a fixed exchange rate with the euro. Thus we expected that our analysis would show strong dependence between EUR/USD and CHF/USD during the period 2011-2015 and some drop in the strength of linkages after 2015. This turned out to be true for returns calculated at 9:05 and 14:35. However, at the beginning (8:05) and end (17:00) of the trading day in Europe the dependence process is much more complicated. There occur quite long periods of very weak linkages. Investors consider the Swiss franc as a “safe haven” asset, so during the periods of uncertainty caused by problems of the Eurozone their behavior generates opposite movements of EUR/USD and CHF/USD. Periods where the returns are independent are observed, for instance, after the Brexit referendum and after unpegging the franc. It is worth mentioning that the first clear drop in the strength of dependence, observed in January 2013, was due to the risk decline on financial markets (Swiss National Bank, 2013). In the case of EUR/USD and GBP/USD, the empirical Spearman’s rho coefficients calculated for returns corresponding to the considered times vary from 0.5373 to 0.555. 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 08:05:00 CET 09:05:00 CET 14:35:00 CET 17:00:00 CET 88 However, the results obtained by means of MSC models show much more wide range for the conditional Spearman’s rho coefficients: from 0.3 to almost 0.8. The dependence is stable at 14:35 and 17:00 except for a drop in its strength at the beginning of 2013, which can be explained by a change of investors’ sentiment caused by an improvement in the UK economy (https://www.fx-mm.com/article/29770/a-change-of-season-a-changeof-sentiment/). This result suggests that the US news and the end of the stock market trading in Europe are not important for the dependence between returns on EUR/USD and GBP/USD. The linkages between these two exchange rate at 8:05 and 9:05 show strong dynamics indicating their sensitiveness on the information flow at the beginning of the trading day in Europe. The dynamic coefficients of dependence in the lower tail for (EUR/USD, GBP/USD) are low (0.06-0.35) for all the selected times, which means that the risk of simultaneous large movements down of these two exchange rates is low. In Figure 3, we present an example of estimates of the conditional lower tail dependence coefficients. The chosen pair is (CZK/USD, HUF/USD) and the value of q is 0.01. The results are quite spectacular. The tail dependence coefficients vary from 0.05 to even 0.77 with the dynamics strongly dependent on the time of day. The highest values are observed in turmoil periods and are achieved for returns calculated at 17:00, which is connected with closing risky positions at the end of the trading day. The pattern for the strength of linkages measured by means of the conditional Spearman’s rho strongly depends on the time of day too. The dynamics is the stablest for returns calculated at 17:00 and the most sensitive on the information flow for returns calculated at 14:35 (US news). The unconditional Spearman’s rho coefficients are about 0.75, but the conditional ones are changing from 0.26 to almost 0.9. Figure 3 Dynamics of lower tail dependence coefficients for (CZK/USD, HUF/USD) Source: authors The linkages of the Czech koruna and Polish zloty with the euro show strong dynamics, very sensitive on the information flow. The empirical Spearman’s rho coefficients are, respectively, about 0.9 and 0.8 and the conditional Spearman’s rhos vary from 0 (occasionally for 14:35) to almost 1 in the case of CZK and EUR, and from 0.6 to 0.9 for PLN and EUR. In the case of HUF and the euro, the dynamics of linkages is less wild, the unconditional Spearman’s rho coefficients are about 0.76, and the conditional ones are between 0.53 and 0.87. The tail dependence coefficients show the strongest dynamics for Czech koruna (vary from 0.05 to 0.82). The observed dramatic movements reveal the difficulties in keeping the koruna pegged to the euro (from 2013 to April 6, 2017). For the pairs (EUR/USD, PLN/USD) and (EUR/USD, HUF/USD) the results are more common – in both cases the dynamic tail dependence coefficients vary from 0.15 to 0.55 and their dynamics is similar to that of observed for main European currencies. Finally, let us consider the structures of dependence between the exchange rates against the US dollar of the Polish zloty and the other two Central European currencies. 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 08:05:00 CET 09:05:00 CET 14:35:00 CET 17:00:00 CET 89 For the pair (PLN/USD, HUF/USD) the impact of the time of a day does not exist. All the dynamic Spearman’s rho estimates show the same reaction on market events and quite stable level (0.63-0.88), and the unconditional values are about 0.81. The dynamics of tail dependence coefficients is similar with values from 0.3 to 0.6. In the case of (PLN/USD, CZK/USD), the dynamics of linkages exhibit much more interesting pattern. Their strength is higher at 9:05 and 17:00 and clearly weaker after opening national markets (8:05) and after releasing the US news (at 14:35). These differences are probably caused by the behavior of local traders. 4 Conclusions In the presented analysis we aimed to describe the dynamics of linkages between exchange rates of six European currencies against the US dollar. Our first question was about the differences in the structure of the linkages observed at different times of day. Moreover, we investigated the changes in the structure of the conditional dependence that could be caused by the most important market events. As a tool to model the dependence we used 3-regime Markov regime switching copula models, which are a tool flexible enough to capture qualitative and/or quantitative changes of the linkages in currency markets. Thanks to the taken approach, we got information about the dynamics of linkages measured using the dynamic conditional Spearman’s rho coefficients as well as the probability of simultaneous extreme movements down described by the dynamic tail dependence coefficients. The analysis was performed from the point of view of a US dollar investor who owns a portfolio of European currencies and analyzes the risk and diversification possibilities. We have shown that, in general, the dependence in currency market changes according to the time of day, which is a result of activities of different groups of traders with different preferences and investment strategies. Even for currencies temporarily pegged to the euro, the dynamics of linkages is quite strong, revealing difficulties to keep them pegged. References Bauwens, E., Laurent, S., Rombouts, J. V. K. (2006). Multivariate GARCH Models: A Survey. Journal of Applied Econometrics, vol. 21(1), pp. 79–109. Breedon, F., Ranaldo, A. (2013). 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(2009). Copula-based models for financial time series. In: T. G. Andersen, T. G., Davies, R. A., Kreiss, J. P., Mikosch, T., eds., Handbook of Financial Time Series, Berlin: Springer, pp. 767-785. Ranaldo, A. (2009). Segmentation and time-of-day patterns in foreign exchange markets. Journal of Banking & Finance, vol. 33(12), pp. 2199–2206. Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges, Publications de l’Institut Statistique de l’Université de Paris, vol. 8, pp. 229-231. Swiss National Bank (2013). 106th Annual Report. Retrieved from: https://www.snb.ch/en/iabout/pub/annrep/id/pub_annrep_2013. Tsay, R. S. (2010). Analysis of Financial Time Series, 3rd ed. Hoboken: Wiley. 91 Closet Indexing in Czechia: An Extended Analysis Tomáš Dráb1 1 Masaryk University Department of Finance at Faculty of Economics and Administration Lipová 41a, 60200 Brno, Czech Republic E-mail: 321854@mail.muni.cz Abstract: All active equity funds levy significant charges in exchange for an opportunity to outperform respective benchmark. However, many of these funds fail to implement active management in practice and resort to closet indexing. Evidence from abroad has shown that closet indexing has been widespread among funds offered on world’s most developed markets. The aim of this paper is a complete assessment of presence of closet indexing on Czech financial market through a deeper analysis of wide scope of funds offered to domestic retail investors. We found out that closet indexing is an issue of more than one third of the overall amount, but there is still sufficient number of truly active funds and certain signals where to expect closet indexer have been identified. Keywords: closet indexing, active investing, mutual funds, benchmark, Czechia JEL codes: G11, G23 1 Introduction Closet indexing is a term known for at least 20 years, firstly quoted by Laderman (1997) to the author’s knowledge, it has rarely been used or investigated for another 12 years, though. It was not until 2009 when Petajisto and Cremers published their paper that shaped a cornerstone of methodology of closet indexing research. Closet indexing is based on a distinction between active and passive portfolio management. As generally known, active management is characterized by an effort to outperform market return and reach an excessive return. On the contrary, passive management seeks to achieve market return. Active investing carries a chance for investor to reach an excessive return in exchange for the risk of underperformance. Passive investing can never lead by definition to lower than market return (Dráb, 2015: 100). Much has been written about active and passive investing, mainly on their mutual comparison in terms of performance and costs. We do not intend to widen that vast list, but instead to focus on an issue similarly pervasive, but incomparably less penetrating the scientific literature so far. Closet indexing is to some extent self-explanatory, an exact rigorous definition of closet indexing does not exist, though (Dráb, Florianová, 2016: 180). Broadly speaking, closet indexing is a phenomenon when an actively managed fund that claims to be maintaining active investment strategy considerably resembles some benchmark. Such a fund can then be called “closet indexer”, “closet index fund”, or “index hugger”. In case a passive fund resembles a benchmark there is nothing to dispute. The real problem emerges, when an active fund claims that it strives to outperform a benchmark or in whatever words to bring investors the opportunity to reach an excessive return, charges hefty fees, but fails to attempt to outperform market return in practice and instead pursue a passive strategy of at least partial resembling a benchmark. Apart from this, fees can be questioned even if they are not charged by a closet indexer. As analyzed by Miller (2010), fees have been successfully disputed at court as outrageous by an individual investor who sued an investment company, despite his case had nothing to do with the fund’s investment strategy. The whole matter of closet indexing came to broader awareness in the early 2016 following the publishing of the Supervisory work on potential closet index tracking by The European Securities and Markets Authority (ESMA). ESMA defines closet indexing rather vaguely: “…according to their fund rules and investor information documentation, to manage their funds in an 92 active manner while the funds are, in fact, staying very close to a benchmark and therefore implementing an investment strategy which requires less input from the investment manager (ESMA, 2016: 1, see Dráb, Florianová, 2016 for broader discussion).” As noted above, closet indexing is still not much covered across existing literature despite its practical significance. Besides Laderman there has only been a few papers targeted exclusively on closet indexing. Braham (2004) published a list of top index huggers among American funds investing in U.S. equity. Taylor (2004) brought certainly one of the first scientific insights into closet indexing. Based on an analysis of trading opportunities he concluded that closet indexing was not a widespread phenomenon in the American mutual funds industry during the 1990s. Petajisto (2013) extended his previously mentioned research with Cremers from 2009, which will be discussed further in the text, and confirmed their findings on closet index funds underperforming their benchmarks while genuinely active funds exhibiting excessive return over their benchmarks fit to financial crisis era too. Cremers et al. (2016) also extended the research and found out that about 20 % of worldwide mutual fund assets are managed by closet indexers. The only analysis of presence of closet index funds in Czechia known to the author is that of Mikeš (2015). He compared 44 active funds investing in European stocks and found only 4 closet indexers. However, this sample is rather small and only partially covers Czech financial market, since it does not actually include any domestic fund. We already analyzed closet indexing on domestic financial market back in 2016. Although we came to certain conclusive results and a significant part of this paper is based on our previous findings, some issues left unanswered. This paper is intended as an extension of our introductory analysis. Its aim is a complete assessment of presence of closet indexing on Czech financial market through a deeper analysis of wide scope of funds offered to domestic retail investors. 2 Methodology and Data Methodology of investigating closet indexing is based on a concept of active share (AS), which, in the meaning we are using, was first introduced by Petajisto and Cremers in 2009. Active share represents the share of portfolio holdings that differ from the benchmark index holdings. AS thus has to lie between 0 and 1 (usually expressed in %). A fund with AS equal to zero has identical portfolio (i.e. actual stocks including their weightings) to selected index. On the other hand, a fund with AS 100 % does not hold a single share that would be a part of a benchmark in its portfolio. Closet index fund is claimed to resemble its benchmark from at least 40 %, which means that AS is up to 60 % but no less than 20 %, which is a frontier of an index fund (Cremers, Petajisto 2009: 3341-3342). An alternative view of active share was provided by Miller (2007), who calculated R2 from a regression model with fund performance as a dependent variable and benchmark performance as an explanatory variable. Such approach does not lack validity, but only the former, as Smith noted, provides us with high specificity which active bets a fund manager is making and how large they are (Smith 2014: 7-8). Despite being introduced less than nine years ago, active share has already spread among scholars and financial industry to a certain extent, and anyone who attempts to analyze closet indexing is forced to cope with this concept whether he acknowledges or disapproves it. Active share measure has been utilized by several scholars so far. Hirschel and Krige (2010) used AS for an analysis of South African unit trusts and more or less confirmed previous findings of Petajisto and Cremers that low active share funds showed lower benchmark adjusted performance, while high AS funds showed higher benchmark adjusted performance. Active share has also penetrated fund factsheets of several significant asset managers, i.e. Threadneedle, Bailie Gifford, Sparinvest or SEB. We can therefore say that AS has started establishing a position of a possible future 93 standardized benchmark which may once become an inseparable part of funds’ key figures. However, the relevance of active share has recently been challenged by Frazzini, Friedman and Pomorski (2016: 20). They cast doubts upon reliability of performance prediction based on active share as they found no evidence that high AS funds earn higher returns than their low AS counterparts. Nevertheless, their conclusions are oversimplified as proofed by Petajisto (2016: 11-12) and do not challenge the actual concept, only some of its features we are not going to utilize in this analysis anyway. Closet indexing can in principle relate to any fund that has a benchmark set or at least there exists an appropriate benchmark that can be set. However, practical application of the concept has been limited to equity funds, which are most prone to closet index tracking. Table 1 shows the amount of mutual funds offered in Czechia according to data provided by Czech National Bank (ČNB). Table 1 Number of funds offered in Czechia as at 01/08/2016 Fund type Count Domesic mutual funds 197 of that standard and special open-ended 132 of that closed-ended 7 of that funds of funds 11 of that qualified investors funds (QIF) 47 Foreign investment funds 1 399 of that standard open-ended 1 232 of that others 167 Total domestic and foreign funds 1 596 Source: ČNB, own calculation These numbers do not change significantly over the short term, so we may proceed from last year’s figures. Moreover, recently launched funds cannot be analyzed either way, since there is no available holdings data for them yet. According to the above shown there was 1 596 funds altogether offered to domestic investors. Out of 197 domestic mutual funds 132 were standard or special open-ended funds (OPF). Standard fund is a fund that has to meet criteria of the European law, namely the UCITS directive, unlike special fund that does not have to. The rest was consisted of closed-ended funds, funds of funds, and QIF. These are particular types of special funds that do not count to the previous group. Out of 1 399 foreign investment funds 1 232 was deemed standard according to ČNB. We included only standard and special domestic OPF and standard foreign OPF in the analysis, since they are generally aimed at retail customers, are mutually comparable, standardized, and should all have a similar degree of transparency necessary to acquiring data. Besides focusing on equity funds, we imposed several other conditions on what funds include in scope for analysis. A major problem of selection, which has to be overcome, stems primarily from the overall number of eligible funds that lies beyond capabilities of an individual to be fully covered. First, we excluded every fund that is not designated for retail customers or its official documents (especially KIID, Annual and Semi-annual reports with full portfolio holdings) are not freely available. Second, we took into account only those funds managed by a member institution of Czech Capital Market Association (AKAT). This condition excluded a considerable amount of funds of several well-known asset managers and financial conglomerates, i.e. Aberdeen, Allianz, Blackrock, BNP Paribas, Fidelity, Franklin Templeton, JPMorgan, and Parvest. However, great portion of these funds has already been decently investigated within the US market or in Mikeš (2015). Third, as we targeted on retail Czech financial market, we strove to cover all domestic (i.e. Czech domiciled) funds and a majority of funds offering a CZK share class both of which are most available and common to local customers. Therefore, we set 94 multiple restrictions on minimum fund size as a general distinctive feature to include a fund in scope - no minimum limit for domestic funds, 2 bil. CZK in assets for foreign funds offered in CZK, and finally 10 bil. CZK for foreign funds offered in foreign currency only. This condition, however, does not apply generally, since we also included many funds that do not comply with the restriction but are attracting a significant amount of domestic capital according to data from AKAT. Not every fund having a majority of stocks in its portfolio and meeting the above conditions is suitable for analysis based on active share. First example are structured funds that are much more structured products than genuine funds and are not actively managed. Another example, but a lot more complex one, are funds of funds (FoF) we have marginally been dealing with in our previous analysis. Despite stating a benchmark in some cases, FoF’s AS cannot be calculated directly, since these funds neither hold stocks directly, but only through other mutual funds. This puts extraordinary demands on actual holdings acquisition, since it is necessary to gain portfolio data for all funds in an FoF portfolio. Moreover, benchmark, when set, is mostly artificial and it proves difficult to select one in case it is not set. Another obstacle - how to assess ETF holdings definitively lead us to a conclusion that funds of funds are not suitable for the concept of active share. On the other hand, feeder funds (FF) are not such case as they are investing predominantly in a single master fund. Index funds - despite being eligible for AS calculation, were not a subject of analysis, because they are not a subject of closet indexing issue. We only included a few index funds for purely referential purpose. Active share which we calculated over the course of analysis is based on stocks holdings only. Other fund assets, i.e. derivatives, rights, receivables, or cash do not show on the calculated figure, although they are not a part of benchmark holdings. 3 Results and Discussion Based on the above criteria we identified a total of 104 funds eligible for analysis plus 4 index fund added for reference. They are ordered by fund size and divided according to domicile and currency into three distinct tables below. Table 2 Active share of foreign funds not offered in CZK Fund Name D C NAV Focus B Benchmark AS Pioneer U.S. Fundamental Growth LU $ 64 715 USA Y Russell 1000 Growth N/A Pioneer Euroland Equity LU € 51 155 EU Y MSCI EMU 71,20* KBC Equity Fund Strategic Cyclicals BE € 49 008 all world cyclical N N/A N/A HSBC Indian Equity LU $ 41 154 India Y S&P / IFCI India Gross N/A KBC Equity Fund Eurozone BE € 31 950 EU N MSCI EMU 56,43 KBC Equity Fund Strategic Finance BE € 31 030 global finance N MSCI World Financials 51,16 Pioneer Global Ecology LU € 28 686 global eco Y MSCI World 95,50 Pioneer European Potential LU € 27 976 Europe small cap Y MSCI Europe Small Cap86,20* Pioneer Global Select LU € 24 287 global Y MSCI World 81,31 Pioneer U.S. Research LU $ 24 250 USA Y S&P 500 72,72 NN (L) Euro High Dividend LU € 23 849 EU dividend Y MSCI EMU 63,70* KBC Equity Fund High Dividend BE € 22 477 global dividend N MSCI World High Dividend Yield 77,83 KBC Equity Fund Buyback America BE $ 19 781 US thematic N N/A N/A Pioneer European Equity Value LU € 18 126 Europe Y MSCI Europe Value 74,40* KBC Index Fund Euroland BE € 18 054 index Y EURO STOXX 50 0,60* 95 Pioneer North American Basic Value LU $ 17 193 USA Y Russell 1000 Value N/A Pioneer European Research LU € 17 066 Europe Y MSCI Europe 73,30* KBC Equity Fund New Markets BE € 16 876 EM N MSCI Emerging Markets 57,79 NN (L) US High Dividend LU $ 14 390 US dividend Y S&P 500 68,50 KBC Equity Fund High Dividend Eurozone BE € 14 097 EMU dividend N MSCI EMU High Dividend Yield 79,24 Raiffeisen Global Aktien AT € 13 260 global N MSCI World 82,77 Pioneer U.S. Midcap Value LU $ 13 144 US mid cap Y Russell Mid Cap Value N/A NN (L) Euro Equity LU € 12 552 EMU Y MSCI EMU 49,70* Raiffeisen Eurasien Aktien AT € 12 058 Asia, RF, TR N N/A N/A Raiffeisen Europa Aktien AT € 11 513 Europe N MSCI Europe 70,17 Pioneer Emerging Markets Equity LU € 11 334 EM Y MSCI Emerging Markets 86,03 KBC Equity Fund Strategic Satellites BE € 10 959 various thematic N N/A N/A KBC Equity Fund Japan BE ¥ 10 713 Japan N MSCI Japan 61,63 NN (L) European High Dividend LU € 10 294 Europe dividend Y MSCI Europe 69,63 Credit Suisse (Lux) European Dividend Plus LU € 10 066 Europe dividend Y MSCI Europe 64,76 Raiffeisen US Aktien AT € 10 053 USA N MSCI USA 80,17 Pioneer Japanese Equity LU € 9 979 Japan Y MSCI Japan 74,10 NN (L) Food & Beverages LU $ 9 589 global sector Y MSCI World Consumer Staples 48,49 Credit Suisse (Lux) USA Growth Opportunities LU $ 9 431 US M&L cap Y MSCI USA 79,91 HSBC GIF Brazil Equity LU $ 9 363 Brazil Y MSCI Brazil 10/40 42,07 Credit Suisse (Lux) Small and Mid Cap Germany LU € 8 800 Germany S&M cap Y Midcap Market Index 27,63 KBC Equity Fund Pharma BE € 8 413 global sector N MSCI World Pharmacy BioLife 42,81 KBC Index Fund United States BE $ 7 632 index Y MSCI USA 5,71 HSBC GIF Euroland Equity Smaller Companies LU € 7 126 EU S&M cap Y MSCI EMU SMID N/A Raiffeisen Emerging Markets Aktien AT € 6 660 EM N MSCI Emerging Markets 81,15 KBC Equity Fund New Asia BE € 6 220 EM Asia N MSCI Emerging Markets Asia 56,38 KBC ECO Fund Water BE € 6 114 global sector N N/A N/A Credit Suisse (Lux) Global Security LU $ 5 222 global Y MSCI World 96,84 KBC Equity Fund Oil BE € 4 997 global oil&gas N MSCI World Energy 27,59 Raiffeisen Pazifik Aktien AT € 4 644 Asia-Pacific N MSCI Asia Pacific 45,60 Raiffeisen TopDividende Aktien AT € 4 283 global dividend N MSCI World High Dividend Yield 84,47 KBC ECO Fund World BE € 3 545 global CSR N MSCI World SRI 81,64 Source: own calculations based on asset managers, index providers, AKAT, and Bloomberg data 96 Table 3 Active share of foreign funds offered in CZK Fund Name D NAV Focus B Benchmark AS Pioneer European Equity Target Income LU 70 513 EU/Europe N MSCI Europe 55,60 Pioneer Global Equity Target Income LU 50 350 global dividend N MSCI World 74,03 Pioneer U.S. Pioneer Fund LU 38 986 USA Y S&P 500 69,62 Pioneer Top European Players LU 34 674 Europe M&L cap Y MSCI Europe 80,66 KBC Equity Fund America BE 27 703 North America N MSCI North America 77,30 Amundi Equity Emerging World LU 24 937 EM Y MSCI Emerging Markets Free N/A NN (L) Global Sustainable Equity LU 24 377 global CSR Y MSCI World 87,60 NN (L) Global High Dividend LU 21 640 global dividend Y MSCI World 75,92 NN (L) US Enhanced Core Concentrated Equity LU 12 665 USA Y S&P 500 51,76 Amundi Equity US Relative Value LU 12 403 US value Y S&P 500 79,32 NN (L) Emerging Markets High Dividend LU 9 324 EM dividend Y MSCI Emerging Markets 74,80 NN (L) International Czech Equity LU 8 893 CZ,SK,R,BG,LT ,LV,EE,HR,SLO Y composite1 26,42 NN (L) Global Equity LU 8 034 global Y MSCI World 79,53 NN (L) Japan Equity LU 7 891 Japan Y MSCI Japan 62,28 NN (L) European Real Estate LU 7 402 Europe sector Y 10/40 GPR 250 Europe 20% UK N/A NN (L) Global Equity Impact Opportunities LU 7 285 all world Y MSCI ACWI 87,30 NN (L) European Equity LU 6 753 Europe Y MSCI Europe 61,50* ESPA Stock Global AT 6 193 global N MSCI World 77,35 Amundi Equity Global Luxury and Lifestyle LU 5 929 global sector Y MSCI World Consumer Discretionary 60,57 ESPA Stock Biotec AT 5 543 global sector N MSCI World Pharmacy BioLife 76,88 ESPA Stock Europe AT 5 460 Europe N MSCI Europe 58,76 Pioneer Emerging Europe and Mediterranean Equity LU 5 231 EM Europe & Middle East Y MSCI EM Europe and Middle East 10/40 N/A Credit Suisse (Lux) Global Value LU 4 258 global value Y MSCI World 99,54 ESPA Stock Global - Emerging Markets AT 4 220 EM N MSCI Emerging Markets 48,93 KBC Multi Track Germany BE 4 189 index Y MSCI Germany 11,17 KBC Equity Fund Europe BE 4 144 Europe N MSCI Europe 46,28 Amundi Equity Europe Concentrated LU 3 379 Europe Y MSCI Europe 73,95 CPR Invest - Global Silver Age [FF] LU 3 173 all world thematic Y MSCI World 87,33 Amundi Equity Japan Value LU 3 093 Japan value Y Topix Tokyo SE 76,26 Erste WWF Stock Environment AT 2 836 global eco N N/A N/A Conseq Invest Akciový IE 2 729 CEE Y composite2 50,05 ESPA Stock Europe Emerging AT 1 818 various EM N N/A N/A Generali Fond východoevropských akcií IE 1 663 CEE N MSCI EM Europe 71,70 ČSOB Akciový dividendových firem BE 1 611 global dividend N MSCI World High Dividend Yield 79,23 97 ESPA Stock Europe Property AT 1 541 Europe sector N MSCI Europe Real Estate 42,93 ČSOB Akciový dlouhodobé spotřeby BE 1 349 all world sector N N/A N/A ESPA Stock Japan AT 1 316 Japan N MSCI Japan 26,26 Erste Responsible Stock America AT 1 213 North America N MSCI North America 71,90 ČSOB Akciový vodního bohatství BE 727 global sector N N/A N/A Generali Fond světových akcií IE 260 global N MSCI World 55,65 ČSOB Akciový fond - BRIC BE 226 BRIC N MSCI BRIC 16,80 ČSOB Český Akciový (PX) BE 176 index Y PX 13,44 Source: own calculations based on asset managers, index providers, AKAT, and Bloomberg data Table 4 Active share of domestic funds Fund Name D NAV Focus B Benchmark AS ČS Top Stocks CZ 11 261 global N MSCI World 96,99 KB privátní správa aktiv 5D CZ 6 617 global dividend N MSCI World High Dividend Yield 69,56 Generali Fond globálních značek CZ 2 525 global N MSCI World 61,95 ČS Sporotrend CZ 2 099 CEE Y composite3 46,00 AXA CEE Akciový fond CZ 1 990 Visegrád 4 Y composite4 29,82 ČSOB Akciový CZ 1 716 global N MSCI World 59,59 Generali Fond nemovitost. akcií CZ 1 651 global sector N MSCI World Real Est. 73,46 Pioneer akciový fond CZ 1 253 OECD N MSCI World 86,24 Generali Fond farmacie a biotechnologie CZ 1 218 global sector N MSCI World Pharmacy BioLife 43,18 Generali Fond ropy a energetiky CZ 1 176 global sector N MSCI World Energy 59,10 AXA Realitní fond CZ 1 108 global sector N MSCI World Real Estate 77,89 Generali Fond nových ekonomik CZ 524 EM N MSCI Emerging Mkts. 74,93 ČSOB Akciový realitní CZ 504 Europe&US sector N MSCI World Real Estate 48,50 IKS Akciový - střední a východní Evropa CZ 429 CEE N MSCI Eastern Europe 44,92 Generali Fond živé planety CZ 353 global eco N N/A N/A AKRO globální akciový fond CZ 350 various all world N MSCI ACWI 15,97 ČSOB Akciový střední a východní Evropa CZ 233 CEE N MSCI Eastern Europe 66,73 AKRO akciový fond nových ekonomik CZ 228 EM N MSCI Emerging Markets 94,23 AKRO fond progresívních spol. CZ 160 global small cap N MSCI World Small Cap17,96 Source: own calculations based on asset managers, index providers, AKAT, and Bloomberg data • D stands for domicile of the fund. • C stands for fund currency. CZK is preferred whenever possible, whatever the fund master currency. All funds in Table 2 and 3 are in CZK. • NAV stands for fund net asset value in mil. CZK. • Focus stands for investment focus of a fund as per KIID or portfolio composition. • B stands for presence/absence (Y/N) of a fund’s benchmark according to KIID. If no benchmark was stated, we selected a benchmark most appropriate to fund’s focus and portfolio. We used N/A (not available) in case it was impossible to set a benchmark which members’ weightings would be available. N/A at AS implies that benchmark members’ weightings were unavailable. • EM stands for Emerging Markets, CSR stands for Corporate Social Responsibility, S&M and M&L cap stands for small and mid and mid and large cap. 98 • 1 - composite index of PX (52%), WIG30 (28%), BUX (10%), BET (10%) • 2 - WIG30 (38%), PX (28%), BUX (28%), SBITOP (6%) • 3 - MSCI Russia (30%), PX (17,5%), BUX (17,5%), MSCI Turkey (17,5%), MSCI Poland (17,5%) • 4 - PX (40%), WIG20 (28%), BUX (20%), SAX (2%), PRIBID 6M (10%) • * - adopted from Mikeš 2015. It has to be noted that AS is never an exact figure. This is caused by a time inconsistency between effective dates of fund and benchmark holdings. Fund holdings are in most cases disclosed in annual and semi-annual reports only, while benchmark holdings are mostly disclosed without an exact date, if at all. Holdings figures are nonetheless not changing at such a pace that would render calculated figures useless. This has also been proved by a time consistency in AS that allowed us to adopt few figures from Mikeš in this analysis. We summarized key findings based on the above data in Table 5. Table 5 Key findings on closet indexing among funds in scope (excl. index funds) Foreign funds Foreign funds in CZK Domestic funds Average AS 67,5 % 65,6 % 59,3 % Overall average AS 65 % AS not available 10/45 7/40 1/19 Closet indexers 24,4 % 27,5 % 47,4 % Overall closet indexers 31/108 (28,7 %) Overall closet indexers excl. AS N/A 31/86 (36 %) Benchmark (Y) 53,3 % 52,5 % 10,5 % Average AS of funds with benchmarks 69,8/64,8 % 71,4/58,6 % 37,9/62 % Overall average AS of funds with/without benchmark 68,9/61,9 % Funds with broad/narrow focus 23/22 26/14 9/10 Average AS of broad/narrow focused funds 72,6/61,4 % 68,8/58,1 % 64,2/54,4 % Overall average AS of broad/narrow focused funds 69,8/57,8 % Source: own calculations The most important figure from the above is the overall number of closet indexers out of all funds with calculated AS. This 36 % confirmed our previous findings in. We identified several interesting trends that surfaced along with funds division into three separate groups. Foreign funds showed the highest AS, tightly followed by foreign funds available in CZK. Domestic funds falls into closet indexers set on average as their AS being slightly below 60 %. A share of closet indexers confirms AS figures, specifically domestic funds showed worryingly high amount of closet index funds. However, relatively small amount of domestic funds has to be taken into account as it might distort the result. We found sharp differences at benchmark presence between foreign and domestic funds. While foreign funds with benchmark prove to be less prone to closet indexing, we can see pretty much the opposite at domestic funds. The latter figure lacks statistical significance, since there were only two domestic funds having benchmarks. Paradoxical as it may seem, funds with benchmarks have much higher AS than their counterparts that do not state any. Absence of benchmark can thus serve as a warning indicator for an investor trying to avoid index huggers. Setting a benchmark seems to be a matter of particular asset manager. While some assign benchmarks to all equity funds or at least their overwhelming majority, others do not assign any. The former group consists of Amundi, Credit Suisse, NN and Pioneer, which all reached higher AS on average than the latter group of KBC (ČSOB), Generali, or ESPA (Erste, ČS). Raiffeisen funds, despite not having benchmarks reached surprisingly high AS. It is sometimes claimed that AS is 99 suffering from an investment focus tilt. Our analysis shows that funds with wider focus tend to have ca. 10 % higher AS than funds with narrow focus. It may turn out more difficult to pick stocks on narrowly defined market, it is not impossible as several funds in above tables prove, though. 4 Conclusions Yet around one third of funds we analyzed could be accused of closet indexing, there is still sufficient amount of equity funds with high active share that an individual investor can invest in, even in Czech Koruna. A combination least prone to closet indexing is a foreign fund of whatever currency that has a benchmark and a broad investment focus, like US stocks or global dividend stocks. On the side are domestic funds, especially that which are narrowly focused. Supply of Czech equity funds is very limited indeed. We tried to provide a complete analysis of retail Czech financial market and answer some issues which left unanswered in our previous research. Some questions newly emerged, though. It has already been mentioned that active share suffers from certain imperfection stemming from data shortage. What is equally important is also the actual benchmark selection. We observed that some funds have a benchmark that does not reflect their investment focus or true holdings. It is then tempting to test these funds against appropriate benchmark despite not being officially stated. This brings us to a conclusion of aggregate testing of these funds against alternative benchmarks, which is certainly worth further investigation. Acknowledgments Support of the Masaryk University within the project MUNI/A/0823/2016 “Behaviorální, znalostní a ekonomické aspekty oceňování a obchodování finančních a jiných aktiv” is gratefully acknowledged. References Braham, L. (2004). How to Spot a Closet Index Fund. BusinessWeek, Sep. 6, pp. 108- 109. Cremers, K. J. M., Petajisto, A. (2009). How Active Is Your Fund Manager? A New Measure That Predicts Performance. The Review of Financial Studies, vol. 22(9), pp. 3329-3365. Cremers, K. J. M., Ferreira, M. A., Matos, P., Starks, L. (2016). Indexing and active fund management: International evidence. Journal of Financial Economics, vol. 120 (2016), pp. 539-560. Dráb, T. (2015). Performance of actively managed funds in various stages of business cycle. In: Krajíček, J., Kajurová, V., eds., European Financial Systems 2015. Brno: Masarykova univerzita, pp. 100-105. Dráb, T., Florianová, H. (2016). Closet Indexing: the Case of Czechia. In Čulík, M., ed. Řízení a modelování finančních rizik. Ostrava: VŠB-Technická univerzita, pp. 179-187. ESMA (2016). Supervisory work on potential closet index tracking. Frazzini, A., Friedman, J., Pomorski, L. (2016). Deactivating Active Share. Financial Analyst Journal, vol. 72(2), pp. 14-21. Hirschel, P., Krige, J. D. (2010). Characterisation of South African Equity Unit Trusts Using the Active Share Measure as a Performance Indicator. Journal for Studies in Economics & Econometrics, vol. 34(1), pp. 54-82. Laderman, J. M. (1997). Shining a Light on “Closet Index Funds”. BusinessWeek, Oct. 20, pp. 162-163. 100 Mikeš, J. (2015). „Aktivní řízení“ v praxi. Fond Shop, Vol. 15-16 (2015), pp. 18-19. Miller, R. M. (2007). Measuring the true cost of active management by mutual funds. Journal of Investment Management, vol. 5(1), pp. 29–49. Miller, R. M. (2010). Paying the High Price of Active Management. A new look at mutual fund fees. World Economics, vol. 11(3), pp. 5-23. Petajisto, A. (2013). Active Share and Mutual Fund Performance. Financial Analysts Journal, vol. 69(4), pp. 73-93. Petajisto, A. (2016). Author Response to “Deactivating Active Share”. Financial Analysts Journal, vol. 72(4), pp. 11-12. Smith, D. M. (2014). Equity Hedge Fund Performance, Cross-Sectional Return Dispersion, and Active Share. Research in Finance series, vol. 30, pp. 1-22. Taylor, J. (2004). A note on closet-indexing. Journal of Economics and Business, vol. 56 (2004), pp. 431-441. 101 Implementation of cost model for effective planning of foreign military operations Jan Drbola1 , Miroslav Krč2 1 University of Defence Faculty of military leadership, Department of economy Kounicova 65, 662 10 Brno, Czech Republic E-mail: jan.drbola@unob.cz 2 University of Defence Faculty of military leadership Kounicova 65, 662 10 Brno, Czech Republic E-mail: miroslav.krc@unob.cz Abstract: Current Military of Defense accounting system is based on expenditure base, thus this state is not suitable for planning or evaluation of each activities of military operation, because there are no sufficient accounting outputs. In accordance with this unsatisfactory state of accounting, this paper focuses on finding some measures that help to improve current system of planning and budgeting foreign operation in the whole life cycle period. The life cycle is divided into three separate time subperiods – preparation, foreign operation and evaluation. Pursuant this conditions, system of cost reporting, which calculate with cost assignment, is designed. This measure help to identify and determine real amount of all costs, relating with foreign acting. Most information come from used papers of Military of Defense, but most materials are used from countries abroad, where similar measures were implemented. In the paper is used method of description, analysis, synthesis, comparison, statistical methods or method of data modelling. Keywords: cost model, public administration, accounting, life cycle of foreign operation, planning process JEL codes: H56, H72, H83 1 Introduction This paper focuses on implementation of cost accounting model for effective planning of foreign operation. The paper is divided into four separated parts. The first part involve some basic information about the topic and general situation that help to introduce readers into the issue. The second part focus on methodology and data source, where the logical succession and progress throughout the work is explained. The third part includes analyses of current state of monitoring costs and expenses in foreign operation, measurement the effectiveness in the conditions of foreign operations and definition of life cycle of foreign operation. Practical application of cost model is also a part of the third part. Public sector is an important part of national economy that is responsible for securing public goods for inhabitants on a non-profit basis and is therefore financed from funds concentrated in the budgetary system, especially in the public budget system (Peková, Pilný, Jetmar, 2012).From economical view defense is purely public good. Defense as a public good is also realized in the form of military missions. Military missions contribute to the implementation of the state´s economic policy (Krč, 2016). Economic control in the field of public sector of the Czech Republic fall behind private sector for a long time. One reason for this delay could be not using any economic tools that helps representatives of subjects of public sector in their decision-making and planning process or seemingly non-existent factor of output, apart from subjects of private sector, where some measurable factor of output are stated (e.g. price). All subjects of public sector 102 must comply Act No. 320/2001 Coll. on financial control in public administration and Act No. 218/2000 Coll. on budget rules. These two acts by the way treat of the principle of economy, effectiveness and efficiency. As described below, representatives of military unit are unable to fulfill these principles because of inadequate inputs of decision-making process. Ministry of Defense of the Czech Republic decided to implement controlling. This commitment was firstly captured in White paper on Defense (2011). At present, an essence of this implementation is a flat-rate monitoring of costs throughout the sector, but only the place of cost rise, rather than the purpose, is being tracked, which greatly limits its own use of cost accounting for next purposes. There are some NATO countries, where cost accounting was implemented (Krč, Golik, 2015). In German army has been accepted many measures, that caused, economic control is closer to economic control of civil firms. It´s important to mention, that there is cost capturing and their evaluation on high level, although there is still a difference between economic theory and economic practice (Krč, 2010). In United Kingdom, cost accounting is also used for many years and accrual accounting is introduced and its benefits are being exploited (Krč, Golik, Vodáková, 2016). The main goal of implementation of cost accounting is to provide to representatives of military units economical outputs based on cost monitoring. These outputs are able to use for improvement planning process, process of control or decision-making. In case of full implementation of cost accounting it is possible to accept other, more difficult economical tools and measures. According to Král (2012), the main goal of cost accounting is to provide source materials for control of reproductive process in conditions, when the basic parameters have been decided. 2 Methodology and Data In the section of introduction is used a method of analysis, because it is needed to provide readers sufficient information to an appropriate extend about the topic. In the part of practical application of cost model is used a method of observation and analysis to track each activity relating with the observed object (foreign operation). These observed activities are captured by using method of description. Other method is synthesis used for identification each kind of cost on the first side and for grouping identified cost into calculation formula on the other side. Also it is necessary to mention method of modelling used throughout the part practical application. It is not possible to describe each activity involved in the military training, therefore it is used a method of abstraction and incremental costs are involved in mentioned activities. Because of stated maximum length of this paper, some calculation and operations doesn´t include all items, but they are represented by at least one example. As main date source similar some published scientific papers and published book aiming these topic, were used for the part of introduction. The part of practical application of cost model is based on concrete papers and discussion with the employees of the economic section of Ministry of Defense. In the field of dividing cost must be distinguished direct costs and indirect costs. Direct cost are easily identified and are easily to relate them to concrete activity or purpose. Direct costs correspond with variable cost expended in dependence of capacity of performance. Indirect costs correspond with fixed costs are single expended for certain time period (Fibírová, Šoljaková, Wagner, 2011). 103 3 Results and Discussion This section involves a description of current situation in the field of expenses and cost monitoring in the foreign operation, measurement the rate of effectiveness in the conditions of foreign operations and definition of life cycle of foreign operation and practical application of cost model. Current situation of costs and expenditures measurement A basic problem of an economical control in the field of Ministry of Defense is reporting a budgeting system based on expenditures. Pursuant this fact, any economical control is not possible, because expenditures don´t capture every real economical operation (e.g. material consumption), thus economic outputs aren´t credible, so representatives of military units has no relevant information for planning and decision-making process. Definition of the effectiveness and life cycle of foreign operations Because there is not a profit category, in the conditions of public sector, outputs must be determined for each activity or each process. This determination is possible to carry out through two basic ways. First way is to determine so called shadow price. Wherever possible, calculation of the shadow price by evaluating the state produced in the public sector either by the price at which these goods are produced in the market sector or by the costs incurred to which average profit achieved in the production of the same goods produced by the private sector (Strecková, 1998). In some cases it´s not possible to use shadow prices, mainly because of specific outputs, that are produced by army in conditions of public sector. In this case it is needed to use different way to determine output of public sector, which is determination the quality elements of the target state against which the costs can be related. It is therefore appropriate to use, for example, a direct point assessment. For example, in the case of shooting training, you can determine the score as a result of the number of 10 shots. Pursuant this two ways it is possible to identify output and this output quantify express. Costs of foreign operations do not arise only during acting soldiers abroad, but they arise during the whole life cycle of a foreign operation. Given the fact that this term does not exist under the terms of the Ministry of Defense's economic governance, this paper focuses, among other things, on explanation of this term. From the logic of things and the reflection on the problem of the costs in relation to the foreign operation, the life cycle needs to be defined in time. It is possible to define this term as a time period from the approval of the foreign operation's intent, to the return from foreign and the settlement of all obligations. In terms of timing, the life cycle of the foreign operation can be divided into 3 individual periods. First period can be defined from the moment of approval of the deployment of units of the Czech Army into a foreign operation by the Parliament of the Czech Republic, or by the Government, until the time of departure to a foreign operation. In this phase, the army is preparing to operate abroad, which entails a number of military exercises, medical preventive measures (e.g. vaccinations), education and training courses, and other activities that are spectrally very extensive and diverse. Given the specific conditions of each foreign operation, it is necessary to purchase the materials and services that are necessary for the actual operation in a specific operation. Transport itself generates large costs because the transportation of containers with the material is ensured by coalition partners (the Czech Army does not have the necessary tools of transport), provided that the costs incurred are paid. All of these activities, which are being carried out in the first phase, carry a large amount of costs to be allocated to the foreign operation in question. Because of large number of participating subjects at this stage, it is necessary to cooperate with the economic bodies of several public administration subjects so that the costs incurred can be assigned in a form that is as close as possible to the real situation. 104 Second phase of the life cycle of the foreign operation is the operation of soldiers abroad. The first cost to be mentioned is the transport of soldiers to a place of action, which is mainly carried out by aircraft of the Czech Armed Forces. The actual operation of soldiers abroad is probably the simplest phase of the entire life cycle of a foreign operation, as most of the costs incurred during this phase are directly related to the procurement of materials and services abroad. Here is a place for an analogous cost allocation procedure, as is the case with the normal organization of the Ministry of Defense, which is deployed on the territory of the Czech Republic. However, as mentioned in previous text, cost sharing itself takes place in a different way. The last phase of the life cycle is the period from the return of the members of the foreign operation to the completion of the material restoration (repair of damaged material, purchase of destroyed or lost material), after payment of all obligations (payment of invoices associated with the cost of living - food, accommodation, waste etc.) to umbrella organizations (coalition partner or supranational organization), settlement of personal expenses (compensation of injuries or other reimbursements to employees, provision of preventive rehabilitation, etc.) Activities that relate to this stage must be related to the actual operation because the costs incurred at this stage arise only because of the existence of a foreign operation and in the absence of a foreign operation, these costs would not arise at all. Practical application of cost model Every incurred cost analyzed during acting of model military unit has to be assign to one of stated activity in appropriate time phase. This principle expresses the essence of cost accounting, because it is necessary as much as possible assign to cost certain activity. For purpose of this paper, there is a military training that is a part of preparation of soldiers, who are supposed to operate in foreign operation. Every element operating abroad, acts externally as a separate accounting entity and is identified by u unique designation (military unit XY). Military unit, soldiers will be part of, is marked as a Military unit 1125. Suggested process of cost determination have to be the same in the whole cycle of foreign operation process and in case of every activity. Pursuant previous theoretical definitions a model cost calculation of the first phase of foreign operation´s life cycle is designed. Detailed description of cost calculation is shown on the example of shooting training as a necessary part of soldier´s preparation. Firstly, it is necessary to identify all activities, to which costs are related in the whole cycle of foreign operation. Each activities relating with the main goal and structure of model situation of acting abroad is depicted in Table 1. Table 1 Activities of foreign operation Military unit Phase Calculation unit 1125 1 1 tactical training 2 medical training 3 shooting training 4 other training 5 domestic courses 6 foreign courses 7 other costs 2 1 main goal of foreign operation 2 support activities (transport) 3 cost of living (food, accommodation etc.) 4 other costs 105 3 1 material recovery (damage, loss) 2 personal cost (injury /death compensation) 3 spa care 4 other costs Source: by author As shown on Table 1, model Military unit 1125 activities are divided according to listed three time subperiods of the foreign operation´s life cycle. Particular activities are set to be comfortable to assigned occur costs. Form and structure of all activities depends on managerial decisions. Together with range of monitored activities determination it is necessary to define structure and form of calculation formula. For model situation calculation formula includes these groups of direct costs: • Direct wages, • Direct material, • Other direct expenses. Further, calculation formula involves these groups of indirect costs: • Productive overheads, • Support overheads, • Staff overheads. To fulfil the essence of cost accounting, it is necessary to assign incurred cost to concrete activities according to listed activities. Capturing of every cost is done by accounting record. Account record without analytical form is expressed as a record of debit side and credit side, but there is no detailed information. Due to listed calculation unit is possible to create accounting record capturing these activities – analytical accounts. For the purpose of this article, concrete numerical expression of debit side and credit side is expressed verbally because of different designation of one account operation in different countries. Due to setting rules of in-house accounting can be reached, each analytical account record express more information, concretely purpose of account record. Record form with analytical account looks like this: wages/112513. Pursuant this enrollment it is clear, this analytical account express wages in relations with shooting training. In connection with shooting training relating with foreign operation´s preparation, following direct cost were identified as costs directly relating with shooting training that relates with preparation for foreign operation. According to calculation unit is possible to relate analyzed cost directly to the activity of shooting training of the first phase of foreign operation. Figure 1 Direct cost relating with shooting training Source: by author These costs directly relates with shooting training and thus it is not difficult to identify them and describe them by analytical account record. In relations with this activities it is possible to identify indirect costs, it means such costs, that are not easily identified in relation with monitored activity and thus it is necessary to determine them. These costs are determined by using cost allocation base. That means, cost allocation base extension leads to increase of indirect costs. 9 650 5 108 2 510 material/112513wages/112513 other direct costs/112513 106 Process of recalculation of indirect cost is represented by example of planning the exercise. Written preparation and all administration activities leading to successful fulfilling recommended exercises is covered by the chief of training department. Monthly salary of this soldier is 40 000 CZK (annual salary 480 000CZK), thus one hour salary is 227,30CZK. Time necessary to creating all of preparation activities is calculated for 7 hours. Other costs relating with planning of shooting exercise is enumerated in the amount of 4 CZK. Total cost per one hour chief of training department is calculated for 231,30CZK. Table 2 Determination of costs of planning shooting exercise Activity Cost per activity Total annual range of activities Cost per unit of activity Relation value Number of monitored activities Cost of monitored activity Planning training 40 000 CZK 7 hrs. 231,30 CZK Value of planned exercise 1 1 619,10 CZK Source: by author Pursuant calculation formula, this type of cost is a part of productive overhead that includes, among other things for example command and administration costs. The same way for determination of indirect cost is needed to use in every case of any cost, those amount is not impossible to determinate directly and those relate to foreign operation of Military unit 1125. It is very important to specify cost allocation base correctly, because there are cost of high values in the overheads, thus specify of cost allocation base has a major influence on a cost rate of each calculation unit. Finally due to carried out analysis of all direct and indirect cost relating with monitored foreign operation, final form of calculation formula is captured on the table 3: Table 3 Final form of calculation formula Direct wages 9 650CZK Direct material 5 108 CZK Other direct costs 2 510 CZK Productive overheads 4 627 CZK Support overheads 2 364 CZK Staff overheads 4 825 CZK Total 29 084 CZK Source: by author Calculation formula consist of direct and indirect costs. There is no big problem to explain origin and contend of direct cost, i.e. direct wages (wages of soldiers directly participating shooting training), direct material (ammunition, grenades, camouflage colors, depreciation of weapons and carried equipment) or other direct costs (weapons and equipment maintaining). Problem of clear explanation of every single item of calculation formula arise in case of indirect costs. This model calculation formula is designed in such a way, that productive overhead include all cost relating with planning, administration secure, services of command, etc. Support overhead involve occurred cost connecting with support activity, such as transportation, medical treatment, repair shops, etc. Staff overhead involves staff wages costs, health and social insurance for all employees, buildings depreciations, energy charges etc. Choose of suitable cost allocation base must be defined according to specific conditions of every case of recalculation cost overhead. The calculation formula capture all incurred cost in relation with shooting training. Therefore this analysis is created after ending this activity. Hence similar calculation is created before starting each activity. Individual values of calculation 107 formula are added based on experience from the same or similar activities carried out in the past. By comparing these two calculations it is possible looking for reasons of different cost values. At the same time, these finding enable improvement of planning the same or similar activity in the future. Pursuant the carried out analysis, the whole process of capturing costs relating with model military unit acting in foreign operation is shown on Figure 2. This figure describes each single activity and milestones in the process of cost analysis. As listed previously, particular range and structure of calculation unit or calculation formula can be modified for every military unit. Figure 2 Process of cost capture Source: by author 4 Conclusions The main goal of the paper is stated to finding measures helping improve current system of planning and budgeting foreign operations in the whole life cycle period. Pursuant assessment of theoretical base, describing primarily current state of investigated issue and definition main weaknesses, it is able to proceed to create an appropriate measure. Firstly, definition of rate of effectiveness determination and life cycle of foreign operation is created. Secondly, practical application of cost model lies in creation calculation activities, parts of calculation formula, system of account records and recalculation identified cost throughout cost allocation base. As a monitored calculation unit a shooting training as a part of preparation for foreign operation is stated. Through assembly of concrete calculation formula total costs of monitored activity are pictured. Specific use of cost model outputs is clear, because by using this model it is able to evaluate the costs of any activity. Source planning becomes more accurate because some activities are the same in case of every foreign operation. Degree of accuracy depends primarily on number of evaluated activities. Due to cost model is also improved a level of decision-making on the use of resources and cost rationalization. Using this tool can be also improved degree of decision-making in the field such as outsourcing. Pursuant knowledge of cost commanders can easily decide, if any service abroad is better secure by own sources, or by any external subject. Pursuant the identified reality, implementation of cost model is a very suitable tool for improvement of planning process of foreign operations. Due to cost model outputs commander of single military unit is able to plan a foreign operations with higher rate of effectivity. 108 References Act No. 320/2001 Coll. On financial control in public administration, 2001. Act No. 218/2000 Coll. On budget rules, 2000. Fibírová, J., Šoljaková, L., Wagner, J. (2011). Manažerské účetnictví – nástroje a metody, 1st ed. Praha: Wolters Kluwer, pp. 135 – 140. Král, B. (2012). Manažerské účetnictví, 3rd ed. Praha: Management Press, pp. 21 – 21, 134 – 137. Krč, M. (2016). Economic Context of the Czech Army Military deployments and export. In: Proceedings of the 20th International conference: Current trends in public sector research. Brno: Masaryk University, pp. 156-162. Krč, M. (2010). Německá vojenská ekonomika, 1st ed. Brno: Tribun EU, pp. 242 – 251. Krč, M., Golik, V. (2015). Resource accounting and budgeting for manning personnel cost ant the amount of capital expenditures in the UK armed forces. In: European Financial Systems 2015. Proceedings of the 12th International Scientific Conference. Brno: Masaryk University, pp. 320-325. Krč, M., Golik, V., Vodáková, J. (2016). Accrual accounting in the army of Great Britain clarity, transparency, and accounting data analysis options. In: European Financial Systems 2016. Proceedings of the 13th International Scientific Conference. Brno: Masaryk University, pp. 385-391. Peková, J., Pilný, J., Jetmar, M. (2012). Veřejný sektor - řízení a financování, 1st ed. Praha: Wolters Kluwer ČR, pp. 23-30. Strecková, Y. (1998). Teorie veřejného sektoru, 1st ed. Brno: Masarykova univerzita, pp. 112 – 117. White paper on Defence (2011). Prague: Ministry of Defense of the Czech Republic – department of communication and promotion. 109 Allocation of environmental taxes in the context of general government expenditures on environmental protection in the European Union Member States Justyna Dyduch1 , Katarzyna Stabryła-Chudzio2 1 AGH University of Science and Technology Faculty of Management, Department of Economics, Finance and Environmental Management Gramatyka Street 10, 30-059 Cracow, Poland E-mail: jdyduch@zarz.agh.edu.pl 2 Cracow University of Economics Faculty of Finance and Law, Finance Department Rakowicka Street 27, 31-510 Cracow, Poland E-mail: stabrylk@poczta.onet.pl Abstract: Since many years in the European Union more attention has been paid to environmental taxes (also fees and other charges) as an important source of public revenues. It is argued that the increase in environmental taxes should raise the quality of the environment while at the same time ensuring budgetary implications. Despite the existence of non-assignment rule in the public finance there is no doubt that in the case of environmental taxes, fiscal targets are related to non-fiscal objectives. Consequently, the state demand for this source of revenues should stem from the necessity for environmental protection. The objective of the paper is to examine to what extent revenues from environmental taxes are allocated to finance environmental protection in the EU countries. The research is based on the Eurostat data on environmental tax revenues and environmental protection expenditures of the general government sector and covers the period 2006 to 2015. In most EU countries less than a half of environmental tax revenues has been spent on environmental protection. The average ratio of environmental protection expenditures to environmental tax revenues in the particular EU countries varies from 10.1% to 55.4%. In order to test the linear relationship between environmental taxes and expenditures it has been conducted the Pearson correlation analysis. A statistically significant, strong and positive relationship between these variables has been observed. The result could be interpreted in two ways: - more environmentally related taxes imply greater spending on environmental issues or - the growing need to comply with the EU obligations concerning environmental protection entails an increase in environmentally related tax burden. Keywords: environmental taxes, tax earmarking, public revenues, environmental protection public expenditures, general government JEL codes: F64, H10, H23, H50, Q58 1 Introduction Environmental taxes (also called ecological taxes, environmentally related taxes or green taxes) are a core economic instrument of pollution control and resource management. They force externalities to be internalized thus enhancing environmental protection and provide a source of additional government revenues (Patterson III, 2000, p. 133). In the European Union, in addition to taxes and charges that are part of environmental policy, other economic tools, both national and EU could be mentioned like: greenhouse gas emission trading system, subsidies, fines, grants from the EU budget and other kinds of incentives like tax deductions or tax exemptions. All instruments could be treated as incentives or disincentives for specific behaviour towards environment, but it should be stressed that environmental policy disposes wider range of tools needed to fulfill a government strategy (including public investments in environmental protection, legal acts or information campaigns) (European Environment Agency, 1996, p. 17). National 110 environmental actions are part of the EU's environmental strategy mainly with the following documents Europe 2020 Strategy and the 7th Environmental Action Programme to 2020 setting objectives concerning reduction of greenhouse gas emissions, natural resources efficiency and creating living conditions based on healthy and wellbeing society. According to the Regulation (EU) No 691/2011 environmental taxes are the taxes “whose tax base is a physical unit (or a proxy of a physical unit) of something that has a proven, specific negative impact on the environment, and which is identified in ESA (the European system of national and regional accounts in the European Union) as a tax”. This definition stresses that a tax base with an environmentally destructive result is the core for identification this type of taxes on the European Union Member States level. Additionally “a tax that can be expected to increase (directly or indirectly) the cost of a product or activity deemed to be harmful to the environment relative to other (less harmful) activities or products should be considered to be environmental” (Eurostat/European Commission, 2013, p. 9 and 18). In some cases environmental taxes could be identified by earmarking perspective i.e. that once collected they are spent on the environmental purposes but it is only supplementary information mainly to divide environmental taxes into specific categories. The distinction between different categories of environmental taxes is not unequivocal if every group is interpreted according to the different meanings such as: name, tax base, purpose included in the legal act, policy objectives or the question of earmarking (Eurostat/European Commission, 2013, p. 11-14, 20-22). In consequence for international comparison it was chosen a tax base as only basis for identification. It could be enumerated four main types of environmental taxes such as: energy taxes, transport taxes, taxes on pollution and resources taxes. Energy taxes (including CO2 taxes but following different interpretations, so-called CO2 taxes could be categorized also as transport taxes or pollution taxes) concern levies on energy products for transport and stationary purposes (e.g. petrol, natural gas, coal and electricity) and emissions of greenhouse gases. Transport taxes (excluding fuel) are related to the sale, import, ownership and use of motor vehicles, but also to flight tickets, insurance of motor vehicles or motorway charges. Pollution and resource taxes focus mainly on the measured and estimated emissions to air (e.g. NOx and SOx) and water, on noise, on the management of waste, extraction of raw materials, conversion of landscapes and harvesting of biological resources. It exists an interesting division of environmental levies which is not used in international statistics but could be observed in different analysis. It concerns the role of such payments (European Environment Agency, 1996, p. 21): • incentive taxes stimulating positive behaviour and discouraging negative actions, • fiscal taxes creating additional important source of revenue for central, regional or local budgets in case of lack of fiscal consolidation, • cost-covering charges (according to the polluter pays principle) paid by a user in return for a specific service or designed for some services but not specifically used by a charge-payer. The aim of the paper is to examine to what extent revenues from environmental taxes are allocated to finance environmental protection in the EU countries. Environmental taxes are a source of public revenues therefore a question arises how to allocate these revenues. There is an opinion that revenues form environmental taxes should be treated as general government revenues and used to maintain spending in other, non-environmental areas, reduce debt or other taxes (OECD, 2011, p.8). Using environmental taxes for non-environmental purposes may however „invite significant political backlash” (Patterson III, 2000, p. 137) and gain less public acceptance than general budget appropriation (Cottrell et al., 2016). 111 Earmarking is often associated with environmental taxes. Earmarking is the process of pre-assigning revenue to particular agencies or allocating it to meet certain expenditure needs (Patterson III, 2000, p. 137). Earmarking may be perceived as a negative casus because it is “an additional constraint to the management of fiscal policy” (Marsiliani, Renström, 2000, p. 123) and results in under-funding or over-funding environmental programmes (OECD, 2011). There are arguments for earmarking as well. It can compensate those facing environmental externalities, reduce externalities (Brink, Mazza, 2013, p.5) and makes the link between environmental taxes and environmental improvement more clear (Cottrell et al., 2016). 2 Methodology and Data The authors of the study have followed the definition of environmental taxes provided by the Regulation (EU) No 691/2011, presented in the introduction of the paper. General government is treated as a specific part of public sector where public units are nonmarket producers “financed, directly or indirectly, by taxes and/or compulsory social contributions” (Pitzer and Dupuis, 2006). Government units produce mainly non-market output, however, it is possible to create market output as a side activity. In the Regulation (EU) No 549/2013 it could be found almost the same definition but with two additional information concerning a purpose of activity i.e. “(…) institutional units (…) whose output is intended for individual and collective consumption, and are financed by compulsory payments made by units belonging to other sectors, and institutional units principally engaged in the redistribution of national income and wealth”. In our research on general government expenditures the Classification of the Functions of Government (COFOG) was used where government expenditures are defined (Eurostat/European Commission, 2011, p. 23) as “a particular set of transactions undertaken by units in the general government sector as defined and recorded in national accounts under ESA”. For purpose of this work it was analysed category no 05 – environmental protection – as one of ten main categories of expenditures (division level). The detailed division of each category into groups was not used. The analysis is based on the Eurostat data on environmental tax revenues and environmental protection expenditures of the general government sector for the period 2006 - 2015. It has been examined the linear relationship between environmental taxes and expenditures by means of the Pearson correlation coefficient. The significance of difference between means of environmental protection expenditures to environmental taxes ratios for “old” and “new” EU countries has been tested using the Student’s t-test. 3 Results and Discussion According to the Eurostat data, revenues from environmental taxes in the EU equaled to EUR 359.3 billion in 2015 (in absolute terms). A share of environmental tax revenues in total revenues from taxes and social security contributions amounted to 6.11% in the whole European Union in 2015 (cf. Figure 1). Nineteen countries exceeded the EU average and three of them reached 10% and more (Croatia, Slovenia and Bulgaria). In all Member States the most important group within the environmental taxes in 2015 were energy taxes (76.65%, more than 90% - the Czech Republic, Lithuania, Luxembourg and Romania), followed by transport taxes (19.84%, over 38% - Malta, Denmark and Ireland) and finally pollution and resources taxes (3.52%, more than 11% - Croatia, the Netherlands and Slovenia). In the period 2006-2015, revenues from environmental taxes systematically increased except for 2009, which should be combined with the effects of the financial and economic crisis. The growing influence of environmental taxes cannot, however, be unequivocally explained without additional information, as a growth can be a consequence of the emergence of new taxes, raising tax rates, or increasing the tax base. As a result, the growth may be a result of the 112 effectiveness of fiscal authorities and vice versa - greater environmental pollution by businesses and households. Although environmental tax earmarking is a controversial issue, in practice it is applied not only in the EU Member States but also among others in the U.S., Canada, Japan (Marsiliani, Renström, 2000), Costa Rica and Thailand (Cottrell et al., 2016). The extent of earmarking of environmental taxes for environmental protection depends on specific tax regulations in particular countries. E.g. in France and Italy earmarking is mostly used with the respect to water pollution charges (Marsiliani, Renström, 2000, p. 123). It should be noticed that environmental tax revenues may be earmarked not only for environmental protection but for other objectives as well. For example, in recent years in Poland even more environmental tax revenues have been legally binding to nonenvironmental expenditures (among others to a construction of motorways and a compensation for power plants due to premature cancellation of their long-term contracts) than to environmental ones (cf. Figure 2). However, overall most of environmental tax revenues in Poland flow into the general budget. This is the case in other Member States as well. 113 Figure 1 Environmental taxes by tax category as a percentage of total revenues from taxes and social contributions, 2015 Source: Eurostat data 0,00 2,00 4,00 6,00 8,00 10,00 12,00 European Union (28 countries) Euro area (19 countries) Belgium Bulgaria Czech Republic Denmark Germany Estonia Ireland Greece Spain France Croatia Italy Cyprus Latvia Lithuania Luxembourg Hungary Malta Netherlands Austria Poland Portugal Romania Slovenia Slovakia Finland Sweden United Kingdom Energy taxes Transport taxes Taxes on pollution and resources 114 Figure 2 Earmarked environmental tax revenues in Poland Source: authors’ calculation based on Eurostat data and analysis of Polish tax law Table 1 presents environmental protection expenditures of the general government sector to environmental taxes ratios in the EU countries. It shows to what extent revenues from these taxes are allocated to finance environmental protection. In most EU countries less than a half of environmental tax revenues has been allocated to finance environmental protection. At the EU level about 32-38% of the environmental tax revenues in 2006-2015 was spent on environmental protection. The average ratios of environmental protection expenditures to environmental tax revenues in the particular EU countries in the analysed period (presented in Table 2) vary from 10.1% (Finland) to 55.4% (Spain). The difference between means of these ratios for “old” and “new” EU countries is not statistically significant. Table 1 Share of environmental protection expenditures of the general government sector in environmental taxes (%) State 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 EU-28 32.2 33.3 34.4 37.8 36.1 34.6 33.9 33.8 33.3 33.0 AT 20.2 19.5 20.0 23.5 24.9 20.3 21.0 21.2 19.6 18.6 BE 31.9 31.2 37.8 42.5 45.4 52.2 55.4 56.4 46.6 41.2 BG 41.7 37.9 20.6 38.6 24.2 25.6 25.8 32.7 25.3 27.2 CY 8.7 8.5 9.0 10.5 10.9 11.1 10.8 16.4 8.5 12.0 CZ 44.4 40.9 39.9 29.4 43.3 54.7 59.3 47.5 49.6 52.4 DE 23.5 23.3 23.9 31.9 28.1 26.9 28.4 30.3 30.6 31.7 DK 12.3 10.9 10.9 11.0 10.2 9.5 10.1 11.4 11.6 11.2 EE 35.7 38.6 45.3 33.5 n.a. n.a. 30.3 24.2 23.1 25.1 EL 39.2 39.3 47.5 45.2 30.5 30.5 34.6 48.0 40.3 39.7 ES 53.4 56.3 59.9 66.5 64.3 60.3 56.9 43.9 47.0 45.8 FI 10.8 12.0 11.8 13.5 10.4 8.1 8.3 8.8 8.7 8.2 FR 45.8 46.3 48.5 53.1 52.6 51.5 51.3 50.5 50.5 46.1 HR 8.4 9.5 10.3 11.4 9.9 10.7 11.6 12.1 9.6 11.0 HU 25.2 22.0 24.1 21.7 21.4 27.2 25.7 35.3 45.7 46.4 IE 42.3 44.9 51.2 49.9 41.6 31.5 32.4 24.8 22.8 20.8 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Non-earmarked revenues Earmarked revenues - non-environmental purposes Earmarked revenues - environmental purposes 115 IT 25.4 28.3 31.6 31.6 30.8 28.9 25.9 28.0 26.3 28.5 LT 41.9 50.3 51.5 58.8 73.4 44.0 49.2 28.6 33.2 29.7 LU 38.6 42.2 43.9 51.0 48.1 46.1 48.1 51.2 54.5 58.7 LV 30.6 45.6 45.0 8.1 12.3 27.7 29.9 27.2 25.5 25.6 MT 47.7 45.4 47.0 49.5 66.7 41.2 48.6 50.0 52.5 69.5 NL 42.6 45.9 44.7 48.7 46.1 46.3 47.5 46.7 43.9 42.2 PL 24.9 22.1 24.8 28.0 27.2 26.2 22.8 25.9 24.2 23.0 PT 22.9 22.2 25.3 24.0 26.1 22.4 19.8 21.0 17.1 15.9 RO 18.9 20.9 28.2 30.7 36.3 48.2 41.9 39.4 33.9 41.4 SI 27.5 25.4 26.2 25.5 18.5 23.0 19.9 19.1 25.1 25.8 SK 36.9 35.9 39.6 50.5 49.1 42.8 48.2 46.7 47.5 58.5 SE 14.4 13.8 13.0 13.1 12.6 13.5 14.1 14.0 14.2 13.1 UK 38.8 40.1 38.0 40.9 39.1 36.0 33.9 31.4 33.0 31.9 Source: authors’ calculation based on Eurostat data Table 2 Average share of environmental protection expenditures of the general government sector in environmental taxes in 2006-2015 (%) Old EU countries Expenditures to taxes ratio New EU countries Expenditures to taxes ratio Austria 20.9 Bulgaria 30.0 Belgium 44.0 Croatia 10.4 Cyprus 10.6 Czech Republic 46.1 Denmark 10.9 Estonia 32.0 Finland 10.1 Hungary 29.5 France 49.6 Latvia 27.7 Germany 27.9 Lithuania 46.1 Greece 39.5 Poland 24.9 Ireland 36.2 Romania 34.0 Italy 28.5 Slovakia 45.6 Luxembourg 48.2 Slovenia 23.6 Malta 51.8 Netherlands 45.5 Portugal 21.7 Spain 55.4 Sweden 13.6 UK 36.3 Mean 32.4 31.8 Standard deviation 15.2 10.5 p-value (t-test) 0.831 Source: authors’ calculation based on Eurostat data A statistically significant, strong and positive linear relationship between environmental taxes and environmental protection expenditures in the EU Member States has been observed (the Pearson correlation coefficient exceeds 0.9, cf. Table 3). Because of the fact that the Pearson correlation cannot determine a cause-and-effect relationship the result could be interpreted in two ways: - more environmentally related taxes imply greater spending on environmental issues or - the growing need to comply with the EU obligations concerning environmental protection entails an increase in environmentally related tax burden. 116 Table 3 Data description and Pearson correlation Variable Environmental taxes Environmental protection expenditures Mean 11406.8 3898.5 Minimum 171.9 34.8 Maximum 63690.3 21984.0 Standard deviation 16499.1 5965.9 Pearson correlation coefficient 0.944 p-value <0.001 Source: authors’ calculation based on Eurostat data 4 Conclusions Despite the existence of non-assignment rule in the public finances, i.e. the prohibition of binding specific revenues to designated categories of expenditures, there is no doubt that in the case of environmental taxes, fiscal targets are related to some extent to non-fiscal objectives. In effect beside the fiscal role it could be enumerated other functions of taxes such as: • allocation – which entities and how should benefit from reduction of negative externalities treating environment as a public good, • redistribution – by imposing taxes public authorities increase burden of companies and households who contribute to environmental pollution in direct or indirect way, as a result, funds raised cover environmental friendly activities of government or other kinds of public expenditures, • stabilization – creating sustainable growth by reducing environmentally harmful effects and by incentives towards innovations on the environmental field (leading to international competitiveness), • stimulation – influence and create positive behaviour of producers and consumers towards environmental protection. Consequently, the state demand for this source of revenue should stem from the necessity for environmental protection (treated as supply of infrastructure and creation of ecological behaviour of households and public and private companies). To some extent environmentally related taxes (as well as fines) constitute a repressive form for entities creating negative external effects (internalization of externalities) and not complying with legal obligations (Hyman, 2010, p. 106-109). Moreover, since the financial and economic crisis, more attention has been paid to environmental taxes as an important source of revenues. It is argued that the increase in environmental taxes should improve the quality of the environment while at the same time ensuring budgetary implications. The results show that from the public authorities perspective the environmental taxes are the instrument used both for fiscal and environmental policies purposes with emphasis on fiscal ones. Due to possible difficulties in estimating and finding a tax rate that would offset negative externalities, multiple in many cases (Heine, Norregaard, Parry, 2012), caused by a specific category of taxpayers it is presumed that all elements of environmental taxation are so defined to achieve the two purposes mentioned above. Acknowledgments Justyna Dyduch – This research received financial support from the resources granted to the Faculty of Management of the AGH University of Science and Technology as part of the subsidy for the maintenance of the research potential. Katarzyna Stabryła-Chudzio – This research received financial support from the resources granted to the Faculty of Finance and Law of the Cracow University of Economics as part of the subsidy for the maintenance of the research potential. 117 References Brink, P., Mazza, L. (2013). Reforming Environmental Taxes and Harmful Subsidies: Challenges and Opportunities. London-Brussels: Institute for European Environmental Policy. Cottrell, J., Schlegelmilch, K., Runkel, M., Mahler, A. (2016). Environmental Tax Reform in Developing, Emerging and Transition Economies. Studies. Deutsches Institut für Entwicklungspolitik, vol. 93, pp. 1-113. European Environment Agency (1996). Environmental Taxes. Implementation and Environmental Effectiveness. Environmental Issues Series No 1, Copenhagen. European Parliament and the Council. Regulation (EU) No 691/2011 of the European Parliament and of the Council of 6 July 2011 on European environmental economic accounts (OJ 22.7.2011 L 192). European Parliament and the Council. Regulation (EU) No 549/2013 of the European Parliament and of the Council of 21 May 2013 on the European system of national and regional accounts in the European Union (OJ 26.6.2013 L 174). Eurostat/European Commission (2011). Manual on sources and methods for the compilation of COFOG statistics - Classification of the Functions of Government (COFOG), Methodologies and Working Papers, 2011 edition. Eurostat/European Commission (2013). Environmental taxes. A statistical guide, 2013 Edition, Luxembourg: Publications Office of the European Union. Heine, D., Norregaard, J., Parry, I. W. H. (2012). Environmental Tax Reform: Principles from Theory and Practice to Date. IMF Working Paper, WP/12/180, International Monetary Fund. Hyman, D. N. (2010). Public Finance. A Contemporary Application of Theory to Policy, 10th edition, Mason: South-Western, Cengage Learning. Marsiliani, L., Renström, T.I. (2000).Time Inconsistency in Environmental Policy: Tax Earmarking as a Commitment Solution. The Economic Journal, vol.110, issue 462, pp. 123-138. OECD (2011). Environmental Taxation. A Guide for Policy Makers. Retrieved from: https://www.oecd.org/env/tools-evaluation/48164926.pdf. Patterson III, C. D. (2000). Environmental Taxes and Subsidies: What is the Appropriate Fiscal Policy for Dealing with Modern Environmental Problems? William & Mary Environmental Law and Policy Review, vol. 24, issue 1, pp. 121-159. Pitzer, J., Dupuis, J.-P. (2006). The General Government and Public Sector, Paper presented at the fifth meeting of the Task Force on Harmonization of Public Sector Accounting (TFHPSA) Chaired by the International Monetary Fund Hosted by the OECD Paris, France—March 8–10. Retrieved from: https://www.imf.org/external/np/sta/tfhpsa/2006/03/pdf/govern.pdf. 118 Regulation of Insurance Market in Slovak republic Michal Fabuš1 , Viktória Čejková2 1 School of economics and management in public administration in Bratislava Department of economics and finance Furdekova 16, SK – 851 04 Bratislava, Slovakia E-mail: michal.fabus@vsemvs.sk 2 School of economics and management in public administration in Bratislava Department of economics and finance Furdekova 16, SK – 851 04 Bratislava, Slovakia E-mail: viktoria.cejkova@vsemvs.sk Abstract: In contribution, we deal with insurance market as part of the financial market. We will examine financial market from an economic and legal point of view, stating its segmentation as economic-legal category. As the insurance market is regulated by different laws and legal norms, we will deal closer with regulation of the insurance market and supervision of the insurance market. The contribution will also focus on the assessment of the level of the insurance market through selected indicators. In the contribution, traditional methods of analysis and synthesis will be used as well as analytical and statistical methods to characterize main selected indicators of the insurance market. At the end, we list the options for improving the regulation of the insurance market and supervision of the insurance market. The activity of commercial insurance companies in the Slovak insurance market will in the future be influenced by the development of information technologies, which must be used also for investment activity. Very important is the interconnection - Information highway between commercial insurance companies as well as between insurance companies and their clients. Qualified use of these information technologies will lead in the future to a reduction in service and thus to a competitive advantage.Clients of commercial insurance companies are inadequately informed, for example, about the residual value of life insurance claims. They do not have enough information on the disposable value of the indemnity in case of early termination of the insurance contract and are not informed of the disadvantage of early termination of the life insurance contract.The main objective of the regulation should be stability, security, credibility in the insurance market for clients of commercial insurance companies. Only an efficiently functioning insurance market can ensure the performance and development of the economy. Keywords: insurance, insurance market, insurance market regulation, supervision of the insurance market, regulatory tools JEL codes: G22 1 Introduction The insurance market is to be understood as an important part of the financial market, with insurance being an important sector of the market economy and its function is irreplaceable. The financial market is a place where the supply and demand for capital meet. The insurance market is an inherent part of it. Insurance market can be classified by several criteria, the decisive criterion being the business direction of the entity (subject of the insurer's activity). The insurance policy is closely related to its regulation, regulatory instruments and relevant insurance market supervision standards. Slovak Republic as an EU Member State is part of the single European insurance market, based on the basic approaches to regulating the insurance market from EU legislation. Problems related to the competencies of regulators and supervisors were also affected by the activities of foreign banks and commercial insurers. Insurance market regulation and insurance market supervision are based on international agreements. The Slovak insurance market opens a new space for development. The 119 Slovak insurance market is dynamically developing, so the regulation and supervision of the insurance market requires more attention from professionals and supervisors. The insurance market and the assessment of its regulation in the Slovak Republic in relation to the European Union is very important and this aspect was also the reason for choosing the subject and content of our contribution. 2 Methodology and Data The paper will deal with insurance market as part of the financial market. We also deal with regulation and supervision of the insurance market. The methods of description, analysis, synthesis, deduction and comparison will be applied. The conclusion will include the current problems and trends and options for improving the regulation and supervision of the insurance market. We will examine financial market from an economic and legal point of view, stating its segmentation as economic-legal category. As the insurance market is regulated by different laws and legal norms, we will deal closer with regulation of the insurance market and supervision of the insurance market. The contribution will also focus on the assessment of the level of the insurance market through selected indicators. In the contribution, traditional methods of analysis and synthesis will be used as well as analytical and statistical methods to characterize main selected indicators of the insurance market. At the end, we list the options for improving the regulation of the insurance market and supervision of the insurance market. Based on data from Slovak Insurance Association on the development of prescribed premiums (as the most important indicator) in 2011-2015 in the Slovak Republic we analyse the changes in lofe and non-life insurance. According the data we can see the rise of premiums in life insurance and decrease in non-life insurance premiums. 3 Results and Discussion Insurance market and its segmentation The insurance market as one of the components of the financial market is of great importance in the national economy. It contributes significantly to the economic growth. (Grmanová and Hošták, 2016). We understand that insurance market as a part of the financial market and its segmentation as an economic-legal category, it is therefore necessary to recognize the importance of the issue. Economic theory defines the market as a mechanism by which buyers and sellers influence each other to determine prices and quantities of goods. (Grmanova and Čejková, 2016) Financial market is characterized by various aspects. In the market-oriented economy, the financial market is at the top of other markets; it is a universal market. In developed economies, it is one of the most effective markets. With continued globalization, its importance is constantly growing. "Financial markets are the heart of the financial system." (Čejková and Nečas, 2008) The financial market, which is part of the financial system, contains various entities, instruments, institutions and transactions. (Grmanova and Čejková, 2016) This enables savings to those who need more funds for their consumption. The insurance market has been developing very dynamically in recent years thanks to the huge capital it has and thus becomes a very important and essential segment of the financial market. It can be characterized as a "system of various market instruments and regulatory measures of the state, the observance and control of which is ensured by the state supervision institution." (Grmanova and Čejková, 2016) Globalization is also marked by the fact that most commercial insurance and reinsurance companies are 120 linked to transnational and international financial markets, enabling them to carry out their business in the most important and most important financial world centers. The insurance market operates on a similar principle as the financial market - i.e. collection and redistribution of funds. It specifies the reserves that are created from the client's money. Reserves have a very important role in the insurance market and are, of course, much more important than money funds in other segments of the financial market. Since the insurance market operates based on provisioning, it means that commercial insurance companies also use free cash to carry out their own investment activity. Investing these temporary free funds by commercial insurance companies also contributes to a more favorable tax system in advanced economies, which is designed to enable commercial insurers to provide their clients with insurance cover as well as risks that are sometimes disadvantageous in their view. One of the major tasks of commercial insurance companies in the insurance market is to focus and adapt their activities to achieve the best possible results in their core roles. This role can also gain a certain very good position in the insurance market in the respective economy. This is their position and position in the insurance market and therefore they are trying to offer many new insurance products or innovate older insurance products and insurance. (Cejková et al., 2016) The insurance market is a place (buyer market, affected by state interventions more than other markets), where supply and demand for insurance cover meets with insurance and reinsurance. (Čejková and Nečas, 2008) We can define insurance and reinsurance as a kind of service that has a fictional character. This service is offered on the insurance market; at the same time, it sells and the buyer must pay for it. The insurance market is a market where the supply predominates. The type of market in question is specific to the market, because, unlike other markets, it does not concentrate on a place, its trades are mainly realized in commercial insurance companies, insurance intermediaries, reinsurance companies and elsewhere. About its clients - insured are traveled by commercial insurance companies and their intermediaries. The insurance market operates and largely affects it: • "the State through legal standards; • supervision of insurance undertakings; • insurers; • policyholders and insurances; • insurance intermediaries; • reinsurers; • various consulting companies. "(Čejková and Nečas, 2008) In a market economy, each insurer must behave as an entrepreneur, must operate under conditions of competition. The business of a commercial insurance company should be an insurance business, and a temporary investment of free cash. The insurance market can be classified per several criteria. Here is a basic classification, the decisive segmentation criterion is the business direction of an entity (subject of the insurer's activity), where the activity can be divided into two headings. This breakdown can be classified as a basic segment of the insurance market: 121 Figure 1 Basic segmentation of the insurance market Source: Own processing according (Čejková et al., 2011) This segmentation of the insurance market is often used in many publications and theories of well-known authors, Čejková, Martinovičová (Čejková et al., 2011) but there is no mention in economic practice. Indicators of the level of the insurance market The insurance market has a significant position with specific roles, principles and meanings. However, it can not be evaluated without its assessment of the development of its level indicators. "(Čejková et al., 2011) In general, we can say that market-level indicators assess the efficiency of the use of resources and spent money. The insurance market and its significance can be characterized by indicators, among which are the main indicators and supplementary indicators. The main indicators include: • Premiums written - includes the entire amount of money earned in the form of premiums in each commercial insurance company for one year in question. It is one of the most important performance indicators of each commercial insurance company, and it is possible to compile the order of commercial insurance companies and their share in the insurance market in the respective year. (See data in Table 1) • Insurance Performance - Denounces the amount of insurance premiums paid by the relevant commercial insurance company to the client that was characterized in the insurance contract. • Damage - Expresses the ratio between the amount of premium received and the amount of insurance benefits provided in the relevant commercial insurance company for a certain period, which is set, for example, in one calendar year. The damage to the insurance market is expressed in percentages and should never exceed 100%. • Insurance - this indicator has been used for a very long time, especially in advanced economies. It evaluates the overall level of the insurance market and at the same time serves to compare the level of the insurance market of different economies. It represents the ratio of the premium written to the gross domestic product at current prices. 122 • Concentration of the insurance market - informs about the concentrated insurance market in the relevant economy. (Cejková and Nečas, 2008) The most important indicator, valid at international level in insurance, is insurance premiums. In each country, the prescribed total premiums and premiums written in life and non-life insurance are determined. In most European countries, life insurance accounts for a larger proportion of the premiums than non-life insurance. In some European countries, life insurance accounts for 80% of the premiums. (Grmanová, 2015). In Table 1, we provide data on the development of prescribed premiums in 2011-2015 in the Slovak Republic. Table 1 Premiums written in commercial insurance companies in the Slovak Republic (in thousands of EUR) Life insurance Non-life insurance Total 2011 1,14 0,97 2,11 2012 1,17 0,94 2,11 2013 1,23 0,94 2,17 2014 1,22 0,96 2,18 2015 1,21 1,0 2,21 Change 2015/2011 (v %) 106 97 104 Source: own processing by the Slovak Insurance Association (www.slaspo.sk) The insurance market and its regulation The insurance market requires state regulation, which is discussed in the next section. The regulation of insurance market regulation is implemented gradually, depending on the development of the insurance market and on the real possibilities of state supervision. The insurance market largely affects the quality and scope of insurance services and, above all, their cost. Compatibility in the provision of insurance products is partly influenced and to a certain extent limited by state interference. In each economy, the insurance market is regulated by special regulations. The state regulates the insurance market by means of laws and various legal standards or legislative adjustments. Regulation is in most cases carried out by the state administration body - a special body called the " State supervision or oversight supervising compliance with the Insurance Act. (Cejková and Nečas, 2008) Figure 2 Insurance market regulation Source: Own processing by authors (Čejková and Nečas, 2008, Ducháčková and Daňhel, 2010) In the Slovak Republic, the insurance legislation is based on two basic legal norms, namely Act No. 39/2015 Coll. on Insurance and Civil Code No. 40/1964, and other legislation. The new phase of insurance opened in 1991. New commercial insurance companies were created and new insurance products and services were offered. Act No. 39/2015 Coll. on insurance in the Slovak Republic, which is currently in force, fulfills the objectives which have been given also in the framework of EU directives and directives, namely the following objectives: (Čejková et al., 2016) 123 • to create a standard, internationally accepted legal environment for the development of the insurance market, in line with OECD and EU requirements: to regulate the conditions for authorization for entities wishing to undertake insurance business; • ensure further liberalization of insurance services; • strengthened the supervisory authority in insurance, its instruments by which it can influence developments in the insurance market and intervene in the event of maladministration and its misconduct; • create a legal framework for the application of consolidated supervision of insurance companies in the group; • improve the level of protection of the rights and interests of insured and investors in the insurance market. Act no. 39/2015 Coll. the insurance sector shall furthermore provide for: • certain relationships relating to the activities of insurance and reinsurance undertakings; • valuation of liabilities and assets; • reorganization and relationships related to the disappearance of insurance and reinsurance undertakings; • relationships related to the operation of foreign insurance companies; • relationships governing insurance supervision. (Cejková et al., 2011) The basic legal regulation is the Act of the National Council of the Slovak Republic No.39/2015 Coll. on Insurance and on amendments to certain acts. Supervision in the Insurance Act is more specifically specified as oversight of the activities of an insurance company, a reinsurance undertaking, a branch of a foreign insurance company, a foreign reinsurance company and the Slovak Insurance Office, etc. where the supervision of the supervised entities (which is carried out by the National Bank of Slovakia in the Slovak Republic) Evaluation of information and documents relating to their business or other activities. Supervision over the insurance market in the Slovak Republic Since the accession of the Slovak Republic to the EU, efforts have been made to integrate and unify the supervision of the financial market. In several countries, integrated unified oversight is already in place, and supervision of the insurance market is already in place. In the Slovak Republic, the supervision of the insurance market is integrated in the supervision of the financial market and implemented by the central bank - the National Bank of Slovakia. Since 1st January 2006, the National Bank of Slovakia has implemented integrated supervision of the financial market. (Cejková et al., 2011) Figure 3 Supervision over the insurance market Source: Own processing by authors (Čejková and Nečas, 2008, Ducháčková and Daňhel, 2010) Regulation and supervision represent an interference with the economic life of insurers. "The higher the powers of the supervisory authority, the more it restricts the business activities of the insurance company concerned" (Čejková and Nečas, 2008) On the other hand, it must be remembered that it ensures a high degree of protection of the rights and interests of policyholders and policyholders. Insurers and policyholders are confident that their interests will be secure even if they have limited powers. Therefore, in any market economy, it is in the interest of regulating the supervision to find a 124 compromise solution to ensure protection and interests or insurers as well as commercial insurers. Insurance market supervision is subject to compliance with the Insurance Act, other laws and other binding laws and conditions specified in the permits issued under applicable legal standards. Possibilities of improving the insurance market regulation in Slovakia The insurance market in Europe represents all European insurance markets. Central and Eastern European countries experienced a significant recovery in the mid-1990s in the insurance market. Some of them achieve relatively good results in the development of the insurance market, but they are still lagging in comparison with the EU average. Worldwide, in the past few years, the position of life insurance within the insurance market has been strengthened. Europe is not an exception, but there are quite significant differences between countries. "It can be said that the higher share of life insurance on the national market is an indicator of its maturity." (Čejková and Nečas, 2008) Growth and prosperity in the insurance market is closely linked to the overall economic development of individual states and continents. The results of insurance market activities are monitored and presented in each economy as well as internationally. There are currently several international organizations that record and evaluate activity in the insurance market. (Paulik et al., 2012) Since several countries, including of course the Slovak Republic, have entered the European Economic Area, some principles and guidelines have been adopted to improve the conditions of the common EU single insurance market. The basic principles of oversight function, which are binding on all EU Member States, have also been laid down. One of the most important is the concession obligation, which means that any insurance undertaking wishing to pursue its business on the insurance market must be authorized to do so by the supervisory authority of the country in question. If this commercial insurance company also wants to operate abroad, it can only be in the area where it has been authorized in the parent country. There is also the possibility of transferring insurance classes from one insurance undertaking to another, but all the conditions laid down by the country concerned must be respected. As the most important principle, we must mention the cooperation of the supervisory and supervisory authorities of individual EU Member States. The primary objective of EU legislation is to achieve integration, globalization and the functioning of the single insurance market in the Member States. The main aim is therefore to remove all obstacles, particularly in national law, which restrict the creation and functioning of this single market, unify the business rules and ensure maximum protection for commercial clients. The activity of commercial insurance companies in the Slovak insurance market will in the future be influenced by the development of information technologies, which must be used also for investment activity. Very important is the interconnection - Information highway between commercial insurance companies as well as between insurance companies and their clients. Qualified use of these information technologies will lead in the future to a reduction in service and thus to a competitive advantage. The facts will significantly affect the whole financial market in Slovakia as well. It is important to clarify to the public and the clients the purpose and functioning of the insurance and insurance market, its regulation and the supervision of insurance. Also, to improve the communication possibilities of clients with commercial insurance companies, clients are unaware of the existence of these instruments of insurance market regulation. Therefore, we propose to introduce insurance training already at secondary schools. Clients of commercial insurance companies are inadequately informed, for example, about the residual value of life insurance claims. They do not have enough information on the disposable value of the indemnity in case of early termination of the insurance 125 contract and are not informed of the disadvantage of early termination of the life insurance contract. Our suggestion is to improve the functioning and gain of greater confidence of our clients by creating an information highway between a commercial insurance company and its clients on the website of a commercial insurance company, the opportunity to see and check the account of each client. Upon signing into this account, the client would see the status of their funds, their value, and in case of early termination of the insurance contract and their interest through the investment of a commercial insurance company. The client must believe that a commercial insurance company will be able to pay the insurance benefit at the agreed time after the expiration of a longer period. 4 Conclusions The subject of this paper was the characteristics and issues of the insurance market and its regulation, as well as its regulation, namely the supervision of the insurance market in the Slovak Republic. Finally, we can state that the current regulation of insurance market regulation is of higher quality (than before the accession of the Slovak Republic to the EU) and offers extensive opportunities for the operation of entities in the insurance market and beyond the borders of the Slovak Republic. The activity of commercial insurance companies in the Slovak insurance market will in the future be influenced by the development of information technologies, which must be used also for investment activity. Very important is the interconnection - Information highway between commercial insurance companies as well as between insurance companies and their clients. Qualified use of these information technologies will lead in the future to a reduction in service and thus to a competitive advantage. Clients of commercial insurance companies are inadequately informed, for example, about the residual value of life insurance claims. They do not have enough information on the disposable value of the indemnity in case of early termination of the insurance contract and are not informed of the disadvantage of early termination of the life insurance contract. The main objective of the regulation should be stability, security, credibility in the insurance market for clients of commercial insurance companies. Only an efficiently functioning insurance market can ensure the performance and development of the economy. The insurance market regulation is based on the overall regulation of the financial market, with the implementation of certain specificities that arise from the fact that in the insurance market we encounter different types of risks of clients and commercial insurers. Acknowledgments The paper is the output of a scientific projects IGA no. 3/2014 „Risk insurance for small and medium-sized enterprises”. References Act No. 39/2015 Coll. On Insurance and on amendments to certain acts. Act No. 747/2004 Coll. on oversight of the financial market. Act No. 40/1964 Coll. Civil Code. Čejková, V., Adamko, J., Kopanický, L. (2016). Insurance, Theory and Practice. Bratislava: VŠEMvs. Čejková, V., Martinovičová, D., Nečas, S. (2011). Insurance Market - Theory and Practice. Bratislava: Iura Edition. Čejková, V., Nečas, S. (2008). Insurance market. 1st ed. Bratislava: Merkury. Ducháčková, E., Daňhel, J. (2010). Theory of Insurance Markets. Prague: Professional Publishing. Grmanová, E., Čejková, V. (2016). Insurance market in the Slovak Republic and the Czech Republic. Trenčín: TnUAD. 126 Grmanová, E., Hošták, P. (2016). Influence of selected environmental factors on the efficiency of commercial insurers. In: Proceedings of the 13th International Scientific Conference. Brno: Masaryk University. pp. 199-206. Grmanová, E. (2015). Efficiency of National Life Insurance Markets in Europe. In: Proceedings of the 12th International Scientific Conference. Brno: Masaryk University. pp. 151-157. Paulík, D., Čejková, V. (2012). Fundamentals of Finance and Currency. 1st ed. Trenčín: Institute of Applied Management. Slovenská asociácia poisťovní. (2017). Official web site. Retrieved from: www.slaspo.sk. 127 To gamble or not to gamble – comparison of decisions made under risk and under uncertainty Maria Forlicz1 , Tomasz Rólczyński2 1 Wroclaw University of Economics Faculty of Engineering and Economics, Department of Economic Sciences. 118-120 Komandorska Street, 53-345 Wrocław, Poland E-mail: mforlicz@ue.wroc.pl 2 WSB University in Wroclaw Faculty of Finance and Accounting, Department of Economics 29-31 Fabryczna Street, 53-609 Wrocław, Poland E-mail: tomasz.rolczynski@wsb.wroclaw.pl Abstract: It’s been noticed that people estimating the probability of occurrence of some events underestimate large probabilities and overestimate small ones (Burns et al, 2010), which in the case of lotteries, where the probability of winning is very low, should effect increased willingness to play when the probabilities are not known. That is why we put forward a hypothesis that in the case of choice made under uncertainty people are more willing to buy lottery tickets than in the case of choice under risk (while the expected value of a gamble is smaller than the price of the lottery ticket). Two experiments with real (but not monetary) payoffs were organized. In both experiments participants were divided in two groups - informed and not informed about the probability of winning (different across experiments). Results of the Experiment I confirm our hypothesis. On average, the number of lottery tickets bought was higher in a group that didn’t know the probability of winning. However, when we lowered the pool in Experiment II this difference disappeared. Keywords: decision under uncertainty, lottery, expected value JEL codes: D120, D800 1 Introduction One could venture a statement that every decision made by people is a decision made under risk or uncertainty. The consequences of those decisions may have more or less significant implications from the economic, health and social perspective. The determinants of daily decisions may be seen to lie in both personal character traits (a different level of knowledge, different propensity to risk, etc.) and external conditions relating to the consequences of potential decisions, access to information, etc. The draws now being offered where one can win a substantial amount of money by sending a chargeable text message provided the inspiration to conduct the study. One person is drawn randomly from all the people who have texted and thus wins the lottery. The person entering the lottery has no information as to the number of participants, which is what determines the probability of winning. Moreover, games such as lotto have been operating for years where you can bet on numbers after having paid a certain price, and the person who bets on the numbers drawn by the lottery organizer wins. In this case, however, it is possible to calculate the probability of winning the jack pot (although one cannot always be sure as to its value). One might also ask whether the games in which players do not know the probability of winning provide organizers with higher lottery ticket sale than the games for which this probability is known (ceteris paribus). The aim of the article is to compare decisions made under uncertainty with those made under risk. The hypothesis to be verified is that in the case of choice made under uncertainty people are more willing to buy lottery tickets than in the case of choice under risk (while the expected value of a gamble is smaller than the price of a lottery ticket). 128 It is central to our considerations to specify, distinguish and interpret the concepts of risk and uncertainty, and to show the difference between these two terms. In literature on the subject, the views on this issue regarded as classic are those of F.H. Knight which he presented in his dissertation essay “Risk, Uncertainty and Profit” (1921). His belief is that risk refers to cases that can be calculated and expressed in quantities, whereas uncertainty does not provide this possibility. Elaborating further on his idea, Knight suggests that in referring to risk we can talk about “objective” probability, while for uncertainty it is “subjective” probability. A similar view in this respect takes Gough, (1988) asserting that a risky situation is such in which a set of possible outcomes is well known and the probability distribution for those outcomes is either known or can be estimated. An uncertain situation, on the other hand, occurs when the set of outcomes is unknown (uncertainty as to the conditions in which the decision is being made) or when the probability distribution for those outcomes is not known. In this paper it was assumed that decisions are made under risk if there is a possibility to calculate the probability of occurrence of an event while if the probability is neither known, nor can it be estimated based on the past, decisions are made under uncertainty. Taking a decision under risk the correct way to assess a certain equivalent of a gamble is to calculate its expected value; yet many scientists indicate that the valuation of a gamble by a person is often affected by other factors (Kahneman, Tversky, 1979). Long ago Bernoulli (1738)1 observed that the expected utility of the prize won in a game is more important than the expected value of this game. In his view, the same game can have different values of utility for different players. Many years later, his theory was named Expected utility theory (EUT) and was formalized by von Neumann and Morgenstern (1944). Since then, in the theory of decision-making under risk, the assumption has been that people try to maximize the expected utility and not the expected value. Nevertheless, a number of scholars rejects the EUT as the proper theory for explaining decision-making under risk (see e.g. Markowitz, 1952). While applying the traditional approach, people are believed to use tools in their judgments offered by the theory of probability and statistics, and they are also capable of automatically updating information on probability along with acquiring information. In this case their actions are rational, in line with the definition of rational human being – homo economicus, that is, people are consistent in their actions and seek to maximize their wealth (Cieślak, 2003). Herbert Simon (1957) was against this perception of human behavior, arguing that the occurrence of bounded rationality was caused by temporary and technological constraints. Likewise, D. Kahneman and A. Tversky (1974) do not agree with the traditional concept of rationality and, supported by their research, they argue that bounded rationality is the result of the time pressure and complexity of information. They believe that people assess the reality intuitively rather than apply complex estimation processes of probability and prediction. The effect is that people make decisions based on: • information easily accessible through one’s memory • conclusions made on the basis of similarities, • information about the originally suggested value which is adjusted in order to estimate the real value. A rationally thinking scientist may wonder why people even buy lottery tickets, since they must be aware of how low the probability of winning is. One of the psychological reasons may be so called “peanuts effect” – people are more willing to take risk dealing with smaller stakes (although unequivocal results were obtained only for gains, see Mitchell and Wilson, 2010; Hogarth and Einhorn, 1990). With small amounts at stake no one thinks about the expected value, losing small amount of your wealth is not important 1 His work “Specumen theoriae novae de monsura sortis” was translated in 1954 and published in Econometrica with the title Exposition of a New Theory on the Measurement of Risk” 129 when you can win quite big money. Also, some researches have proven that the probability of winning has no significant influence on the number of lottery tickets sold (Beenstock, Haitovsky, 2001), and that if not for a very big jackpot, few people would take part in a lottery (Shapira, Venezia 1992). The availability bias probably plays an important role (Tversky, Kahneman, 1973) - stories about people who have won are widely broadcasted and thus easily brought to mind. In the case of choice under uncertainty, the estimation of probability seems an important factor. It’s been noticed that people overestimate small probabilities (Erev, Wallsten, 1993), which may cause the increased belief in winning. On the other hand, it has been shown that small probabilities (when known) are underweight (Weber, Blais, & Shafir, 2004), which may influence the willingness to buy lottery tickets in the case of decisions made under risk. Camerer and Weber (1992) found that people, when having the opportunity of choosing between playing in a lottery, where the probability of occurrence is known (under risk), and playing in a lottery with unknown probability distribution (under uncertainty), will choose the first type of the lottery. This principle is known as the ambiguity effect. However, the research conducted by Rode et al (1999) showed that in the conditions created for the purpose of the experiment people did not avoid making decisions under uncertainty and thus the ambiguity effect did not occur. Charness and Gneezy (2003) found that, in fact, people prefer lotteries with known probabilities but these preferences do not affect the way “participants allocate their investment capital between an asset with a sure return and a risky asset with a higher expected rate of return” in case of choices under risk and under uncertainty. In the experiment conducted by the authors of this paper, the objective was to compare the propensity to playing a game which was the game under risk and a game played under uncertainty. According to the research Rode et al conducted, the participants of the experiment should prefer playing under uncertainty, while in line with the findings made by Camerer and Weber (1992) the ambiguity effect should occur and the subjects examined should be more willing to participate in the game under risk. 2 Methodology and Data In order to verify hypothesis that in the case of choice made under uncertainty people are more willing to buy lottery tickets than in the case of choice under risk and to achieve assumed purpose of the article two experiments were conducted. Experiment I Subjects: 137 first year students taking their course in mathematics. Around 67% were females. Procedure: Students were divided randomly in two groups. To each group the same proposal was made: “Dear Ladies and Gentlemen, I’d like to offer you, as part of a certain scientific study, participation in a lottery where you can win extra points to obtain credit in my subject. The lottery is that for the points you have already obtained you can buy any number of tickets which will be then drawn in the lottery. You can win one prize, which is 50 extra points added to those already collected during the semester (of course, less the points spent on lottery tickets). Each ticket costs 0.5 point” Additionally the second group received information about the pool, which was 400 tickets. 400 tickets in pool with 50 points to win gives the expected value of points 130 gained 0.125, so the price of the lottery ticket offered (0.5 point) was 4 times bigger than the expected value. We called the first group (the one without information about pool) „uncertainty group” and the second one (that knew the pool) „risk group”. It is important to notice that every group had the same conditions for receiving credit. The maximum amount of points to be gained was 100 and to receive a positive grade, one needed to gain above 50 points. Students had to complete two tests during a semester (each giving a maximum of 40 points), the rest of points could be earned by activity during the semester. We chose a moment in the semester when students had already taken one test and could gain no more points in any other way than by the second test. That is why from the data obtained in the experiment, we decided to exclude information on decisions of the people who had at the moment of decision not more than 10 points. Experiment II Subjects: 97 first year students taking their course in financial mathematics. Around 75% were females. Procedure: The same as in Experiment I students were divided randomly in two groups and to each group the same proposal as in Experiment I was made although we adjusted the amounts of points because of different scoring. This year the maximum amount of points to be gained during a semester was 50 and to receive a positive grade, one needed to gain above 25 points. Students had to complete two tests during the semester (each giving a maximum of 20 points), the rest of the points could be earned by activity during the semester. The proposal was as follows: “Dear Ladies and Gentlemen, I’d like to offer you, as part of a certain scientific study, participation in a lottery where you can win extra points to obtain credit in my subject. The lottery is that for the points you have already obtained you can buy any number of tickets which will be then drawn in the lottery. You can win one prize, which is 25 extra points added to those already collected during the semester (of course, less the points spent on lottery tickets). Each ticket costs 0.25 point” This time the “risk group” was informed that the pool was 200 tickets, which meant that the expected value of the lottery equalled 0.125 point. Still, the price of a ticket remained higher than the expected value, but only two times bigger. This time we did not have to exclude anyone’s answers from our calculations because theoretically every one still had a chance to pass the subject. 3 Results and Discussion Experiment I After excluding the data on decisions of people who had at the moment of decision not more than 10 points 59 answers in the “uncertainty group” and 56 answers in the “risk group” were received. We then calculated descriptive statistics for each group, which can be seen in Table 1. On average, more tickets were sold in the “uncertainty group”. Also the median is higher in the “uncertainty group”. The most commonly bought number of tickets was 2; however, when checking the particular data, it did not appear so frequently in the “risk group” as in the “uncertainty group”. In general, the number of tickets sold per person was significantly higher in the “uncertainty group” than in the “risk group” (p-value = 0.000606917), which is unequivocal to saying that on average 131 people not knowing the probability of winning were more willing to buy lottery tickets than those who were aware of it. Table 1 Descriptive statistics on number of lottery tickets bought in the case of choice under uncertainty and under risk Uncertainty group Risk group Mean 1.88 1.21 Median 2 1 Most common answer 2 2 Standard deviation 1.40 1.06 Skewness 1.92 0.47 Source: authors’ own study Decisions about tickets bought were more skewed in the “uncertainty group”. A Greater skewness can be observed in Figure 1, which shows the percentage of students buying a particular number of lottery tickets. We can also observe that in the “risk group” there were almost as many people not wanting to buy any ticket as the ones wanting to buy 2 tickets. The visible difference between distributions of answers was not, however, confirmed by chi-square test (p – value = 0.17). Figure 1 Percentage of people buying a given number of lottery tickets Source: Authors’ own study Experiment II In the second experiment 47 students were assigned to the “uncertainty group” and 50 to the “risk group”. In contrast to Experiment I we have found that on average the number of tickets bought was not significantly different in the two groups (p – value = 0.55). However, it seems that it is not caused by a higher (than in Experiment I) demand for lottery tickets in the “risk group” but by a lower demand in the “uncertainty group”. In both groups the modal and median choice (see Table 2) was to buy no tickets, which is less than in Experiment one. We can only suppose that it was caused by the different number of points possessed before the experiment. In Experiment I the average number of points was 35.22 (with 100 maximum) and in Experiment II 10.23 (with 50 points maximum). According to Friedman and Savage (1948), low-wealth people should be attracted to gambles with small chance of large gain, in our case that would mean that in Experiment II more people should be willing to participate in the lottery. It may be 0% 10% 20% 30% 40% 50% 60% 0 1 2 3 4 5 6 7 8 Uncertainty group Risk group 132 explained by other research (Guiso, Paiella 2008) that finds that “individuals who are more likely to face income uncertainty or to become liquidity constrained exhibit a higher degree of absolute risk aversion”. Conducting the experiments we often heard people saying “what if I buy this ticket and then I will not pass because of missing 0.1 point”. With the lower number of points this concern must have been more vivid. Table 2 Descriptive statistics on number of lottery tickets bought in case of choice under uncertainty and under risk Uncertainty group Risk group Mean 1.15 1.2 Median 0 0 Most common answer 0 0 Standard deviation 1.84 1.89 Skewness 1.66 1.58 Source: authors’ own study Figure 2 depicts distributions of number of lottery tickets bought in the “uncertainty” and “risk group”. In both groups answers were distributed in almost the same way, which is confirmed by chi-square test (p – value = 0.73). When we compare Figures 1 and 2 we notice that the answers were more concentrated in Experiment II. Chi – square tests showed that there is a significant difference between distributions in the “uncertainty” groups (p – value 3.4E-08) and in the “risk” groups (p – value = 0.0001). Figure 2 Percentage of people buying given number of lottery tickets Source: Authors’ own study 4 Conclusions The hypothesis that in the case of choice made under uncertainty people are more willing to buy lottery tickets than in the case of choice under risk was confirmed by the Experiment I, where it was found that the average number of lottery tickets that participants bought was higher in the uncertainty group. However, when we lowered the pool in Experiment II this difference disappeared. We cannot be sure, however, if it was wiped out by the lower pool or by the fact that the samples in Experiment I and II were different in terms of points possessed at the beginning of the experiment. We did not observe the ambiguity effect in neither of the experiments, which is consistent with 0% 10% 20% 30% 40% 50% 60% 70% 0 1 2 3 4 5 6 7 8 Uncertainty group Risk group 133 results obtained by Charness and Gneezy and contradicts the results obtained by Camerer and Weber (1992). Nevertheless, it is possible that in the case of a pool that is even lower than in Experiment II, one could find more people willing to participate in a lottery with known risk. A knew hypothesis to be verified can be that the occurrence of the ambiguity effect depends on the value of known probabilities and also on the material status of a decision maker. References Beenstock, M. Haitovsky, Y. (2001). Lottomania and other anomalies in the market for lotto. In: Journal of Economic Psychology 22, pp. 721-744. Bernoulli, D. (1954). Exposition of a New Theory on the Measurement of Risk. In: Econometrica, Vol. 22, No. 1 (Jan., 1954), pp. 23-36. Camerer, C., Weber, M. (1992). Recent developments in modelling preferences: uncertainty and ambiguity. In: Journal of Risk and Uncertainty , 5 , pp. 325–370. Charness, G., Gneezy, U. (2003). Portfolio Choice and Risk Attitudes: An Experiment, Department of Economics, UCSB. UC Santa Barbara: Department of Economics, UCSB. Cieślak, A. (2003). Behawioralna ekonomia finansowa. Modyfikacja paradygmatów funkcjonujących w nowoczesnej teorii finansów. In: Materiały i studia, NBP, Warszawa. Erev, I., Wallsten, T. S. (1993). The effect of explicit probabilities on the decision weights and the reflection effect. In: Journal of Behavioral Decision Making, 6, pp. 221– 241. Friedman, M., Savage, L. J. (1948). The Utility Analysis of Choices Involving Risk. In: The Journal of Political Economy, 56( 4), pp. 279-304. Gough, J. (1988). Risk and uncertainty. In: Information Paper No. 10, Centre for Resource Management, University of Canterbury and Lincoln College. Guiso, L., Paiella, M. (2008), Risk Aversion, Wealth, and Background Risk. In: Journal of the European Economic Association, 6, pp. 1109–1150. Hogarth, R., Einhorn H. (1990). Venture Theory: A model of decision weights. In: Management Science, 36(7), pp. 780–803. Kahneman, D., Tversky, A. (1974). Judgement under uncertainty: Heuristics and biases. 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New York, Wiley. 134 Tversky, A., Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. In: Cognitive Psychology, Volume 5, Issue 2, pp. 207–232. Von Neumann, J., Morgenstern, O. (1944). Theory of games and economic behaviour, Princeton N.J.: Princeton University Press. Weber, E. U., Blais, A. R., Shafir, S. (2004). Predicting risk sensitivity in humans and lower animals: Risk as variance or coefficient of variation. In: Psychological Review, 111, pp. 430–445. 135 Comparison of the tax burden on natural persons in the Slovak Republic and Spain Katarína Gašová1 , Katarína Repková Štofková2 1 University of Žilina Faculty of Operation and Economics of Transport and Communications, Department of Communications Univerzitná 8215/1, 010 26 Žilina, the Slovak Republic E-mail: katarina.gasova@fpedas.uniza.sk 2 University of Žilina Faculty of Operation and Economics of Transport and Communications, Department of Communications Univerzitná 8215/1, 010 26 Žilina, the Slovak Republic E-mail: katarina.stofkova@fpedas.uniza.sk Abstract: Redistributive function of public finance is provided through the state budget, which is formed as a balance of revenue and expenditure side. Revenues are redistributed and provided to citizens through public services. The primary source of the state budget revenues are taxes, which represent up to 95% of total revenues, based on individual years. Tax policy is a part of the state's fiscal policy and represents a set of measures, which govern the tax system. The Slovak Republic and the Kingdom of Spain are European Union member states which use the same currency. Both countries differentiate in many areas e.g. economics, geography etc. despite some common features. The aim of the contribution is to analyze the current situation in the field of tax burden on personal entity in the Slovak Republic and Spain. Authors conduct comparison and evaluation of the tax burden on personal entity in both countries, based on the example of an employee working in selected countries. Spain, unlike the Slovak Republic, also takes into account the origin of the tax during the tax burden calculation i.e. the area in which is tax levied. It results into different tax rates for different regions of Spain. Keywords: taxes, income tax, public finance, tax-free income, child tax bonus JEL codes: H21, H23, H24, H60, H71 1 Introduction The aim of effective state policy is to achieve prosperity, abundance, sustainable development and to well manage public finances. (Toshihiro, 2013). One of the main public finances functions is to ensure a fair redistribution of fund among individual groups of citizens and to remove the main differences in their socio-economic status. (Backhaus and Wagner, 2004). The redistribution function reflects in the structure of the tax system i.e. optimal distribution of tax burden among individual entities. (Stiglitz, 2000) The literature gives various definitions of taxes. From a formal point of view the tax is: “a mandatory, statutory, non-purpose and non-equivalent payment, which is usually repeated regularly and which taxpayers carry at a specified amount and within specified time limits to the relevant public budget”. (Štofková et al., 2015) According to economic and financial point of view, the tax is: “the financial category, represents the fiscal relationship between taxpayer and the state, governed by the law, which is used to ensure its aims, is based on the principle of inequality”. (Štofková and Štofko, 2015) Taxes are divided into direct and indirect. Direct taxes are deducted directly from the income or wealth of personal or legal entity (taxpayer). Their payment is not passed on to anyone else and personal or legal entity bears the tax liability itself, calculates it and transfers it to the paid administrators. Indirect taxes are attached to products or services. The consumer (taxpayer) is indirectly liable for the price of the goods or 136 provided services.Personal or legal entity which is legally required to calculate the tax, to withdraw it from the taxpayer and to pay within a certain period to the tax administrator is called the taxpayer. The taxpayer is liable for the tax levied or deducted and is present in direct and indirect taxes paid in a withholding way. (Sivák, 2007) Taxes have become a term used every day. Taxes represents the most significant revenue item of the state budget, so it is in the state interest to monitor compliance with the tax obligations. (Sivák, 2015) Income tax of personal entity in the Slovak Republic The tax is governed by Act. No. 595/2003 on income tax (last amended on 23rd November 2016). The tax liability applies to every citizen who, during a taxable period – during a calendar year, has taxed income higher than € 1,901.67. The tax return for 2016 have to be submitted by 31st March 2017. The submission deadline is prolonged for the taxpayer of three months i.e. to 30th June 2017, based on the notification filed before 31st March 2017. The deadline for filling the tax return is extended by a maximum of six months (to 2nd October 2017), based on the notification submitted before 31st March 2017, but only if incomes from foreign sources are a part of total incomes of taxpayer. (Finančné riaditeľstvo SR, 2017) Tax free income Employee is entitled to claim a non-taxable portion of the tax base. If the taxpayer's tax base is equal to or less than € 19,809, the non-taxable portion is € 3,803.33. If the taxable amount exceeds € 19,809 the non-taxable amount is: the difference between € 8,775.578 and one quarter of the taxpayer's tax base. If this sum is less than zero, the non-taxable portion of the tax base on the taxpayer is equal to zero. (iSITA, 2016) Income tax of natural entity has two levels. The tax rate for incomes below or equal to € 35,022.31 is 19%. For incomes exceeding this limit is applied 25% income tax. The basic deadline for the tax return submission is always before the end of March of the year following the tax period. Since 2013 there is also a so-called “special rate” of 5%, which is applied on selected state officials, e.g. President, Government and Members of the National Council of the Slovak Republic. (Ministerstvo financií SR, 2017) Taxpayer can reduce the tax liability by a child tax bonus of € 21.41 per month for each dependent child living with a taxpayer. Income tax of personal entity in Spain The tax is governed by Act. from 28th November No. 35/2006 on income tax of personal entity and its partial adjustment (valid until 1st January 2017). The tax return have to be submitted by 6th April 2017, with a request for deadline extension and assistance in filling, submission of the tax return may be postponed until 30th June 2017. (BOE, 2017a) In 2011, the Act. on income tax No. 35/2006 determined the basic interval with the tax rates for annual gross wages, which were changed in 2015, based on the Real Decreto – ley 9/2015. Authors therefore used these new tax rates in the calculation. The following table provides a comparison of the above mentioned rates. (El economista.es, 2014) 137 Table 1 Comparison of income tax rates. Gross wage in € Tax rate 2015 2016 <12,450 20% 19% 12,450 – 20,200 25% 24% 20,200 – 35,200 31% 30% 35,200 – 60,000 39% 37% > 60,000 47% 45% Source: own processing based on El economista.es. The tax burden on income also depend on the origin of the tax, i.e. the area in which is income tax levied. Currently it is allocated partly to the individual autonomous regions of Spain (together 17 on the territory of the country). That is whyare individual tax rates different in each area from the basic rates determined by the law. (BOE, 2017a) Tax quotas used from 2012 to 2014 (sum of state and autonomous rates plus complementary load in 2012, 2013 and 2014) for the autonomous region of Madrid are expressed in following table. Table 2 Tax quotas for the autonomous region of Madrid. Gross wage State rate Additional rate Autonomous rate of Madrid SUM From To 0.00 € 17,707.20 € 12.0 % 0.8 % 11.6 % 24.4 % 17,707.21 € 33,007.20 € 14.0 % 2.0 % 13.7 % 29.7 % 33,007.21 € 53,407.20 € 18.5 % 3.0 % 18.3 % 39.8 % 53,407.21 € 120,000.20 € 21.5 % 4.0 % 21.4 % 46.9 % 120,000.21 € 175,000.20 € 22.5 % 5.0 % 21.4 % 48.9 % 175,000.21 € 300,000.20 € 23.5 % 6.0 % 21.4 % 50.9 % 300,000.21 € More 23.5 % 7.0 % 21.4 % 51.9 % Source: own processing based on Expansión Económica. The currently highest rates oscillate between 51.9% (La Rioja, Madrid) and 56% (Catalonia). (Expansión Económica, 2017). Tax free income There exist certain types of income for which is income tax liability equal zero. Such incomes are not added do total taxable income for assessment year and thereby remain tax-free. The previous non-taxable portion of the tax base in an amount of € 2,652 was canceled by the 2015 reform and replaced by a new amount of € 2,000 per year. This amount will increase by an additional € 2,000 per year in the case of unemployed taxpayers, who accept a new job requiring a change of residence and in the case of active taxpayer with a disability, this non-taxable portion of the tax base increases from € 3,500 to € 7,750 per year depending on the degree of disability. (BOE, 2017b) In the case of taxpayers whose net income does not exceed € 14,450 per year without any additional income exceeding € 6,500, the following tax understatements are applied: • taxpayers with a net income equal to or less than € 11,250 - income tax is reduced by € 3,700 per year, 138 • taxpayer with a net income in the range from € 11,250 to € 14,450 – the tax reduction is calculated as follows: 3,700 – 1.15625 x (net income – 11,250). These non-taxable portions of the tax base were increased by 100% for working taxpayers at age over 65. However, the reform canceled these benefits without the replacement by other deductible expenses.(Expansión Económica, 2017) The subsistence minimum has increased from € 5,151 to € 5,550 after the reform. The reform also increases the values according to the age of the taxpayers. If is a taxpayer older than 65 years, the value of subsistence minimum increases from € 6,069 to € 6,700. If a taxpayer is older than 75 years, the value of subsistence minimum increases from € 7,191 to € 8,100. (Agencia Tributaria, 2017) Disability tax bonus The 2015 reform also covers tax bonus for taxpayers with disabilities. Comparison of tax bonuses before and after the 2015 reform for disabled taxpayers, their direct ancestors or offspring with disabilities is expressed in the following table. Table 3 Disability tax bonus. 2014 2015 Disability degree Disability tax bonus Assistance expenditures SUM Disability tax bonus Assistance expenditures SUM 33%-65% 2,316 € 2,316 € 3,000 € 3,000 € 33%-65% with the assistance 2,316 € 2,316 € 4,632 € 3,000 € 3,000 € 6,000 € ≥ 65% 7,038 € 2,316 € 9,354 € 9,000 € 3,000 € 12,000 € Source: own processing based on Agencia Tributaria. Child tax bonus The bonuses applicable to offspring has increased significantly, especially for the first two children, based on the reform. For third, fourth and other children, the increase is smaller, since from the third child have working taxpayer a tax relief for a large family. Table 4 Child tax bonus. Child 2014 2015 First 1,836 € 2,400 € Second 2,040 € 2,700 € Third 3,672 € 4,000 € Fourth and next 4,182 € 4,500 € Source: own processing based on Agencia Tributaria. If is child younger than 3 years the minimum is increased to € 2,800 per year (before the reform € 2,244). To get the children tax bonus it is necessary to provide care for a child in a common dwelling. (Agencia Tributaria, 2017) 2 Methodology and Data Domestic and foreign book literature, scientific publications and articles represents the base of the literature review of this contribution. Data for calculation were retrieved from official state websites and legal documents of the Slovak Republic and Spain. Authors used a number of various methods during the contribution, which lead to the achievement of its primary aim. Methods of induction, deduction, analysis and synthesis 139 were used to define the theoretical backgrounds of the researched issue, as well as to map the current state of tax burden of natural entities in the Slovak Republic and Spain. The authors conducted comparison of tax burden in selected countries based on the data and the model example. The MS Excel program was used to calculate the social and health insurance contributions, tax burden and net employee income. 3 Results and Discussion For the tax burden comparison in the Slovak Republic and Spain was used the following model example: taxpayer, 30 years old, employee of the medium-sized enterprise, earned in 2016 € 25,000 from employment. Taxpayer has one child, older than 3 years, without disability. Taxpayer's incomes flow only from home state without mobility within the state. (Spain considers whether an employee has moved to another autonomous community). For the child tax bonus is important number and age of the child, from which are derived other benefits from the state. The income tax calculation in the Slovak Republic The gross taxpayer's income is € 25,000 per year. Our model taxpayer is 30 years old and has one child, which is over 3 years old. Social and health insurance contributions are set at 13.4%, representing € 3,350 out of € 25,000. The next step is to express the non-taxable portion of the tax base, which have to be deducted from the current tax base. Non-taxable portion of the tax base is calculated as following: 8,755.578 – ¼ * 21,650. The calculation is expressed by following table. Table 5 Income tax calculation in the Slovak Republic Gross wage 25,000 € Social and health insurance contributions (13,4% of € 25,000) 3,350 € Partial tax base (gross wage – insurance contributions) 21,650 € Non-taxable portion of the tax base (8,755.578 – ¼ * 21,650) 3,343.078 € Reduced tax base (partial tax base – non-taxable portion) 18,306.92 € Tax liability (19% of 18,306.92) 3,478.31 € Child tax bonus (12*21.41) 256.92 € Income tax = tax liability – child tax bonus 3,221.39 € = Net wage (gross wage – insurance contributions – income tax) 18,428.61 € Source: authors We deduced from the reduced tax base the monetary tax rate, which is set at 19% because the tax base is less than € 35,022.31. Then we need to deduct the tax bonus for a child at a rate of 12*21.41 € from tax liability. The result is the amount of income tax which has to be paid by taxpayer to the state. The income tax calculation in Spain Gross taxpayer's income is € 25,000 per year. The taxpayer is 30 years old and has one child older than 3 years. In addition to salary, he does not receive any other income from other activities. According to the 2015 reform, the subsistence minimum is € 5,550 per year. The child tax bonus is € 2,400 (child tax bonus are applied only to the taxpayer). The result is a family minimum of € 7,950. In the next step we deducted all deductible expenses in order to obtain the tax base. The non-taxable portion of the tax base is set at € 2,000. In our case, we did not apply any additional deductible expenses to it, since the taxpayer in our example is not disabled and earns more than € 14,450 per year. Social insurance is in our case at € 1,587. Social insurance includes healthcare, pension and unemployment insurance contributions. We 140 had to choose a minimum and maximum amount according to the applicable standards depending on which category of employees our taxpayer belongs to. The basis are different for an official, a technical engineer, an engineer/master etc.. Our taxpayer was included in the group of engineers/masters, who have the highest minimum and maximum according to the social insurance. The minimum is € 1,051.50 and a maximum is € 3,642. Currently 6.35% is paid for social insurance. We applied this percent to gross income and received the total amount of social insurance contributions. In order to calculate the tax base, we deducted the social insurance contribution and non-taxable portion of the tax base from the taxpayer's gross annual income. Table 6 Tax base calculation in Spain. Non-taxable portion of tax base 2,000 € Social insurance contribution 1,587.50 € SUM of contributions 3,587.50 € Tax base 21,412.50 € Source: authors To calculate the net annual income we need to know the percentage of total contributions deducted from the social security and income tax. The result is in our case 18.67%. Since this percentage represents the total quantity, we derive from gross income, we have to determine the remaining percentage. By deducting from 100% we get 81.33%, which we applied to gross wage. By subtracting 12 months of the year we report the monthly net income. Table 7 Monthly net income calculation in Spain. Total contributions % 18.67% Annual net income 20,333.75 € Monthly net income 1,694.48 € Source: authors The difference between the second and first deduction will lead to the total amount of income tax our taxpayer has to pay. The first deduction was calculated as a family minimum, resulting in € 1,510.50. In the second deduction we used the tax base as the basis for the calculations. The result of second deduction is € 4,589.25. The total tax statement of income tax which has our taxpayer to pay has been obtained by the difference of these two deductions, which is € 3,078.75. 4 Conclusions The data expressed in tables reflects that despite the same initial income of € 25,000 the resulting amount of income tax, which a taxpayer has to pay is different in both countries. The resulting tax is higher in the Slovak Republic, exactly € 142.64 more. Apart from the differences in the calculation procedure of income tax we see the largest difference in tax rates applicable according to the income amount. Completing and submitting tax return is a lengthy and often incomprehensible process for many of us. Although we can be pretty sure that this act is not very popular regardless of where we live, somewhere the overall tax system is easier than elsewhere. By the comparison of the basic terms and conditions of setting and submitting a tax return to the income of natural persons in the Slovak Republic and Spain we can see certain differences. We consider as the substantial difference the division of incomes in the intervals by which the income tax rates are assigned. Unlike in the Slovak Republic, the Spanish system also takes into account the origin of the tax during the tax burden calculation i.e. the area in which is tax levied. It results into different tax rates for different regions of Spain. 141 Acknowledgments This contribution was undertaken as a part of research project VEGA 1/0693/16 and VEGA 1/0733/15. References Agencia Tributaria (2017). Reducciones en la base imponible de la IRPF. Retrieved from: http://www.agenciatributaria.es/AEAT.internet/Inicio/_Segmentos_/Ciudadanos/Minimos __reducciones_y_deducciones_en_el_IRPF/Reducciones_de_la_base_imponible_en_el_IR PF/Reduccion_por_aportaciones_a_personas_con_discapacidad_.shtml. Backhaus, J., G., Wagner E. R. (2004). Handbook of public finance. Boston: Kluwer Academic Publishers. BOE (2017a). Ministerio de la presidencia y para las administraciones territoriales. Ley 35/2006, de 28 de noviembre, del Impuesto sobre la Renta de las Personas Físicas y de modificación parcial de las leyes de los Impuestos sobre Sociedades, sobre la Renta de no Residentes y sobre el Patrimonio. Retrieved from: https://www.boe.es/buscar/doc.php?id=BOE-A-2006-20764. BOE (2017b). Ministerio de la presidencia y para las administraciones territoriales. Impuesto sobre la renta de las personas físicas. Retrieved from: https://www.boe.es/legislacion/codigos/codigo.php?id=64&modo=1¬a=0. El economista.es (2014). Así es la reforma fiscal: la rebaja media del IRPF será del 8% en 2015 y alcanzará el 12,5% en el año 2016. Retrieved from: http://www.eleconomista.es/economia/noticias/5878708/06/14/La-rebaja-media-del- IRPF-para-2015-sera-del-125.html. Expansión Económica (2017). Así quedará su nómina en enero con el nuevo IRPF. Retrieved from: http://www.expansion.com/economia/2016/01/03/5689689be2704e7a288b45cd.html. Finančné riaditeľstvo SR (2017). Zákon č. 595/2003 Z.z. o dani z príjmov. Retrieved from: https://www.financnasprava.sk/_img/pfsedit/Dokumenty_PFS/Zverejnovanie_dok/Spriev odca/Sprievodca_danami/2017.01.04_dan_z_prijmov.pdf. iSITA (2016). Výška dane z príjmu. Retrieved from: http://podnikam.webnoviny.sk/vyska-dane-prijmu/. Ministerstvo financií SR (2017). Fyzické osoby. Retrieved from: https://www.financnasprava.sk/sk/podnikatelia/dane/dan-z-prijmov/fyzicke-osoby. Sivák, R. (2007). Verejné financie. Bratislava: Wolters Kluwer (Iura Edition). Sivák, R. (2015). Financie. Bratislava: Wolters Kluwer (Iura Edition). Stiglitz, J. E. (2000). Economics of the Public Sector. 3rd edition. New York: W.W. Norton & Company, Inc. Štofková, J., Repková Štofková, K., Kaštánek, P. (2015). Finance. Bratislava: Dolis. Štofková, J., Štofko, S. (2015). Financial policies as a crucial element of economic development in the Slovak republic. In: Instytucjonalne uwarunkowania systemu 142 finansowego jako środowiska rozwoju sfery reálnej. Bielsko-Biała: Wydawnictwo Wyższej Szkoły Finansów i Prawa. pp. 188-198. Toshihiro, I. (2013). Principles of Public Finance. Singapore: Springer. 143 Comparison of Selected Aspects of Financial Literacy and their Differences in the Conditions of University Education in Slovakia Beata Gavurova1 , Eva Huculova2 1 Technical University of Kosice Faculty of Economics, Department of Banking and Investment B. Nemcovej 32, 040 01 Kosice, Slovak Republic E-mail: beata.gavurova@tuke.sk 2 Technical University of Kosice Faculty of Economics, Department of Banking and Investment B. Nemcovej 32, 040 01 Kosice, Slovak Republic E-mail: eva.huculova@tuke.sk Abstract: The inability to make qualified financial decisions has a very negative impact on peoples’ lives. From this point of view financial literacy represents a basic and inevitable skill that is important for human existence in the 21st century. In many countries, there is a trend towards decreasing state support of individual retirement income, in result of which individuals have to manage their financial resources by themselves in order to secure sustainable retirement and to retain their financial wellbeing standard in the future. Supporting the raise of financial literacy, in particular for young generation, could contribute to better knowledgeability and readiness for household and retirement planning. The authors focus on financial habits and financial behaviour of respondents and their skills in terms of financial literacy concept. This study is aimed at comparison of levels of financial literacy of chosen economic faculties in Slovakia, as well as comparison of levels of financial literacy between the first year undergraduates and those graduates who are in the final year of their studies. The authors statistically process primary data which were collected through a survey in 2015- 2016. The data were obtained from economic faculties of three different universities in Slovakia. Correlative data dependence of selected variables and their intensity is analysed using Probit model. The research line is defined by a few hypotheses, and the main goal is to explain the impact of respondents’ financial literacy in terms of their sex, high school background and university study program on financial habits. Keywords: financial literacy, financial habits, education, retirement planning JEL codes: I21, I22, I25 1 Introduction The field of finance and money management have been changed over few decades in many ways with impact as on national as on the individual and household level. The inability to make qualified financial decisions has a negative impact on peoples’ lives and financial literacy (FL) has became a basic and inevitable skill that is important for human existence in the 21st century. Topic of financial literacy has been increasingly brought to common people’s attention and there are evident activities of policy makers from many countries of the world aimed at the support of financial education. Nowadays, young generation can learn about the financial possibilities and obtain financial habits from extensive resources, including their parents, friends, schools, out-of-school activities, as well as personal experiences such as opening a bank account. These available resources, however, provide not only different but often incomplete or outdated information to use. In this context, regarding social aspects is important and crucial the environment in which individual grows and lives. Within this context Driva et al. (2016) and Grohmann et al. (2015) confirm the existence of the gap of FL among teenagers that relates to household finance. 144 Financial education provided to young people can help to overcome the gap in financial skills due to inequality in the socio-economic status of students and improve their financial habits. Socio-economic status of an individual and consequently, family environment and education play an important role in this process (Pixley, 2010; Grohmann et al., 2015; Popescul et al., 2016). In this case, there is a strong parental and household planning influence, which can lead directly to children within their money management in the future (ČNB, 2012; Kiliyanni and Sivaraman, 2016). Without basic financial knowledge, it is not possible to make eligible decisions because they affect personal and family budgets, often with a serious impact on the next generation (Szovics, 2012). Effective money management should be a part of childcare needed for their future life. We suppose additional financial education can be effectively extended directly in the primary and secondary school environment, where young people are gathering on a daily basis and should be added to the education standards systematically with the respect of diversity in society, one that will take into account the social and economic status of the individual as well as local and regional aspects. The ability to understand and utilize financial information in way that contribute to optimal financial outcomes is a crucial element for independence and sustainable wellbeing in old age (Han et al. 2016). Increasing financial skills by individuals and making sound personal financial decisions over the course of the adult life span is one of the keys to individual self- and household sufficiency with consequences on better quality of life (Hershey et al. 2015). Level of FL around the world can be affected by many factors, which are not equally important in all participating countries and economies. Differences in FL can be associated with gender, parental background, educated system or economic status in country. Problems with insufficient financial habits and lower FL relate not only to young generations but also to elderly. One foreign study shows that FL is associated with greater functional connectivity between brain regions in old age (Han et al. 2016). Financial literacy in relation with retirement planning Education of FL has become a global priority (Blue et al. 2014). Level of FL significantly varies in many countries around the world. McGraw Hill Financial shows significant disparities of FL levels within the EU countries. For instance, in Romania only 22 % of its inhabitants are financially literate as opposed to 71 % of inhabitants in Denmark and Sweden. In Europe, there are 69 % of adults with academic degree whose financial literacy is significantly higher than of 54 % of adults with secondary education and of 28% of citizens with elementary education (Klapper et al. 2015). A lack of personal financial skills and habits in many cases contributes to the increase in consumer credit debt, and from the other hand over-indebtedness is positively associated with poor level of FL and self-control problems. One of the effective way to combat this growing problem is through supporting financial education (DeLaune et al. 2010). By using available data from PISA and OECD statistics we put to the relation scores of FL of the year 2012 which were officially released on July in 2014, because findings about data of PISA 2015 will be available later in May 2017, GDP per capita as the main factor of economic growth in US Dollar since year 2015 or latest and expenditure on education as % of total government expenditure since year 2013. Indicator expenditure on education is represented by different size of points. The bigger point is, the more financial recourses given country spend on education. Figure 1 shows relationship between student’s score on FL assessment, GDP per capita and expenditure on education. Living in a rich country does not seem as a strong implication on the FL scores of 15 years old students. We cannot say that higher expenditure on education will cause higher level of FL of students as well (p > 0.05). While higher GDP per capita is associated with higher level of FL, the plot in Figure 1 indicate that lot of countries with lower value of GDP per capita (Latvia, Poland, Czech, Estonia) perform better level of FL than countries with higher economic growth as Italy, France or United States. 145 Figure 1 Relation of financial literacy, GDP per capita and expenditure on education in given country. Source: own processing according to PISA and OECD data This implies that students and youth in countries with advanced economy do not achieve higher score of FL than students in poorer countries. This is determined by a number of socio-economic, political and cultural aspects. A detailed examination of them requires access to more structured data. Those conditions also create a platform for a subsequent research. In this context Lusardi (2015a), OECD (2014), Stauvermann and Kumar (2017) and Kočišová (2015) indicate that following independency simultaneously underscores the importance and relevance of having a well-functioning educational system or its efficiency. Results of research study of Dragoescu (2015) show positive relation between GDP per capita and the number of students with higher education and no connection between GDP per capita, size of students respondents and public education expenditure. In one of the OECD study (French et al. 2015) was found out negative impact of gender on public expenditure on education and positive effect of individualism and long-term orientation. Hence, authors emphasize relevance of cultural dimensions on education expenditure and country differences of FL (Feranecová and Krigovská, 2016; Bartosik-Purgat et al. 2017). In many countries, there is a trend towards decreasing state support of individual retirement income, in result of which individuals have to manage their financial resources by themselves in order to secure sustainable retirement and to retain their financial wellbeing standard in the future. To meet the challenges of an ageing population, standard ages for retirement have increased and occupational pensions have become less generous. In many countries substantial part of the risk and responsibility for an adequate standard of living after retirement has been shifted from the government and employers to individuals or private households (OECD, 2013; Oehler and Werner, 2008; Prast and Soest, 2016; Lusardi 2015a, 2015b; Kočišová, 2014). Supporting the raise of financial literacy, in particular for young generation, could contribute to better knowledgeability and readiness for household and retirement planning. Greater personal responsibility toward financial decision-making is being advocated on a global basis and as abovementioned, individuals or households are encouraged to take a more active approach to personal finance (Kabók et al. 2017; Brounen et al. 2016; Krpálek and 146 Krpálková Krelová, 2016; Jovovic et al. 2016). World financial situation today indicate low savings rates and inadequate long-term financial planning for retirement, in this regard financial well-being has become an important topic for individuals and households as well as for societies and countries. Findings about low and different saving behavior across generations are the outcomes by other study (Brounen et al. 2016; Fachrudin and Fachrudin, 2016; Lusardi 2015a; Peng et al. 2007). According the study of McGraw Hill Financial (2015) relatively few EU adults save for old age, and even those who save often have weaker financial literacy. The savings of individuals must be adequate enough to cover longer retirement periods due to higher life expectancies (Lusardi, 2015a) and in this context FL appears to be positively strong associated with retirement planning and with management of financial resources needed for sustainable retirement (Prast and Soest, 2016). Examination of regional, national and international disparities and discrepancies in the financial literacy of special populations is in the recent years the subject of interest to many research teams, as well as polemic and debate. It is related to process of globalization and related changes in the financial markets, the increasing internationalization of economic and business processes, etc. In Slovakia, there absents complex research and expert studies within given issue in spite of their importance in strategic concepts’ formation, planning of educational processes, and creating of relevant policies. The above mentioned facts have made us more interested in a close and deep examination of the level of FL and disparities at selected universities, and in the confrontation with the partial results of international research. The principal aim of this research was a comparison of levels of financial literacy and financial habits at both input and output, i.e. between the first year undergraduates and those graduates who are in the final year of their studies and a research of causal links, which provide a list of differences in horizontal (researched universities) and vertical (other categories) FL dimensions. 2 Methodology and Data The authors focus on financial habits and financial behaviour of university students and their skills in terms of financial literacy concept. This study is aimed at comparison of levels of financial literacy of chosen economic faculties in Slovakia, as well as comparison of levels of financial literacy between the first year undergraduates and those graduates who are in the final year of their studies. The authors statistically process primary data which were collected through a survey in 2015-2016. There participated 496 students from three selected universities in Slovakia: Faculty of Economics, Technical University of Košice (EKF TUKE) with overall frequency 228 respondents, Faculty of Management of University of Prešov in Prešov (FM PU) with 93 respondents and Faculty of National Economy of the University of Economics in Bratislava (NHF EUBA) with 195 respondents. These Slovak universities have been active for many years and they provide various economically oriented study programs. They also realize research activities besides those educational ones. The written form of survey by means of structured questionnaire that consisted of 54 questions was used in order to collect the necessary data. Questionnaire structure was divided into three types of questions so it also considered key categories of a given issue. The targets as well as character of a survey were determined by a form of questionnaire and questions’ concept. The students’ FL was evaluated by 7 specific and practical (mathematical) tasks with multiple choices and one correct answer on the basis of content point of view. In the research, there was applied a verified and internationally respected procedure, which has also been used by the Global Financial Literacy Excellence Center (GFLEC) at the Washington university in the U.S. (e.g. Lusardi and Tufano, 2009). We analyze correlative data dependence of selected variables and their intensity is analysed using Probit model. The research line is defined by a few hypotheses, and the main goal is to explain the impact of respondents’ financial literacy in terms of their sex, high school background and university study program on financial habits: 147 H1: Level of respondents’ FL connected to respondents’ sex. The primary source of this hypothesis was a research made by an international study, PISA (2012, in cooperation with GFLEC). This research made a conclusion that male respondents are more frequently ranked at higher, but also lower levels of FL evaluation (wide variance of achieved FL level). Female respondents reached average results in this research. Our research primarily focused on a possibility of such differentiation between sexes in terms of FL in students of specific economic faculties in Slovakia. H2: Level of respondents’ FL defined as an input of students, which is connected to high school type. Many high school study programs offer different knowledge of FL. On the other hand, FL is inevitable in daily life of each individual who becomes a client, customer, debtor, creditor, etc. In this context, each individual should have a certain level of FL. The differences presumed by this hypothesis are of main interest of this research, while accepting a fact that only a part of population continues in the university studies. H3: Level of FL is increased by completing economic study programs at the university. The individuals achieve the highest level of education by completing the university study programs also in economic field. It is estimated that those individuals who did not complete any economic study programs, or are at the beginning of such studies have significantly lower level of FL than those respondents who are about to complete their economic studies. H4: Level of FL is connected to respondent’s financial habits. Individuals learn to manage their money from childhood and thus obtain financial habits in diverse ways that affect their wellbeing throughout whole lives. We assume that those who have the appropriate financial habits will be able to achieve higher level of FL. 3 Results and Discussion In our empirical research we run Probit regression that is interpreted by marginal effects in contrary of the Logit model (binary logistic regression) measuring the odds ratio of studied problem. Practical tasks used in survey questionnaire which enabled to evaluate the level of students’ FL in selected economical faculties were thematically-oriented to financial skills and abilities of respondents in simple and complex interest rate, inflation, influence of interest rates on particular types of investments. In our analysis, the dependent variable is financial literacy, or financial illiteracy of students. Our dataset consists of 496 observation, where 320 were females and 176 were males. In Table 1 are presented results of Probit analysis. This Probit model as a whole is statistically significant and overall percentage of cases that are correctly predicted by the model is 57.15 %. Model indicates, that expect variable Gender, no other variable has a statistically significant impact on financial literacy of our sample. Results show that a one unit change in the Gender variable, decreases the probability of being financial literate by -0.066575470. In this sense of analyzed variable Gender we can point out that men are 6.7 % less likely to be financial literate than comparable female respondents, while controlling for other variables in the regression. Table 1 Probit regression Coefficients: Estimate Std. Error z value Pr(>|z|) Marginal Effects (Intercept) 5.47862 146.95417 0.037 0.9703 Gender -0.27503 0.12031 -2.286 0.0223 * -0.066575470 Education Business Secondary School 0.24059 0.16764 1.435 0.1512 0.058238704 Hotel academy 0.18705 0.42730 0.438 0.6616 0.045280294 148 Secondary vocational school -0.07195 0.34980 -0.206 0.8370 -0.017416282 Year of study -5.09719 146.95412 -0.035 0.9723 -1.233879175 Financial habits 0.01140 0.12228 0.093 0.9257 0.002759785 Source: own processing Studied model as a whole defined gender as a statistically significant variable. Concerning compound interest and risk diversification, men of all respondents in our survey are less likely to respond correctly to the question compared to women. Generally, fewer men at all three Slovak economic faculties can answer all questions correctly compared to women. But, for instance, if we look deeper on students at EKF TUKE, male students of this faculty achieve higher level of FL than female students. For instance, where only 10.13 % of male respondents are financially illiterate, while this number is in female respondents much higher, 30.87 %. Similar results are consistent with majority of studies (Lusardi and Mitchell 2011; Prast and Soest, 2016; Klapper et al. 2015) where show significantly higher male success concerning financial knowledge than female one. Interestingly, the analysis does not prove statistically significant dependence (p > 0.05) of year of study between the first year undergraduates and those graduates who are in the final year of their studies. Comparison of levels of financial literacy as a whole at both input and output achieved comparable worth. Although the results of Probit regression showed no significance of this variable throughout the study sample, among the faculties themselves included in the questionnaire we can find certain differences. A frequency rate of financially illiterate students from EKF TUKE was lower at the end of their studies (9.68 %) as at the beginning of their studies (33.33 %). Similar connection was found out at NHF EUBA, where a rate of financially literate students of the first year (53.13 %) was lower than in students of the last year at particular faculty (72.34 %). Logical connection was found in two universities, in Košice and Bratislava, while analyzing the relation between FL level of respondents and level of their studies at the university. In both cases, the statistically significant dependency was confirmed (p < 0.05), which means that financial knowledge depends on completed level of study at particular university. However, this validity was not confirmed at FM PU (p > 0,05), where the rate of financially literate first-year students at FM PU forms 31.58 %, while only 27.78 % of the last year students are financially literate. Business Secondary School in comparison to Grammar School, or other types of high schools teach many subjects of economy and they provide primary or broadened knowledge of finances and economy for their students. Therefore, it was supposed that respondents who attended Business Secondary School would reach a higher level of FL, but based on these results we reject this hypothesis where we do not recognize any statistically significant dependency. According to fourth hypothesis linked to students’ financial habits, there surprisingly prevail findings leading to no significant interdependence (p > 0.05). The analysis does not show that better financial habits of students necessary cause higher level of FL our sample. It could be caused by insufficient amount of tasks related to financial habits oriented to retirement planning and management. If we take a deeper look on these results, up to 73.19 % of students reported that they have not think about the financing of their pensions yet and did not search any information on retirement planning. Only fewer than one third of the sample of students indicated that they actually attempted to do a retirement saving calculation. Since the saving of funds for retirement is a long process dependent on savings strategies, the pension savings themselves should be long-term and regular, because only in that case could be the saver progressive in investment strategies and only in that case could he achieve adequate recovery (Cenker, 2017; Zvaríková and Majerová, 2014). One of research limits was uneven representation of respondents at researched faculties and the second one lower number of respondents of our sample. Three selected faculties of economy were examined due to procedural and technical difficulties of a given survey. This focus limits the outputs’ generalization of the whole Slovak population. 149 4 Conclusions It can take many years of experience to develop good financial habits, but the benefits of being responsible with spending are well worth any effort it takes to develop good financial skills needed for at least adequate and sustainable wellbeing and household budgeting. Financial problems and debts result from bad decision making and lower financial knowledge. One of the way to protect better financial future of students is highlight and amplify financial education at school and help students with understanding the ways in which individuals can avoid making financial mistakes on a regular basis and it is necessary to make sense of the funding policies on education programs to increase of their efficiency and equity. This study deals with evaluating of FL among university students with economic field. The aim of the research line contained explanation of the impact of respondents’ financial literacy in terms of their sex, high school background and university study program on financial habits. By using Probit model we can conclude a statistically significant dependency between variables FL and gender where female students achieved higher level of FL and are more likely to be financial skilled compared to male, no other variable has a similar impact on FL of our sample. Further, analyzed model does not prove statistically significant relation of year of study, it means that first year undergraduates and those graduates who are in the final year of their studies achieved comparable score point of FL and we do not see significant development of student’s financial skills after graduation in the majority of study programs. University students of our sample showed lower skills related to acquired financial habits than we assumed. 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Procedia Social and Behavioral Sciences, vol. 110, pp. 1106–1115. 152 Tobacco Tax and Tobacco Consumption in Slovakia Martin Geško1 1 Assistant Professor, University of Economics in Bratislava Faculty of National Economy, Department of Finance Dolnozemská cesta 1, 852 35 Bratislava, Slovak Republic E-mail: martin.gesko@euba.sk Abstract: Selective consumption taxes and particularly the excise tax on tobacco and tobacco products represent the important source of income for the state budget of the Slovak Republic. In 2016 the tobacco tax revenue amounted to EUR 673.2 million, which represents 6.12% share of the total tax revenues.As the argument for increasing the excise taxes on tobacco products the idea that selective excise taxes have a significant impact on reducing the consumption of the taxed commodities, and thus contribute to mitigating the negative effects associated with their consumption, is widely used. The price sensitivity of the tobacco consumers has been indicated by several scientific papers, mainly from various health organizations. The aim of this paper is to verify whether the development of the excise tax on tobacco and tobacco products in SR indicates the sensitivity of the Slovak tobacco consumers to increased tax rates and prices as well as to determine the factors which could influence their elasticity. Keywords: tobacco tax, economics of tobacco, tobacco policy, demand for cigarettes, elasticity JEL codes: D11, H21, H23 1 Introduction This paper is a part of the ongoing research focused on the impact of the tobacco taxation on the consumption of tobacco products in Slovakia. The aim of the research project is to determine the main factors forming the consumer's response to the increased tax rate as well as to assess the real price elasticity of the demand for tobacco products in the Slovak Republic. We aim to analyse whether the small regular increases of the tobacco tax rates (as done by the Slovakia and most of the states worldwide) is an effective measure to reduce the tobacco consumption and if so under what conditions. By analysing the ways how consumers react to the increased tax, assessing the influence of the price on the new consumers and analysing the development of the tax revenues reaction to the increased tax we would like to sketch a theoretical framework for the future tax policy measures to be taken in this field. Selective tax on consumption, and particularly excise duty on tobacco products represent an important source of income for the state budget of the Slovak Republic. In 2016 the revenue from the tax amounted to EUR 673.2 million, which represents 6.12% share of the total tax revenues. In order to justify the existence of a higher excise duties on tobacco products the governments often claim that selective excise taxes have a significant impact on reducing the consumption of taxed commodities, and thereby contribute to mitigating the negative effects related to their consumption while achieving preventive function. This argument appears also in the official legislative documents related to this tax. For example, in the explanatory memorandum of the law concerning the introduction of a minimum price for cigarettes in packs from November 2009 (Slovak Parliament Press, 2009) we can read: "...according to the World Bank price increases of tobacco products are the most effective single measure to prevent smoking. A price increase of 10% decreases consumption on average adults about 4% in Member States with high income. More importantly, the impact of higher prices is likely to be greatest on young people, who are responsive to price rises than older people." With the continuous increase of the tax rates and the prices of tobacco products (see for example Válek, 2008) various questions arise. We will focus on the factors determining 153 the elasticity of the tobacco demand. The question is: What is the pattern of the smoker's reaction to the increased price of cigarettes? After determining the framework of the elasticity factors we will focus on assessing the characteristics of the tobacco demand elasticity in Slovakia. 2 Analysis Regarding the methodology of the research - we use the indirect indicators in order to assess the real cigarette consumption and thus to assess the impact of the increased tax. The reason is that we are not able to collect relevant data for all components of the tobacco consumption. Results of many analyses of elasticity and the impact of the excise tax on tobacco products has confirmed relatively low elasticity of demand for tobacco products. According to the World Bank study 10% increase in tax has the effect of 4% reduction in consumption. Similar results can be found in work related to the impact of excise duty on tobacco products in developing countries. According Aloui (2003) an increase in excise duty on tobacco products in Morocco led to a decrease in consumption. Increase of the tax by 10% should reduce the demand for cigarettes among adults by 3.3%. The World Health Organization has several analyses which show similar results (e.g. Nassar, 2003). These analyses are based on estimates of elasticity and all recommended to increase the tobacco tax as one of the major tools to reduce the tobacco consumption. This reduction in consumption may occur as a result of reduced consumption of current smokers, and as a result of discouraging youth from start smoking (Rozada, 2002). At the same time the positive impact of the tax increase on the government revenue remained as well when the tax increase of 10% resulted in an increase in budget revenues of about 6%. Similarly in China, where the elasticity of demand is estimated between -0.65 and -1.0, analyses reveal mainly the fiscal importance of the tobacco tax. As reported by Hu (1997), if we assume a lower level of demand elasticity -0.65, then increased tax led to a price increase of 10%, a 6.5% decrease in sales volumes and simultaneously increase sales revenues by 2,9%. Given the effective tax rate 38% in 1992 these estimates imply the tax revenue growth by 18.2%. Conversely, if we assumed price elasticity of demand of -1.0 and assume that the tax is fully passed on to consumers, Hu stated that the doubling of the tax rate would reduce consumption by 40% while increasing tax revenues by about 20%. The particular goal of our paper is to find the answer to the question: How tobacco consumers in Slovakia responds to increased price of tobacco products due to the increase in the tobacco tax rate? Is the current system of tobacco taxation in Slovakia forcing existing smokers to quit and deter the potential smokers to become permanent? As first we shall accept the fact that the increase of the tax is at least fully reflected in the price of the tobacco product. On the basis of empirical data, it can be confirmed that an increase in excise duty on tobacco leads to at least complete shift of burden on to consumers, which means an increase of the price of cigarettes by the amount of the tax increase. Until 2010 the basis for the tax burden calculations was the price of the most sold cigarettes brand. Since 2010 all the brands on the market (cheaper brands as well as premium ones) are being considered. The WAP is calculated as the total value of all cigarettes released into tax free circulation, based on the retail selling price inclusive of all taxes, and the total number of cigarettes released into tax free circulation. As we can see on Figure 1 the tax rate increase is reflected in prices. However, since the WAP is available for a limited time period only, we still use the most sold cigarettes price (Marlboro King Size), which is annually published by the Statistical Office of the SR. 154 Figure 1 Average Marlboro cigarettes price EUR/19 pcs. 1997 - 2016 Source: author, based on Slovak Statistical Office (SO) data These values are not coherent to the Weighted Average Price of cigarettes. The WAP indicator would be more precise but it was introduced in European Union since 2010 only. However, the trend of the price development is comparable within the relevant time period. We can conclude that the tax increase is at least fully shifted on to existing consumers. If we want to asses the impact of the increased price on consumption we would need the real data on the total tobacco consumption within the country. Total consumption of cigarettes (tobacco products) Ctotal = consumption of the cigarettes sold (taxed) within the country (Ctax) + consumption of the cigarettes purchased outside the country (Cout) + consumption of the cigarettes smuggled into the country (Csmu) Ctotal= Ctax + Cout + Csmu In order to asses the impact of the tax increase on consumption we shall eliminate the influence of the Cout and Csmu factors. Due to the nature if these factors it is not possible to rationally asses their volume2 . In order to proceed in our analyse we shall assume that their volume is not increasing (given the tobacco prices in neighboring EU countries and Schengen area arrangements). However, regarding the size of the total consumption, estimations done by the biggest tobacco products distributor in Slovakia Philip Morris International exist. We shall still keep in mind that these estimates do not cover the Cout and Csmu factors. Figure 2 Total tobacco products consumption estimates of the Philip Morris International (billions of pieces) Source: Author, based on the Philip Morris International (PMI) data 2 Yurekli and Sayginsoy estimated that in 1999, 3.4% of global cigarette consumption was illegal, whereas a study by Joossens et al. found that 11.6% of the global cigarette market is illicit. 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 0,00 € 0,50 € 1,00 € 1,50 € 2,00 € 2,50 € 3,00 € 3,50 € 4,00 € 2008 2009 2010 2011 2012 2013 2014 2015 2016 6,200 6,400 6,600 6,800 7,000 7,200 7,400 7,600 7,800 8,000 8,200 155 However, if we take a look at the government revenue we will see a different picture. Figure 3 Total tobacco tax revenues in Slovakia 1994 - 2016 Source: author, based on Financial Directorate of the SR (FD) and SO data Similarly, if we take the number of the people between 15 and 100 years of age (possible smokers) into consideration, we can observe the following development (Figure 6). Figure 4 Total tobacco tax revenues per capita in Slovakia 1997 - 2015 Source: author, based on the FD and SO data Other indirect indicator comprises of the total tobacco tax revenues divided by the average cigarette price (based on the statistical prices). This indicator, average tobacco unit, represents an indirect indicator of the consumed units of tobacco products (cigarettes). 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 0 100 200 300 400 500 600 700 800 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 0,00 € 20,00 € 40,00 € 60,00 € 80,00 € 100,00 € 120,00 € 140,00 € 160,00 € 156 Figure 7 Volume of the average tobacco units 1997 - 2016 Source: author, based on FD and SO data Previously stated indicators provide an indirect insight into the tobacco products consumption. These shall be considered carefully in order to asses the real effects of the tax on consumption. Despite the PMI estimates, given the indirect indicators, it is possible that the overall tobacco consumption in Slovakia is not declining in longer period. It seems that smoking is not just a question of money. Smoking is an addictive consumption which means that smokers are “irrationally” unwilling to quit smoking. The impact of the increased tax on new smokers is unclear and shall be studied further. If we want to proceed further in our analysis we shall understand how existing smokers react in case of the increased tax rate and price. The demand for cigarettes is characterized by a low demand response to a moderate rise in the price of cigarettes. The demand for tobacco is specific and we can also call it "addictive" demand. A taxpayer who is forced to bear the selective consumption tax will have natural tendencies to avoid or at least mitigate it. His interest is to maintain the same level of utility / consumption as before introduction or increase of the tax. Observed the smokers habits we formulated the following possibilities for the substitution (in hierarchical order): 1. to change 'expensive' cigarettes for cheaper cigarettes (cheaper brands); 2. to smoke relatively less taxed tobacco (hand-rolled cigarettes); 3. to search for smuggled (illegal) cigarettes; 4. to reduce the volume of consumed tobacco products 5. to quit smoking - the complete cessation of consumption – non-smoker. Relevancy of such ways of substitution indicates the material Forecast of tax revenues and social contributions of government for the years 2013 - 2016 by the Institute of Financial Policy at the Ministry of Finance of the Slovak Republic (Hagara, Sporina, 2013): "The performance of the tobacco tax remain slightly behind the expectations. Slovak Financial Authority and distributors of tobacco products suspect that since the end of last 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 0 500 000 000 1 000 000 000 1 500 000 000 2 000 000 000 2 500 000 000 3 000 000 000 3 500 000 000 4 000 000 000 4 500 000 000 157 year consumers of the cheaper brands of cigarettes substitute towards hand-rolled cigarettes and alternative tobacco products. In some regions of Slovakia the sale of the tobacco for own production of cigarettes continues at a price lower than the actual amount of excise duty, which indicates illegal (untaxed) production of tobacco. From these reasons the estimate of the tobacco tax revenue has been revised for about EUR 10 million in 2013." So for the existing smokers to quit smoking is probably the least wanted reaction. Given the small price increases the consumers probably still have potential to absorb the higher price without significant change in the volume of consumption. 3 Results and Discussion Observed development of the selected indicators show that the continuous tax increase is at least fully reflected by the price increase (full tax shift). The tax revenue measured by the absolute amount as well as per capita rises. This could mean either harder burden for the existing smokers or even more new smokers within the population. The indirect indicator, the average tobacco unit, shows stable development of the tobacco consumption. The real consumption is hard to observe directly. The estimates made by the biggest tobacco distributor might not reflect the Cout and Csmu factors. According to their estimates the real consumption decreases. Starting decline of the tobacco consumption and tax revenues in the last years could mean: 1. higher impact of the other factors (Cout and Csmu); 2. shift to the other tobacco products (less taxed) as hand rolled tobacco; 3. lower per capita volume of smoked cigarettes or decline in number of smokers; Ad 1) This is very hard to asses. The out-of-country purchases are supposed to decline since the prices are getting comparable within Slovak neighbours. The real effectiveness of the border controls in case of Ukraine is complicated to asses. Ad 2) This shift is very probable. It was indirectly confirmed for instance by the tobacco industry representatives, as well as the KPMG analysis. Richie Gretler – Regional Director Central Europe of the Imperial Tobacco in his investor day presentation on September 2010 stated as one of their business successes a growth of their „white stick“ (handrolled cigarettes) share in four accession markets (Poland, Slovakia, Czech Republic and Hungary) from 22.3 per cent in 2004 to an estimated 25.7 per cent in 2010 (Gretler, 2010; similarly the KPMG, 2013). All mentioned reactions of the consumers mean the higher propensity to substitution. This could mean that we have reached (at least temporarily) the breaking point on the Laffer curve in case of tobacco. However, it is not clear which way of substitution prevails. We assume that the cessation of smoking is hierarchically least reaction to a higher price. The deterrent effect of the price to the new smokers is also questionable since the motivations to start smoking are probably not sensitive to prices. 4 Conclusions The total consumption of tobacco products in Slovakia is despite the data from tobacco distributors very difficult to asses. The level of tobacco products smuggled into the country or purchased abroad is impossible to determine. We were not able to acquire the official consumption data from the tobacco distributors. Based on the increase in tobacco tax rates, subsequent increase of tobacco products prices and tax revenues we could express a presumption that the demand for tobacco products in Slovakia is still relatively low elastic. The indirect indicator, the average tobacco unit, shows stable development of the tobacco consumption over observed period. This is contradictory to the estimates done by the biggest tobacco products distributor. 158 Given the current system of tobacco taxation (gradual small changes) the reaction pattern of the existing smokers suggests very low propensity to reduction or elimination of smoking. The potential smokers are probably not sensitive to current price changes of cigarettes as well. The other, non-financial, factors could play more important role in their case. Reduction of the level of the tobacco products consumption by the tobacco tax could be seen more as the secondary effect and not as the main purpose of the existence of this tax. Therefore, we can assume that in the case of selective consumption taxes, it is rather a rigorous use of Ramsey's approach to taxation (i.e. fiscal function) than a real impact on the consumption of a harmful commodity in a short period of time. Research shows that with the gradually rising tobacco tax rates the absolute tax revenue rises as well. Thus, selective excise taxes are rather an effective source of public funds than a mean to smoking reduction. Acknowledgments Supported by VEGA scheme No. 1/0776/16: Taxation of the financial sector and harmonizing tendencies in the European Union. References Aloui, O. (2003). Analysis of the economics of tobacco in Morocco, WHO Tobacco Control Papers, Center for Tobacco Control Research and Education, UC San Francisco. Gretler, R. (2010). Anticipating Demand Shifts. Regional Director Central Europe presentation for the Investor Day Presentations Central Europe. Hagara, E., Sporina, J. (2013). Vyššie príjmy z lepšej domácej spotreby a účinnejšieho výberu, Prognóza daňových a odvodových príjmov verejnej správy na roky 2013 – 2016, Komentár Inštitútu finančnej politiky pri Ministerstve financií SR. Hu, T. W. (1997). Cigarette taxation in China: Lessons from international experiences. Tobacco Control, vol. 6(2), pp. 136–140. Joossens, L., Merriman, D., Ross, H., Raw, M. (2009). How eliminating the global illicit cigarette trade would increase tax revenues and save lives. Paris, International Union Against Tuberculosis and Lung Disease. KPMG (2013). Project Star results, Report on tobacco consumption. Nassar, H. (2003). Economics of Tobacco in Egypt: A New Analysis of Demand, WHO Tobacco Control Papers, Center for Tobacco Control Research and Education, UC San Francisco. Rozada, M. G., Sanchez, G., Angueira, A., Verra, F. J. B. (2002). Analisis economico del consumo de tabaco en Argentina, Informe final. Slovak Parliament Press. (2009). Nr. 1356/2009; Retrieved from: www.nrsr.sk/Dynamic/Download.aspx?DocID=340878. Tax burden on tobacco (2007). Washington, DC, The Campaign for Tobacco Free Kids; Retrieved from: http://global.tobaccofreekids.org/en. Válek, J. (2008). Komparácia príjmov zo spotrebnej dane z tabakových výrobkov a zo spotrebnej dane z liehu na príjem do štátneho rozpočtu. In Finance and risk: proceedings of the 10th international scientific conference. Bratislava:EKONÓM, pp. 649-654. Zákon o štátnom rozpočte na rok 2016 č. 411/2015 Z.z. Retrieved from: http://www.finance.gov.sk/Default.aspx?CatID=10398. 159 Multiple state models for critical illness policy Ján Gogola, Lucie Kopecká University of Pardubice Faculty of Economics and Administration, Institute of Mathematics and Quantitative Methods Address: University of Pardubice Faculty of Economics and Administration Studentská 84, 532 10 Pardubice E-mail: jan.gogola@upce.cz, lucie.kopecka@upce.cz Abstract: The main goal of this paper is to apply multiple state models for an insurance policy combining disability income insurance benefits and critical illness benefits. We consider a policy with term 40 years to a life aged 25 which provides a death benefit, a disability benefit and a critical illness benefit. Using the data supplied by the Continuous Mortality Investigation (CMI) we calculate the premium payable continuously for this policy. Key words: multiple state model, stochastic process, Markov process, critical illness, JEL Classifications: C51, C52, G22, J11 1 Introduction The main goal of this paper is to apply multiple state models for an insurance policy combining disability income insurance benefits and critical illness benefits. The data we used in our contribution were supplied by the Continuous Mortality Investigation (CMI). The CMI is a research organisation established by UK actuarial profession. Disability insurance, long-term care insurance and critical illness cover are becoming increasingly important in developed countries as the problems of demographic aging (Pacáková, V., Jindrová, P. (2014)) come to the fore. The private sector insurance industry is providing solutions to problems resulting from these pressures and other demands of better educated and more prosperous populations. Critical illness insurance (CII) is a type of long term insurance that provides a lump sum on the diagnosis of one of a specified list of critical illnesses within the policy conditions. CII coverage includes (but is not limited) cancer, heart attack, stroke, coronary artery by-pass graft, kidney failure, major organ transplant, multiple sclerosis and other causes. CII has been very popular in the UK. UK sales peaked in 2002 when around 1 million new policies were issued by CMI Working paper 50 (2011). There is no restriction on how to spend the CII benefit. Most of the CII policies in the UK are linked to mortgages as this is a considerable financial commitment and diagnosis with a critical illness could affect the individual’s ability to repay the mortgage. There are two types of critical illness policy: Full Accelerated, which covers both critical illness and death, and Stand Alone, which covers only critical illness. Most of the policies in UK are accelerated policies (88%) and they are attached to life insurance, term insurance or endowments. Typically, regular premiums are payable throughout the term while the policy is in force. We describe the actuarial structure of disability insurance, long-term care insurance, and critical illness cover. Actuarial problems such as pricing and reserving are considered within the context of multiple state modelling, providing a strong and sound framework for analysing personal insurances. Our contribution is based on Markov process that can be used to develop a general, unified and rigorous approach for describing and analysing disability and related insurance benefits. The use of Markov process or Markov chain in life contingencies and their extensions has been proposed by several authors; for example Dickson, D. C., Hardy, M. R., & Waters, H. R. (2013), Haberman, S., & Pitacco, E. (1998). 160 2 Methodology and Data Multiple state models are one of the most exciting developments in actuarial science nowadays. They are a natural tool for many important areas of practical interest to actuaries. They provide solid foundation for pricing and valuing complex insurance contracts. We can represent life insurance survival model diagrammatically as shows Figure 1. An individual is, at any time, in one of two states, “Alive” or “Dead”. Figure 1 The alive-dead model Source: Own processing Transition from state ‘0’ to ‘1’ is allowed, as indicated by the direction of the arrow, but transition in the opposite direction is not possible. We can use this simple two state model to reformulate the survival model such as we define a random variable )(tY which takes one of the two values ‘0’ and ‘1’. Suppose we have an individual aged x years at time t=0. The event 0)( =tY means that an individual is alive at age x+t, and 1)( =tY means that an individual died before age x+t. The set of random variables { } 0)( ≥ttY is an example of a continuous time stochastic process. We will assume that { } 0)( ≥ttY is a Markov process. The alive-dead model represented by Figure 1 captures all the life contingent information that is necessary for calculating insurance premiums and policy values. The force of mortality 01 xµ fully describes the lifetime distribution. But there are more complicated insurance policies which require more sophisticated models. These policies consist of a finite set of states with arrows indicating possible movements between them. Each model appropriate for a given insurance policy is constructed in a similar manner. Figure 2 The disability income model Source: Own processing Alive 0 Dead 1 Healthy 0 Sick 1 Dead 2 161 The condition for a payment relating to the policy, for example a premium, sum insured, is either that the individual is in a specified state at that time or that the individual makes an instantaneous transfer between a specified pair of states at that time. The disability income insurance pays a benefit during periods of sickness, the benefit ceases on recovery. Figure 2 shows a model suitable for policy which provides an annuity while person is sick, with premiums payable while the person is healthy. The model represented by Figure 2 differs from that in Figure 1 in one important aspect: it is possible to transfer from state ‘1’ to state ‘0’, that is, to recover from an illness. This model illustrates an important general feature of multiple state models that is the possibility of entering one or more states many times. This means that several periods of sickness could occur before death, with healthy (premium paying) periods in between. This model has three states, and we can define a continuous time Markov process, { } 0)( ≥ttY , where random variable )(tY takes one of the value ‘0’, ‘1’ and ‘2’. Other extension of the model illustrated in Figure 1 is a multiple decrement model. A multiple decrement model is characterized by having a single starting state and several exit states (absorbing states), but no further transitions. Figure 3 illustrates a multiple decrement model with n+1 states. A policyholder is supposed to be healthy at the time of the commencement of the policy and he/she stays in this state until at some time he/she transits to one of the n possible exit states that means a death or a critical illness occurred. Figure 3 A multiple model with several exits Source: Own processing In general case, with states 0, 1, 2, …, n, we refer to ij xµ as the force of transition or transition intensity between states i and j at age x. The transition intensities are fundamental quantities which determine everything we need to know about a multiple state model. Consider an insurance policy issued at age x and with term m years described by a multiple state model with n+1 states, labelled 0, 1, 2, …, n. Let ij yµ denote the transition intensity between states i and j at age y, tδ denote the force of interest per year at time t, )(i tB denote the rate of payment of benefit at time t while the policyholder is in state i, )(ij tS denote the lump sum benefit payable instantaneously at time t on transition from state i to state j. Healthy 0 Exit 1 Exit 2 Exit n. . . . 162 Then the policy value )(i tV for a life in state i at time t is given by the Thiele’s differential equation ( )∑≠= + −+⋅−−⋅= n ijj i t j t ij t ij tx i t i tt i t VVSBVV t ,0 )()()()()()( d d µδ , (1) for i = 0, 1, …, n and 0 < t < m. We assume that )( , i tt Bδ and )(ij tS are continuous function of t. The premium is included within this model as negative benefit and expenses can be included as addition to the benefits. We can use formula (1) to calculate policy values numerically. We choose a small step size h and replace the left-hand side of (1) by h VV i ht i t )()( −− . We will then use Euler’s method (starting with 0)( =i mV ) to calculate the policy values at durations m-h, m-2h, …, h, 0. 3 Results Consider the model (Figure 4) for an insurance policy combining disability income insurance benefits and critical illness benefits. An insurance company issues a policy with term 40 years to a life aged 25 which provides a death benefit, a disability benefit and a critical illness benefit as follows: • a lump sum payment of 200 000 USD is payable immediately on the life becoming critically ill, • a lump sum payment of 100 000 USD is payable immediately on death, provided that the life has not already been paid a critical illness benefit, • a disability income annuity of 25 000 USD per year payable whilst the life is disabled payable continuously, with no lapses and no expenses. (Expenses can be included in as additions to the benefits) Premium is payable continuously provided that the policyholder is healthy. We assume an interest rate of 2 % p.a. 163 Figure 4 Schema of our model Source: Own processing For calculation we use transition intensities from the CMI Working paper 50. From these data we apply our model for particular critical illnesses: cancer and stroke for female population. The transition intensities are as follows: { }xx ⋅⋅⋅+⋅= −− 14,0exp105,3104 6401 µ , x x 094174,1106,7105 5402 ⋅⋅+⋅= −− µ , (Gompertz-Makeham’s law of mortality) { }xx ⋅+−= 09313,0425,13exp03 µ , for Stroke and { }xx ⋅+−= 08347,0135,10exp03 µ , for Cancer, 0110 1,0 xx µµ ⋅= , 0212 xx µµ = , 0313 xx µµ = . Thiele’s differential equations for )0( V t and )1( V t are       −⋅ + −      −⋅ + −      −⋅ + −+⋅= )0( 00020003 25 )0( 00010002 25 )0()1(01 25 )0()0( d d V tt V tt V t V tt PV tt V tt µµµδ , (2)       −⋅ + −      −⋅ + −      −⋅ + −−⋅= )1( 00020013 25 )1( 00010012 25 )1()0(10 25 00025 )1()1( d d V tt V tt V t V tt V tt V tt µµµδ .(3) Using Euler’s method with a step size 12 1 =h and with the boundary conditions 0)1( 40 )0( 40 == VV we calculate the policy values. Healthy 0 Sick 1 Dead 2 Critically ill 3 164 These equations we solve by using Excel build-in tool “Solver”. Requiring )0( 0V to be equal to 0 (using equivalence principle) gives PS = 1 435.59 USD for stroke and PC = 1 773.26 USD for cancer. 4 Conclusions We have presented an application of multiple state models to problems in actuarial science. There are various extensions of multiple state models. One way is to allow the transition intensities out of a state to depend not only on individual’s current age but also on how long they have been in current state. This breaks the Markov property assumption and leads to the new process known as a semi-Markov process. This could be appropriate for the disability income insurance process where the intensities of recovery and death from the sick state could be assumed to depend on how long the individual had been sick, as well as on current age. For the numerical solution of differential equations we used Euler’s method. Its advantage is that it is relatively simple to implement. There are more sophisticated ways of solving such equations, for example the Runge-Kutta method. The transition intensities are fundamental quantities which determine everything we need to know about a multiple state models. Therefore it would be useful to have data from domestic insurance industry. Our further research will focus on estimation of transition intensities for the Czech Republic (or other central European countries) in similar manner as in Pacáková, V., Jindrová, P., Seinerová, K. (2013). There is a need for awareness of model risk when assessing an insurance policy combining disability income insurance benefits and critical illness benefits, especially with long term. The fact that transition intensities can be estimated does not imply that they can sensibly describe future medical development. Acknowledgments This research could be performed due to the support of the University of Pardubice student project grant no. SGS_2017_022 (Faculty of Economics and Administration). References Continuous Mortality Investigation Committee (2011). Working Paper 50 - CMI critical illness diagnosis rates for accelerated business, 2003-2006. Institute and Faculty of Actuaries. Dickson, D. C., Hardy, M. R., Waters, H. R. (2013). Actuarial mathematics for life contingent risks. Cambridge University Press. Gogola, J. (2014a). Stochastic Mortality Models. Application to CR mortality data, Mathematical and Statistical Methods for Actuarial Sciences and Finance, pp. 113-116. Haberman, S., Pitacco, E. (1998). Actuarial models for disability insurance. CRC Press. Pacáková, V., Jindrová, P. (2014). Quantification of Selected Factors of Longevity,” Proceedings of the 2014 International Conference on Mathematical Methods in Applied Sciences (MMAS’14), Saint Petersburg State Polytechnic University, pp. 170 – 174. Pacáková, V., Jindrová, P., Seinerová, K. (2013). Mortality Models of Insured Population in the Slovak Republic. In: Proceedings of the 7th Professor Aleksander Zelias International Conference on Modelling and Forecasting of Socio-Economic Phenomena. Zakopané, pp. 99-106. 165 Impact of R&D investments on earnings predictability Grabińska Barbara1 , Grabiński Konrad2 1 Cracow University of Economics Department of Finance Rakowicka Street 27, 31-510 Cracow, Poland E-mail: grabinsb@uek.krakow.pl 2 Cracow University of Economics Department of Financial Accounting Rakowicka Street 27, 31-510 Cracow, Poland E-mail: kg@uek.krakow.pl Abstract: The aim of this paper is to empirically investigate the impact of R&D investments on earnings predictability. R&D investments are considered to generate one of the most valuable companies’ assets in the era of knowledge economy. This type of investment is however very different from more traditional forms of capital or financial investments. Firstly, R&D projects create more informational asymmetry between a reporting entity and investors. Information about firm’s innovation activities is confidential and expected to be hidden from competitors and as a result disclosure level of R&D intensive firms is supposedly low. Secondly, technological and market outcomes of R&D are usually associated with uncertainty and it is very difficult to determine market success of invented products, innovative services and other research accomplishments. On the basis of these two assumptions, we hypothesize that earnings of R&D intensive firms are less predictable. On the sample of more than 900 firms listed on US stock exchange, we examine the relation between firm’s R&D intensity and earnings predictability, controlling for firm’s profitability, leverage ratio, size and industry affiliation. For measuring the predictability of earnings, we use accounting based metrics proposed by Francis et al. (2006) and Lipe (1990). Earnings predictability is very desirable property of company required by analysts and investors, our findings can have practical implications for estimating cost of capital and valuation of R&D intensive firms. Keywords: R&D, earnings predictability, intangible assets, R&D determinants, R&D disclosure JEL codes: G30, G32, O32 1 Introduction Intangible investment is a broad concept, which consists of several categories of expenditures like: employee training, brand enhancement, software development, building customer relationships and many more. Accounting rules put special emphasis on R&D investments requiring disclosing specific information in financial reports while other intangible investments are generally aggregated with other expense items and usually are not visible separately in financial statements (Guo et al., 2006). In consequence R&D outlays are supposedly one of a very few categories of intangibles reported directly in financial statements and can be easily investigated from researcher’s point of view. R&D investments are considered to generate one of the most precious assets in the economy. Companies put a lot of effort to invent not only new products and services, but also improvements in all aspects of company’s activity like logistics, distribution, marketing, H&R, etc. (Fagerberg et al., 2009). Nowadays this type of investments involves a lot of resources and at the same time is very different from typical capital or financial investments like inputs in property, plant and equipment or governmental bonds. This distinction of intangible investments is especially important and troublesome for financial reporting. 166 The first and the most significant difference is uniqueness of R&D investment. As Aboody and Lev (2000) point out, radically new drugs under development or software programs are unique for the firm while other investments usually share common characteristics across companies within an industry. Similarly, the knowledge about R&D output and its profitability in one company is not very useful in estimating the output and profitability of R&D project in another firm, even in the same industry. As a result it is very difficult to estimate market success of invented products, services and other research accomplishments. The second distinguishing feature and specificity of R&D investment, related to financial reporting, is informational asymmetry. As Zhao (2002) points out, information about research activity is confidential and is expected to be very hard to copy for competitors. Inventions are guarded internally by corporate procedures and outside the company by patents, trademarks, etc. Language used by scientists and inventors is very technical and hardly understandable for accountants. Therefore reporting R&D activity is a matter of disclosure requirements from one side and reluctance or even inability to report required information. This is the main reason of the underreporting of R&D expenditures. The third problem in measuring the research output is that there is no organized market for R&D. Market prices serve an important role in the accounting process of asset recognition and measurement (marking-to-market). Physical and financial asset are usually standardized, homogenous products for which market prices are available to the public and this is not possible for R&D. So far at least several studies have investigated the impact of R&D investment on the reported earnings, especially company’s profitability and qualitative characteristics of earnings. One strand of research is focused on the relation between R&D expenditures and future profitability (Bublitz and Ettredge, 1989; Sougiannis, 1994, Ballester, GarciaAyuso, Livnat, 2003). The other group of studies is related to qualitative characteristics of earnings like value relevance and earnings management. Some of them documented a positive relation between R&D spending measures as current R&D intensity or growth and stock returns or market value (Hirschey and Weygandt, 1985; Shevlin, 1991; Lev and Sougiannis 1996; Chan et al. 2001; Chambers et al. 2002; Penman and Zhang 2002; Healy et al., 2002; Eberhart et al. 2004). Results of most studies suggest that information on R&D expenditures/assets is value relevant for investors. The others investigate the relation between R&D and earnings management (Perry and Grinaker, 1994; Mande et al. 2000; Seybert, 2010; Markarian et al. 2008). But there is still a paucity of research that explores other qualitative characteristics like earnings predictability. Our study aims to fill this gap and test what is the impact of R&D investments on earnings predictability. Predictability is a very desirable property of earnings required both by analysts and investors. Lipe (1990) defines this as the ability of earnings to explain themselves. High predictability means that historical earnings are good estimates of current ones. Dichev and Tang (2009) point out that time-horizon of prediction is very limited, usually no more than one year. Conceptually there are two methodological approaches to predict future earnings. The first method is based on historical accounting data and time-series analysis (regression) and for this reason the measure of earnings predictability is called accounting-based. The second approach uses measures based on forecasts of market analysts. Das et al. (1998) note that this approach is superior because analysts’ forecasts encompass much wider spectrum of publicly available information than reported in financial statements. Hope et al. (2006) argue that low disclosure level negatively impacts earnings predictability what may translate into higher cost of capital and impairment of shareholder value. Furthermore they theorize that a low level of disclosure and unpredictable earnings are symptoms of informational asymmetry between management and shareholders. The level of earnings predictability is the function of earnings 167 surprises. The nature of R&D investments suggests that they may be a source of these surprises. Disclosure level of R&D intensive firms is supposedly lower and informational asymmetry is higher. Amir and Livne (2005) compare riskiness of investment in R&D to a football player contract. The output of research projects is in most cases very unpredictable and may result in a big success, but equally likely in failure. Investing in R&D resembles gambling what also should translate in less predictable earnings. The results of other studies provide more arguments supporting the above hypothesis. For example studies of Berk et. al (2002) and Ho et al. (2004) shows that investors require a higher rate of return from investments in R&D intensive firms. Another study of Kothari et al. (2002) documents that the future earnings stream generated by R&D investments is more uncertain than that generated by investments in tangible long-lived assets. The luxury to invest in research projects is reserved only for companies with a stable cash position. R&D costs are usually one of the first to be cut in the time of financial stress. Hence companies with worse economic condition and less predictable earnings are less willing to invest in intangibles. Company’s cash position and its profitability potentially may be important factors influencing relation between R&D spending and earnings predictability and that is necessary to control for them in our study. Another important feature of R&D project is the time needed for a result to show off. It takes on average two years from the completion of the project to its commercial launch. There are few studies related to this issue, but most of them suggest that the result of a research project is determined after the period of two or three years (Lome et al., 2016; Leonard, 1971; Rapoport, 1971; Pakes and Shankerman, 1984). For the purpose of our research we assume that on average it will be a two-year period of time. For this reason we formulate the following hypothesis: R&D investments negatively impact earnings predictability after a two-year period. Characteristics of the reporting entity have influence on the relation between R&D spending and the probability of its success. More than 70 years ago Schumpter (1942) noticed that large companies are better prepared to commercially exploit research inventions. Cifti and Cready (2011) provide empirical evidence that larger firms are able to generate more value per dollar invested in R&D and that they cause less earnings variability. They explain this phenomenon by a greater ability of larger firms to: (1) assure commercial success of newly invented products, (2) diversify R&D investments risks and (3) achieve spillover effect and (4) “cost spreading” advantages of R&D project. The latter one refers to the ability of larger companies to spread the R&D costs across larger sales bases and usually larger firms have more products, services and scale of operations, which can benefit from R&D inventions. Another phenomena – a “spillover effect” is defined as the ability of one discovery to stimulate breakthroughs in other related areas. The results of this study reveal a very important distinction. The size of the company may not be an important factor for inducing production of new inventions, however it is crucial to bring such innovations to the market and realize high profit margins. For this reason in our study we need to control for firm’s size. Another characteristic of high R&D companies is reluctance of bank sector to invest in R&D intensive companies. Barclay et al. (1995) document that companies in high R&D industries like biotechnology, pharmaceuticals, computers, software, etc. finance their operations mostly with equity. As a result we can expect lower R&D intensity in high leveraged firms. In our study we assume that: firstly, investments in research projects create more informational asymmetry between reporting entity and investors and secondly, technological and market outcomes of such projects are in majority of cases difficult to predict. On the basis of these we theorize that earnings of R&D intensive firms are less predictable. In other words we hypothesize that level of R&D investments is a negative determinant of earnings predictability. 168 2 Methodology and Data In order to test our hypothesis, firstly we need to measure predictability of earnings. We use accounting based metric of earnings predictability and follow in this regard model proposed by Francis et al. (2004) and Lipe (1990): NI t + 1 = α + β NIt + εt (1) where: NI – net income The measure of earnings predictability (EP) is estimated by regressing earnings in time t on historical data using the following formula (autoregressive model): + ,- ε (2) The lower the value of P the higher earnings predictability of earnings. We estimate P for each firm-year observation. In order to normalize and make earnings predictability measure more usable in the regression analysis, we use EP measure as a natural logarithm of P. We calculate earnings predictability using time-series regression on historical accounting earnings for ten-years period (2007–2016). We eliminate companies for which we have less than nine observations in time-series. For each company we obtain two measures of earnings predictability for the year 2016. One measure is calculated on the basis of the net income before tax (NI_BT) and the second measure on the basis of net income after tax (NI_AT). We also use two measures of R&D intensity we are commonly used in accounting research. The first (RD_INT1) is calculated as R&D expenses to sales and the second one (RD_INT2) as R&D expenses to total assets. We test our hypothesis using the following model: EPi,t = RD_INTi,t-2 + SIZEi,t + LEVi,t + ROAi,t + INDi,t (3) where : EPi,t – measure of earnings predictability for i-firm in t time; RD_INTi,t-2 – R&D intensity for i-firm in t-2 time measured as R&D expenses to sales (RD_INT1) or R&D expenses to total assets (RD_INT2); SIZEi,t – firm’s size measured as natural logarithm of total assets; LEVi,t – leverage of i-firm in t time measured as total debt to total assets; ROAi,t – i-firm’s profitability in t-time measured by return on assets (two measures, the first one ROA_BT is calculated on the basis of net income before tax and the ROA_AT – is calculated using net income after tax); INDi,t – firm’s sector affiliation. In model we use values of all variables for the year 2016 except for R&D intensity, for which we use two-years lagged values - for the year 2014. We use OLS regression to test if R&D intensity is a significant determinant of earnings predictability. Our sample consists of US stock listed companies. US GAAPs create a very unique regulatory setting with a conservative approach to R&D disclosure and require full expensing all R&D costs in income statement (with very few exceptions - e.g. software). In consequence it is possible to determine the level of R&D investment for each company-year observation. Our initial sample consists of 10 003 US listed companies. We eliminate banks and financial institutions and observations with negative value of equity. Due to lack of available data required to calculate earnings predictability measure and R&D intensity ratio we end up with 943 firms for RD_INT1 and 909 firms for RD_INT2. 169 3 Results and Discussion Correlation analysis presented in Table 1 and Table 2 shows that in most cases there is no strong correlation between variables used in the model. The only exception is correlation between firm’s size and earnings predictability, what was previously expected given the Cifti and Cready (2011) theory. Additionally correlation analysis demonstrates that correlation between two R&D intensity measures (RD_INT1 and RD_INT2) is high (77%) but at the same time it demonstrates that they are not identical. Table 1 Correlation matrix for variables EP and ROA before tax Variables EP_BT RD_INT1t-2 RD_INT1t-2 SIZE LEV ROA_BT EP_BT 1.000 RD_INT1t-2 -0.157 1.000 RD_INT2t-2 -0.208 0.770 1.000 SIZE 0.889 -0.314 -0.362 1.000 LEV 0.403 -0.176 -0.152 0.422 1.000 ROA_BT 0.237 -0.578 -0.508 0.412 0.004 1.000 Source: authors’ own elaboration Table 2 Correlation matrix for variables EP and ROA after tax Variables EP RD_INT1t-2 RD_INT1t-2 SIZE LEV ROA EP 1.000 RD_INT1t-2 -0.132 1.000 RD_INT2t-2 -0.182 0.770 1.000 SIZE 0.880 -0.314 -0.362 1.000 LEV 0.400 -0.176 -0.152 0.422 1.000 ROA 0.211 -0.576 0.505 0.410 0.012 1.000 Source: authors’ own elaboration In Table 3 descriptive statistics are presented. Some of variables were tailored and extreme values were cut off in order to eliminate outliers. Table 3 Descriptive statistics of variables Variables Min. Max. Mean Median St. Dev. Variance Skewness Kurtosis EP_BT 0.759 16.928 10.181 10.185 2.065 4.265 -0.041 3.217 EP_AT 0.751 16.600 10.229 10.243 1.966 3.697 -0.073 3.457 RD_INT1t-2 0.000 1.000 0.157 0.060 0.260 0.068 2.518 8.223 RD_INT2t-2 0.000 1.000 0.108 0.045 0.176 0.031 3.093 13.465 SIZE 0.000 1.061 13.479 13.715 2.441 5.959 -0.250 3.129 LEV 0.000 0.992 0.522 0.539 0.262 0.069 -0.230 2.129 ROA_BT -1.000 1.000 0.001 0.032 0.214 0.046 -2.089 13.080 ROA_AT -1.000 1.000 -0.018 0.030 0.221 0.049 -2.080 12.284 Source: authors’ own elaboration 170 In order to check robustness of our results we test our hypothesis using two measures of earnings predictability as dependent variables (EP_BT and EP_AT) and two measures of two-years lagged R&D intensity (RD_INT1 and RD_INT2). As a result we performed four regression analyzes reflecting all possible combinations (see Table 4). Results of the regression analysis provide evidence that higher R&D investments lead to lower earnings predictability. In all combinations of EP and R&D intensity measures the latter one is a significant negative determinant of earnings predictability (the higher the value of EP the lower earnings predictability). Table 4 Results of regression analysis for combinations of EP and RD_INT dependent vaiable independent vaiable Results of regression analysis Coeff Std. error t-statistic p-value RD_INT1t-2 EP_BT 0,28852 0,14184 2,03 ** 0,042 RD_INT2t-2 EP_BT 0,79350 0,25482 3,11 *** 0,002 RD_INT1t-2 EP_AT 0,34581 0,14852 2,33 ** 0,020 RD_INT2t-2 EP_AT 0,90944 0,26640 3,41 *** 0,001 No. of obs. Prob > F R- squared Adj. Rsquared Root MSE RD_INT1t-2 EP_BT 909 0,000 0,819 0,818 0,857 RD_INT2t-2 EP_BT 943 0,000 0,818 0,817 0,861 RD_INT1t-2 EP_AT 909 0,000 0,793 0,793 0,897 RD_INT2t-2 EP_AT 943 0,000 0,794 0,793 0,897 Notes: *** Significance at 1% level; ** significance at 5% level; * significance at 10% level. Source: authors’ own elaboration The results of the study for other – control variables reveals interesting and intriguing relations. Firstly, the theory formulated by Cifti and Cready (2011) postulates that larger firms are more able to control and exploit the benefits of R&D investments. We expected a positive relation between the size and earnings predictability. Unexpectedly regression analysis showed a negative relation, suggesting that the larger company the less predictable earnings are. Secondly, we noticed that leverage ratio is negatively correlated with R&D intensity (see Table 1 and Table 2), what is consistent with the observations that high-R&D companies finance their activity mostly with equity. However when we control for R&D intensity, regression analysis shows that the more indebted company the less predictable earnings are. The third control variable is consistent with our predictions and common sense. Regression analysis demonstrates that firm’s profitability is a positive determinant of earnings predictability. Output of our empirical study also confirms that sector affiliation is an important control variable. 4 Conclusions Our empirical findings provide evidence that the level of two-year lagged R&D investments is a negative determinant of current earnings predictability. We tested our hypothesis on the sample of more than 900 US listed companies using different measures of earnings predictability and R&D intensity. We learn from our study that profitability is a positive determinant and leverage ratio and firm’s size are negative 171 determinants of earnings predictability. The latter one is difficult to explain and needs further investigation. Additionally the regression analysis shows that industry affiliation plays an important role in shaping earnings predictability. Our study has several limitations. Firstly, we use only accounting-based measures of earnings predictability, which does not provide a complete picture of the problem. Further investigation is required to test the impact of R&D investments on earnings predictability using analysts’ forecast measures. Secondly, we use small sample covering a short period of time. Extending time-horizon of the study would provide more convincing arguments. Thirdly, future research should extend the analytical framework of this study by including other variables, which potentially may influence quality of earnings, like for example audit quality (BIG-4 auditor) and influence of macroeconomic factors (i.e. financial crisis). Our study provides a new insight into the nature of R&D investments showing its negative impact on earnings predictability. Empirical results may be of use for company’s valuation process and financial analysts. 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The Accounting Review, vol. 66, pp. 44–69. 174 Financial resources of local government units in respect of the financial perspective of the European Union Ewa Gubernat University of Economics Department of Finance, Faculty of Economics Komandorska 118/120, 53-345 Wroclaw, Poland E-mail: ewa.gubernat@ue.wroc.pl Abstract: Finance is the basis for the implementation of public tasks and determines the conditions of local economic development. One of the key issues relevant for local and regional development is the local government unit’s capacity in investment activities. They are the most important category of beneficiaries of EU funds. Based on literature review, I have posed the following research hypothesis: there are four key indicators that determine the absorption capacity of the local government units as a beneficiary of EU funds. I have also analysed the relationship between total budget revenue, budget expenditure, results achieved and debt. I have found that: absorption capacity is closely correlated between the financial condition and the state of local finances, what means that changing in the financial system of local governments units does not affect the absorption capacity and can significantly reduce it. Data were obtained from the GUS Local Data Bank and studies by the Ministry of Finance, as well as data from the National Regional Councils of the Accounting Chamber, covering years 2007-2013. Keywords: local government unit, absorption capacity of local government unit JEL codes: H72, H77, F42 1 Introduction Finance is the basis for the implementation of public tasks and determines the conditions of local economic development. One of the key issues relevant for local and regional development is the local government unit’s capacity in investment activities. They are the most important category of beneficiaries of EU funds. Nowadays, one of the basic problems of functioning of local government units is the limited financial resources in relation to the scope of current tasks and reported investment needs. The rate of socioeconomic development depends on the level and structure of income, which determines the investment activity of local government units [Poniatowicz, 2016]. In period 2007- 2013, local government units received 17.3 bilion EUR, which constituted about 25% of all EU funds allocated to Poland [Raport…, 2013]. Approximately 80% of the funds raised by local governments are investment subsidies, which are an important source of financing local government investment [Kobiałka, 2013]. The aim of this paper is to identify the current key components that determine the absorption capacity of the local government units as a beneficiary of EU funds. The author is also trying to answer the question: how important it was to support EU funds in the implementation of the investment and how much caused the deterioration of the financial situation of local government units? This problem is important not only from the point of view academic discussion, but also has many practical and political implementations. The unfavorable economic processes that have emerged in recent years in the polish local finance system have not affected the absorption capacities of local government units and can significantly reduce them. In this sense, the following determinants are important: limited stability and revenue efficiency, additional government performance without adequate financial compensation, and restrictive local debt monitoring system. Absorption capacity is seen as the ability of the region to make efficient use of external financial resources and is essential to achieving economic and social cohesion [Brdulak, 175 2001]. Demand Absorption Capacity refers to the actual capacity of applicants for support to create good projects. Supply Absorption Capacity means financial capacity to cofinance programs and projects supported by the European Union. Based on the research, it should be underlined, that local government units are a large group of beneficiaries of EU funds, [Kornberger-Sokołowska, 2010]. Assuming that, in the local finance system, there are four most important key components deciding on the capacity of absorbing local governments as beneficiaries of EU funds. These are: budget revenue, budget expenditure, results achieved and debt. I have posted the following research hypothesis: (H1) absorption capacity depends on the level of budgetary income and expenditure (including current and capital), budgetary performance (general and operational), and local government debt. Wojciechowski [2012] found, that the state of public finances allows to assess the functioning of the local government unit and its development possibilities. The level and structure of income that determines investment activity is decisive. Research showed, that total income is a strategic element in the local government finance system, not only from the point of view of the financial autonomy of local government units but also in the context of the possibility of applying for EU funds. The resources of own funds of local government units determine the possibility of using other, supplementary sources of financing [Sierak, 2015], what was confirmed also in other studies [Gonet 2013]. The limited stability of income of local government unit is due, inter alia, to frequent changes in the legal regulations related directly or indirectly to the local government unit. On the other hand, the yield of local government unit income sources is primarily influenced by macroeconomic factors, including economic fluctuations. Another important factor negatively affecting local government finances is the commissioning by the state of additional tasks to local government units without guaranteed financial sources. The result of such actions is primarily the increase of local government current expenditures and, consequently, the need to reduce capital expenditures, including investment ones, also cofinanced from EU. The next issue, significant from the point of view of absorption capacity, is the introduction of new debt limits, called as new debt limits, from January 2014 to the Polish local government system. Individual debt ratio (IWZ), as laid down in Art. 243 of the Public Finance Act of 2009 (Act, 2009). The new indicators are based on the operating surplus category and are much more restrictive than the existing regulations limiting the debt of local government. Taking into account that repayable instruments (loans, municipal bonds) are treated by the local government units as an absorption tool, all mechanisms controlling the debt are at the same time mechanisms regulating absorption capacity. In the context of the tightening of the mechanisms for regulating local government debt, it is highly probable that some territorial government units may have a difficult access to financial resources from the European Union, primarily due to lack of capacity to supplement the own financial sources. 2 Methodology and Data My hypothesis requires the analysis on national level. In order to measure the level of absorption of EU funds I have employed total income and total EU-CAPEX income, and for expenditure – total capex expenditure, debt level caused by the implementation of capex expenditure, due to the fact, that most studies confirmed, that these factors are most important determinant at EU funds absorption. Every variables are in bln PLN. I have used a descriptive statistics method, using data from national accounts, covering the years 2007-2013. Data were obtained from the GUS Local Data Bank and studies by 176 the Ministry of Finance, as well as data from the National Regional Councils of the Accounting Chamber. 3 Results and Discussion Important factors of effective absorption of EU funds are the factors resulting from the overall financial condition of local government units. Their measure is the financial capacity of local government units to guarantee adequate funds for the implementation of investment projects. Financial capacity is characterized by the level of income, the level of co-financing, the operating result, the deficit. My analyses has shown the following conclusions, table 1. Table 1 Funds for financing and cofinancing of EU programs and projects in total income of local government units in the years 2007-2013 2007 2008 2009 2010 2011 2012 2013 Total INCOME, BLN PLN 131,38 142,57 154,84 162,80 171,31 177,41 183,46 EU-CAPEX INCOME, BLN PLN 6,62 5,45 14,51 13,79 16,35 17,08 16,01 CAPEX EXPENDITURE, BLN PLN 24,6 30,8 41,6 43,3 41,2 34,4 33,5 EU-CAPEX INCOME/TOTAL INCOME, % 5,03 3,82 9,37 8,47 9,54 9,63 8,73 Source: own study based on the Local Data Bank 2007-2013 Over the seven-year period, funds received annually by the local government units related to the implementation of EU programs and projects increased three times from the level of 6.6 billion PLN in 2007 to 16.01 billion in 2013. The share of EU subsidies in total incomes was different in different years (the lowest level was recorded in 2008 - 3.82%, the highest in 2011 - 9.54%). The share of investment expenditures makes it possible to draw conclusions about the freedom of local government administration. It can be considered that during the period 2007-2010 investment expenditures did not show any fluctuations in business conditions. From 2011 there is a decrease in investment expenditure, especially in 2012-2013. Limited investment scale was characteristic for the whole sector. It is also important to indicate how the debt of the local government unit was developed for programs and projects co-financed by EU funds, presenting table 2. Table 2 Debt of local government units for programs and projects co-financed by EU funds in the years 2007-2013 2007 2008 2009 2010 2011 2012 2013 Total DEBT, BLN PLN 25,88 28,76 40,29 55,09 65,76 67,84 69,16 EU-CAPEX DEBT, BLN PLN 2,38 2,06 2,42 4,95 6,96 6,44 10,21 EU-CAPEX DEBT/TOTAL DEBT, % 9,20 7,16 6,00 8,99 10,58 9,49 14,76 Source: own study based on the Local Data Bank 2007-2013 At present, the share of total indebtedness does not exceed several percent, and in the analyzed period it was the highest in 2013 (14,76%), the lowest in 2009 (6%). The reason for the increase in the debt of local governments is also the new regulation 177 introduced in the Act on Public Finance. It mobilized local governments to intensify their indebtedness to old rules. The remaining, important determinants of absorption capacity and investment capacity are general and operational budgetary outcomes. Table 3 presents changes in the general performance of local government unit (budget deficits / budget surpluses), collectively, in 2007-2013. The construction of an individual debt ratio depends on the operating margin, so the presented results are very relevant in terms of absorption capacity. On the basis of the data presented, one can postulate a thesis about systematically improving, roughly from 2011, aggregate operational results of local government in Poland. This is mainly the effect of the rule of at least balanced current balance of the current budget, effective since 2011. (Article 242 paragraph 1 of the Public Finance Act ). Table 3 Deficit and budget surpluses of local government units in period 2007-2013 2007 2008 2009 2010 2011 2012 2013 Total DEFICIT, BLN PLN 2,27 -2,62 -12,99 -14,97 -10,29 -3,05 -0,38 BUDGET SURPLUSES, BLN PLN 17,72 17,74 11,96 9,28 10,99 11,63 14,34 EU-CAPEX DEBT/TOTAL DEBT, % 9,2 7,1 6,0 9,0 10,6 9,5 14,8 Source: own study based on the Local Data Bank 2007-2013 4 Conclusions European Union funds are a significant source of funding undertakings in the area of local and regional development. Absorption capacity is perceived as the ability of the region to make effective use of external funds and depends on the conditions of making available these resources and on the level of socio-economic development and financial condition of local government units. In the financial economy in 2007-2013 there were disturbing phenomena that affected their financial standing. This can significantly reduce the possibility of raising funds from the EU in the future, because the capacity of the local government unit to apply for EU funding depends on the possibility of providing own. The most important threats include the reduction of financial autonomy of local governments and the reduction of their ability to finance investments. Consequently, the scale of local government investment has been reduced. Especially important is the increase in total indebtedness of local government units. However, the European Union does not fund projects, but it subsidizes, requires a specific contribution from the beneficiary. Growing budget deficit led to a significant increase in the level of debt. However, on the other hand, the limited total income of local governments units increases the demand for debt. Debt is also an indispensable instrument to increase the absorption capacity of local government units. Finally, my conclusions are following: • absorption capacity of local government units is determined by the financial condition and state of local finance. This applies to budgetary revenue and expenditure (including current and capital / investment), budgetary performance (general and operational), and local government debt. • the implemented investment activity, supported by the EU investment subsidies, did not significantly influence the increase in the debt of the local government sector, • investment expenditures during the analysed period stay resistant for prosperity changes, 178 • in the period 2017-2013, the funds received annually by the local government unit related to the implementation of EU programs and projects increased three times (from the level of PLN 6.6 billion in 2007 to the level of PLN 16 billion in 2013). • changing external conditions (prosperity, the law) may negatively affect the ability of local governments to secure their own funds necessary to absorb European funds in next years. References Brdulak, J. (2001). Problemy rozwoju regionalnego, T. 5, Dom Wydawniczy Bellona, Warszawa, pp. 25. Gonet, W., (2013). Naprawa finansów jednostki samorządu terytorialnego, Difin, Warszawa, pp. 125. Kobiałka, A. (2013) Wykorzystanie funduszy unijnych w finansowaniu wydatków inwestycyjnych jednostek samorządu terytorialnego Lubelszczyzny: Finanse publiczne. Red. J. Sokołowski, A. Żabiński, Wrocław, pp. 196. Kornberger-Sokolowska E., Zdanukiewicz J., Cieślak R. (2010) Jednostki samorządu terytorialnego jako beneficjenci środków europejskich, Oficyna Wolters Kluwer Polska sp. z o.o., Warszawa, pp. 179. Poniatowicz, M. (2016). Stabilność finansowa jednostek samorządu terytorialnego w aspekcie nowej perspektywy finansowej Unii Europejskiej i zmian w systemie dochodów samorządowych, Ekonomiczne Problemy Usług, vol. 125, pp. 8-10. Raport z postępu wdrażania Regionalnych Programów Operacyjnych na lata 2007-2013, (2013). Ministerstwo Rozwoju Regionalnego, Warszawa, Retrieved from: http://www.funduszeeuropejskie.gov.pl/AnalizyRaportyPodsumowania/poziom/Document s/RPO_raport2012_15222013.pdf. Sierak, J. (2015) Finansowanie inwestycji komunalnych w nowej perspektywie funduszy UE 2014-2020. „Samorządowa Akademia Finansów III edycja”. Wojciechowski, E. (2012). Zarządzanie w samorządzie terytorialnym, Warszawa, pp. 234. 179 Taxonomy of EU Member States from the View of VAT Imposed on Immovable Property (year 2017) Dagmar Halabrinová1 , Karel Brychta2 1 Vysoké učení technické v Brně Fakulta podnikatelská Kolejní 2906/4, 612 00 Brno, Česká republika E-mail: xphalabrnova@fbm.vutbr.cz 2 Vysoké učení technické v Brně Fakulta podnikatelská Kolejní 2906/4, 612 00 Brno, Česká republika E-mail: brychta@fbm.vutbr.cz Abstract: The aim of the paper is to identify and systematize selected rules contained in the Council Directive 2006/112/ES on the common system of value added tax relating to taxation of immovable property and subsequently, while using cluster analysis, to divide EU Member States into groups according to VAT tax rates applicable. To identify the relevant legal norms contained in the Council Directive 2006/112/ES the legal regulation as valid and effective as to 01 January 2017 was taken into account. To carry out a classification of the EU Member States, the data published by the European Commission was utilized. For the reasons of cluster analysis, five possible VAT objects were taken into consideration (social housing, renovation and repairing, building land, supplies of new buildings and construction work on new buildings). On the basis of results reached one can observe that there is a relatively high variability in the rules established in legal regulations covering issues connected with immovable property. A presumption as to the utilization of exceptions and discretion given to the Member States can be considered as valid. At the same time the results reached prove that Member States can be divided into several groups that demonstrate high similarity in VAT taxation of immovable property. Key words: European Union, harmonization, immovable property, tax rate, VAT JEL codes: H25, K34 1 Introduction The Council Directive 2006/112/EC of 28 November 2006 on the common system of value added tax, as amended (hereinafter referred to as „Directive“ only) represents a fundamental legal regulation of value added tax (hereinafter referred to as „VAT“ only) in the area of the European tax law. As stated in the Directive, its aim is to contribute to the establishment of an internal market where conditions of competition are not distorted and the free movement of goods and services is not hindered (see item (4) of the Preamble of the Directive). The aim of the Directive is also to eliminate tax evasion or avoidance which occur in connection with the abolishment of internal frontiers (see e. g. items (27), (42), (55) and (59) of the Preamble of the Directive). Despite the existence of many (and in some aspects very strict) rules binding for the Member States there has been a certain level of discretion left. The Directive also lays a huge number of exceptions for many Member States. Above stated can be observed also in relation to the rules stated for the taxation of immovable property at which this paper is aimed. Its purpose is to identify and systematize some rules as rooted in the Directive just for the VAT object given and further more to divide the EU Member States into groups according to the VAT burden imposed on immovable property. 180 2 Methods and data A part which deals with the identification and systematization of fundamental rules as stated in the Directive is based on a qualitative research – namely on a case study while using content analysis of legal documents to gain relevant information. The legal state as valid and effective as to 01 January 2017, if not stated otherwise, has been taken into consideration. Gathered information in connection with the data published by the European Commission (2017) creates the fundament for a cluster analysis. Its aim was to divide EU Member States on the basis of tax rates imposed on different types of transactions connected with immovable property. For the analysis carried out a tax burden for transactions as specified below was taken into consideration: • Social housing; • Renovation and repairing; • Building land; • Supplies of new buildings and • Construction work on new buildings. States which imply several tax regimes (exemption with or without refund of tax paid at preceding stage – zero rate) and/or different types of tax rates (standard tax rate, reduced tax rate and/or super-reduced tax rate) for the same transaction were excluded from the created data matrix. These States were included in one separate cluster for which above stated fact was the feature (see the chapter “Results and Discussion”). For the lack of data available also Luxembourg was excluded. To carry out the cluster analysis for the remaining EU Member States, the Ward method was used. 3 Results and Discussion Fundamental terms connected with the VAT have been determined partly in the Directive itself and partly in its implementing legal regulation which is the Council Implementing Regulation (EU) No. 282/2011, as amended (hereinafter referred to as „Regulation“). The basic terms and criteria are shown in Table 1 below. Table 1 Fundamental terms Term Definition Specification Building Any structure fixed to or in the ground. Article 12 of the Directive Land on which a building stands Detailed rules can be lay down by a Member States. Article 12 of the Directive Building land Any unimproved or improved land defined as such by the Member States. Article 12 of the Directive Immovable Property a) any specific part of the earth, on or below its surface, over which title and possession can be created; b) any building or construction fixed to or in the ground above or below sea level which cannot be easily dismantled or moved; c) any item that has been installed and makes up an integral part of a building or construction without which the building or construction is incomplete, such as doors, windows, roofs, staircases and lifts; d) any item, equipment or machine permanently installed in a building or construction which cannot be moved without destroying or altering the building or construction. Article 13b of the Regulation 181 Term Definition Specification Services connected with immovable property Services with a sufficiently direct connection the immovable property, e. g.: a) the drawing up of plans for a building or parts of a building designated for a particular plot of land regardless of whether or not the building is erected; b) the provision of on site supervision or security services; c) the construction of a building on land, as well as construction and demolition work performed on a building or parts of a building. Where equipment is put at the disposal of a customer with a view to carrying out work on immovable property, that transaction shall only be a supply of services connected with immovable property if the supplier assumes responsibility for the execution of the work. A supplier who provides the customer with equipment together with sufficient staff for its operation with a view to carrying out work shall be presumed to have assumed responsibility for the execution of that work. The presumption that the supplier has the responsibility for the execution of the work may be rebutted by any relevant means in fact or law. Article 31a of the Regulation Article 31b of the Regulation Source: own elaboration using Directive and Regulation. As to the determination of the place of supply services connected with immovable property the Directive is very strict and logical – it is solely the place where the immovable property is located (see Article 47 of the Directive). In relation to tax rates, the Directive is not so hard and fast. In fact there are set some rules (limitations), however, while respecting them the Member States can state their amount by themselves. Four types of tax rates are used: namely standard rate, reduced rates, zero rate and parking rate (see Article 96 – 122 of the Directive). The aim has been to reach the state where similar goods and services would bear the same tax burden in the territory of particular Member States. A Member State can use so called transition periods – they provide the Member State an extra time for the continuous adoption of domestic legal regulations to the rules stated by the Directive. During this transitional period, certain derogations concerning the number and the level of rates are possible (see item (31) of the Preamble of the Directive). The parking rate is a rate which is used on the basis of the transitional provisions of the Directive which aim is to moderate the impact of tax rate increase (Szarowska, 2009). Until 31 December 2017 the standard rate shall be at least 15 % (Article 97 of the Directive). Reduced rates shall be two maximally (Article 98 of the Directive) not being lower than 5 % (Article 99 of the Directive). Some States apply so called super-reduced rate – that is a tax rate lower than 5 % (see Article 109 – 122 of the Directive). Member States which, at 1 January 1991, were applying a reduced rate to housing may continue to apply such a rate. (Article 114 of the Directive) Member States can also exempt a supply of buildings or parts of a building before their first occupation including the lands on which the building stands and including the building lands. Member States can apply also criterion other than that of first occupation, e. g.: 182 • the period elapsing between the date of completion of the building and the date of first supply provided that this period does not exceed five years; • the period elapsing between the date of first occupation and the date of subsequent supply provided that this period does not exceed two years. In this respect it is worth mentioning that the European Union supports social housing as a service of general economic interest. Charter of Fundamental Rights of the European Union states, “In order to combat social exclusion and poverty, the Union recognises and respects the right to social and housing assistance so as to ensure a decent existence for all those who lack sufficient resources, in accordance with the rules laid down by Union law and national laws and practices.”. (see Article 34 para. 3 of the Charter of Fundamental Rights of the European Union). Legal regulations contained in the Directive seem to be in perfect line with those contained in the Charter of Fundamental Rights of the European Union. Member States may also allow taxable persons a right of option for taxation in respect of following transactions: • the supply of a building or of parts thereof, and of the land on which the building stands, other than the supply referred to under Article 12 para. 1 letter a) of the Directive; • the supply of land which has not been built on other than the supply of building land referred to in point (b) of Article 12(1) of the Directive; • the leasing or letting of immovable property. (Article 137 of the Directive) Totally 11 of all the EU Member States introduced the exemption – it means that supply of the goods and services are exempted from taxation. However, prevailing number of countries does not make refund of tax paid at preceding stage possible (hereinafter referred to as “ex”) in such cases. United Kingdom represents the only state which makes refund of tax paid at preceding stage possible (hereinafter referred to as “0”) with the exception of the sale of building lands. (European Commission, 2017) For more details see Table 2 below. Table 2 Exempted transactions with and without refund of tax paid at preceding stage BE DE CY LU MT AT PT SK FI SE UK Social housing ex ex ex 0 Building land ex ex ex ex ex ex ex ex ex ex Supplies of new buildings ex ex ex ex ex ex ex 0 Construction work on new buildings 0 Source: own elaboration using (European Commission, 2017). Used abbreviations: BE – Belgium, BG – Bulgaria, CZ – Czech Republic, DK – Denmark, DE – Germany, EE – Estonia, IE – Ireland, EL – Greece, ES – Spain, FR – France, HR – Croatia, IT – Italy, CY – Cyprus, LV – Latvia, LT – Lithuania, LU – Luxembourg, HU – Hungary, MT – Malta, NL – Netherlands, AT – Austria, PL – Poland, PT – Portugal, RO – Romania, SI – Slovenia, SK – Slovakia, FI – Finland, SE – Sweden, UK – United Kingdom. The list of supplies of goods and services to which the reduced rates may be applied is determined in the Annex III of the Directive as follows: “provision, construction, renovation and alteration of housing, as part of a social policy”. (item (10) of the Annex III of the Directive); (European Commission, 2016b) VAT reduced tax rate can be applied not only for the construction but also for a renovation of social housing. Tax rates lower than 5 % (super-reduced tax rate) are applied by Estonia, Italy and Luxembourg. (European Commission, 2017) This rate can be used by the Member States which, on 01 January 1993, were obliged to increase their standard rate in force at 01 January 1991 183 by more than 2 %. Member States applying super reduced tax rate are shown in Table 3 below. (Article 114 of the Directive) Table 3 Super-reduced tax rate in % ES IT LU Social housing 4 4 Supply of new buildings 4 3 Construction works on new buildings 4 4 3 Source: own elaboration using (European Commission, 2017). A task of the European Union is to create a VAT system providing a possibility to apply a reduced rate for the construction and reconstruction of social housing by state, social and private providers as a fundamental living need of local nature which does not influence business activities among Member States, neither correct working of internal market. (European Commission, 2012) Reduced tax rate in connection with the support of housing is applied by a half of EU Member States. The lowest one (5 %) is applied in Cyprus, Hungary and United Kingdom. The highest level of the reduced tax rate is applied in the Czech Republic – the tax rate is 15 % (European Commission, 2017). Reduced tax rates used in the EU Member States are show in Table 4 below. Table 4 Reduced tax rate in % BE CZ IE ES FR IT CY HU NL PL PT RO SI UK Social housing 6 12 15 13.5 10 5.5 10 10 5 5 8 6 5 9.5 5 Renovation and repairing of the building for housing 6 15 13.5 10 5.5 10 10 5 6 8 6 9.5 5 Building land 13.5 Supply of new buildings 13.5 10 10 5 8 9.5 Construction work on new buildings 6 12 13.5 10 10 8 6 9.5 Source: own elaboration using (European Commission, 2017). Standard tax rate is used for the supplies for which no preferential regime is applied. The lowest standard rate in the amount of 17 % is used by Luxembourg. The highest one is in Hungary (27 %). Median of the standard tax rates amounts to 21 %. The summary of the standard tax rates is shown in Table 5 and Figure 1 below. Table 5 Standard tax rates in % BE BG CZ DK DE EE IE EL ES FR HR IT CY LV Social housing 20 25 19 20 24 20 25 21 Renovation and repairing of the building for housing 21 20 25 19 20 24 20 25 21 Building land 20 21 25 20 24 21 20 25 22 21 Supply of new buildings 21 20 21 25 20 24 21 20 25 22 19 21 Construction work on new buildings 21 20 21 25 19 20 24 20 25 19 21 184 Source: own elaboration using (European Commission, 2017). Used abbreviations: N/A = not applicable. Ireland is the only EU Member State which does not apply standard rate for any assessed categories of transactions. Most of goods and services in this area are the subject to reduced tax rate in the amount of 13.5 %. Figure 1 Standard VAT rates in 2017 Source: Own elaboration using (European Commission, 2017). Above stated similarity disappears when taking account of tax burden of other types of transactions in connection with immovable property. Based on the results from the cluster analysis, one can observe that EU Member States can be divided into three basic clusters (see Table 6 below). Table 6 Results of cluster analysis States included in the cluster Cluster I AT, BE, ES, FR, IT, HU, NL, PL, PT, SE, SP, UK Cluster II BG, DK, EE, EL, HR, LV, LT Cluster III CZ, CY, FI, GE, MA, SK, RO, IR Source: own elaboration using results of cluster analysis. The first cluster includes the Member States which apply different regimes, eventually different types of rates, for the same transaction (depending on specific conditions of the transaction). Cluster II and cluster III (see also Table 6 above and Figure 2 below) represent Member States which apply one regime/tax rate on particular transactions in question. 27 25 24 23 22 21 20 19 18 17 0 5 10 15 20 25 30 HU DK, HR, SE EL, FI IE, PL, PT IT, SI BE, CZ, ES, LV, LT, NL BG, EE, FR, AT, SK, UK DE, CY, RO MT LU STANDARDRATE[%] LT LU HU MT NL AT PL PT RO SI SK FI SE UK Social housing 21 N/A 27 21 20 20 24 25 20 Renovation and repairing of the building for housing 21 N/A 27 18 21 20 23 23 19 20 24 25 20 Building land 21 27 21 23 19 22 20 Supply of new buildings 21 27 21 20 23 19 22 20 20 Construction work on new buildings 21 17 27 18 21 20 23 23 19 22 20 24 25 20 185 Figure 2 Results of cluster analysis Str. diagram pro 15 případů Wardova metoda Euklid. vzdálenosti 0 20 40 60 80 100 Vzdálenost spoje Malta Finland Germany Slovakia Cyprus Ireland Romania Czech Republic Greece Croatia Denmark Lithuania Latvia Estonia Bulgaria Source: Own elaboration. Member States included in Cluster II are those which apply only one standard tax rate for all types of transactions in question. In this respect, Chalupka (2017) gives that the existence of a single VAT rate is in harmony with the theory of optimal taxation; it leads to the reductions in administrative costs and in tax evasions. EU Member States included in cluster III represent those which apply different regimes/tax rates for particular transactions, however, with no variability as to the regimes/tax rates for a particular transaction. 4 Conclusions As stated by Široký (2008), the essence of the VAT is taxation of added value which is a value that a payer add to the value of purchased goods and services. Thus, taking account of the value of immovable property and connected services, the rules for VAT and tax rates are of great importance. That is to say, the VAT can influence significantly the final price of the immovable property and/or services connected with immovable property. Then, it is a very sensitive political issue, especially in respect of new construction and social housing. From the results reached one can observe that there are significant differences in the rules stated by the Member States for imposing the VAT on immovable property and connected services. It seems that the discretion given by the Directive is widely used. Only several Member States use one tax rate for all the transactions in question. Member States evidently prefer other aspects than those of administrative costs. Some of them even find themselves in breach of the rules in relation to tax rates (European Commission, 2016a). The results reached also suggest that it is possible to distinguish three basic groups according to the VAT rules for the immovable property and connected services. The rules for VAT rates are currently considered as inconvenient for several reasons (for more details see e. g. (European Commission, 2016a)). There are some proposals how to solve it. A relatively progressive proposal stated by the European Commission (2016a) is that “… all currently existing reduced rates, including derogations, legally applied in Member Stated would be maintained, the possibility to apply them could be made available to all Member States”. This solution would be, according to the opinion of the authors of the paper, in line with the principle of prohibition of discrimination. The fact that some Member States met certain conditions in the past has not been a sufficient 186 reason for taking the same advantage from other Member States. However, such a solution could create other serious problems (including those for public budgets). Acknowledgement The paper was supported by internal project Entrepreneurship in the Era of Industry 4.0 (FP-S-17-4634) realized at the Brno University of Technology, Faculty of Business and Management. Reference Chalupka, R. (2017). Tax Reform in Slovakia. Finance a úvěr/Czech Journal Of Economics And Finance, vol.54(9-10), pp. 382-390. Charter of Fundamental Rights of the European Union as published in the Official Journal of the European Union (C 326/39) on 26 October 2012, pp. 391-407. Council Directive 2006/112/EC of 28 November 2006 on the common system of value added tax, as amended. Council Implementing Regulation (EU), No. 282/2011, as amended. European commission (2012). Opinion of the European Economic and Social Committee on ‘Issues with defining social housing as a service of general economic interest’ (owninitiative opinion). Retrieved from: http://eur-lex.europa.eu/legal- content/CS/TXT/?uri=CELEX:52012IE0597. European Commission (2016a). Communication from the Commission to the European Parliament, the Council and the European Economic and Social Committee on an action plan on VAT Towards a single EU VAT area - Time to decide. Retrieved from: http://eur- lex.europa.eu/legal-content/CS/TXT/?uri=CELEX:52016DC0148. European Commission (2016b). Position on the European Commission’s Action plan on VAT – Policy on Reduced Rates. Retrieved from: http://www.europeanhistorichouses.eu/wp-content/uploads/2013/06/EHHA-ELO-EPF- TEGoVA-UIPI-Position-on-EC-Action-Plan-on-VAT-5-July-2016.pdf. European commission (2017). VAT rates applied in the Member States of the European Union. Retrieved from: http://ec.europa.eu/taxation_customs/sites/taxation/files/resources/documents/taxation /vat/how_vat_works/rates/vat_rates_en.pdf. Szarowska, I. (2009). Stav harmonizace daně z přidané hodnoty v Evropské unii. [Status of Harmonization of VAT in the European Union]. Acta academica karviniensia, vol.12(1), pp. 232-243. Široký, J. (2008). Daňové teorie: s praktickou aplikací. [Tax Theories with Practical Applications]. 2. vyd. V Praze: C.H. Beck, 2008. xvi, 301 s. Beckovy ekonomické učebnice. 187 The Impact of E-sales on Birth and Death Rate of Businesses in the Czech Republic Eva Hamplová1 , Jaroslav Kovárník2 1 University of Hradec Králové Faculty of Informatics and Management, Department of Economics Rokitanského 62, 500 03 Hradec Králové, Czech Republic E-mail: eva.hamplova@uhk.cz 2 University of Hradec Králové Faculty of Informatics and Management, Department of Economics Rokitanského 62, 500 03 Hradec Králové, Czech Republic E-mail: jaroslav.kovarnik@uhk.cz Abstract: Since 2016 the Czech Republic has gradually been implementing the system of electronic records of sales of goods and services in cash – ´E-sales´ (Czech abbreviation: EET). It is a modern communication means between entrepreneurs and the Financial Administration of the Czech Republic. The objective of this article is to evaluate, with the support of the latest data by the Czech Statistical Office, the influence of the electronic register on the birth and death rate of businesses in the Czech Republic in 2016. The contribution compares the differences between the number of newly-established and closed business units according to their size, the declared legal form of enterprise, and the basic level of nomenclature of economic activities (CZ-NACE) in the years 2010 – 2016. Methods of comparative analysis and analysis of trends are used. At the end of the paper, thanks to the net balance analysis and the average annual growth rate of new businesses analysis, we disprove the hypotheses about the significant impact of the new system introduction on the number of small and medium-sized businesses in the Czech Republic (mainly natural persons and legal entities – limited liability companies), as well as on the number of active business units according to selected economic activities in which the system was started in the first phase. Keywords: business, business environment, E-sales, national economy JEL codes: L25, L26, O10 1 Introduction On 1st December 2016, a new sales reporting system has been launched in the Czech Republic. The main purpose of the electronic records of sales is for businesses to report their cash sales directly to the tax authority via an online system. Technically, the tax payers’ cash registers have been connected to a server of Ministry of Finance (“MF”), and have to exchange information about sales in real time. The MF server provides a unique transaction code, which has to be printed at each receipt from the cash register. (TPA, 2016) This obligation applies to all business entities taxable by income tax in the Czech Republic, if they receive payments for their goods or services in a different way other than a wire transfer (e.g. cash, a credit/debit card, a check, etc.). The Tab. 1 shows the starting dates for different industrial sectors. 188 Table 1 – Phases of Electronic Records Phase Starting as of Applicable to 1st Phase 1st December 2016 Accommodation and catering services 2nd Phase 1st March 2017 Retail and wholesale 3rd Phase 1st March 2018 Other activities except those included in the 4th phase, for example freelancers, transport, agriculture 4th Phase 1st June 2018 Selected crafts and production activities Source: collection of Laws (2016), Records of Sales Act The biggest group of opponents of this Act are self-employed persons (according to Matzner, 2015), where this author also explains the main difference between the cash register and the E-sales. The cash register saves the data about sales into the internal memory, while system E-sales saves data online into the system of Financial Administration. This solution looks more effective in written form, because it uses the modern technology. However, exactly this moment could be the biggest problem of this method, because it requires not only constant access to these technologies, but also the ability to use them, for both sides – state, as well as the obligated entities. The constant access to the internet enabling the connection with the server of Financial Administration is more or less achievable. However, both large retail chain and small self-employed persons need to get all necessary technical equipment and all required software, and both of them have to learn to use these technologies. This could be the most critical point for small self-employed persons, because this request could have fatal consequences for many of them. The concerns of self-employed persons are understandable, because especially small selfemployed people will be under the greatest administrative and financial pressure, where huge companies will still continue doing tax evasions. Moreover, considering turnover of these companies, these evasions will be probably significantly higher than can be done by any self-employed person. The business entities are one of the most important aspects in modern history. They are a source of basic economic values, they determine the pace of economic and technological development, and they are the object of this development too. Due to employing a large part of the population they play a significant role in social systems of society, affect the character of the regions, the development of society, politics, culture, and many other branches. (Geršlová, 2012) The institutions have the impact on the quality of entrepreneurial activity and they affect the process of economic development. (Acs, Desai, Hessels, 2008) The survey about the number of newly established and closed down business units is one of the most important indicator of the condition and of the level of entrepreneurial environment in macroeconomic context. The annual increase in number of business units can be considered as a signal of climate favourable for entrepreneurship, which consequently helps to develop the business sector, increases the employment, and increases the economy growth as well. (Hamplová, Kovárník, 2016), (Hamplová, Kovárník, Jedlička, 2016) 2 Methodology and Data The internal database of Czech Statistical Office obtained from the Register of economic subjects has been used during the analysis in this article. The information about newly established and closed down business units between 2010 – 2016 has been obtained on the Department of Information Services of Czech Statistical Office. The data are analysed according to the legal form of business units, according to their size (number of employees), and according to the nomenclature of economic activities “CZ-NACE”. Authors analyse only data for the whole Czech Republic, they do not analyse any regional disparities in this article. As far as methodology is concerned, the method of trend 189 analysis has been used for evaluation of number of newly established and closed down business units. This trend analysis means mostly year-on-year growth rate, which measures relative change of analysed variable with respect to previous period. Based on the aim of this article, the value in the year 2016 is crucial. For complex evaluation of the tendency in the number of newly established and closed down business entities between 2010 – 2016 has been used the value of average growth rate as a geometric mean of each growth coefficients. 3 Results and Discussion The first part of analysis is about the development of number of business entities according to the legal form between 2010 – 2016. It means analysis of tendency among self-employed persons in total, and among the biggest group of them – private entrepreneurs in business under the Trade Act. As self-employed persons are considered also agricultural entrepreneurs – natural persons, and others (e.g. physicians, veterinarians, auditors, etc.). It is obvious that the number of newly established selfemployed persons (SEP) is for 7% higher in 2016 than in 2015, where the number of closed down units in 2016 is higher for 5% than in 2015 (Figure 1). Figure 1 Number of newly established and closed down units between 2010 – 2016 in the legal form Self-Employed Person (SEP); Year-on-Year Growth Rate of SEP Source: own research based on data from the Czech Statistical Office (2016) The analysis shows (Figure 2) that in the A group (private entrepreneurs in business under the Trade Act) is the number of newly established in 2016 only for 2% higher than in 2015. The number of closed down units is even for 3% lower in 2016 than in 2015. Significant decrease in this category has not been shown. 2010 2011 2012 2013 2014 2015 2016 Ø10 -16 SEP_BIRTH 1,12 0,97 0,88 0,94 0,92 1,02 1,07 0,9841 SEP_DEATH 0,54 0,85 1,05 2,35 0,47 1,01 1,05 0,9217 SEP_BALANCE -1,16 1,19 0,65 -2,04 -0,16 1,11 1,21 190 Figure 2 Number of newly established and closed down units between 2010 – 2016 in Private entrepreneurs in business under the Trade Act (A); Year-on-Year Growth Rate Source: own research based on data from the Czech Statistical Office (2016) Next analysis focuses on the tendency among entrepreneurs – legal persons, where these legal persons are in the Czech Republic limited liability companies (LLC), jointstock companies, limited partnerships, general commercial partnerships, and cooperatives. The most frequently used type is Limited Liability Company. It is obvious that the number of newly established business units in total (see Figure 3) is for 7% higher in 2016 than in 2015, as well as in 2015 and in 2014. Consequently, the number of closed down business units in 2016 is for 21% higher than in 2015, however, this value is even higher in 2015 compared with 2014 (39%). Same results can be seen in the analysis of LLC only (see Figure 4). It is possible to see increased business units loss, however, this loss is not any extreme happening only in the year 2016. Figure 3 Number of newly established and closed down units between 2010 – 2016 among the legal persons (Business Companies); Year-on-Year Growth Rate of Business Companies Source: own research based on Czech Statistical Office (2016) 2010 2011 2012 2013 2014 2015 2016 Ø10 -16 A_BIRTH 1,07 1,02 0,89 0,86 0,98 0,97 1,02 0,9719 A_DEATH 0,94 1,08 1,10 2,36 0,46 1,00 0,97 1,0251 A_BALANCE 1,27 0,96 0,64 -2,24 -0,15 0,74 1,46 2010 2011 2012 2013 2014 2015 2016 Ø10 -16 BUSINESS COMPANIES_BIRTH 1,04 0,95 1,01 1,01 1,08 1,07 1,07 1,0340 BUSINESS COMPANIES_DEATH 1,01 1,03 1,01 1,01 0,89 1,39 1,21 1,0688 BUSINESS COMPANIES_BALANCE 1,05 0,93 1,01 1,02 1,14 1,00 1,02 1,0224 191 Figure 4 Number of newly established and closed down units between 2010 – 2016 among the legal persons Limited Liability Companies (LLC); Year-on-Year Growth Rate of LLC Source: own research based on Czech Statistical Office (2016) Next analysis focuses on the development in the number of business units according to the size of these units (number of employees, see Table 2) between 2010 – 2016. The authors are using 11 different groups, where the analysis of year-on-year growth rate has been done. The analysis shows that the highest number of newly established compared with 2015 is in the group with 50 – 99 employees (56%), but relatively high numbers of newly established units in 2016 compared with 2015 are in the group with 250 – 499 employees (25%) and in the group with 6 – 9 employees (15%). The analysis of closed down units is the highest number in 2016 compared with 2015 in the group with 250 – 499 employees (40%), next in the group with 100 – 199 employees (8%), and after that in the group without any employees (5%). With respect to the other groups, there can be seen even decrease in the number of closed down units in 2016 compared with 2015. The significant decrease in the number of micro-, small-, and medium-sized companies has not been proven. Table 2 Year-on-Year Growth Rate of newly established and closed down units between 2010 – 2016 in different groups according to the size Size of business (number of employees) 2010 2011 2012 2013 2014 2015 2016 Ø2010 - 2016 not reported_BIRTH 1,04 0,79 0,92 1,69 0,64 1,01 1,01 0,9759 not reported_DEATH 0,55 0,83 1,09 3,04 0,35 1,04 0,99 0,9165 0*_BIRTH 0,97 1,26 0,85 0,16 4,15 1,12 1,01 0,9625 0*_DEATH 0,78 1,17 1,16 1,39 0,75 1,00 1,05 1,0203 1 - 5_BIRTH 1,36 1,00 1,01 0,96 1,01 0,99 1,04 1,0479 1 - 5_DEATH 1,05 0,99 1,11 1,09 0,86 1,06 0,83 0,9928 6 - 9 _BIRTH 1,14 1,01 1,09 0,88 0,88 1,06 1,15 1,0227 6 - 9 _DEATH 0,92 0,75 0,80 1,22 0,66 1,13 0,89 0,8936 10 - 19 _BIRTH 1,27 0,92 1,05 0,89 0,92 0,94 1,09 1,0052 10 - 19 _DEATH 0,70 0,76 0,74 0,80 0,70 1,14 0,98 0,8193 20 - 24_BIRTH 1,62 0,48 1,74 0,79 0,77 1,24 0,85 0,9798 20 - 24_DEATH 0,74 0,81 0,86 0,53 0,70 1,21 0,71 0,7726 25 - 49 _BIRTH 1,32 1,01 1,04 0,81 0,86 0,84 1,05 0,9785 25 - 49 _DEATH 0,61 0,94 0,71 0,62 0,79 0,91 0,94 0,7774 2010 2011 2012 2013 2014 2015 2016 Ø10 -16 LLC_BIRTH 1,03 0,94 1,02 1,03 1,10 1,07 1,07 1,0363 LLC_DEATH 1,02 1,04 1,01 1,01 0,91 1,39 1,23 1,0763 LLC_BALANCE 1,04 0,92 1,02 1,04 1,15 1,00 1,02 1,0244 192 50 - 99 _BIRTH 1,82 0,61 1,03 0,84 0,78 0,86 1,56 1,0000 50 - 99 _DEATH 0,73 1,72 0,42 0,65 0,65 0,85 0,76 0,7561 100 - 199 _BIRTH 0,81 1,54 0,95 0,58 0,82 1,89 0,53 0,9211 100 - 199 _DEATH 1,05 2,13 0,34 0,34 1,20 1,08 1,08 0,8671 200 - 249 _BIRTH 0,33 4,00 0,75 1,00 1,00 0,33 0,8327 200 - 249 _DEATH 0,42 1,80 0,44 1,00 1,00 1,25 0,40 0,7742 250 - 499 _BIRTH 3,00 0,33 1,00 1,00 0,25 4,00 1,25 1,0324 250 - 499 _DEATH 0,80 1,38 0,45 1,00 0,50 1,00 1,40 0,8607 * without employees Source: own research based on Czech Statistical Office (2016) The last part of trend analysis focuses on last important factor, which can reveal the fact that the introducing of E-sales in 2016 affected in a way the establishment and the termination of business units in the Czech Republic. Based on the analysis of newly established and closed down business units according to the economic activity is possible to see following results. 21 different nomenclatures of economic activities have been analysed. It is possible to identify (see Figure 5) high relative increase of newly established units in nomenclature I – Accommodation and food service activities, where this increase was for 27%. The second highest increase (16%) has nomenclature H – Transportation and storage, and the third highest has S – Other service activities (13%). With respect to the relative decrease in the number of business units, the biggest change has nomenclature D – Electricity, gas, steam and air conditioning supply (increase for 56%), the second highest (34%) has nomenclature R – Arts, entertainment and recreation, and the third highest (31%) has B – Mining and quarrying. The most surprising is non-standard increase of business units in nomenclature O – Public administration and defence, compulsory social security (for 276% in 2016), and even higher decrease of business units in this nomenclature, where this decrease for 21times higher in 2016 than in 2015. However, this extraordinary situation cannot have influence on the verification of hypothesis about the influence of E-sales on the number of newly established and closed down business units. It can be assumed that in NACE No. 84 will be nonstandard business units, where these units will be probably freed from registration. Based on the Act No. 112/2016, §12 (according to Collection of Laws, 2016), about the registration of sales, the contributory organizations are freed from the electronic registration of sales, where this legal form is assumed in this business sector. It was assumed that this part of analysis will bring the answer, whether the introduction of E-sales has had the impact on the number of business units in the NACE nomenclature No. 55 – 56 (I – Accommodation and food service activities), where above mentioned Act was implemented since 1st December 2016. The significant decrease in the number of business units has not be proven. 193 Figure 5 Number of newly established and closed down units between 2010 – 2016 in different business sectors and Year-on-Year Growth Rate of newly established and closed down units between 2010 – 2016 CZ-NACE 2010 2011 2012 2013 2014 2015 2016 Ø2010 - 2016 B_BIRTH 1,05 0,87 0,91 1,23 0,66 1,72 0,74 0,98 B_DEATH 0,48 1,50 0,72 5,15 0,21 0,93 1,31 0,95 B_BALANCE 2,25 0,59 1,13 -1,61 -0,38 2,73 0,50 D_BIRTH 1,25 0,05 7,72 3,19 0,09 0,74 0,98 0,72 D_DEATH 3,32 0,38 0,89 3,12 1,33 0,88 1,56 1,31 D_BALANCE 1,20 0,03 12,50 3,20 0,03 0,42 -1,79 H_BIRTH 0,85 0,97 0,83 1,06 0,92 1,29 1,16 1,00 H_DEATH 0,83 1,08 1,14 2,87 0,32 1,02 0,99 0,99 H_BALANCE 2,33 -2,47 5,10 6,63 0,12 0,35 -0,57 I_BIRTH 0,98 0,96 0,96 0,94 1,00 0,99 1,27 1,01 I_DEATH 0,98 1,18 1,05 1,98 0,69 1,01 0,90 1,06 I_BALANCE 0,98 0,63 0,73 -3,12 0,24 1,16 -1,12 R_BIRTH 1,18 1,01 0,94 0,86 0,91 1,23 1,12 1,03 R_DEATH 1,00 1,12 0,88 3,03 0,28 1,11 1,34 1,04 R_BALANCE 1,26 0,97 0,97 0,08 8,89 1,28 1,04 S_BIRTH 1,03 0,99 0,90 0,98 0,75 1,14 1,13 0,98 S_DEATH 1,18 0,78 1,13 2,48 0,40 1,03 1,07 1,02 S_BALANCE 0,97 1,09 0,83 0,35 1,81 1,22 1,16 O_BIRTH 0,76 0,50 1,28 0,84 0,35 6,09 2,76 1,14 O_DEATH 0,69 4,91 0,22 1,79 0,70 0,67 20,90 1,44 O_BALANCE 0,82 -2,19 -0,16 -0,92 1,58 -2,47 -4,96 B - Mining and quarrying, D - Electricity, gas, steam and air conditioning supply, H - Transportation and storage, I - Accommodation and food service activities, R - Arts, entertainment and recreation, S - Other service activities, O - Public administration and defence; compulsory social security Source: own research based on Czech Statistical Office (2016) 4 Conclusions The aim of this article was to create an overview of the number of newly established and closed down business units in the Czech Republic based on the date from the Czech Statistical Office between 2010 – 2016. The trend analysis of this period and especially the analysis of the year 2016 can reveal the fact, whether the introducing of the 1st phase of E-sales has had the influence on the number of newly established or closed down business units. The trend analysis has been made according to the legal form of -4 000 -2 000 0 2 000 4 000 6 000 8 000 2010 2011 2012 2013 2014 2015 2016 I BALANCE I Accommodationand food service activities_BIRTH I Accommodationand food service activities_DEATH -500 0 500 1 000 1 500 2 000 2008 2009 2010 2011 2012 2013 2014 2015 2016 D BALANCE D Electricity, gas, steamandair conditioning supply_BIRTH D Electricity, gas, steamandair conditioning supply_DEATH 194 business units, according to their size (number of employees), and according to their economic activity. Based on the results is possible to make a conclusion that the significant loss of business units in different groups has not been proven. The 1st phase of introducing of E-sales has had no impact on either significant number of closed down or newly established business units of natural persons, legal persons, micro-sized, small-sized, or medium-sized companies. Moreover, there is no significant impact even in the business sector which has been affected by the E-sales at the end of previous year mostly. The authors of this article are aware that the year 2017 will be more important for the evaluation of the influence of E-sales, of course. The number of affected business units will be higher, where only 5% of business units were affected in the year 2016, and already 20% of them will be affected in the year 2017. Therefore, the evaluation of the influence of Esales on the number of newly established and closed down unit in the year 2016 is limited; however, it gives us the opportunity for the next analysis in the following year. Acknowledgments This study is supported by internal research project No. 2103 “Investment evaluation within concept Industry 4.0”, and by project IGA 2017 No. 1904 “Support of pedagogical work, technical equipment, and communication with experts”, both at Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic. It was prepared in cooperation with students of Ph.D. study at Department of Economics, namely with Ing. Lucie Černá and Ing. Martin Král. References Acs, J., Desai, S., Hessels, J. (2008). Entrepreneurship, economic development and institutions. Small business economics, Volume 31, Issue 3, pp. 219 – 234. Collection of Laws (2016). Records of Sales Act No. 112/2016. Retrieved from: http://business.center.cz/business/pravo/zakony/eet/. Czech Statistical Office (2016). Births and deaths of businesses. Database of the Statistical Business Register. Not public, internal document. Geršlová, J. (2012). Dějiny moderního podnikání. Praha: Professional Publishing. Hamplova, E., Kovarnik, J. (2016). Analysis of Entrepreneurship Development in the Czech Republic from 2008 to 2014. In Klimova,V., Zitek, V. (eds.) 19th International Colloquium on Regional Sciences. Brno: Masaryk University, pp. 226-232. Hamplova, E., Kovarnik, J., Jedlicka, P. (2016). Analysis of Various Entrepreneurial Activities and their Development in the Czech Republic from 2008 to 2015. In Krajicek, J., Nesleha, J., Urbanovsky, K. (eds.) 13th International Scientific Conference of the European Financial Systems. Brno: Czech Republic, pp. 227-233. Matzner, J. (2015). U elektronické evidence tržeb není důvod k euforii. Retrieved from: http://www.pravniprostor.cz/clanky/ostatni-pravo/u-elektronicke-evidence-trzeb-neni- duvod-k-euforii. TPA Czech Republic (2016). Electronic Records of Sales. Retrieved from: http://www.tpa- group.com/en/publications-news/news/czech-republic-electronic-records-sales. 195 Loan-To-Deposit Ratio and Financial Stability: macroprudential policy perspective Martin Hodula1 , Stanislav Poloucek2 1 VŠB-Technical University Ostrava Faculty of Economics, Department of Economics Sokolská třída 33, 702 00 Ostrava, Czech Republic E-mail: martin.hodula@vsb.cz 2 VŠB-Technical University Ostrava Faculty of Economics, Department of Economics Sokolská třída 33, 702 00 Ostrava, Czech Republic E-mail: stanislav.poloucek@vsb.cz Abstract: This paper evaluates the relation between the loan-to-deposit ratio (LTD) and financial stability and hence the usefulness of the LTD as a macroprudential policy indicator or instrument. In general, an increase of the LTD should indicate a financial stability decrease. Subsequently we adopt a macroprudential policy perspective and test financial sector stability with respect to the currency (domestic/foreign) in which loans and deposits are denominated. This is done in a linear empirical framework by using large factor-augmented VAR model (FAVAR) which is not limited to number of variables used. For such analysis, it is appropriate to choose an open economy with a bank-based financial system and potential substitutability between domestic and foreign currency loans and deposits. Therefore, we analyze the Czech Republic and Hungary as these countries meet the above listed characteristics. Our results suggest that the structure of financial transactions in terms of domestic/foreign currency denomination cannot be ignored or disregarded in relation to financial stability objective. Keywords: FAVAR, financial stability, LTD, domestic/foreign currency loans and deposits JEL codes: C32, E32, E44, G18 1 Introduction Sources of investments as well as the role of savings and their origins attract the attention of economists for quite some time. J. M. Keynes has even made the balance of savings and investments a key condition for the balance of the entire economy. The macroeconomic nature of his analysis directly encouraged an empirical analysis, which is very common to the present day. In financial systems in which banks play a significant role, deposits are undoubtedly a principal factor influencing credit activity. On the other hand, the openness of the economy, the free movement of capital, and mergers and acquisitions in the financial sector has weakened the domestic nature of savings and investments, as well as the importance of domestic deposits as a source of credit provided by banks. However, recent financial crisis has shown that domestic deposits could be one of the stabilizing factors that may mitigate the negative spillover effects of increasing financial risks coming from the international markets into the domestic economy. Most studies argue that Central European countries have relied on foreign funding to fuel their credit growth (Impavido et al., 2013). They underline the high foreign ownership of banks and the parent bank policy and strategy towards the foreign subsidiaries. Several papers recorded the fact that especially foreign exchange mismatches were the reason of the build-up phase of the financial cycle in some emerging markets (Brockmeijer et al., 2011, page 22). Nevertheless all these studies omit a very important factor (besides the fact that subsidiaries have their own capital and that they are under legislation and supervision of the country where they are located): how much are credits financed by 196 domestic deposits in each country and how important are foreign sources for domestic credits. Recently, the loan-to-deposit (LTD) ratio and its structure is more and more often discussed as a representative indicator of banking sector stability and hence the financial stability. According to some studies, if macroprudential policy should be able to encompass all important providers of credit, liquidity, and maturity transformation, then the LTD ratio could be one of representative indicators of macroprudential policymaking (see Brockmeijer et al., 2011 and Park et al., 2012, among others). ESRB (2016) states that simpler structural liquidity ratios such as the LTD ratio are promising both in their role as indicators and as regulatory instruments addressing maturity mismatches and market illiquidity. In Europe, LTD ratio is currently used as a supplementary indicator of individual bank stability by some central banks without any official numerical target or limitation. Basel III accords consider the net stable funding ratio (NSFR) which provides a sustainable maturity structure of assets and liabilities (BCBS, 2013, page 1). Explaining virtually the same relations as LTD ratio, NSFR seems unnecessarily complicated and hard to operate. In this paper, we aim to evaluate the usefulness of LTD ratio as a potential standalone macroprudential policy tool and to see if we can find a rationale for setting some mandatory numerical target. Since the objective of a macroprudential policy is to curb systemic risk and reduce procyclicality of a financial sector, we aim to verify: (i) the relationship between the LTD ratio stability and the stability of the financial sector and (ii) the pro-cyclicality of the LTD ratio and its two components (loans and deposits). We also consider the currency structure of loans and deposits in the analysis and argue that while exploring LTD ratio in some countries, the currency structure of loans and deposits needs to be considered as well. For such analysis, it is appropriate to choose an economy with a bank-based financial system and potential substitutability between domestic and foreign currency loans and deposits. Therefore, we choose the Czech Republic and Hungary as our training sample as these countries meet the above listed characteristics. The rest of the paper is organized as follows: Section 2 lays down some stylized facts on the matter; Section 3 explains our used methodology and data; Section 4 discusses empirical results and Section 5 concludes. 2 Stylized Facts Most studies find that in general, higher LTD ratios are associated with greater risk of bank distress (IMF, 2013) and therefore with more unstable financial environment and lower stability of financial system. Another conclusion is that a huge increase of loan-todeposit levels brings the likelihood of a bank failure; Bologna (2013) claims such failures occur two to three years later. If such chronological succession exists the LTD ratio can be used as a predictive policy tool. The idea behind the LTD ratio regulation is that the amount of bank loans could depend on the amount of its deposits. In the Czech Republic banks supervised by the Czech National Bank maintain relatively low LTD ratio. However, if we separate the LTD ratio according to the currency in which considered transactions take place, we may identify potential risks. First, as evident from Figure 1, there is a huge gap between domestic (in CZK) and foreign (other currencies) LTD ratios ranging from around 50 percent to more than 150 percent in 2000-2016 period. And even though foreign currency loans form only a minor part off total lending in the Czech Republic, they exhibit much steeper growth compared to domestic currency loans (5.1 percent to 1.7 percent on average, 2011-2017 data). In total, this may be seen as a potential future threat to financial sector stability and needs to be monitored closely. 197 Figure 1 Loan-To-Deposit Ratio – Czech Republic Source: Czech National Bank data, own processing Hungarian domestic banks experienced different development compared to the Czech banking sector (Figure 2). In the great moderation period (2002-2006) their LTD ratio was increasing steadily, and peaked at 148 percent in January 2009. Again, it is useful to separate the LTD ratio per currencies to uncover potential risks. As expected, the domestic/foreign LTD ratios in Hungary exhibit huge differences in levels and in growth rates. Prior to the financial crisis, foreign currency loans grew much faster than domestic currency loans (about ten percentage point faster on average, 2002-2008 data) but the dynamics changed after the crisis and foreign currency loans growth rate has been falling steadily while the domestic currency loans exhibit positive growth rate. However, this does not change the fact that the foreign LTD ratio is still relatively high in Hungary (141 percent at end-2016) and pose a significant threat to financial sector stability. Figure 2 Loan-To-Deposit Ratio – Hungary Source: Hungarian National Bank data, own processing 3 Methodology The major interest of this study is to verify: (i) the relationship between the LTD indicator and the stability of the financial sector and (ii) the pro-cyclicality of the LTD indicator and its two components (loans and deposits). Performing such analysis requires a great number of variables (including both macroeconomic and financial data) to be 40 60 80 100 120 140 160 2000 2002 2004 2006 2008 2010 2012 2014 2016 Loan-to-deposit ratio (domestic) Loan-to-deposit ratio (foreign) LTD (all curencies) -25 -15 -5 5 15 2001 2003 2005 2007 2009 2011 2013 2015 2017 Difference Domestic currency loans (y-on-y % change) Foreign currency loans (y-on-y % change) 40 140 240 340 440 540 2000 2002 2004 2006 2008 2010 2012 2014 2016 Loan-to-deposit ratio (domestic) Loan-to-deposit ratio (foreign) LTD (all curencies) -15 -5 5 15 2001 2003 2005 2007 2009 2011 2013 2015 2017 Difference Domestic currency loans (y-on-y % change) Foreign currency loans (y-on-y % change) 198 incorporated into the model and analyzed in detail. However, most of the widely used and accepted macroeconomic models, such as the vector autoregression (VAR) models, suffer from major limitations. The information sets utilized by VAR models or single equation models are rather small to retain the degrees of freedom. To get around this fact, we use a factor-augmented VAR (FAVAR) model introduced in Bernanke et al. (2005). The basic idea of the FAVAR model rests on incorporating a large amount of data series into a small number of factors which are then used for the estimation of a VAR model. We specify an 1×M vector of macroeconomic time series tY and a 1×K vector of unobserved factors tF. We assume that the joint dynamics of tt YF ′′, is given by the following equation: ( ) t t t t t Y F L Y F ε+      Φ=      − − 1 1 , (1) where ( )LΦ is a lag polynomial and tε is an error term with a zero mean and a covariance matrix Q. Equation (1) describes a standard VAR model which represents a reduced form of a linear rational-expectations model including both observed and unobserved variables. However, due to the unobserved variables, the model is impossible to estimate. To deal with this issue we assume that additional informational time series tX are linked to the unobservable factors tF and the observable factors tY by: tt y t f t eYFX ′+′Λ+′Λ=′ , (2) where f Λ and y Λ are matrices of factor loadings and te′ is a serially uncorrelated error term with a zero mean (innovation shock). Equation (2) captures the idea that both vectors tY and tF are pervasive forces that might drive the common dynamics of tX . We use a two-step principal components approach, which is a nonparametric way of estimating the space spanned by the common components ( )ttt YFC ′′=′ , in (2). Data and Identification Scheme Our vector tX incorporates 100 monthly time series representing the Czech and Hungarian economy and the rest of the world. The national data are drawn mainly from Czech National Bank and Central Bank of Hungary databases and the international data are drawn from European Central Bank database. Our sample period covers data from January 2000 to February 2017. The choice of sample period is driven by data availability. The set of variables can be divided into five blocks: (i) real economy variables (industrial production index, retail sales, labour market indicators), (ii) prices (consumer price index, industrial producer price index, house prices, real wages), (iii) credit and interest rates, (iv) financial sector variables (regulatory variables, exchange rates, market indexes, financial cycle indicator, asset prices), and (v) open economy variables (real economy and financial sector development in Eurozone). To identify the policy innovations, we apply recursive Cholesky decomposition to the covariance matrix. For this purpose, we divide our panel of variables into two groups: slow and fast-moving variables (Stock and Watson, 2002). We assume that slow-moving variables display a lagged response to a shock, whereas fast-moving variables react contemporaneously. In our setting, blocks describing the real economy, prices and the external environment are classed as slow-moving (in the given order). The remaining variables are classed as fast-moving. 199 4 Results and Discussion The main objective of a macroprudential policy is to curb systemic risk and procyclical behavior of banks. Hence, to verify the usefulness of LTD as a macroprudential policy instrument, we first evaluate the impact of an increase in loan-to-deposit ratio on real economy and financial sector (LTD shock) and second, we consider how the LTD components respond to an increase in real economic activity (economic cycle shock). The increase in LTD shock is normalized so that the increase itself is assigned by 50 % to the loans increase and by 50 % to the deposits decrease. Evidently in real economy the proportion could be different and a change in LTD ratio has been usually triggered by loans increase or decrease. That is one of reasons why the LTD ratio is a suitable indicator from the point of view of banking (financial) sector stability. We present the effects of identified shocks in Figures 3 to 4 using impulse response functions (IRFs). To account for any uncertainty in the factor estimation, we also calculate accurate confidence intervals as in Kilian (1998). The IRF can be constructed for any variable in our information set. However, due to space constrains, we only report here those relevant for our analysis. The baseline model specification, including optimal lag-length and number of factors, is based on Schwarz information criteria and employs 3 lags of explanatory variables and 3 factors. Since there is always a considerable time lag between for instance, a decision to apply for a loan and the moment of granting the loan, we decided that all the variables would enter the estimated equations with the lag of 3 months (one quarter). To check for robustness of our results, we also try to estimate the FAVAR model with different number of lags and 5 and 7 factors which yield comparatively identical results. Relationship between LTD Ratio and Financial Sector Stability The logic behind the LTD ratio indicator suggests that the higher is the banks’ participation in wholesale funding, the bigger is its contribution to the systemic risk of the financial sector. In this regard, associating higher LTD ratios with higher risk (higher instability) of financial sector, we may consider to set some LTD limits. We simulate two shocks associated with LTD ratio increase – one for transactions in domestic currency and the other for transactions in foreign currency. Doing this we want to emphasize that in some countries it is important to take into account the currency structure of loans and deposits. 200 Figure 3 Impulse Responses to LTD Shock Domestic currency loan-to-deposit ratio increase by 100 basis points (domestic LTD) Foreign currency loan-to-deposit ratio increase by 100 basis points (foreign LTD) Source: CNB data, own computations Note: Responses were normalized to account for a 100 basis point positive innovation; 10th and 90th percentiles of the distribution reported. Except for the inter-bank rates, the variables are in year-on-year changes, annualized. First, increasing domestic/foreign currency LTD ratios gives quite different outcomes in the Czech Republic and Hungary (Figure 3). The results are heavily influenced by the relative importance of foreign currency loans and deposits in both countries. In the Czech Republic, foreign currency loans and deposits play much less important role than in Hungary (Brzoza-Brzezina et al., 2010). This is one of very important reasons why an increase in banks’ share in wholesale funding (domestic LTD shock) increases the risk of financial instability in the Czech Republic and forces hands of the CNB to become more restrictive (we use the inter-bank rate to proxy for the main central bank policy tool) whereas responses to foreign LTD shocks are statistically insignificant. In Hungary, the situation is quite different. Increasing domestic LTD ratio has no significant effect on real economic activity on average and it temporarily improves the financial sector stability. However, increasing foreign LTD ratio gives completely different results. It boosts real economic activity but hugely destabilizes the financial sector. And since these transactions are in foreign currency, there is little the national bank can actually do to strengthen financial stability and mitigate increasing credit and exchange rate risks. This country-specific example shows that the currency of financial transaction matters and this fact should be taken into account when considering the use of the LTD ratio as a macroprudential policy instrument. Pro-cyclicality Aspect of Banks Lending In a similar logic we try to find evidence of banks pro-cyclical behavior. This would point to LTD ratio usefulness as macroprudential policy tool. We use countries industrial production index to proxy for real economic activity and simulate a positive economic cycle shock. In general, especially bank loans show pro-cyclicality in terms of the amounts of their supply and demand. But even deposits may become pro-cyclical, as the economic subjects do not behave rationally and do not necessarily modify their typical saving behavior in good times. As apparent from Figure 4, results of our estimation are again heavily dependent on the relative importance of foreign currency loans and deposits in the Czech Republic and Hungary. 201 Figure 4 Impulse Responses to Economic Cycle Shock Industrial Production Index increase by 100 basis points Source: CNB data, own computations Note: Figure 3 note applies. In general, positive economic cycle shock causes domestic currency loans to grow as economic subjects are becoming less risk-adverted. However, domestic currency deposits are decreasing. This means that during economic boom, economic subjects are more likely to increase spending and borrow more, but they do not increase their savings. In total, domestic LTD ratio indicator would grow more than proportionally because of the combination of nominator and denominator effects. These results confirm that banks tend to increase their wholesale funding during times of economic expansion, in active response to rising demand for loans. And the wholesale funding is considered to be a factor that expands the pro-cyclicality of lending. This alone suggest the potential usefulness of the LTD ratio indicator. We may observe different responses in case of foreign LTD ratio components. While the responses in the Czech Republic are insignificant or very little in size, in Hungary both deposits and loans increased following a positive economic cycle shock. This may be viewed as a suitable development but one must not forget the risk stemming from exchange rate movements. The issues related to the substitution of domestic currency loans for foreign in Hungary are well covered in the literature (Endresz and Harasztosi, 2014; Beckmann and Stix, 2015 among others). 5 Conclusions The LTD ratio regulation is a strong policy instrument that directly limits the ratio of deposits to loans and thus puts a restriction on banks assets management. In this paper, we take a closer look at the usefulness of LTD ratio as a standalone macroprudential policy tool. While keeping in mind the macroprudential policy goals, we analyze whether the LTD ratio has the potential to curb the systemic risks and reduce banks procyclical behavior. We choose the Czech and Hungarian banking systems as a training sample. Our results can be summarized as follows: (i) the currency structure of LTD ratio components cannot be neglected while performing a close-up analysis of an individual bank or the entire banking sector; (ii) the higher the banks’ shares in wholesale funding, the bigger their contribution in increasing systemic risk and (iii) during economic booms, economic subjects are more likely to increase their demand for credits while their saving behavior remains unchanged, thus further increasing the LTD ratio on aggregate. 202 Overall, the empirical evidence suggest that the LTD ratio is a useful macroprudential policy tool as it reduces lending procyclicality and limits systemic risk build-up. Considering the criticism of NSFR operationalization (see ECB, 2014, page 127), we may advocate the promotion of a LTD ratio to a standalone macroprudential policy tool with a numerical target and possibly time-variant nature. However, the exact upper value of the LTD ratio remains an open question and should be the subject of further research in this area. Moreover, we highlight the need to consider the currency structure of loans and deposits in particular countries. Acknowledgments This paper greatly benefited from data support provided by the Czech National Bank and Hungarian National Bank. The authors would like to acknowledge the 2017 Research Grant (SP2017/110) from Technical University of Ostrava and the support of Czech Science Agency grant GA16-13784S. The usual disclaimer applies. References Beckmann, E., Stix, H. (2015). Foreign currency borrowing and knowledge about exchange rate risk. Journal of Economic Behavior & Organization, vol. 112(1), pp. 1-16. Bernanke, B., Boivin, J., Eliasz, P. (2005). Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach. The Quarterly Journal of Economics, vol. 120(1), pp. 387-422. Bologna, P. (2013). Structural funding and bank failures: Does Basel 3 net stable funding ratio target the right problem? Journal of Financial Services Research, vol. 47(1), pp. 81- 113. Brockmeijer, J., Moretti, M., Osinski, J. (2011). Macroprudential Policy: An Organizing Framework. IMF Policy Paper, March 14. Brzoza-Brzezina, M., Chmielewski, T., Niedźwiedzińska, J. (2010). Substitution between domestic and foreign currency loans in Central Europe: Do central banks matter? ECB Working Paper, vol. 1187. BCBS (2013). Basel III: The Liquidity Coverage Ratio and liquidity risk monitoring tools. Retrieved from: http://www.bis.org/publ/bcbs238.pdf. ECB (2014). Financial Stability Review, November 2014. Retrieved from: https://www.ecb.europa.eu/pub/fsr/shared/pdf/sfcfinancialstabilityreview201411.en.pdf? ba8c581256881e702880b3bc9d7ad8ac. Endresz, M., Harasztosi, P. (2014). Corporate foreign currency borrowing and investment: The case of Hungary. Emerging Markets Review, vol. 21(1), pp. 265-287. ESRB (2015). The ESRB handbook on operationalising macro-prudential policy in the banking sector. Retrieved from: https://www.esrb.europa.eu/pub/pdf/other/140303_esrb_handbook_mp.en.pdf. IMF (2013). Global Financial Stability Report: Transition Challenges to Stability. Retrieved from: http://www.imf.org/External/Pubs/FT/GFSR/2013/02/. Impavido, F., Rudolph, H., Ruggerone, L. (2013). Bank Funding in Central, Eastern and South Eastern Europe Post Lehman: a “New Normal”? IMF Working Paper, vol. 148. Kilian, L. (1988). Small-sample Confidence Intervals for Impulse Response Functions. Review of Economics and Statistics, vol. 80(2), pp. 218-230. 203 Stock, J. H., Watson, M. H. (2002). Forecasting Using Principal Components from a Large Number of Predictors. Journal of the American Statistical Association, vol. 91(1), pp. 1167-1179. 204 Macroeconomic Determinants of Shadow Banking: evidence from Spain Martin Hodula1 , Martin Machacek2 and Ales Melecky3 1,2,3 VŠB-Technical University Ostrava Faculty of Economics, Department of Economics Sokolská třída 33, 702 00 Ostrava, Czech Republic E-mail: martin.hodula@vsb.cz, martin.machacek@vsb.cz and ales.melecky@vsb.cz Abstract: Shadow banking is a term that corresponds to credit intermediation outside the regulated banking system. Shadow banking system constitutes over one-quarter of total financial assets in the euro Area. Traditional banks now rely more on short-term funding from other financial intermediaries, which also cover shadow banking entities. Due to this fact they may become more susceptible to runs and liquidity shortages and endanger overall financial stability. Macroprudential authorities should therefore carefully monitor and supervise the growing share of shadow banking activities in the financial system. This paper presents a novel evidence on the shadow banking system dynamics in Spain. We chose Spain as a training sample because of its highly dynamic housing markets and related high securitization of loans. We use different methods to estimate the shadow banking size. We estimate a Bayesian ARDL model to uncover possible determinants of shadow banking system dynamics. Based on preliminary results, we conclude that macroeconomic environment may significantly influence the evolution of shadow banking system. Keywords: shadow banking, credit intermediation, Bayesian ARDL JEL codes: E44, G21, G28 1 Introduction Shadow banking is a term that refers to credit intermediation, liquidity and maturity transformation activities outside the conventional banking system and gained substantial attention worldwide after the global financial crisis events. The Financial Stability Board was engaged since 2011 in a global project to closely monitor and evaluate risks stemming from shadow banking system development. Early estimates show that the United States, the euro area and the United Kingdom have the largest shadow banking systems (IMF, 2014 a). However, literature coverage of the topic varies among these groups tremendously. While in the United States, the shadow banking estimation and associated micro- and macroeconomic analysis is a growing field (Adrian and Shin, 2009; Pozsar et al., 2013; etc.), it has been paid very little attention in the UK or in the euro area. This is caused mainly by the fact that there are no official data sets available for European countries. Recent ECB and IMF studies have provided methods to estimate shadow banking system size in Europe but either they do not engage in any deeper data analysis (Bakk-Simon et al., 2012) or it is not the main point of interest (IMF, 2014 a). In this paper, we use newly available data from ECB/Eurosystem and propose an empirical framework to uncover the effects of selected determinants on shadow banking system development. Due to space constrains, we perform a country-specific analysis as an illustrative example. We chose Spain as a training sample for several reasons. First, Spain was one of the European countries most heavily affected by the Great Recession, which hit the country in 2008. Second, the Spanish banking system has been financing a frantic real estate boom of the 2000s by securitization and almost collapsed after the housing bubble burst, only to be rescued by a massive public bail-out representing 5.8% of the country´s GDP (the second largest GDP share after Ireland). Despite a subsequent bank restructuring process started in 2012, four years later the 205 main 6 banks´ real estate exposure was still about 15% of their balance sheet, being five times higher than the equivalent exposure in France and more than seven times higher than this exposure in the Nordic countries (Stúcklin, 2017). Third, the banking crisis in Spain mainly originated in and affected domestic savings banks (cajas de ahorros) which represented an alternative credit source for regional economic development and held 45% of total assets in 2008. Albeit these institutions were subject to similar banking supervision as the commercial banks, they were managed and controlled by local and regional governments, thus effectively being under the influence of political parties and trade unions (see Martin-Aceňa, 2014 or Ruiz et al., 2016). Undoubtedly, this system of “capitalismo de amiguetes” (crony capitalism) and related misconduct finally led to the destabilization of the whole financial system. However, in a rather loose sense the prerecession ”cajas” can be considered shadow banks which makes the Spanish story even more interesting, no matter that we measure the size of shadow banking system here in a more traditional way. The paper is organized as follows: Section 2 provides working definitions and some stylized facts on shadow banking system size and dynamics in Spain; Section 3 describes model details and data sources; Section 4 discusses our results and Section 5 concludes. 2 Shadow Banking Development in Spain A definition of shadow banking is not straightforward and it deserves very close attention. Most of the existing studies build shadow banking definition on the grounds of the entity that carries it out. By this definition, a shadow bank is an entity that conduct maturity, credit, and liquidity transformations without government guarantees or access to central bank liquidity (Pozsar et al., 2013 among others). Other definition focus instead on instruments, which the entities use. Claessens and Ratnovski (2014) states that all financial activities, except traditional banking, requiring private or public backstop to operate are classed as shadow. An ideal definition is impossible to obtain due to the complexity of financial system and the large differences in shadow banking activities across countries. However, we believe that the second-best definition should incorporate both, an entity-based and instrument-based definition (such as ones from FSB, 2013 or IMF, 2014a). For the purpose of this paper, we define shadow banking on the base of its main components. Shadow banks are than described as credit intermediation entities outside the regular banking system that are involved in securitization activities and are active in money market mutual funds, repo markets and hedge funds. Spanish banks are mostly concentrated on credit intermediation but are highly active in securitization, promoted and initiated by banks, and carried by financial vehicle corporations. In short, the original lender can sell his claims to another unregulated entity or she/he can issue securities backed by the underlying assets. This process can be described as liquidity transformation. Depending on the underlying assets, a maturity transformation might also take place. Other institutions such as specialized credit institutions and money market funds, which may be of relative importance in other countries, exhibits relatively low volumes in Spain. Panel A at Figure 1 shows our original estimate of shadow banking system size in Spain. Compared to the size of traditional banking, we can see that it roughly takes around 30% of total financial sector. In total, credit intermediation grew rapidly prior to the financial crisis. After the Lehman’s in 2008, both traditional and shadow banking sector growth experienced significant slowdown (Figure 1, panel B) and the size of banking sector in Spain even started to decrease since 2010. Since 2014 the shadow banking sector growth started to outperform the traditional banking sector which may pose a future threat to financial stability. 206 Figure 1 Shadow vs. Traditional Banking System Size and Growth a) total assets in levels (billions of Euros) b) total assets annual growth rate (percentages) Source: authors’ calculations based on ECB/Eurosystem data To see the mutual development of a relation between traditional and shadow banking sector, we compute dynamic correlations with different rolling window size for both, the data in levels (Figure, panel A) and in annualized growth rates (Figure 2, panel B). Overall, data in levels exhibits strong correlation prior the crisis but the correlation become weaker and unstable ever since starting with year 2009-2010. This finding suggests that the relationship between traditional and shadow banking changed significantly after the crisis. This claim is also supported by the rolling window correlation of data in growth rates, even though these are much more time-variant and evolve with severe dynamics. Figure 2 Estimated Rolling Window Correlations for n Observations a) rolling window correlations (levels) b) rolling window correlations (growth rates) Source: authors’ calculations based on ECB/Eurosystem data Figure 3 shows the breakdown of shadow banking sector in Spain by single components. Nowadays, the shadow banking system is concentrated around pension funds and insurance corporations (52%), investment funds (31%) and financial vehicle corporations (15%). The development of shadow banking sector from 2009 to 2016 documents that after the crisis, various types of funds have been stepping in (often as intermediaries for insurance companies and pension funds) to provide long-term credit to the private sector while banks have been repairing their balance sheets and retrenching from certain 0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 200020022004200620082010201220142016 Shadow banking system Traditional banking system -10 -5 0 5 10 15 2000 2002 2004 2006 2008 2010 2012 2014 2016 Shadow banking system Traditional banking system -1,0 -0,5 0,0 0,5 1,0 2001 2003 2005 2007 2009 2011 2013 2015 ρ=0.94 2Y rolling window (n=8) 3Y rolling window (n=12) 4Y rolling window (n=16) 5Y rolling window (n=20) -1,0 -0,5 0,0 0,5 1,0 2001 2003 2005 2007 2009 2011 2013 2015 ρ=0.69 2Y rolling window (n=8) 3Y rolling window (n=12) 4Y rolling window (n=16) 5Y rolling window (n=20) 207 activities (see IMF, 2014b). This also explains the continuous decrease of financial vehicle corporations. Figure 3 Spanish Shadow Banking Sector – Component Breakdown a) total assets in levels (billions of Euros) b) annual growth rate decomposition Source: authors’ calculations based on ECB/Eurosystem data 3 Data and Methodology To estimate shadow banking dynamics, we use quarterly time series obtained from ECB/Eurosystem database. The data span ranges from the first quarter of 1999 to the fourth quarter of 2016. The shadow banking system size is estimated in line with our proposed definition as the sum of total assets of financial vehicle corporations, investment funds, insurance corporations, pension funds and money market fund shares (see Table 1 for description). Further, we consider traditional banking system size, shortterm nominal interest rates, term spread and real GDP as potential drivers of shadow banking system dynamics. It should be noted that all data are publicly available and come from a single source so that our results can be replicated or extended on other counties. Table 1 Data Description Variables Mnemonics Description Shadow banking assets (broad measure) sb_broad comprises of financial corporations other than MFIs: financial vehicle corporations, investment funds, insurance corporations, pension funds and money market fund shares (total assets, mil. EUR) Traditional banking assets banks regulated banks size (total assets, mil. EUR) Short-term interest rates euribor_r 3-months inter-bank rate (EURIBOR), average of observations through period Term spread spread calculated as a difference between 10Y government bond yields and 3M EURIBOR rate Real GDP gdp_r real gross domestic product (mil. EUR, seasonally adjusted data) Source: ECB/Eurosystem data 0 500 000 1 000 000 1 500 000 2009 2010 2011 2012 2013 2014 2015 2016 Financial Vehicle Corporations Investment Funds Money Market Funds Insurance Corporations and Pension Funds -8 -6 -4 -2 0 2 4 2010 2011 2012 2013 2014 2015 2016 Financial Vehicle Corporations Investment Funds Money Market Funds Insurance Corporations and Pension Funds 208 Model Selection To verify the potential determinants of shadow banking system development, we use Bayesian estimation method with instrumental variables. This method allows us to combine the a priori knowledge gained from relevant international studies or other believes along with information from specific economy. This method is particularly useful for economies with short time series, which is our case. We argue that the dynamics of shadow banking development can be described by the following equation: tttt XbroadSBbroadSB εβα ++= −1__ , (1) where [ ]spreadreuriborbanksrgdpX t ,_,,_= . The expected directions of action of the individual variables are captured in the equation (2). In line with IMF (2014a), we assume the estimated coefficients to be positive for real GDP and traditional banking system size but negative for EURIBOR and term spread. The expected direction of the effect of individual variables is in line with the empirical results presented in the literature. ttttttt spreadeuriborbanksrgdpbroadSBbroadSB εββββα +−−++= − 43211 ___ , (2) For econometric estimation purposes, the component tε is considered a residual with the process described as follows: ttt e+= −1µεε , (3) where µ is an autoregressive coefficient and tε is i.i.d. shock with normal distribution ( )εσ,0N . For model estimation using the instrumental variables method, we further assume that: ttt XX ϑρ += −1 , (4) where ρ is an autoregressive coefficient linked to the vector of instrumental variables and ( )ϑσϑ ,0~ Nt . 4 Results and Discussion Figure 4 illustrates a satisfactory diagnosis of Bayesian estimation. Specifically, it illustrates the convergence statistics of two generated Markov chains, which gradually converge. The horizontal axis shows the chain length (number of iterations) and individual statistics are captured on the vertical axis. The chains statistics converge fast to each other. Afterwards, the values are gradually stabilized. The values are becoming stable prior 10,000 iterations in all three cases - interval, m2 and m3. The use of generated parameters to calculate the distribution of parameters and their main statistics in the range of 10,000-20,000 is therefore credible. 209 Figure 4 Convergence Statistics Source: authors’ calculations In case of the effect of a previous values of shadow banking system size (coefficient a_sb_broad), the posterior modus and posterior mean are higher than the presumed a priori mean value that lies beyond the posterior confidence interval and confirms the high persistence of this indicator. Regarding the magnitude of the impact of real GDP on the size of the shadow banking system (a_gdp_r), the posterior modus and posterior mean are higher than the presumed a priori mean value which however lies inside the posterior confidence interval. This result is statistically significant at the chosen significance level and confirms the hypothesis that the shadow banking sector, in most of the cases, reacts pro-cyclically to the development of real GDP in Spain. In a similar logic, we identify a positive influence of the size of the traditional banking sector on the size of shadow banking (a_banks). A priory mean value is close to posterior mean and mode, lies inside the 90% confidence interval and is statistically significant. This finding shows that banks tend to use shadow banking activities often as complementarities and therefore move in the same direction. In Spain, more than 99% of securitized assets are originated by banks and carried on by financial vehicle corporations. Mandel et al. (2012) already confirmed that banks often sponsor shadow banking activities. It should be noted however, that these findings may vary among particular countries. For instance, other shadow banking components, such as the investment funds, insurance corporations and pension funds may act as rather subsidiaries to traditional banking. However, this was not the case of Spain in most of our sample period as these subjects started to gain on importance only in the last couple of years. The impact of the EURIBOR interest rate on the size of the shadow banking system (a_euribor_r) is identified as negative, which is in line with the theoretical assumptions. Decreasing nominal interest rate boosts the search for a yield motive of traditional banks who are searching for more profitable sources of income and often engage in securitization activities. In line with IMF study (2014) we identified this effect as statistically less significant, which might be also due to rather flat prior. The impact of the term spread on the size of the shadow banking sector (a_spread) is identified as statistically insignificant albeit positive. This finding is also in line with the impact identified in the IMF study (2014a), if we consider the entire observed period in their paper. Again, this is logical and it confirms the fact that traditional banks often use shadow banking activities for liquidity and maturity transformations (search for liquidity motive). 210 Table 2 Estimated Parameter Results Parameters Prior distribution Posterior mode Posterior mean 90% Bayesian posterior interval a_sb_broad B(0.5; 0.1) 0.6763 0.6487 [0.5272; 0.7633] a_gdp_r N(0.41; 0.24) 0.6653 0.6347 [0.3425; 0.9838] a_banks N(0.36; 0.1) 0.3127 0.3262 [0.1911; 0.4519] a_euribor_r N(0; 1.00) -0.2266 -0.2422 [-0.6775; 0.1293] a_spread N(0; 1.00) 0.2652 0.2315 [-0.2513; 0.7370] p_sb_broad B(0,2; 0,10) 0.3857 0.4226 [0.2435; 0.6088] r_gdp_r B(0,2; 0,10) 0.8768 0.8642 [0.8349 ; 0.8974] r_banks B(0,2; 0,10) 0.8927 0.874 [0.8489; 0.8975] r_euribor_r B(0,2; 0,10) 0.6539 0.648 [0.5449; 0.7538] r_spread B(0,2; 0,10) 0.8107 0.8073 [0.7453; 0.8777] Standard errors of shocks u_sb_broad IG(0.4; 0.2) 1.3864 1.4491 [1.2361; 1.6666] u_gdp_r IG(0.4; 0.2) 0.3983 0.4136 [0.3512; 0.4736] u_banks IG(0.4; 0.2) 0.9363 0.9823 [0.8293; 1.1050] u_eurobor_r IG(0.4; 0.2) 0.7290 0.744 [0.6399; 0.8666] u_spread IG(0.4; 0.2) 0.5563 0.5736 [0.4916; 0.6597] Source: authors’ calculations All autoregressive components are statistically significant at 10% level and confirm that lag values are relevant instruments for individual macroeconomic variables. According to the results presented in the table, the most rigid variables are real GDP and the size of banking sector (coefficients r_gdp_r and r_banks). Finally, we interpret the standard deviation of shocks based on the a priori assumed inverse gamma distribution. In this case, the posterior mode and mean value are significantly higher than a priori mean values in the case of the four shocks with the highest posterior values estimated. Table 1 presents also the results on standard errors of shocks affecting the size of the shadow banking sector (u_sb_broad), the size of the banking sector (u_banks), the interest rate (u_euribor_r) and the term premium (u_spread). Prior and posterior distributions are available in Figure 1A the Appendix. 5 Conclusions Shadow banking activities in Spain constitutes of over one third of total banking sector and pose a notable threat to financial sector stability. Nowadays, the main components of Spanish shadow banking system are pension funds and insurance corporations, investments funds and financial vehicle corporations. In this paper, we aim to uncover the driving macroeconomic forces behind the shadow banking system development. Our findings can be summarized as follows: (i) the shadow banking system is highly procyclical; (ii) its components may act as both, complementarities (financial vehicle corporations) or substitutes (investment funds, insurance corporations and pension funds) to traditional banking, however in Spain the role of financial vehicle corporations is dominant; (iii) low interest rates and an increase of term spreads tend to be associated with more rapid growth of shadow banks in most of the cases and (iv) country-specific factors and the relative importance of individual shadow banking components play a role in the analysis and should not be disregarded. Overall, we may expect the shadow banking system to continue to growth, especially in the current environment of tighter bank regulations and low interest rates. Migration of lending intermediation from banks to nonbanks may be associated with increased market and liquidity risk but this is difficult to assess yet. In the end, there are some important 211 challenges ahead. First, data gaps remain challenging and need to be addressed. The ECB/Eurosystem data is definitely a step forward but is still far from perfect as it does not offer sufficiently long information on a sectoral level. Second, incorporating the shadow banking development into regulatory policy remains an open question. Third, cross-border effects of shadow banking needs to be considered as well. In future research, we plan to address some of these issues. We will broaden the sample and consider a cross-sectional dimension and measure the effect of a regulatory framework. We will also aim to analyze the individual components of shadow banking system. Acknowledgments This paper greatly benefited from data support provided by the ECB/Eurosystem. Authors would like to acknowledge the 2017 Research Grant (SP2017/110) from VSB - Technical University Ostrava and the support of Czech Science Agency grant GA16-13784S. References Adrian, T., Shin, H. S. (2009). The Shadow Banking System: Implications for Financial Regulation. Federal Reserve Bank of New York Staff Reports, no. 382. Bakk-Simon, K., Borgioli, S. Giron, C., Hempell, H., Maddaloni, A., Recine, F. and Rosati, S. (2012). Shadow Banking in the Euro Area: An Overview. European Central Bank Occasional Paper, no. 133. Claessens, S., Ratnovski, L. (2014). What is shadow banking? IMF Working Paper, no. 14/25. FSB (2013). Policy Framework for Strengthening Oversight and Regulation of Shadow Banking Entities. Retrieved from: http://www.financialstabilityboard.org/publications/r_130829c.htm. IMF (2014a). Global Financial Stability Report, Chapter 2: Shadow Banking around the Globe: How Large, and How Risky? Retrieved from: https://www.imf.org/external/pubs/ft/gfsr/2014/02/pdf/c2.pdf. IMF (2014b). Global Financial Stability Report: Moving from Liquidity- to Growth-Driven Markets. Retrieved from: http://www.imf.org/en/Publications/GFSR/Issues/2016/12/31/Moving-from-Liquidity-to- Growth-Driven-Markets. Mandel, B., Morgan, D., Wei, Ch. (2012). The Role of Bank Credit Enhancements in Securitization. Federal Reserve Bank of New York Economic Policy Review, vol. 35(2), pp. 225-254. Martin-Aceňa, P. (2014). The savings banks crisis in Spain: When and how? Asociación Espaňola de Historia Económica (AEHE) Documento de Trabajo, no. 1404. Pozsar, Z., Adrian, T., Ashcraft, A., Boesky, H. (2013). Shadow Banking. Economic Policy Review, vol. 19(2), pp. 1-16. Ruiz, J., Stupariu, P., Vilariňo, A. (2016). The crisis of Spanish savings banks. Cambridge Journal of Economics, vol. 40(6), pp. 1455-1477. Stúcklin, M. (2017). Spanish banks still heavily exposed to real estate. Retrieved from: https://www.spanishpropertyinsight.com/2017/03/07/spanish-banks-still-heavily- exposed-real-estate/. 212 Appendix Figure 1A Prior and Posterior Distributions Source: authors’ calculations 213 A Comparative Analysis of Shadow Economy in Croatia and Slovakia Sabina Hodzic1 , Jana Kockovicova2 1 University of Rijeka Faculty of Tourism and Hospitality Management, Department of Public Finance Primorska 42, Ika, POB 97, 51410 Opatija, Croatia E-mail: sabinah@fthm.hr 2 University of Economics in Bratislava Faculty of National Economics, Department of Finance Dolnozemská cesta 1, 852 35 Bratislava, Slovakia E-mail: kockovicova.euba@gmail.com Abstract: Shadow economy includes all unrecorded economic activities, employment and income. All shadow economy activities are unregulated, untaxed, unregistered and unmeasured by official statistics. Since Croatia and Slovakia are countries in transition, numerous elements exist that support shadow economy in international exchange. Shadow economies in Croatia and Slovakia influence all kinds of economic activities with allocative, stabilizing, welfare and redistributive effects. The objective of this paper is a comparative analysis of shadow economy in Croatia and Slovakia in the period 2005 – 2014. The labour approach is used because it relies on the difference between real and registered use of labour. Results show that, in this period, the level of Croatian shadow economy under the labour approach was highest in 2008 (3.65 percent), while in Slovakia it was highest in 2006 (27.47 percent). In order to improve the quality of the fight against corruption and to reduce the level of shadow economy, it is necessary to continue the development of the judiciary, take actions to improve the independence of the judiciary, review the principles of financial support for law enforcement and provide measures to improve their wages. Keywords: fiscal policy, shadow economy, labour approach, Croatia, Slovakia JEL codes: H20, H26, O17 1 Introduction Since Croatia and Slovakia are countries in transition, numerous elements exists that support shadow economy in international exchange. Some of these elements include inefficiency in privatisation, non-existent industrial policy, frequent changes of legislation, lenient criminal measures for combating illegal activities, etc. It is characteristic of countries in transition to achieve a faster and more successful transition due to higher rates of economic growth, lower unemployment rates and faster development of the private sector. There are numerous factors that affect the growth of shadow economy in countries in transition. Vehovec (2002), North (1997) and Kaufmann and Kaliberda (1996) claim these are political repression, an inadequate legal system, high tax burdens, macroeconomic instability and the culture of non-payment. Vehovec (2002) and North (1997) list the following factors: non-functioning of property rights, high entrepreneurial risk and high transaction costs. The World Bank (2001) claims these are large-scale legislative and regulatory changes, massive redistribution of national wealth, lack or inefficiency of institutions of public control. Bejakovic (2002) lists the following factors: lack of democratic, economic and regulatory institutions, lack of tax-paying experience, discretionary rights of public servants and state prodigality. In 2004, the Croatian Government adopted a plan of short-term and long-term measures for combating shadow economy. Therefore, the Ministry of Finance of the Republic of Croatia continued with consistent supervisory activities in its scope, particularly in cooperation with the Tax Administration and the Customs Administration. These activities 214 mainly refer to increased supervision of fiscalization and value added taxes as well as supervision and detection of illegal activities in the field of transport of excise goods, performance of unregistered activities, residence tax and vessel control. Taxpayers whose goods and services are paid in cash are under supervision. Special attention is given to supervision on the Adriatic coast and in the City of Zagreb. The main initiative of the Slovak Government is focused on tax evasion and tax avoidance. The Action Plan to improve the fight against tax evasion was introduced in 2012. The unification of the collection of taxes, duties and levies, and the suppression of tax evasion indicate clearly that its aim is ensuring sufficient budgetary resources. The government considers its key role to be the uncompromising, conceptual and systematic fight against tax evasion. The effectiveness of existing legislation and the Code of Criminal Procedure should be assessed and the possibility of introducing new tax offenses should be analysed. The strategy of the Slovak Government and Ministry of Finance in the area of tax administration puts emphasis on strengthening tax collection and the fight against tax evasion and fraud. Achieving this goal should help introduce new and stricter rules to tax legislation, increase the frequency and quality of tax audits through organizational changes and, overall, increase the number of staff in audit departments. It will also extend the territorial scope of the Tax Office for selected taxpayers to the entire territory of the Slovak Republic. The aim of this paper is to estimate under the labour approach the size of shadow economy in Croatia and Slovakia in the 2005-2014 period. The empirical approach is based on methodology used by Svec (2009) and Crnkovic-Pozaic (1997). For the purposes of this paper, shadow economy is defined as paid work which is legal in all respects other than it is not declared to authorities for tax, social security or labour law purposes (Williams and Franic 2015; Aliyev 2015; Boels 2014; Williams, Franic and Dzhekova 2014; OECD 2012; Polese and Rodgers 2011; European Commission 2007). 2 Methodology and Data Shadow economy can be difficult to quantify because of the complexity of the phenomenon itself. Depending on the analysed period and theoretical assumptions, different assessment methods have been used. The methods can be divided into those with a direct and those with an indirect approach. The direct approach encompasses the use of micro approaches, or well-designed surveys and samples based on voluntary responses or tax inspection and other effects of determining compliance with the law. The indirect approach focuses on developing a psychological contact between the state and its citizens to encourage commitment to compliance among citizens and thus greater self-regulation. The indirect approach, also known as “indicating“, offers 4 indicators. These are: • The currency demand method (Tanzi 1983, 1980; Gutmann 1977; Cagan 1958); • The electricity consumption method–two indicators: Kaufmann-Kaliberda method (1996) and Lackó method (Lackó 1998, 1996) and • The Multiple Indicators Multiple Causes (MIMIC) method (Giles, Linsey and Werkneh, 1999; Schneider 1997, 1994; Thomas 1992; Frey and WeckHannemann 1984) According to Nastav and Bojnec (2007), the three main groups of methods to quantify the size of the shadow economy can be identified as direct methods, indirect methods and modelling. In this paper, the labour approach is used because it belongs to indirect methods and relies on the difference between real and registered use of labour. Davidescu (2014) considers that “the labour approach method is one of the indirect methods and is meaningful only if the changes in official population activity rates are caused by factors related to the shadow economy. The advantages of this method are data availability and simplicity. The disadvantage is the fact that the initial value of 215 unofficial employment is always zero” (p. 26). The assumption is not realistic, but the method algorithm itself gives this value. The obstacles of this method are: • The assumption of the full-time participation of the unemployed in the shadow economy (and that no one with an official job participates (part-time) in the shadow economy) is not properly grounded due to sampling errors, underreporting and the fact that not all of those registered as unemployed according to the Labour Force Survey work full-time in the shadow economy. • The problem of underreporting of data to the Labour Force Survey. • Different data sources on the labour force in the economy are very limited in their consistency: the Labour Force Survey measures employment, whereas the administrative sources usually report the jobs. • The method doesn’t include and measure people with a second job. According to Svec (2009) and Crnkovic-Pozaic (1997), the activity rate can be defined as a ratio of persons who either are or wish to be economically active in relation to all persons of working age: Activity rate = (the employed + the unemployed) / persons of working age (1) The employed + the unemployed=labour force (total labour supply, total working population,de facto economically active population) (2) Alternative definition: Activity rate = (the employed + the unemployed) / total population (3) Share of the employed in the unofficial economy = (hypothetically active – de facto active) / de facto active (4) The main source of data is the Croatian Bureau of Statistics-Statistical Yearbook 2005- 2014 and Statistical Office of the Slovak Republic-Statistical Yearbook 2005-2014. 3 Results and Discussion The aim of this paper is to use the labour approach to calculate and compare the value of shadow economy in Croatia and Slovakia: the method based on historical activity rates (administrative data) using annual data covering the 2005-2014 period. Moreover, the paper also presents a correlation coefficient under the assumption of regression analysis. For the calculation of unofficial employment, the following data are needed: population, number of employed and unemployed people. Based on that, an algorithm is used to calculate the activity rate, hypothetically active population and unofficial economy for a certain time series. The results of Croatian data are shown in Table 1. 216 Table 1 Administrative data from 2005-2014 (in thousands) 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Total population 4442. 0 4440. 0 4436. 0 4434. 0 4429. 0 4418 .0 4280 .0 4268 .0 4256 .0 4239 .0 Total employed 1420. 5 1467. 9 1516. 9 1554. 8 1498. 7 1432 .4 1411 .2 1395 .1 1364 .0 1342 .0 Total unemployed 308.7 291.6 264.4 236.7 263.2 302. 4 305. 3 324. 3 345. 1 328. 2 De facto active population 1729. 2 1759. 5 1781. 3 1791. 5 1761. 9 1734 .8 1716 .5 1719 .4 1709 .1 1670 .2 Activity rate (%) 38.9 39.6 40.1 40.4 39.7 39.2 40.1 40.2 40.1 39.4 Hypothetica lly active 1729. 2 1728. 4 1726. 8 1726. 0 1724. 1 1719 .8 1666 .1 1661 .4 1656 .7 1650 .1 Unofficial employed (%) 0.00 1.77 3.06 3.65 2.14 0.86 2.93 3.37 3.06 1.20 Source: authors’ research based on data from the Statistical Yearbook 2005-2014, Croatian Bureau of Statistics. Table 1 leads to the conclusion that, in the observed period, there is a significant decrease in the activity rate until 2006, followed by a growth and then again by a decrease until 2009. In the 2005-2014 period, the de facto active population declined much faster than the total population grew. After 2010, the de facto active population grew and, thus, so did the rate, especially after 2011. After 2013, the de facto active population recorded a high decline despite the accession to the European Union (after 1 July 2013). The results of Slovakian data are presented in Table 2. Table 2 Administrative data from 2005-2014 (in thousands) 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Total population 5393 .6 5400 .9 5400 .9 5412 .2 5424 .9 5435 .2 5404 .3 5410 .8 5415 .9 5421 .3 Total employed 2216 .0 2301 .0 2357 .0 2434 .0 2366 .0 2318 .0 2315 .0 2329 .0 2329 .0 2363 .0 Total unemployed 427. 50 353. 40 291. 90 257. 50 324. 2 389. 0 367. 90 377. 50 386. 0 358. 70 De facto active population 2643 .5 2654 .4 2648 .9 2691 .5 2690 .2 2707 .0 2682 .9 2706 .5 2367 .6 2721 .7 Activity rate (%) 49.0 49.1 49.0 49.7 49.5 49.8 49.6 50.0 43.7 50.2 Hypothetically active 2643 .5 2647 .1 2647 .1 2652 .6 2658 .8 2663 .9 2648 .7 2651 .9 2654 .4 2657 .1 Unofficial employed (%) 0.00 27.4 7 0.07 1.44 1.17 1.59 1.27 2.02 12.1 2 2.37 Source: authors’ research based on data from the Statistical Yearbook 2005-2014, Slovakian Bureau of Statistics. During the observed period, Slovakia recorded a constant activity rate, except for 2014 when it reached its highest level (50.2). When compared to the Croatian activity rate, the situation is completely different because the activity rate was (39.4). Slovakia found itself in an interesting environment in 2006 when its activity rate was 49.1 per cent and the rate of unofficial employment was very high (27.47). The activity rate is inversely proportional to the estimate of people employed in unofficial economy. As the activity rate falls, the unofficial employment grows. The obtained result is meaningful as the Croatian population switches from official to unofficial economy. Therefore, the assumption that everybody who leaves the labour force enters unofficial economy is not entirely correct. There is always a share of active population that becomes inactive, but 217 this method cannot be used to calculate its percentage. By analysing possible correlations between employment in the official economy, unemployment in official economy and employment in unofficial economy during the observed 2006-2014 period (the initial year has been ignored, in which the labour approach supposes that the unofficial employment is zero, because the difference between the hypothetically active and de facto active population is zero), a negative significant relationship between official employment and official registered unemployment was noticed. The results are presented in Table 3. Table 3 Pearson's correlation coefficients for the 2006-2014 period (administrative data) in Croatia and Slovakia VARIABLE CROATIA SLOVAKIA Official employment and official unemployment -0.97 -0.18 Unofficial employment and official employment 0.31 -0.12 Unofficial employment and official unemployment -0.24 -0.87 Source: authors’ research. In Croatian economy, it is realistic to assume that the increase in the number of employed persons in official economy at the same time decreases the number of registered unemployed persons. The positive relationship between unofficial employment and official employment (0.31) can be explained as follows: persons employed in official employment are satisfied with their jobs and income and do not think about moving to the shadow economy. In Slovakia, the situation is totally different because the relationship between unofficial employment and official employment is negative (-0.12). This can be explained by the fact that persons employed in official employment are unsatisfied with their jobs and income and are thinking of moving to the shadow economy. An interesting situation can also be observed in the relationship between unofficial employment and official unemployment – it is negative in both countries, although Slovakia recorded a higher negative correlation coefficient (-0.87) compared to Croatia (-0.24). This can be explained by the fact that the decrease in the number of unemployed persons leads to an increase in unofficial employment because the unemployed become part of the unofficial sector. A comparison of these data with the data presented by Svec (2009), in which the author also analyzed the correlation coefficient between official unemployment, official employment and unofficial employment for the 2002-2007 period in Croatia, shows that the results are very similar. The author also found a negative correlation between official unemployment and official employment (-0.92). What is interesting is the fact that there is a huge difference between unofficial employment and official employment (-0.74) which is negative compared to these results, while the difference between unofficial employment and official unemployment (0.41) was positive as opposed to results which gave a negative correlation coefficient (-0.24). For Slovakia, this correlation is even more negative (- 0.87). By analyzing empirical results from this paper, it has been estimated that there are two negative correlations in the observed 2006-2014 period (official employment and official unemployment and unofficial employment and official unemployment) and one positive correlation between unofficial employment and official employment in Croatia. In Slovakia, negative correlations are recorded for all three relationships. 4 Conclusions The main reasons that have affected the rise of the shadow economy in the past decades in Croatia and Slovakia are the following: tax policy characterised by substantial tax rate increases; increasing burden of labour costs; obtaining different social supports; dissatisfaction with actions and goals of the government; and changes in work ethics. The Croatian and Slovak governments have to carry out an economic policy that will ensure economic growth and increase the living standard of their citizens. If real income 218 increases and economic growth is achieved, it can be expected that the size of shadow economy will decrease. The size of shadow economy in Croatia and Slovakia was estimated using the labour approach that relies on the difference between actual (real) and official (registered) use of labour for the 2005-2014 period. In Croatian economy, it is realistic to assume that the increase in the number of employed persons in official economy at the same time decreases the number of the registered unemployed persons. The positive relationship between unofficial employment and official employment (0.31) can be explained by the fact that persons employed in official employment are satisfied with their jobs and income and do not think about moving to shadow economy. On the other side, the situation is different in Slovakia because a negative relationship is recorded (-0.12) for the same variables. This can be explained by the fact that persons employed in official employment are unsatisfied with their jobs and income and are thinking of moving to the shadow economy. By analysing empirical results, it has been estimated that there are two negative correlations in the observed 2005-2014 period (official employment and official unemployment, -0.97, and unofficial employment and official unemployment, - 0.24) and one positive correlation between unofficial employment and official employment (0.31) in Croatia. In Slovakia, negative correlations are recorded in all three relationships (official employment and official unemployment -0.18, unofficial employment and official employment – 0.12 and official unemployment and unofficial employment – 0.87). Due to a lack of accurate evidence, the use of labour approach (only administrative data) is limited for concrete policy measures. Even so, in order to reduce the shadow economy, the Croatian and Slovakian governments need to implement serious sets of measures, especially structural reforms. 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In Financie a riziko : zborník príspevkov z XIII. ročníka medzinárodnej vedeckej konferencie. - Bratislava: Vydavateľstvo EKONÓM, pp. 211-218. 222 The changes in capital structure of selected banking markets as a result of new regulations Günter Hofbauer1 , Monika Klimontowicz2 , Aleksandra Nocoń3 1 Technische Hochschule Ingolstadt Business School, Esplanade 10, D-85049 Ingolstadt/Germany E-mail: guenter.hofbauer@thi.de 2 University of Economics in Katowice Faculty of Finance and Insurance, Department of Banking and Financial Markets ul. 1 Maja 50, 40-287 Katowice, Poland E-mail: monika.klimontowicz@ue.katowice.pl 3 University of Economics in Katowice Faculty of Finance and Insurance, Department of Banking and Financial Markets ul. 1 Maja 50, 40-287 Katowice, Poland E-mail: aleksandra.nocon@ue.katowice.pl Abstract: Among many different factors that has influenced banking market for the last few decades the regulations are thought to be the crucial ones. The new prudential standards implemented by the Basel Committee treat banks’ capital as a foundation for safety. The appropriate level of bank’s capital helps to manage all kinds of risks. European directives that have had to be implemented to national regulations have remarkably changed the market environment and influenced banks and credit institutions’ market policy. Fulfilling capital requirements has become one of their most important objectives. On the other hand, capital safety may favour maintaining the stability of banks.The paper presents the review of European regulation in this field and their impact on selected banking market. The author has chosen Germany and Poland as an representation of European largest economies. The purpose of the paper is to present the similarities and differences between the regulation impact on the west and east Europe. Keywords: Basel III, capital regulations, banking market, credit institutions’ liabilities, bank capital, structure of bank capital JEL codes: G21, G28 1 Introduction Events that took place in the twentieth century fostered alternately different doctrines relating to the scope and scale of regulations of a banking sector. Supporters of the ‘free banking’ called for more loosening in regulatory discipline, believing in the ability of banking sector self-regulation and market supervision. In turn, their opponents pointed to the advantages of banks’ strictly regulations, by creating more and more restrictions and requirements for capital adequacy. Since the 30s of the twentieth century, so since the Great Depression, prudential standards were gradually implemented, aimed at countering the threat of banking institutions' bankruptcy. At the beginning, there were set the guidelines, regulating the scope of activity carried out by banks and prudential standards depending on the scale of this activity as well as risk exposure. A particular role was attributed to the banks’ own funds - as a non-repayable source of financing, enabling the absorption of losses and guaranteeing the fulfilment of claims for creditors in case of potential bank’s insolvency. So that equity became a main axis of banks’ prudential regulations (Marcinkowska, 2005). The paper presents the evolution of prudential regulation, the contemporary capital requirements and their impact on Polish and German banking market. 223 2 Basel I and Basel II: History of the regulations’ evolution In the late 80s of the twentieth century with the growing importance of risk management and the need to regulate banking supervision, the Basel Committee on Banking Supervision developed innovative rules relating to the security of the banking system. The reason was to create a level playing field for ‘internationally active banks’. Basel Committee is not a supervision authority, and its recommendations are not applicable legal standards. These are only recommendations for good practice that national authorities may implement to their legislation in the form of relevant law acts, adapting to financial system specificities. At the same time, due to the high authority of the Committee, they are recognised in many countries around the world as a basis of created legal acts. In 1988 the Basel Committee has introduced a capital measurement system, presented in the document known as Basel Capital Accord (Basel I). The most important element of Basel I became a synthetic measure of the Capital Adequacy Ratio (CAR). This indicator, also known as Cooke ratio (Total Capital Ratio – TCR, Capital to Risk Weighted Assets Ratio - CRAR), presents how much capital a bank must hold to its activity to be safe (Nocoń, 2015). Initially, Capital Adequacy Ratio was referred only to credit risk. Therefore, it was defined as a relation between bank’s capital base (own funds, consisting of Tier I capital, as a basis to cover losses, and Tier II capital as supplementary capital for a bank) to risk-weighted assets (Iwanicz-Drozdowska, 2012): ./ 0 1 234 0 1 234 00 153567 289 :7435 ; 8% wherein: rcred = rbs+robs where: CAR1 – Capital Adequacy Ratio ctier I – basic funds (core capital, basic equity) - tier I capital ctier II – supplementary funds – tier II capital cdeduction – positions which reduce the total amount of funds rcred – exposure to credit risk (risk-weighted assets) rbs – exposure to credit risk on balance sheet items robs – exposure to credit risk on off-balance sheet items. Basel I required that the capital adequacy ratio can not be lower than 8%(IwaniczDrozdowska, 2012). It also defined four asset classes and four non-balance sheet liabilities classes, by assigning them risk weights (0%, 20%, 50% and 100%). A significant advantage of this approach was simplicity, which on the other hand - was also a main basis of criticism, because the assets were equally treated in the same group, without the possibility of varying the level of risk (Marcinkowska, 2010). However, changing environment of the banking sector, dynamic development of financial markets, high volatility of prices on the financial markets, orientation only on credit risk, and in particular the criticism of the oversimplified measurement in Basel I, led to the need to include in the measurement of capital adequacy, in addition to credit risk, also other kinds of risk, like: price (market) risk and operational risk. The work on the improvement of the Basel Agreement was revealed in 2004, presenting its new framework known as Basel II - The New Basel Capital Accord (Basel Committee on Banking Supervision, 2004). In June 2006 revised and improved version of the concept of Basel II was published (Basel Committee on Banking Supervision, 2006). Its main foundation became the idea of economic capital, defined as the minimum value of own funds, which secures all unexpected losses, taking into account the bank’s preferences regarding the accepted level of risk (Adamowicz, 2005). Additionally, a third category of 224 capital was introduced - Tier III capital (available to cover market risk). Basel II took into consideration a proposal of measuring capital adequacy, which was based on three complementary pillars (Iwanicz-Drozdowska, 2012) - see figure 1. Figure 1 The basic concept of Basel II Source: Own work. The New Basel Capital Accord maintained previously used definition of own funds and the minimum 8% level of Capital Adequacy Ratio. However, it implemented in its estimation apart from credit risk – also market and operational risk (see figure 2). In place of previously used approach – equally for all banks - was recommended the system, which consisted of three methods, allowing banking institutions to choose the most appropriate for them: a simple approach (assigning weights depending on the assets credit quality), as well as intermediate and advanced approaches. Therefore, capital requirements according to Basel II are much more dependent on the risks incurred by a bank (Marcinkowska, 2010). Furthermore, the New Basel Capital Accord allowed banks to use their own risk models (based on the VaR methodology) in estimation of market risk exposure. Figure 2 Evaluation of Basel Committee Initiatives. Source: Own work. Pillar I - Minimum Capital Requirements • consists of setting the minimum requirements for capital adequacy, including credit market and operational risk Pillar II - Supervisory Review Process • giving supervision authorities the additional task of assessing, whether the own funds, hold by a bank, are sufficient in relation to the scale and risk profile of its business Pillar III - Market discipline • applies to market discipline, performed by market participants, assessing bank’s risk on the basis of obtained information Settlement Risk 1974 Credit Risk 1988 Market Risk 1996 Operational Risk 2004 Cross Border Regulation Capital Adequacy Internal Models Market Discipline Economic Capital 225 The capital adequacy ratio according to Basel II is defined as follows (IwaniczDrozdowska, 2012, Marcinkowska, 2009, Kopiński 2008): ./ 00 1 234 0 1 234 00 153567 289 1 234 000 :7435 12,5 :>?4 :8@34 ; 8% where: ctier III – third category of own funds - tier III capital rmark – exposure to market risk roper – exposure to operational risk However, during implementation of the new recommendations of Basel II in early 2007 (also underwent severe criticism), the first symptoms of the global financial crisis occurred. It revealed many imperfections in risk management and existing supervisory regulations. Therefore, the international bodies, including the Basel Committee on Banking Supervision, were forced to revise the mandatory amount of banks’ equity capital. As a result, in the years of 2010-2011, the Basel III framework was presented, which will come into force successively until 2019. 3 Post-crisis capital requirements - Basel III As a reaction of low level of capital security of banks during the global financial crisis, in December 2010 the Basel Committee has published two documents (IwaniczDrozdowska, 2012): • Basel III: A global regulatory framework for more resilient banks and banking systems, • Basel III: International framework for liquidity risk measurement, standards and monitoring. These documents constitute a set of regulations known as Basel III, which strengthens previous equity requirements. It distinguishes two categories of capital in banks (Iwanicz-Drozdowska, 2012a, Nocoń, 2016): • tier I capital described as going concern capital, • tier II capital described as gone concern capital. Thereby, Tier III capital - introduced in The New Basel Capital Accord, disappeared (Iwanicz-Drozdowska, 2012a). The Basel Committee has also proposed tightening the rules for qualifying specific positions as core capital, to fully meet the requirements associated with the ability to cover losses. The amount of the capital adequacy ratio was left at the previous level of 8%. According to Basel III (Basel Committee on Banking Supervision, 2011), Tier I capital consists of common equity tier I (CET1) and additional tier I capital. However, the main emphasis is on common equity Tier I, which includes: • ordinary shares issued by a bank, • the issue premium arising from the issuance of instruments classified as common equity Tier I, • retained profits and other accumulated profits and disclosed reserves. Basel III has tightened existing recommendations, assigning a greater role of Tier I capital. Banks should, therefore, maintain capital adequacy ratios at the following levels (Basel Committee on Banking Supervision, 2011): • Common Equity Tier I ratio (CET1) ≥ 4,5% • Tier I Capital ratio ≥ 6% • Capital Adequacy Ratio (Tier I + Tier II) ≥ 8% 226 Despite the fact that the Basel Committee maintained the current level of capital adequacy ratio at a level of 8%, it also introduced two capital buffers: • capital conservation buffer – which has protective character, • countercyclical buffer – which has countercyclical character. Their aim is to increase the security of banks and banking sector, increasing requirements for the level of capital adequacy ratio, taking into account common equity tier I. Capital conservation buffer has appeared in 2016 at a level of 0.625%, in 2017 it will increase to 1.25%, after that to 1.875%, and from the beginning of 2019 it will amount to 2.5% (Basel Committee on Banking Supervision, 2011). Recommendations proposed in Basel III seem to be the a result of a concern for an adequate level of capital security for banks. Basically, Basel III is intended to strengthen banks' stability and increase funds security. Nevertheless, Basel III identifies the same capital adequacy ratios for all banking institutions, as a guarantor of safe global banking. However, it is highly disputable to adapt Basel regulations to national banking systems. Each country is governed by its own law referring to banks and attempts of transnational regulations common for all, which often turn out to be difficult to implement. 4 The changes in capital structure on Polish and German banking market As a result of the last financial crisis and the implementation of new regulations, the European banking market has changed remarkably. Different economies adapt to all this changes in a different way. Germany and Poland has been chosen to present the results of new regulation implementation as they are the representation of European largest economies. The primary result of the crisis was banking market consolidation. It has led to increasing market concentration. The commonly used indexes for measuring the market concentration are Herfindhal-Hirshman index for credit institutions’ total assets and concentration ratio. Both, Polish and German banking market become slightly more concentrated today, and the position of the five largest credit institution on the market has increased (see Table 1). Comparing with other European countries, both countries remain quite competitive ones. In 2016 the average level of HHI index was 1107. The level of this index in both countries is definitely below the mean. Similarly, the concentration ratio of both banking market is lower than the average for Europe that was 61.1 in 2016. Germany is still one of the less concentrated market in Europe. (see Figure 3). Table 1 The concentration of Polish and German banking market 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Poland HHI 640 562 574 559 563 568 586 656 670 659 CR5 46.6 44.2 43.9 43.4 43.7 44.4 45.2 48.3 48.6 47.7 Germany HHI 183 191 206 301 317 307 266 300 273 277 CR5 22.0 22.7 25.0 32.6 33.6 33.0 30.6 32.1 30.6 31.4 Source: own work based on ECB Banking Structural Statistical Indicators 227 Figure 3 The concentration of European countries’ banking markets in 2016 Source: Own work based on ECB Banking Structural Statistical Indicators Such a market structure means that there are many credit institutions conducting their activity on the market. All of them must fulfill the regulatory requirements concerning the capital as a guarantee of safety. Since the beginning of the financial crisis, the capital base has increased in both countries. In Poland, the total assets has almost doubled, from the level of 526 757 to the level of 778 298 million Polish zloty. In Germany, since 2007 it has tripled from the level of 302 891 to the level of 921 785 million euro (see Table 2). Table 2 The CI’s assets conducting activity on Polish and German banking market 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Poland (in million Polish zloty) the assets of branches 34590 56927 56477 57426 30198 29475 33739 33926 34088 35194 the assets of subsidiaries 492167 636851 606633 674432 774585 800689 860450 904654 894712 743104 Germany (in million euro) the assets of branches 137576 156185 151784 164883 189429 257255 185858 196033 252254 309763 the assets of subsidiaries 165315 172391 513322 631519 674870 630983 550653 595308 575902 612022 Source: own work based on ECB Banking Structural Statistical Indicators The capital increase enabled creating the stable banking system in both economies. The overall solvency ratio exceeds the minimum requirements of 8% in both countries. During the period considered it has been at the comfortable level. Today it has reached the level of 15.81% in Poland and 17.91% in Germany. Tier 1 ratio has also been above the expected requirements. According to Articles 83 and 96 of the Act of 5 August 2015 on Macroprudential Supervision over the Financial System and Crisis Management, since 1 January 2016, a countercyclical buffer rate has been set at 0% for credit exposures on 0 500 1000 1500 2000 2500 Greece Estonia Lithuania Netherland Malta Croatia Slovakia Portugal Denmark Finland Latvia Belgium Cyprus CzechRepublic Spain Slovenia Romania Bulgaria Sweden Hungary Poland France Italy Ireland UnitedKingdom Austria Germany Luxemburg 0 10 20 30 40 50 60 70 80 90 100 CR5 - right axis HHI - left axis 228 the territory of the Republic of Poland. This rate shall apply until it is changed by a regulation of the Minister of Finance. According to German Banking Act, the domestic countercyclical buffer rate shall be between 0% and 2.5% of the total risk exposure amount (German Banking Act, Article 10c). Since the 1st January 2016, the recommended level is the same like in Poland (ESRB, 2016). Both countries are prepared for Basel III standards in this case (see Table III) Table 3 The Basel indicators for Polish and German banking market 2007 2008 2009 2010 2011 2012 2013 2014 2015 Poland Overall solvency ratio 12.44 11.34 13.46 14.01 13.29 14.87 15.57 14.90 15.81 Tier 1 ratio 11.86 10.17 12.10 12.59 11.88 13.14 13.96 13.73 14.51 Capital buffer (%) n/a 3.34 5.46 6.01 5.29 6.87 7.57 6.90 7.81 Germany Overall solvency ratio 11.43 12.99 14.27 15.28 15.78 17.39 18.67 17.25 17.91 Tier 1 ratio 7.9 9.26 10.63 11.43 11.72 13.80 15.19 14.75 15.44 Capital buffer (%) n/a 4.99 6.27 7.28 7.78 9.39 10.67 9.25 9.91 Source: own work based on ECB Banking Structural Statistical Indicators Both, the Polish and German banking market are characterised by strong capital base that enables banks to remain stable during periods of stress when losses materialise. It will help to create resilience on those markets and banks’ sustainable development. 5 Conclusions The banking systems are the most significant sectors of today's’ economy that enable the development of other areas. Since the last few decades, the role and importance of financial institutions have been systematically increasing. Their resilience is one of the conditions necessary for other enterprises development and the stability of the whole country’s economy. The experience of the last financial crisis show that the free market is not able to create self-regulation that will be able to assure long-term stability. As a result, many regulatory initiatives has occurred. The crucial for banking market are Basel regulations. They focus on banks’ capital as a guarantee for banking systems’ safety. The necessity of fulfilling Basel equity regulations and measures has caused some changes in banking sectors’ capital level. Presented data shows that, during the last ten years, the capital base of Polish and German banking markets has increased remarkably. The changes in the capital level enable not only fulfill but even exceed all the Basel I, II and III requirements. Therefore, both market might be assessed as stable and prepared to absorb any unexpected losses that might occur in the future. References Adamowicz, T. (2005). Practical aspects of the second pillar of the New Basel Capital Accord. Safe Bank, No. 2. Basel Committee on Banking Supervision (1988). International Convergence of Capital Measurement and Capital Standards. Basel Committee on Banking Supervision (2004). International Convergence of Capital Measurement and Capital Standards. A revised framework. Bank for International 229 Settlements, Basel. Basel Committee on Banking Supervision (2006). International Convergence of Capital Measurement and Capital Standards. A revised framework. Comprehensive Version. Bank for International Settlements. Basel Committee on Banking Supervision (2011). Basel III: A Global Regulatory Framework for More Resilient Banks and Banking Systems. December 2010 (modified version: June 2011), pp. 12-13. ESRB (2016). Applicable CCyB rates, European Systemic Risk Board. Retrieved from: https://www.esrb.europa.eu/national_policy/ccb/applicable/html/index.en.html. Iwanicz-Drozdowska, M. (2012). Bank’s financial management. PTE, Warsaw, pp. 132- 137. Iwanicz-Drozdowska, M. (2012a). Banking risk management. Poltext, Warsaw. Kopiński, A. (2008). Bank’s financial analysis. PTE, Warsaw 2008, pp. 140. Marcinkowska, M. (2010). Basel’s tortuous patos… or bank capital standards: yesterday, today, tomorrow. Annales Universitatis Maria Curie-Skłodowska, Lublin, Vol. XLIV, 2, Sectio H, pp. 47. Marcinkowska, M. (2005). The evolution of banks’ capital adequacy standards. In: Transformation of financial market in Poland, Vol. I: Financial institutions and mechanisms of their operation. P. Karpuś, J. Węcławski (ed.), UMCS Publishing House, Lublin. Marcinkowska, M. (2009). Banks’ capital standards. The New Base Capital Accord in Polish supervisory regulations. Regan Press 2009, pp. 106. Nocoń, A. (2015). Capital adequacy ratios in early warning system of modern central banks. In: Challenges, Research and Perspectives. Trust in social, economic and financial relations. G. Hofbauer und Kollegen (ed.), European Research and Working Group, Uniedition GmbH, Berlin 2015, pp. 224-225. Nocoń, A. (2016). The response system of modern central banks on banking system instability. Difin, Warsaw, pp. 210-212. 230 Two Variables Affecting the Economic Value Added (EVA) Irena Honková University of Pardubice Faculty of Economics and Administration, Institute of Business Economics and Management Studentská 95, 532 10 Pardubice, Czech Republic E-mail: irena.honkova@upce.cz Abstract: Economic value added EVA is one of the most important indicators of financial analysis, however, it has not been being used widely in practice yet. A possible reason is its calculation that is more complicated than for conventional one, e.g. ratios. This complexity primarily involves calculating the average weighted cost of capital (WACC) and unequal approach to the calculation of net operating profit after tax (NOPAT), the WACC and NOPAT are initial variables for the calculation of EVA. The aim of this paper is to summarize the basic approaches to calculating WACC, particularly the cost of equity and to calculate NOPAT and finally to simplify the calculation of EVA with mathematical analogy modifications. Mathematical adjustment has proved that economic value added is essentially a net profit weighted by the debt proportion in the overall capital structure. This post is to submit a simplification of the indicator of economic value added in an effort to extend its application in practice. Keywords: Economic value added, EVA, Average weighted cost of capital, WACC, Net operating profit after tax, NOPAT. JEL codes: M21 1 Introduction Economic value added EVA is one of the most important indicators of financial analysis, however, it has not been being used widely in practice yet. A possible reason is its calculation that is more complicated than for conventional ones, e.g. ratios. This complexity primarily involves calculating the weighted average cost of capital (WACC) and unequal approach to the calculation of net operating profit after tax (NOPAT), the WACC and NOPAT are initial variables for the calculation of EVA. The aim of this paper is to summarize the basic approaches to calculating WACC, particularly the cost of equity and to calculate NOPAT and finally to simplify the calculation of EVA with mathematical analogy modifications. The goal of business should not be only profit maximisation. Profit is to be regarded as an accounting entry, which should be viewed in the context of other indicators (Businessvize, 2017). In recent decades, the pressure put on shareholder value has been strengthened, i.e. the pressure to increase shareholder value, the result of which was the creation of EVA that better affects the interests of shareholders and other stakeholders (Synek, 2002). The goal of business should therefore be to maximise market value (Accenture, 2011). In response to this request, economic value added EVA occurred at the beginning of 1990s. It was first published in 1991 by the American consulting firm Stern Stewart & Co. Its informative value became one of the most important indicators of financial analysis. According to Synek (2002), the creation of economic value added and its maximisation is the goal of business. This governs everything - evaluation of investment, new products, and internal departments. EVA is the basis of material involvement, on which the reward system is based. It forms a complex management system, of which main task is to analyse the factors that contribute to the value creation and ensures the decisions that will bring increasing value for shareholders (shareholder value) and increasing benefits 231 for everybody who is closely connected with the existence of the company (stakeholder value). Despite the undoubted importance of this indicator, it is not used by many small companies, because its calculation is complicated, to some extent. Only 35.6% percent of companies monitor EVA, while 88.9% profit after tax (Horová and Hrdý, 2007). Unlike common ratios indicators of financial analysis, the variables entering EVA calculation cannot easily be found in the financial statements. The aim of this article is to show that EVA is basically affected only by two variables. This will result not only in more frequent use in practice, but a financial manager also gets the information on how to particularly act to improve this indicator using a very simple pyramidal decomposition. 2 Methodology and Data Economic value added EVA (Eng. conomic value added) is a part of the so-called value performance criteria (Hrdý and Krechovská, 2013). This indicator is based on economic profit, which counts with all the costs of invested capital (equity and debt), which distinguishes it from the financial profits on which common financial analysis is based. EVA is given by the relation (Stewart, 1999): AB/ CD+/ E/.. ( . (1) Where: • NOPAT is net operating profit after tax (Eng. net operating profit after tax) • WACC is the weighted average cost of capital (Eng. weighted average cost of capital) • C is invested capital. Thus, economic profit is defined as earnings that exceed the cost of capital, i.e. EVA should be greater than zero so that the enterprise creates a new, the so-called added value, and thereby increases the original value of the company. NOPAT NOPAT, or net operating profit after tax, which is a rather complicated indicator, even in the Anglophone literature, enters the calculation of EVA. The English version of Wikipedia (2010) states two different calculations. The first works with strictly terminological approach, where it only deducts the operating profit from the amount paid in taxes – i.e. it multiplies it by a tax shield (1-t). Investopedia (2010) says that NOPAT does not include the tax savings resulting from the deduction of interest expense. Therefore, NOPAT is already defined differently in its domestic environment and the harder it is to transfer it into the Czech environment. NOPAT can be expresses in two ways (2), (3). CD+/ =AFG ( (1− ) (2) Where: • EBIT is earning before interest and tax (Eng. Earnings before interest and tax), • t is income tax rate Or 232 CD+/ =A/ +G (1− ) (3) Where: • EAT is net profit after tax, • I are cost interests, • t is income tax rate. WACC The second indicator entering the EVA calculation is the weighted average cost of capital WACC (Eng. weighted average cost of capital). WACC can be calculated as the sum of the unit costs of equity and debt, where the capital structure acts as a weight (Hrdý and Krechovská, 2013): E/.. HI ∗ KLM K H7 ∗ 1 ∗ KNM K (4) Where: • nvk are costs of equity (required profitability), • nck are costs of debt, • t is income tax rate, • Kvk is equity, • Kck is debt, • K is total capital. While the capital structure can be found in the balance sheet and the costs can of debt be easily calculated, mostly as the average interest rate, the costs of equity are difficult to be determined. Generally, the costs of equity can be determined either on the basis of market-based approaches, or methods and models based on accounting data. According to Dluhošová, the basic methods, used to estimate the costs of equity (Dluhošová, 2006), are: • Capital Asset Pricing Model CAPM (Capital Asset Pricing Model), • Arbitrage Pricing Model – APM (Arbitrage Pricing Model), • Modular models, • Dividend growth model. The economist William Sharpe’s Capital Asset Pricing Model CAPM was created in the 1960s. This market-based approach to determine the cost of equity indicates that the expected risk changes in direct proportion to the beta coefficient. Beta coefficient expresses the rate of a specific market risk through the weighing the shares sensitivity to changes in market portfolio (Kislingerová, 2010). APM model also belongs to a market valuation of assets, but unlike the CAPM, it is a multi-factor model. This model takes into account both macroeconomic factors (GDP, inflation) and microeconomic factors (profitability, indebtedness, liquidity, size). The modular method of determining the WACC is used in an economy with imperfect capital market and short-term functioning market economy. Alternative cost of equity is defined as the sum of the risk-free asset profitability and risk premiums. In this case, the risk premiums are not derived from the capital market, but from the business accounting data as (Dluhošová, 2006): E/.. O P OQ RS (5) Where: • WACC is the total capital cost of a debt-free company, • RF is a risk-free interest rate, 233 • RP is a risk surcharge for the commercial business risk, • RFS is a risk surcharge for the risk resulting from the financial stability, • RLA is a risk surcharge characterising the company size. In the modular method, the starting point is the current profitability of risk-free securities, to which, on the basis of an expert estimate, surcharges are added for different types of risks, resulting e.g. from indebtedness or a reduced level of company liquidity. Determining costs with the use of the modular method is possible even without expert estimates. There is a method, which describes a specific method of calculation, see Kislingerová (2010). The dividend model is used for shares valuation, when the market price of the shares is determined by the present value of future dividends from the shares in individual years. Assuming an infinite period of holding shares and a constant value of dividends, the shares market price can be determined as perpetuity (Nývltová and Marinič, 2010). The disadvantage of market models CAPM and APM is their limited applicability only for joint stock companies with public shares. CAPM and APM models and the modular method do not assume expert estimates. Neither the dividend model can be generally recommended because few companies (if any) can be found in current conditions in the Czech Republic, which regularly pay dividends and the amount of dividends paid fully reflects the requirements of shareholders for increased value of invested capital, and they no longer expect, in addition to the payment of dividends, increase in the market share rate (Nývltová and Marinič, 2010). Given that the aim of this paper is to simplify, or rather allow, the calculation of EVA, and the calculation of WACC and the components of costs of equity for all businesses, it is necessary to offer businesses a different method of calculation. 3 Results and Discussion Similarly to the dividend model of costs of equity calculation, it can be stated that the dividends are essentially the cost of equity, as well as the interests are the cost of debt. Given that equity is a potential liability to the owners, the entire profit after tax can also be understood a potential pay-out to the owners. Dividends may be substantially equal to the profit after tax and the profit after tax is thus the cost of equity. The cost of equity is thus calculated as capital profitability, which is essentially the value of ROA: HTK US K 6 Where: • EAT is profit after tax, • K is the capital. Now, we can proceed to the simplification of EVA, which is demonstrated mathematically and through the exercise experiment in this paper. As we stated, EVA is calculated: AB/ CD+/ E/.. ( ., (7) NOPAT can be calculated: CD+/ A/ G ∗ 1 (8) And WACC as in (4) can be by a modification of (6) calculated: 234 E/.. US K ∗ TK K 0 VK ∗ 1 ∗ VK K . (9) If NOPAT and WACC are put into the calculation of EVA, we get: AB/ A/ G ∗ 1 US ∗TK∗K K WX∗Y∗ Z ∗X WX∗X (10) If we cancel out the variable of total capital K and the variable of debt CK, we get this equation: AB/ A/ G ∗ 1 A/ ∗ TK K – I * 1- t (11) If we adjust it mathematically, we get: AB/ A/ A/ ∗ TK K , (12) After factoring out we get: AB/ A/ ∗ `1 TK K a, (13) This can be finally written as: AB/ A/ ∗ WX X . (14) From the derivation above, we gained an equation, when EVA is only affected by the net profit after tax and debt structure. From a simple multiplier pyramidal decomposition, Fig. 1, it is clear that it is not necessary to calculate cost of equity and debt for the positive influence on EVA. EVA will grow with increasing net profit and debt. Figure 1 Multiplier Decomposition of EVA EVA VK K Source: own The following example will practically prove the new equation for the calculation of EVA. It is therefore necessary to calculate the value of EVA in the traditional way and the new suggested way to verify that the results are the same. Exercise: Equity is 500 000 CZK, debt is 400 000 CZK, the profit after tax is 200 000 CZK, the effective tax rate of 18%, the cost of debt is 0.0721% per annum, interests are 28 500 CZK. The exercise is first solved by the original equation of (1), (3) and (4): AB/ 200 000 28 500 ∗ 1 0,18 ∗c *d d e ∗ fc ∗ , f ∗d e ∗d . (15) AB/ 88 889. (16) When calculating using a new equation (1) (3) and (7), EVA is calculated very easily: AB/ 200 000 ∗ e d (17) EAT * 235 AB/ 88 889. (18) The results (16) and (18) prove that the significant simplification of equation led to the same result as in the use of a more complex equation. 4 Conclusions The aforementioned indicates that the economic value added is essentially a net profit weighted by the debt proportion in the overall capital structure. Besides the net profit indicator, which is an absolute indicator itself, there is also a noticeable tendency of the inclusion of debt. Since there is a rule that debt is a generally cheaper type of financing in comparison with equity, cost of capital is basically integrated here as well, albeit it does not appear in a simplified concept of EVA. This post is to submit a simplification of the indicator of economic value added in an effort to extend its application in practice. References Accenture, Charted Institute of Management Accountants (CIMA) (2011). Sustainability performance management: How CFOs can unlock value. Londýn: Chartered Institut of Management Accountants. Dluhošová, D. (2006). Finanční řízení a rozhodování podniku: analýza, investování, oceňování, riziko, flexibilita. Praha: Ekopress. Businessvize (2017). Finanční analýza. Retrieved from: www.businessvize.cz Horová, M., Hrdý., M. (2007). Aktuální problémy strategického finančního řízení podniků v ČR. E+M. Ekonomie a management. Vol. 10(4), pp. 80-86 Hrdý, M., Krechovská, M. (2013). Podnikové finance v teorii a praxi. Praha: Wolters Kluwer Česká republika. Kislingerová, E. (2010). Manažerské finance. Praha: C. H. Beck. Investopedia (2010). Net Operating Profit After Tax. Retrieved from: http://www.investopedia.com/terms/n/nopat.asp. Wikipedia (2010). NOPAT. Retrieved from: http://en.wikipedia.org/wiki/NOPAT. Nývltová, R., Marinič, P. (2010). Finanční řízení podniku: moderní metody a trendy. Praha: Grada Publishing. Stewart, G. B. (1999). The Quest for Value. New York: Harper Business. Synek, M. (2002). Podniková ekonomika Praha: C.H. Beck. 236 The Structure of Banks' Assets in Terms of Portfolio Theory and Bank Capital Regulation Jan Horvat1 , Eva Horvatova2 1 University of Economics Bratislava Faculty of National Economy, Department Banking and International Finance Dolnozemská cesta 1, 85235 Bratislava, Slovakia jan.horvat3@gmail.com 2 University of Economics Bratislava Faculty of National Economy, Department Banking and International Finance Dolnozemská cesta 1, 85235 Bratislava, Slovakia Masaryk University Brno Faculty of Economics and Administration, Department of Finance, Lipová 41 a, 60200 Brno, Czech Republic E-mail: eva.horvatova@euba.sk Abstract: In the period of the financial crisis, the current question is how banks should invest, as interest rates and market yields are low, and capital growth is required as a result of bank regulation requirements. Balancing between risk and return in the banking sector is still sustaining and contributing to the pro-cyclical behavior of banks. This article addresses the question whether it is appropriate to undertake a high risk in the banking sector and whether the risk is adequately compensated by adequate returns. It compares the portfolio risk of traditional loans and portfolio of securities eligible for inclusion into the HTM (Held to Maturity) portfolio. Keywords: bank assets portfolio, portfolio risk JEL codes: G11, G18 1 Introduction • Markowitz (1952) or (Markowitz, 1952) • Black and Scholes (1973) or (Black and Scholes, 1973) • Phillips et al. (2009) or (Phillips et al., 2009) This issue of balancing between risk and returns in the banking sector and pro-cyclical behavior of banks confirm several authors such as Gordon, Alexandre, Shu (2014) that if regulation allows a hidden increase in risk in banks' trading books without penalty in the form of a capital increase, then the pro-cyclical nature. This problem is not new; it represents the relationship between regulation and market behavior of banks. This question is visible to other authors, such as Koehn a Santomero (1980), who argue that banks with low risk aversion had higher capital requirements but also a higher probability of bankruptcy. Van Hoose (2007) looked at banks as portfolios managers. The primary effect of any system of capital requirements is, according to the leverage effect of the portfolio, to change the ratio between capital and assets. It is probable that the result will be a change in the assets portfolio of the financial institution. Koehn and Santomero (1980) have generalized that the capital requirements and the reliability of the banking system as a whole depend on the distribution of risk aversion across banking systems. Kahane (1977) argued that it is not possible to effectively reduce the risk by regulating if the subject of regulation is not the composition of assets. 237 In addition, we have been inspired by research of the author Tanda (2015) on the relationship between capital and risk-taking, the behavior of banks, as a key link in the regulation of capital. The author points out the differences in the behavior of banks under of selected characters. Its benefits are mainly in comparison to several studies and the methods used. For example, bank size may have a negative impact on capital growth (Berger et al., 2008), because larger banks have easier access to capital markets (Ahmad et al., 2009), which is associated with greater flexibility in the use of hybrid instruments or subordinated debt, they can rely on public pressure in case of emergency. The paper of Beltratti, A. and Stulz, R. (2009) deals with the question of why banks behave better during the financial crisis. They say the hypothesis that the release of regulation leads to problematic behavior of banks, and vice versa, the regulation is tightening during the crisis and this leads to better bank behavior. They also point out that during the crisis; large banks have a lower performance than small ones. The bank's assets can be viewed as a portfolio in the sense of Markowitz's modern theory of the portfolio. The question of the optimal composition of the portfolio looks at the yield of each component and its correlation. Conversely, it does not take into account the liquidity risk or the credit risk, which gives some limitations to the results and recommendations of Markowitz's modern portfolio theory for the bank portfolios. 2 Methodology and Data We have created a portfolio of loans as a market portfolio on the basis of Markowitz's portfolio theory. We compared the portfolio theory recommendations with gap analysis recommendations. Gap analysis took into account the maturity of loans and the expected rate of interest, Markowitz's portfolio theory takes into account volatility and covariance between portfolio components. In addition, it must be included in mind that in the real world, investors can achieve better results by combining the market portfolio with risk-free investments. In this respect, we use the capital market line theory. Inspiration to combine a market portfolio with a risk-free investment was mainly bank practice during the financial crisis. Figure 1 Capital Market Line Source: own processing Effective border -P rf A AC rP C P 238 All portfolios constructed on the basis of a combination of market (in our case credit portfolio) and risk-free investment will lie on the rf-AC line. The higher the portion of the market portfolio, the more profitable the yield will be. The higher the share of risk-free investment, the yield will be closer to rf. For portfolio creation we used data on loans in the euro area, namely their volumes and interest rates. Loans were broken down by maturity and by type. These data are from the ECB (Statistical Data Warehouse) for the period 2015. Data on the German and Greek bond prices are from the Bloomberg site. Model Specification The Capital Market Line (CML) is an effective combination of the risk-adjusted market risk portfolio with the risk-free asset. Markowitz extended his theory to risk-free assets, which are mainly government bonds. The mathematical expression of the capital market line is as follows: p m fr fr rE rE m p σ σ . )( )( − += (1) where: Erp = expected market yield of the market portfolio rf = risk-free yield Erm = expected market yield of the market portfolio σm = market portfolio risk σp = portfolio risk with risk-free asset On the capital market line CML are portfolios that combine market portfolio and the riskfree assets (Sharpe, Alexander, 1999). 3 Results and Discussion At the beginning, we wanted to point out the sensitivity of loan development to selected factors, namely inflation, deposits and capital in the euro area. It appears that the most significant impact on the development of loans has had deposits, and that inflation and capital have had no significant impact on the credits development. 239 Figure 1 Impulse- Response Function between Credits and Inflation, Credits and Deposits, Credits and Capital Source: www.ecb.int. Statistical Data Warehouse. We have prepared a portfolio of loans as a market portfolio based on Markowitz's portfolio theory. Table 1 Impact of Predicted Interest Rates, Credit Volumes and Portfolio Theory on the Recommended Loan Portfolio Type of credit Weight in portfoli o design Weight in the portfoli o - real Recom mended weight in % What is recom mend ed to do Expecte d Interes t Rate Movem ent Short- term/ Long- term Credit Profit / loss (on the basis of gap analys is) What to do? Recomm ended moveme nt of credits (as result of gap analysis and portfolio theory) TO_3M NONFINCORP - 0,4475 27,958 0 ↓ ↓ ST ↓ ↓ TO1Y NONFINCORP 2,8747 12,68 2,87 ↓ ↓ ST ↓ ↓ OVER5NONFINCOR P - 1,7725 1,731 0 ↓ ↓ LT ↑ Discrep ancy TO1YHOSING 4,130 4,212 4,133 ↑ ↑ ST ↑ ↑ 1_5HOUSING 21,12 1,378 21,12 ↑ ↓ LT ↑ ↑ OVER5HOUSING 6,871 1,645 6,87 ↑ ↓ LT ↑ ↑ TO1YCONS 11,266 0,007 11,26 ↑ ↓ ST ↓ Discrep ancy 1_5CONS 21,938 2,446 21,93 ↑ ↓ ST ↓ Discrep ancy OVER5CONS 28,803 2,482 28,80 ↑ ↑ LT ↓ Discrep ancy TO1Y TO1MIL NONFINCORP 0,1054 3,846 0,105 ↓ ↓ ST ↓ ↓ HOUSE PURCHASE 5,1004 8,729 5,100 ↓ ↓ LT ↑ Discrep 240 OVER 10Y ancy TO1Y OVER1MIL NONFINCORP - 0,0250 32,17 0 ↓ ↓ ST ↓ ↓ Source: own processing On the basis of the analysis, we have come to the conclusion that in the euro area, the housing credits groups have profitable potential and this is shown in the Table 1 in highlighted rows. The risk is related to a possible change in the interest rate, so caution is required over 5 years. The portfolio of loans (it means the market portfolio) has a yield of 5,733,728 and a risk of 0.000092968. When investing in a market portfolio and a risk-free asset, new options will be added to the eligible set. In our case, we will combine the loan portfolio as a market-efficient portfolio and risk-free assets in the form of government bonds of Greece and Germany. In the case of Greece's government bonds, we will see the paradox that the asset is considered risk-free on the basis of membership of sovereign bonds. In fact, the market risk is high and the site is able to capture Markowitz modern portfolio theory. On the basis of the theoretical backgrounds, we will combine the market portfolio of loans with the government bond component. Government bonds are referred to in the theoretical literature as a risk-free component because they are the state's issuer and the credit risk is 0. On the other hand, these bonds, especially at the time of the financial crisis, have market risk. In order to express their market risk, we can build a portfolio of Greek and German government securities. Table 2 Optimal Portfolio of Securities, Yield and Risk Securities type Recommended share in the portfolio GTGR6M* 0,25922923 GTDEM6M** 0,008287163 GTGR25Y*** 0,483489 GTDEM25Y**** 0,24899929 Risk 0,003895 Yield 6,893793 GTGR6M* = Government bonds, Greece, maturity 6 Month GTDEM6M** = Government bonds, Germany, maturity 6 Month GTGR25Y*** = Government bonds, Greece, maturity 25 Years GTDEM25Y**** = Government bonds, Greece, maturity 25 Years Source: own processing Given the risk of the securities portfolio, it is not possible to talk about a risk-free investment. It may be considered risk-free in view of the fact that the OECD government securities have zero credit risk. 241 Table 3 Optimal Portfolio of Securities, Market Portfolio, Yield and Risk Yield Risk Risk to Yield Rate Portfolio of Securities 6,8937 0,0038953 0,000565051 Portfolio of Credits (Market Portfolio) 5,7337 0,0000929 1,62142E-05 Combined Portfolio 50:50 6,3137 0,001994 0,000315843 An increase of how much (Combined – Market) 0,5800 0,001901 0,003277736 An increase how many times (Combined/ Market) 1,1011 21,449961 19,47939795 Source: own processing The increase in risk is disproportionately high in relation to the increase in the combined portfolio yield. As the table shows, the yield of the combined portfolio growth is a 1,101 fold; increase of the risk was 21.4499 fold. If we assume that the optimal yield growth is such an increase, which is accompanied by the least increase in risk, the porphyria does not meet the assumptions of optimal yield growth. Banks thus have an inadequate low yield increase exposed to an unreasonable high risk. 4 Conclusions As we can see, the market risk of government securities in this case is even higher than the market risk of the loan portfolio. This means that, in fact, banks face a higher risk of purchasing government securities than is assumed by their risk-free assets. Market risk is often not taken into account. The increased risk is offset by a higher return, and it is questionable whether this increase in risk is adequate to the growth of the mixed portfolio yield. In order to reduce risk, the banks will need to consider alternative solutions that would identify the onset of new risks, crises and the solutions offered in the regulation so as to reduce the rate cyclical nature of regulation. Several steps in this direction have already been made. Acknowledgments This article is processed within the project VEGA 1/0693/17 „Banková únia: systémový prístup k vyhodnoteniu príčin a dopadov zavedenia bankovej únie na bankový sektor SR a štátov Eurozóny“ and within the project of specific research A/1039/2016 “Modelování volatility na finančních trzích a její aplikace v oblasti řízení rizik a oceňování aktiv”. References Ahmad, R., Ariff, M., Skully, M. J. (2009). Determinants of bank capital ratios in a developing economy. Asia-Pacific Financial Markets 15(3–4), pp. 255–272. Beltratti, A., Stulz, R. (2009). Why Did Some Banks Perform Better during the Credit Crisis? A Cross-Country Study of the Impact of Governance and Regulation. ECGI Working Paper Series in Finance, Working Paper N°. Berger, A., De Young, R., Flannery, M. J., Lee, D., Oztekin, O. (2008). How do large banking organizations manage their capital ratios? Journal of Financial Services Research 34(2–3), pp. 123–149. Kahane, Y. (1977). Determination of the Product Mix and the Business Policy of Insurance Company - a Portfolio Approach. Management Science, 23(10), pp. 1060- 1069. Koehn, M., Santomero, A. (1980). Regulation of Bank Capital and Portfolio Risk. The Journal of Finance, 35(5), pp. 1235-1244. 242 Sharpe, W. F., Alexander, G. J. (1994). Investice. Praha: Victoria Publishing. Tanda, A. (2015). The Effects of Bank Regulation on the Relationship between Capital and Risk. Comparative Economic Studies, 2015, (31–54). Van Hoose, D. (2007). Theories of bank behavior under the capital regulation. Journal of Banking & Finance 31, 2007, pp. 3680–3697. 243 Analysis of the impact of capital structure on business performance Jarmila Horváthová1 , Martina Mokrišová2 1 University of Prešov in Prešov Faculty of Management, Department of Accounting and Controlling Konštantínova 16, 080 01 Prešov, Slovakia E-mail: jarmila.horvathova@unipo.sk 2 University of Prešov in Prešov Faculty of Management, Department of Accounting and Controlling Konštantínova16, 080 01 Prešov, Slovakia E-mail: martina.mokrisova@unipo.sk Abstract: The aim of the paper was to analyse the impact of company's capital structure on its performance. To achieve goal, the data of Slovak businesses were used. An input analysis of the capital structure of selected sector was carried out in order to generalize and elaborate conclusions aimed at the capital structure of analysed businesses. Selected indicators of capital structure were calculated to analyse the relationships between these indicators and business performance. The results of the correlation analysis were complemented by examining the impact of selected independent variables on business performance applying regression analysis and principal component analysis. On the basis of the findings, capital structure model was formulated to quantify the impact of changes in capital structure on business performance. The contribution of the paper is the identification of capital structure indicators that affect business performance as well as the construction of capital structure model. The article as well as the research, which is the basis for paper elaboration, is the result of professional public interest focused on the finding whether the capital structure is the determinant of business performance. Keywords: business performance, capital structure, indicators, model JEL codes: C10, G32, G17, M21, C53 1 Introduction One of the fundamental problems of business financial management is to determine the correct composition of the funds needed to finance business activity, characterized by the term financial structure (Růčková, 2008). Capital structure is less comprehensive term and expresses only the structure of long-term capital. According to another definition, capital structure informs users about the type of capital, the period during which funds are fixed and business stability as well. It provides information on whether an enterprise makes optimal use of this capital in terms of indebtedness and capital commitment (Sedláček, 2003). According to Jánošová (2008), the company's capital structure is quantified using a wide range of indicators. The most common used indicators are: Total debt to total assets, Equity ratio, Debt to equity ratio, Equity to debt ratio, Interest coverage (key performance indicator, driver of the risk of capital structure), Interest burden, Equity to fixed assets ratio, Financial leverage, Stability and other indicators. Debt ratios create a network with strong relations between them (Štefko, Gallo, 2015), which results in synergic effect of indicators` impact on business performance. Development of debt ratios of performance evaluation should be focused on the processing and design of indicators which are most closely connected to the performance evaluation (Suhányiová, Suhányi, 2011). The idea of proper capital structure was dealt with many financial management experts. Several theories (static and dynamic theories on capital structure) have been processed and many views on the issue of sources of financing of business activities (Závarská, 2012) have been published. An intensive discussion on this issue has started since the 244 original work of Modigliani and Miller (1958) was published. Frequently discussed was the issue of capital structure in terms of ownership that would maximize the economic profit and business performance. In general, these capital theories can be divided into two basic groups. The first group consists of static theories and second group consists of dynamic theories of capital structure. Static theories deal with the issue of optimal indebtedness and look for answers to the question of whether there is optimal indebtedness, how to define it, on the basis of which criteria and in terms of who (the owners, managers or creditors). This group involves classic theory, traditional approach (U curve theory), theory of Miller and Modigliani (MM model) and trade off model. On the contrary representatives of dynamic theories argue that there is no uniform methodology for determining optimal capital structure due to the specific conditions of each enterprise. Dynamic theories include theory of hierarchical order and signalling model. Financial theory definitely confirms a certain link between capital structure and performance. In general debt is cheaper than equity. Business owner accepts higher risk than the creditors, since the return on an investment for the creditors takes priority over the return on capital invested by business owners. Therefore business owners require higher appreciation of capital invested in the enterprise. Increasing the share of debt in the financial structure of an enterprise can therefore positively affect the amount of economic profit. On the other hand with the rise in indebtedness the risk of bankruptcy is growing (Kiseľákova, Šofranková, 2015). This risk is gradually projected into the expectation of creditors who start to demand an increase in return on their investment to offset the risk they incur. The aim of the business owner is therefore to optimize the capital structure, with the intention of reducing the Weighted average cost of capital (WACC). Research problem: Does the company's capital structure affect business performance? What is business performance at different equity to debt ratio? What is the impact of capital structure on Cost of equity and business performance? How does the capital structure affect Cost of equity evaluation calculated by CAPM (Capital Assets Pricing Model) and Build-up model? 2 Methodology and Data Analysed company is an electrical engineering stock company, which produces terminal telecommunication equipment such as standard, over-standard and special phones, residential equipment, electrical installation material and others. The company's range of products is mainly oriented to the electrical engineering industry, but a large part of the production is focused on the automotive and construction industries. The company's data for the years 2010-2016 were used as part of the analysis. From the capital structure point of view, company is financed by equity, while the average value of the indicator Equity ratio is 78%. The aim of the paper was to find out the impact of capital structure on business performance. Methods: EVA Equity and EVA Entity models were used to calculate performance. The formula (1) indicates that the Economic Value Added is expressed in two ways. AB/Ug62 h DA :3 i A (1) where AB/ stands for Economic Value Added, DA is Return on Equity, A is Equity and :3 represents Rate of Alternative Cost of Equity. One formula shows what is known as the spread DA :3 , which expresses the relative AB/ A⁄ (Neumaierová, Neumaier, 2016). The relative EVA is needed as input to the correlation matrix. Formula for the calculation of EVA Entity is as follows: 245 AB/U9 2 h CD+/ E/.. i . (2) where CD+/ stands for Net Operating Profit after Tax, E/.. is Weighted Average Cost of Capital and . represents Paid Capital. For the calculation of Cost of equity we used CAPM with the acceptance of market, external and systematic risks. In order to compare performance results and the influence of risks on performance, we applied Build-up model for Cost of equity evaluation. This model accepts internal - financial risks and internal and external business risks. Cost of equity, which enters into the calculation of EVA indicator, was quantified applying CAPM we used formula: :3VSP :klQS i A +lQS . + (3) where :k stands for Risk-free rate of return of US T-bonds, A + is Equity Risk Premium of US market, represents coefficient of systematic risk and . + is Country Risk Premium (Damodaran, 2001) and Build-up model - we applied formula: :3ml :k :n :k29 (4) where :n stands for Risk premium for business risk and :k29 is Risk premium for financial risk (Mařík et al., 2011). To analyse relationships between capital structure indicators (Total debt to total assets, Equity to debt ratio, Current liabilities to total assets, Equity to fixed assets ratio, Interest coverage, Financial leverage) and performance indicator (AB/ A⁄ ) we used correlation matrix (Table 1) and Principal Component Analysis (Figure 1). To confirm the impact of capital structure and cost of capital on performance, we used multiple linear regression model. Table 1 Correlation matrix Total debt to total assets Equity to debt ratio Financial leverage Interest coverage Equity to fixed assets ratio Current liabilities to total assets EVA/E Total debt to total assets 1.0000 -.9971 .9998 -.0150 -.4541 .9607 .2332 p= --- p=.000 p=.000 p=.975 p=.306 p=.001 p=.615 Equity to debt ratio -.9971 1.0000 -.9954 -.0122 .4564 -.9638 -.2651 p=.000 p= --- p=.000 p=.979 p=.303 p=.000 p=.566 Financial leverage .9998 -.9954 1.0000 -.0230 -.4535 .9589 .2225 p=.000 p=.000 p= --- p=.961 p=.307 p=.001 p=.632 Interest coverage -.0150 -.0122 -.0230 1.0000 .5659 .1688 .1551 p=.975 p=.979 p=.961 p= --- p=.185 p=.717 p=.740 Equity to fixed assets ratio -.4541 .4564 -.4535 .5659 1.0000 -.2105 -.1179 p=.306 p=.303 p=.307 p=.185 p= --- p=.650 p=.801 Current liabilities to total assets .9607 -.9638 .9589 .1688 -.2105 1.0000 .2128 p=.001 p=.000 p=.001 p=.717 p=.650 p= --- p=.647 EVA/E .2332 -.2651 .2225 .1551 -.1179 .2128 1.0000 p=.615 p=.566 p=.632 p=.740 p=.801 p=.647 p= --- Source: processed by authors in STATISTICA From the matrix (Table 1) is evident, that there is a strong, indirectly proportional relationship between Equity to debt ratio and Total debt to total assets, and there is a strong, directly proportional relationship between Total debt to total assets and Financial 246 leverage. Similarly there is a strong directly proportional linear relationship between Current liabilities to total assets and Total debt to total assets. In the case of Interest coverage, we did not notice a significant relationship with the analysed indicators. Interest coverage does not create a pair with any indicator because the way of its calculation is considerably different from other analysed indicators. Interest coverage can be considered as a key performance indicator that does not correlate with any indicator within a given group. None of capital structure indicators correlate with the indicator AB/ A⁄ . Based on above mentioned, it can be stated that there is no significant relationship between the selected debt ratios and performance. Applying Principal component analysis for the analysis of indicators of indebtedness we obtained two main components which involve 94% of the data variability. The first principal component evaluates Total debt to total assets, Equity to debt ratio, Financial leverage, and Current liabilities to total assets. The second principal component gives information about Interest coverage and Equity to fixed assets ratio (Figure 1). Figure 1 Projection of the variables on the factor - plane Projection of the variables on the factor-plane ( 1 x 2) Interest coverage Equity to fixed assets ratio -1,0 -0,5 0,0 0,5 1,0 Factor 1 : 68,98% -1,0 -0,5 0,0 0,5 1,0 Factor2:25,26% Total debt to total assets Equity to debt ratio Financial leverage Current liabilities to total assets Source: processed by authors in STATISTICA Since the correlation matrix did not confirm significant relationships between capital structure indicators and indicator AB/ A⁄ , we did not create a regression model with these independent variables. Nevertheless, we tried to reveal the impact of the capital structure on performance. Therefore we used equity, debt, Cost of equity, Cost of debt and Cost of capital as independent variables. Two regression models were statistically significant - EVA Equity model applying Build-up model for Cost of equity calculation. This model demonstrated significant impact of Cost of equity and equity on EVA Equity. In the case of these indicators P – Value was less than 0.05. Estimated multiple regression model for AB/Ug62 hml: AB/Ug62 hml o DA2 :3Q 2 pA2 (5) AB/Ug62 hml 2.941A 06 389.218 DA2 1.557A 07:3Q 2 0.124A2 (6) 247 In the second model all indicators show statistically significant impact on EVAEntity, P - Value < 0.05. Estimated multiple regression model for AB/U9 2 hml: AB/U9 2 hml o q2 :52 p:3Q 2 eE/..2 (7) AB/U9 2 hml 291,581 0.028q2 7.575A 06:52 3,868A 06:3Q 2 48,289.6E/..2 (8) 3 Results and Discussion When constructing different regression models, we found out that the impact of capital structure on performance is determined by the way how performance is calculated. In the case of EVA Equity application, the impact of equity on business performance was confirmed only when we calculated the Cost of equity applying Build-up model. This can be explained by the fact that individual inputs of Build-up model are influenced by business capital structure. When calculating EVA Equity indicator applying CAPM, the impact of the change in capital structure on performance was not confirmed. In the case of EVA Entity, the impact of the change in debt and therefore in equity on the performance was confirmed when we calculated the Cost of equity applying Build-up model. Based on the above mentioned, we can suppose that in the case of calculating Cost of equity applying CAPM, the statistically significant impact of business capital structure on business performance was not confirmed. In the next part of the paper, we presented the results of simulation and research of the impact of change in the capital structure on cost of capital and performance. In this analysis of capital structure and its impact on performance, we gradually reduced equity by 10% and increased debt by the same percentage. We recorded the comparison of Cost of equity calculated by CAPM and Build-up model. Comparison of Cost of equity is shown in Figure 2. Figure 2 Cost of equity for different capital structure Source: Authors Figure 2 shows the change in Cost of equity calculated by CAPM and Build-up model. This change occurred as a result of a change in the capital structure (we changed the capital structure by gradual replacement of equity by debt) in favour of debt. For the capital structure of 50% equity and 50% debt, we can see that Cost of equity calculated by both models is the same. The difference arises in the capital structure of 60% debt and 40% equity, at which Cost of equity calculated by CAPM begins to grow. From this capital structure, the impact of increased indebtedness begins to appear in calculating systematic risk. We also noticed an increase in Cost of equity calculated by Build-up model, but this growth was more moderate. Based on above mentioned calculation it can 0,00% 5,00% 10,00% 15,00% 20,00% 25,00% - 10% - 10% - 10% - 10% - 10% - 10% - 10% - 10% - 10% reCAPM reBU 248 be stated, that on a certain debt line, Cost of equity increases because the risks which the owners of capital have to bear, increases too. Figure 3 Cost of capital for different capital structure Source: Authors In Figure 3 we can see the development of Weighted average cost of capital. These costs increase approximately from the capital structure of 60% equity and 40% debt. This increase is associated with rising risks for both owners and creditors as a result of growth of debt. Similarly to Figure 2, we can see that Cost of equity is higher in the case of CAPM application. Figure 4 Development of economic profit for different capital structure Source: Authors In Figure 4 we can see that by increasing the share of debt in capital, the economic profit increases and therefore the value of the performance rises too. In the figure, it is possible to identify deviations in the case of application of the relevant model for quantification of EVA economic profit and for quantification of the Cost of equity. If the economic profit is calculated by EVA Entity model, the value of economic profit for the capital structure of 60% equity and 40% debt is negative. Subsequently, the economic profit reached positive values due to the impact of debt, which positively reflected in the NOPAT through interests. Despite the increasing risk of both owners and creditors, the impact of interests on NOPAT is higher. Based on the above mentioned it can be stated that the influence of the change in the capital structure in favour of debt is positive for the development of economic profit. In the case of EVA Equity the value of the economic profit is increasing by a gradual increase in debt, despite increasing risks. In this case, 0,00% 5,00% 10,00% 15,00% 20,00% -10% -10% -10% -10% -10% -10% -10% -10% -10% WACCreCAPM WACCreBU Polynomial WACC re CAPM Polynomial (WACC re BU) -400 000 -200 000 0 200 000 400 000 600 000 800 000 1 000 000 1 200 000 2015 - 10% - 10% - 10% - 10% - 10% - 10% - 10% - 10% - 10% EVAequityCAPM (€) EVAequityBU (€) EVAentity CAPM (€) EVAentity BU(€) 249 Financial leverage, Return on equity and Cost of equity increase as a result of growth of systematic risk. However, the increase in profitability is faster than the increase in the Cost of equity. 4 Conclusions From the calculations of the selected indicators, we found out that the indicator Interest coverage does not correlate with any capital structure indicator. This indicator can be considered as a key performance indicator. We can describe the capital structure indicators using two main components. The calculations of the indicators that enter into the quantification of the EVA indicator and the calculation of the EVA indicator itself showed that the change in the capital structure changes the values of the EVA indicator. Based on the regression analysis, we were able to confirm the impact of the capital structure on performance only in the case of Build-up model application, while in the case of CAPM application the impact was not confirmed. It is given by the method of calculating the Cost of equity. In the case of Build-up model, we quantified financial risk, which was partly based on the capital structure indicators. Therefore their impact reflected in the value of Cost of equity. For the CAPM, we applied only market risks, without the impact of internal risks. This analysis will be subject to future research, we will focus on more extensive data collection and more detailed analyses. Acknowledgments This paper was prepared within the grant scheme VEGA no. 1/0887/17 – Increasing the competitiveness of Slovakia within the EU by improving efficiency and performance of production systems and grant scheme VEGA no. 1/0791/16 - Modern approaches to improving enterprise performance and competitiveness using the innovative model – Enterprise Performance Model to streamline Management Decision-Making Processes. References Damodaran, A. (2001). The Dark Side of Valuation. Retrieved from: http://pages.stern.nyu.edu/~adamodar/. Jánošová, V. (2008). Zdroje financovania podniku. Bratislava: Ekonóm. Kiseľáková, D., Šofranková, B. (2015). Effects and risks of mergers and acquisitions on entrepreneurship in banking and finance: Empirical study from Slovakia. Review of European Studies. Canadian Center of Science and Education, vol.7(7), pp.23-35. Mařík, M., Čada, K., Dušek, D., Maříková, P., Rýdlová, B., Rajdl, J. (2011). Metody oceňování podniku: proces ocenění - základní metody a postupy, 3. vyd. Praha: Ekopress. Modigliani, F., Miller, K. H. (1958). The Cost of Capital, Corporation Finance and the Theory of Investment. American Economic Review, vol. 48(3), pp. 261- 297. Neumaierová, I., Neumaier, I. (2016). The Performance Ranking of Chosen Manufacturing Division. European financial systems 2016. In: Proceedings of the 13_th International Scientific Conference. Masaryk University, Faculty of Economics and Administration, pp. 503-508. Růčková, P. (2008). Finanční analýza metódy, ukazatele, využití v praxi. Praha: Grada Publishing, a. s. Sedláček, J. (2003). Finanční analýza podniku. Brno: Computer press, a. s. Suhányiová, A., Suhányi, L. (2011). Application of international financial reporting standards (IFRS) in the Slovak republic. Lucrari stiintifice, seria I, vol. 13(3), pp. 267- 274. 250 Štefko, R., Gallo, P. (2015). Using Management Tools to Manage Network Organizations and Network Models. In: Sroka, W., Hittmár, Š., ed., Management of Network Organizations Theoretical Problems and the Dilemmas in Practice, pp. 249-264. Springer International Publishing Switzerland. Závarská, Z. (2012). Manažment kapitálovej štruktúry pri financovaní rozvoja podniku ako nástroj zvyšovania finančnej výkonnosti. Prešov: Prešovská univerzita. 251 Technical Efficiency of Banks Selected countries of Eastern Europe Eva Horvátová1 1 University of Economics in Bratislava Faculty of National Economy Dolnozemská 1, 85235 Bratislava, Slovak Republic e.mail: eva.horvatova@euba.sk Masaryk University Faculty of Economics and Administration, Department of Finance Lipová 41a, 603 00 Brno, Czech Republic E-mail: 136615@mail.muni.cz Abstract: Increased capital requirements are associated with the need to create additional income, with the expansion of banks' performance. The growth of the banking margin can generate downward pressure on prices of resources and the rise in interest rates on loans. Expected growth in the interest margin can be linked to the growth of capital by a certain unit. It should be monitored as to meet the expectations of regulators and how they manifest themselves in countries with different levels of development of the banking sector. In this article we will focus on the efficiency of banks selected countries of Eastern Europe. We will analyze how the technical efficiency of banks is if we consider for their output the net interest margin as presupposes to achieving expectations connected with increasing capital. For these purposes will be used the method of DEA – Data Envelope Analysis. Key words: technical efficiency of selected banks of Eastern Europe, DEA analysis JEL Classification: G21, C58 1 Introduction The financial crisis has shown the fact that the regulation could not prevent bank failures, that excessive risk has not been adequately covered by capital requirements. Often discussed problem is high level of pro-cyclicality of the bank regulation. Procyclicality is defined as common designation of mutually reinforcing ("positive correlation") mechanisms by which the financial system can amplify economic fluctuations and eventually induce or worsen financial instability. Appendix 1 of the document of the Bank for International Settlement (2008) "Addressing pro-cyclicality financial system: a possible framework" sets out possible approaches and solutions in the field of prudential and financial reporting, which can be used to dampen pro-cyclicality of the financial system. Increased capital requirements are associated with the need to create additional income and with the expansion of banks' net interest margins. The growth of the banking margin can generate pressure on prices of resources and the rise in interest rates on loans. Expecting these trends we analysed whether banks are now enough efficiently in the use its resources with regard to their net interest margins. Newly discussed is a problem of the duty of the banks to create additional reserves to cover losses. The measures proposed EBA about the creation of so-called "Eligible Liabilities" referred to as system MREL (Minimum Required Eligible Liabilities) that the bank must have. The new proposal foresees that banks will have to increasing the amount of eligible liabilities in the form of additional long-term debt. The problem may be in them that the banks of Central and Eastern Europe will create the additional resources in a greater extent than banks in Western Europe, as do not have the types of liabilities that may be recognized as eligible liabilities, but they have another 252 Despite a high degree of globalization and integration of banking markets and services, there are arguments for maintaining the national character of the banking system. Therefore, we see meaningful analysis of the conditions of technical efficiency in these selected markets. 2 Methodology and Data For evaluation of banks efficiency we chose the technical efficiency based on DEA analysis. Technical efficiency refers to the ability of decision making units (banks, insurance companies, firms, universities, faculties, hospitals and others types of production units) to obtain the maximum amount of output by a given volume of inputs, or indicates the minimum necessary input to produce a given volume of the output. It means the DEA Models are input oriented or output oriented. Input oriented models give recommendation about what should be done with inputs (the amount of decreasing inputs) and output oriented models give recommendations, what should be done with the output (the amount of increasing output by given inputs). The DEA Models not only recognize efficient and no efficient production units, but DEA gives recommendations, what should do production units to achieve efficiency frontier. In contrast to technical efficiency, the allocative efficiency takes into account the size of the bank as a production unit. This contribution is focused on technical efficiency. DEA analysis belongs to the non-parametric methods of efficiency measuring. The disadvantage of DEA Models is that there is not possible to separate the effect of random errors and errors in the measurement of inefficiency. DEA measures the relative efficiency of production units in the examined group of units. Changing the group involving the unit, we can expect changing of efficiency of evaluated units. Among the first scientists who have developed a DEA Model is M. J. Farrell. Best known is his work "The Measurement of Productive Efficiency" published in the Journal of the Royal Statistics Society in 1957. Model Specification In this contribution will be used an input-oriented CCR model and BCC model. Input-oriented CCR-I (Charnes-Cooper-Rhodes-Input) model can be written in the form of linear programming problems (Jablonský, Dlouhý, 2004): max u v w2 > 2 ∗ 2g 1 Under the conditions: v w2 > 2 ∗ 2g x v yz 4 z ∗ (z ; | 1,2, … . . H v w2 > 2 ∗ 2g v yz 4 z ∗ (z x 0 | 1,2, … . . H v yz > z ∗ (z 1 253 w2 ; 0, ~ 1,2, … . . • yz ; 0, ~ 1,2, … . . : Input-oriented BCC-I (Banker-Charnes-Cooper-Input) model can be written in the form (Jablonský, Dlouhý, 2004. max u v w2 > 2 ∗ 2g € 2 Under the conditions: v w2 > 2 ∗ 2g € x v yz 4 z ∗ (z ; | 1,2, … . . H v yz > z ∗ (z 1 w2 ; 0, ~ 1,2, … . . • yz ; 0, ~ 1,2, … . . : Parameter µ reflects the conditions of convexity of the BCC-I model. The output oriented CCR Model can be written in this form: •~H v y2 > 2 ∗ (28 3 Under the conditions: v w4 • 4 ∗ 4z v y2 > 2 ∗ (2z x 0 ‚ 1,2, … . . H v w4 9 z ∗ 48 1 w4 ; 0, : 1,2, … . . ƒ y2 ; 0, ~ 1,2, … . . • 3 Results and Discussion This article analyzes the efficiency of banks in selected countries of Europe on the basis of data published by the Bank Scope, National Bank of Slovakia and the ECB. The analysis uses the standard tools of descriptive and analytical statistics and statistical program MaxDEA is used for the DEA (Data Envelopment Analysis) applied to the example of the selected banks. Our aim was to examine whether the banks in selected counties of Eastern Europe have different results in terms of overcoming the financial crisis. The theoretical nature of the DEA requires that subjects observed in the group (DMU = Decision Making Unit) have to be banks with similar focus. Therefore, we omitted specialized banks, housing savings banks and state guarantee bank. The number of production unit has an impact on the number of analyzed factors in efficiency measurement. By rule the number of analyzed inputs and outputs should not be greater than one third of analyzed production units. 254 To the development of the theory and practice of using DEA models in the Czech Republic contributed works of authors such as Jablonský, Dlouhý, Novosádová (2006). The application of DEA Analysis deals for example Palečková (Řepková) (2012). In Slovakia, we can mention the works of Luptáčik (2010), Zimková (2014) and Vincová (2006). This contribution aims to analyze the technical efficiency of banks in the banking sectors of some of the countries of Eastern and Central Europe in terms of overcoming the financial crisis. Since we wanted to get a picture of how the financial crisis influenced the efficiency of banks. This analyzes has been done for 2013, because this period is considered to be a shift in the post-crisis development. The output is net interest margin and inputs are total equity and personnel expenses. On this basis, we obtained results of CCR and BCC-I models. Table 1 The results of efficiency of banks in Croatia in terms of CCR and BCC-I Model Coutry DMU Name CCR CRS Proportionate Movement (Total_Equity) CRS Proportionate Movement (Personnel_Expe nses) BCC-I VRS Proportionate Movement (Total_Equity) VRS Proportionate Movement (Personnel_Expe nses) CROATIA Hrvatska Postanska Bank DD 0,1201 -167091 -20245,5 0,121564 -166832 -20214 CROATIA Hypo Alpe-Adria-Bank dd 0,0536 -608203 -34997,6 0,072438 -596106 -34301,5 CROATIA Partner Banka dd 1 0 0 1 0 0 CROATIA Podravska Banka 0,4760 -26995,9 -3423,27 1 0 0 CROATIA Societe Generale Splitska Banka dd 0,0561 -417615 -40207,4 0,062883 -414633 -39920,4 CROATIA Veneto Banka d.d. 1 0 0 1 0 0 CROATIA Zagrebacka Banka dd 0,0217 -2003639 -103282 0,025371 -1996319 -102904 Table 2 The results of efficiency of banks in Slovenia in terms of CCR and BCC-I Model Coutry DMU Name CCR CRS Proportionate Movement (Total_Equity) CRS Proportionate Movement (Personnel_Expe nses) BCC-I VRS Proportionate Movement (Total_Equity) VRS Proportionate Movement (Personnel_Expe nses) SLOVENIA Gorenjska Banka d.d. Kranj 0,8919 -17899,2 -1587,92 1 0 0 SLOVENIA Nova Kreditna Banka Maribor d.d. 0,2490 -388784 -29138,2 0,270619 -377601 -28300 SLOVENIA Raiffeisen Banka dd 1 0 0 1 0 0 SLOVENIA SKB Banka DD 0,4827 -138465 -15672,3 1 0 0 SLOVENIA UniCredit Banka Slovenija d.d. 0,4872 -116762 -11589,1 0,500953 -113633 -11278,5 255 Table 3 The results of efficiency of banks in Lithuania in terms of CCR and BCC-I Model Coutry DMU Name CCR CRS Proportionate Movement (Total_Equity) CRS Proportionate Movement (Personnel_Expe nses) BCC-I VRS Proportionate Movement (Total_Equity) VRS Proportionate Movement (Personnel_Expe nses) LITHUANIA AB DNB Bankas 0,0926 -382091 -29489,3 0,166233 -351099 -27097,4 LITHUANIA AB SEB Bankas 0,0599 -707968 -34500,6 0,147209 -642237 -31297,4 LITHUANIA Citadele Bankas AB 0,6500 -16156,7 -1890,68 1 0 0 LITHUANIA Danske Bank A/S 0,3582 -17372,2 -6730,11 0,820077 -4870,63 -1886,91 LITHUANIA Siauliu Bankas 0,3426 -61130,8 -7164,8 0,498433 -46645,7 -5467,08 LITHUANIA Swedbank AB 0,0684 -881838 -39158,7 0,128522 -824971 -36633,5 LITHUANIA UAB Medicinos Bankas 1 0 0 1 0 0 Table 4 The results of efficiency of banks in Latvia in terms of CCR and BCC-I Model Coutry DMU Name CCR CRS Proportionate Movement (Total_Equity) CRS Proportionate Movement (Personnel_Expe nses) BCC-I VRS Proportionate Movement (Total_Equity) VRS Proportionate Movement (Personnel_Expe nses) LATVIA ABLV Bank AS 0,1131 -165836 -25274,4 0,119643 -164627 -25090,2 LATVIA AS DNB Banka 0,2085 -188440 -14720,6 0,21063 -187949 -14682,3 LATVIA Baltic International Bank-Baltijas Starptautiska Banka 1 0 0 1 0 0 LATVIA Jsc Latvian Development Financial Institution Altum 0,7263 -21263,8 -1559,89 0,730928 -20906,9 -1533,71 LATVIA Meridian Trade Bank 0,9667 -684,799 -93,0794 1 0 0 LATVIA Norvik Banka 0,3317 -32343,9 -7952,33 0,42562 -27800 -6835,12 LATVIA Rietumu Banka 0,2590 -201694 -15041,9 1 0 0 LATVIA SEB banka AS 0,1569 -362239 -19642 0,159085 -361341 -19593,3 256 Table 5 The results of efficiency of banks in Slovakia in terms of CCR and BCC-I Model Coutry DMU Name CCR CRS Proportionate Movement (Total_Equity) CRS Proportionate Movement (Deposits) BCC-I VRS Proportionate Movement (Deposits) VRS Proportionate Movement (Personnel_Expe nses) SLOVAKIA Vseobecna Uverova Banka 0,8901 -861263 -10682,1 0,89406 -830467 -10300,1 SLOVAKIA Slovenska sporitelna 1 0 0 1 0 0 SLOVAKIA OTP Banka Slovensko 0,8057 -218467 -2323,76 1 0 00 SLOVAKIA CSOB SK 0,7537 -1007998 -14159,3 0,787183 -871012 -12235,1 SLOVAKIA UniCredit Bank Slovakia 0,6426 -3641023 -30617,7 0,656858 -3495977 -29398 SLOVAKIA Postova banka 1 0 0 1 0 0 SLOVAKIA Prima banka Slovensko 0,4565 -823976 -20782,1 0,741586 -391814 -9882,25 SLOVAKIA Sberbank Slovensko 0,5542 -704241 -8862,46 0,738484 -413146 -5199,2 SLOVAKIA Tatra Banka 0,6982 -2121431 -32149,3 0,701367 -2099301 -31813,9 The column “Proportionate movement” shows what the bank should do with their inputs in order to achieve efficiency frontier. And so, as problematic we can see the need to increase capital required by regulation, although banks do not have conditions to create sufficient outputs. Acknowledgments This article is processed within the project VEGA 1/0693/17 „Banková únia: systémový prístup k vyhodnoteniu príčin a dopadov zavedenia bankovej únie na bankový sektor SR a štátov Eurozóny“ and within the project of specific research A/1039/2016 “Modelování volatility na finančních trzích a její aplikace v oblasti řízení rizik a oceňování aktiv” and project of SCIEX who supported the cooperation between the University of Applied Sciences in Lucerne and University of Economics in Bratislava and provided the grant SCIEX Nr. 14028. 4 Conclusions The analysis of technical efficiency banks refer to the risk that the banks' interest margins are not sufficient to create additional capital. Personnel cost are too high in banks of Central and Eastern Europe. The results show differences in efficiency according to the BCC and CCR models. This means that it is necessary to take into account the size of the bank. Results indicate that the slowdown in credit activities in the sphere of the real economy has a significant impact on the banking sector. Actions to support of the development and lending are needed. This trend is observed in all surveyed countries of Central and Eastern Europe. Individual measures in the regulation should be aligned so as to fulfill the defined objectives and are not in conflict with each other with other measures, and to act counter-cyclically. References Bank for International Settlement: Addressing financial system procyclicality: a possible framework. Note for the FSF Working Group on Market and Institutional Resilience. 257 September 2008. Retrieved from: http://www.financialstabilityboard.org/wp- content/uploads/r_0904e.pdf?page_moved=1 Jablonský, J., Dlouhý, M. (2004). Modely hodnocení efektivnosti produkčních jednotek. Praha: Professional Publishing. Jablonský, J., Dlouhý, M., Novosádová, I. (2007). Využití analýzy obalu dat pro hodnocení efektivnosti českých nemocnic. Politická ekonomie, No 1, pp. 60 – 71. Řepková, I. (2012). Measuring the efficiency in the Czech banking industry: Data Envelopment Analysis and Malmquist index. In: RAMÍK, J. and STAVÁREK, D. (eds.) Proceedings of 30th International Conference Mathematical Methods in Economics. Karviná: Silesian University, School of Business Administration, pp. 781 – 786. Vincová, K. (2006). Neefektívnosť z rozsahu v bankovom sektore: Komparácia slovenského a českého bankového sektora. In: International Conference Proceedings: National and Regional Economics VI. Ekonomická Fakulta, Technická Univerzita v Košiciach, Košice, pp. 440–445. Zimková, E. (2014). Technical Efficiency and Super-Efficiency of the Banking Sector in Slovakia. Enterprise and the Competitive Environment 2014 conference, ECE 2014, 6–7 March 2014, Brno, Czech Republic. Procedia Economics and Finance 12, pp. 780 – 787. 258 Assessing the Impact of the Financial Crisis on Global Insurance Regulation Eliška Hrabalová1 , Eva Vávrová1 , Lenka Přečková2 1 Mendel University in Brno Faculty of Business and Economics, Dept. of Finance Zemědělská 1, 61300 Brno, Czech Republic E-mail: vavrova@mendelu.cz 2 Silesian University in Opava School of Business Administration in Karviná, Dept. of Finance and Accounting Univerzitní nám. 1934/3, 73340 Karviná, Czech Republic E-mail: preckova@opf.slu.cz Abstract: The aim of this paper is to analyze the impact of the financial crisis on insurance markets in different regions of the global insurance market and to evaluate changes in global approaches to insurance regulation depending on the effects of the financial crisis. The financial crisis has triggered an identified banking crisis and has shifted through the contagion channels from the US mortgage market to other financial sectors and regions of the world. With regard to the integration of financial institutions and the globalization of financial markets, a number of regulatory proposals has emerged in recent years to address the impact of the crisis, to eliminate the trigger of the crisis and to prevent recurrence of the causes of the crisis. This paper builds on the contribution of the authors (Vávrová, Nečas, 2016) published at the international scientific conference European Financial System 2016, whose main objective was to assess the development of financial health of insurers within the global insurance market in the period of lingering financial crisis and to draw conclusions based on the analysis of the insurance sector. Key words: financial crisis, globalization, insurance market, regulation, authority, contagion channel, trigger JEL codes: G01, G15, G22, G28 1 Introduction The financial crisis hit the economies of states in a significant way. In 2009, real GDP fell by 4.4% in the EU Member States and by 4.5% in Euro area countries (Eurostat, 2015). In this sense, one can even speak about one of the worst crises since the Great Economic Crisis in the 1920s and 1930s. The cause of the financial crisis and the subsequent economic downturn was not one particular fact, but it was the effect of multiple factors, shocks and imbalances. Thus, short-term and long-term factors can be found that resulted in the outbreak of the financial crisis, which dates back to 2008-2009. Problems can be found in economic policy, over-expected earnings expectations, poor coordination of crisis management by international institutions, and underestimation of the risk of the outbreak of the crisis. Governments and regulators have begun to pursue a set of measures to address the financial crisis that can be summarized into specific areas (for more see Baldwin, Wyplosz, 2013). Central banks have had to provide liquidity to the financial system and to start substantial expansionary policies. Large financial institutions, called systemically important institutions, had to be rescued. Governments subsequently had to use fiscal policy to prevent a recession. Also, the response to the financial crisis was the introduction of more prudent, higher regulation of financial markets and the establishment of regulatory institutions and mechanisms to prevent financial instability and the recurrence of the financial crisis (Baldwin, Wyplosz, 2013). The aim of this paper is to analyze the impact of the financial crisis on insurance markets in different regions of the global insurance market and to evaluate changes in global 259 approaches to insurance regulation depending on the effects of the financial crisis. The financial crisis has triggered an identified banking crisis and has shifted through the contagion channels from the US mortgage market to other financial sectors and regions of the world. With regard to the integration of financial institutions and the globalization of financial markets, a number of regulatory proposals has emerged in recent years to address the impact of the crisis, to eliminate the trigger of the crisis and to prevent recurrence of the causes of the crisis. This paper builds on the contribution of the authors (Vávrová, Nečas, 2016) published at the international scientific conference European Financial System 2016, whose main objective was to assess the development of financial health of insurers within the global insurance market in the period of lingering financial crisis and to draw conclusions based on the analysis of the insurance sector. 2 Methodology and Data The methodological approach used for the processing of this paper was as follows: used methodical procedures focused mainly on descriptive method, causal analysis, synthesis and comparative analysis. Data sources mainly served as secondary sources, both published studies on the topic of the impact of the financial crisis on global insurance regulation, studies of both foreign and Czech origin as well as publicly accessible sources characterizing the most important regulatory and supervisory institutions operating on global insurance markets. Other methods used were induction and deduction, where induction helped to draw general conclusions based on the analysis of underlying data and deduction led to the assessment of the impact of financial crisis on global insurance regulation. 3 Results and Discussion In the context of the global view of the insurance markets, it should be noted that the regulatory issues vary considerably and were addressed individually for the insurance markets in individual countries (Burling, Lazarus, 2011). The reactions of regulatory and supervisory institutions and the approach to modification of regulatory procedures differed globally at the time of the outbreak of the crisis and its scale, as well as the experience and ability of regulators to develop effective measures. It was important to respond individually to the country's insurance market, but at the same time, to coordinate measures internationally (Kriele, Wolf, 2014). Regulatory institutions discussed and prepared scenarios of longer-term changes; to provide the immediate interventions the following measures were used: provision of repayable financial assistance, order to sell certain parts of financial institutions, ban on the transfer of dividends and profits abroad, limitation of managers' payouts and transactions of speculative character (see Pavlát, Kubíček, 2010). As for questions regarding global regulatory issues, there is a need to mention international organizations representing the global insurance markets. An important role in the insurance industry is played by the International Association of Insurance Supervisors (IAIS). This association deals with regulatory issues and supervisory issues in the insurance industry. In order to achieve the goal of maintaining stable insurance markets and globally supporting financial stability on a global scale, it provides its members a forum to share information and experience from the development of insurance markets, as well as share experience from supervision. One of the results of this association's activity is the issue of the Insurance Core Principles (ICPs). These are globally recognized insurance supervision requirements that are structured to suit diverse insurance markets. 260 Table 1 Insurance Core Principles ICP 1 Objectives, Powers and Responsibilities of the Supervisor ICP 2 Supervisor ICP 3 Information Exchange and Confidentiality Requirements ICP 4 Licensing ICP 5 Suitability of Persons ICP 6 Changes in Control and Portfolio Transfers ICP 7 Corporate Governance ICP 8 Risk Management and Internal Controls ICP 9 Supervisory Review and Reporting ICP 10 Preventive and Corrective Measures ICP 11 Enforcement ICP 12 Winding-up and Exit from the Market ICP 13 Reinsurance and Other Forms of Risk Transfer ICP 14 Valuation ICP 15 Investment ICP 16 Enterprise Risk Management for Solvency Purposes ICP 17 Capital Adequancy ICP 18 Intermediaries ICP 19 Conduct of Business ICP 20 Public Disclosure ICP 21 Countering Fraud in Insurance ICP 22 Anti-Money Laundering and Combating the Financing of Terrorism ICP 23 Group-wide Supervision ICP 24 Macroprudential Surveillance and Insurance Supervision ICP 25 Supervisory Cooperation and Coordination ICP 26 Cross-border Cooperation and Coordination on Crisis Management Source: International Association of Insurance Supervisors Tab. 1 represents an overview of Insurance Core Principles that were issued to support global supervision of insurance industry. ICPs provide a framework for global supervision of all insurers and insurance groups. This framework does not affect reinsurance companies in any significant way, only in predefined situations, this also applies to insurance intermediaries, where this framework is very rarely applied. Based on Insurance Core Principles, the International Monetary Fund (IMF) assesses systems and levels of insurance supervision (Mesršmíd, 2015). IAIS is a non-profit organization based on in the form of voluntary membership. This association belongs to global reach organizations whose task is to seek efficient and consistent supervision in order to achieve stable insurance markets. It is also a member of the Financial Stability Board. To meet its objectives the IAIS focuses on creating support materials for supervisory work and developing methodology for global systemically important insurers and internationally active insurance groups. Institution continued with the ICPs and issued the Basic capital requirements for global systemically important insurers and the Principles of higher absorption of loss. These requirements belong to the important points of global regulation, as their application will lead to the Global insurance capital standards. The scheduled date of receipt after the calibrations in 2017 and 2018 is October 2018. "After completed, Global capital requirements will replace the Basic capital requirements as the basis for higher absorption of losses" (see IAIS, 2015). 261 Global capital requirements will measure the capital adequacy of internationally active insurance groups (IAIGs) and for global systemically important insurers (G-SIIs). Global systemically important insurers are financial institutions that are defined by the FSB as: "institutions of such magnitude, market importance and such global interconnectedness that their difficulties or bankruptcy could cause substantial disruption of the global financial system and have adverse economic consequences for a number of countries”. According to IAIS, the internationally active insurance group is defined as: "an insurance group that has total assets of at least USD 50 billion or gross written premiums of at least USD 10 billion (on a rolling three year average basis). In addition, its premiums are written in three or more jurisdictions and at least 10% of the group’s total gross written premium is written outside the home jurisdiction”. By the end of 2016, the FSB together with IAIS published an updated list of global systemically important insurers for 2017: • Aegon N. V., • Allianz SE, • American International Group, Inc., • Aviva plc, • Axa S.A., • MetLife, Inc., • Ping An Insurance (Group) Company of China, Ltd., • Prudential Financial, Inc., • Prudential plc. The G-SIIs represent the role of global players and a considerable attention must be given to them because their decline would endanger the financial stability of global insurance markets. They would have to meet the requirements of higher absorption of losses, the principle of intensified and coordinated supervision and the resolution of possible crisis of insurers and plan of recovery procedures. The International Monetary Fund is an organization that often acts as a commentator and evaluator of the level of supervision in the insurance sector, when its control, monitoring and oversight activities help to ensure the stability of the global financial system. The International Monetary Fund focuses on stress tests in the field of insurance but it also provides reports on overall observation and publishes a detailed assessment of compliance with the core principles, especially with regarding to regulation of insurance. A detailed assessment of compliance with core principles and reports about the results of organizations' activities, such as IAIS, are published in the Global Financial Stability Report, in particular to make the information available and inspiring for other institutions (Mesršmíd, 2015). Another institution that cannot be omitted is the Financial Stability Board (FSB), which was mentioned above in connection with global regulation linked to the new requirements for G-SIIs and IAIGs. In the context of the effort to resolve and overcome the global financial crisis, the G-20 Summit was convened in London in November 2008, where it was agreed to adopt the Action plan to implement the principles of financial sector reform. This plan addressed short-term, medium and long-term measures, focusing on transparency, strengthening of regulation and coordination at international level, as well as strengthening of supervision, especially in the case of rating agencies (Pavlát, Kubíček, 2010). In connection with the financial crisis and the adoption of the Action plan to realize the principles of financial sector reform, the Financial Stability Board was established. The FSB was formed in 2009 as the successor of the Financial 262 Stability Forum (FSF). The next G-20 meeting was held in Pittsburgh, also in 2009, where the FSB Charter was approved, which included the organizational structure and objectives of the organization. The aim of this organization is to strengthen global financial stability by coordinating the agreed policies in order to facilitate coordination of the international exchange of information between the institutions responsible for financial stability and other international organizations and institutions (FSB, 2016). The Financial Stability Board often cooperates with the G-20, which acts as the forum for international economic cooperation. The G-20 is often presented as a group of the world's richest economies. The aim of the G-20 is to promote global economic growth, investment and also to increase employment (Mesršmíd, 2015). The effort to meet these goals is also evident from current activities when the Berlin government (Germany is a current G-20 chairman) intends to target at the G20 summer summit in 2017 to support private investment that should lead to job creation globally and to improve infrastructure. Other global institutions addressing insurance issues include the Global Federation of Insurance Associations (GFIA), which was established in 2012. This organization is based in Washington where the establishing meeting was held. The GFIA has 41 member associations of insurers, which create 87% of the world's total premiums written. The Federation sets up working groups considering current global insurance markets priority axes. There are currently 12 working groups in the federation (GFIA, 2016). The federation's task is to solve problematic points in insurance, such as collective supervision and regulation on a global scale, systemic risks, but also the fight against money laundering. The organization should take into account individual views from all area, to unify them and submit them to other organizations, such as IAIS. The GFIA acts in the form of a non-profit association that aims to increase efficiency in the insurance industry by exchanging information to each other between member associations, addressing issues of mutual interest to associations, and collaborating with other international organizations to further coordinate the sector's regulation on a global scale (Mesršmíd, 2015). The federation is represented by Europe, Australia, North and South America, Africa and Asia. Europe is represented through 13 insurance associations in this organization, but mainly through Insurance Europe, which is also a member of the Czech Association of insurance companies. The regulation of the insurance markets of certain countries of the group G8 is specific. E.g., the financial system of Canada belongs to stable financial systems that were only very slightly affected by the financial crisis (Pánek, Valová, 2008). In Canada, the Ministry of Finance and four other independent agencies are subjects that supervise banks, insurance companies, and other financial institutions. The most important independent agency is Office of the Superintendent of Financial Institutions (OSFI) for the insurance market. This institution provides supervision not only of insurance companies but also of banks and federally managed pension plans. It has the task of gathering information from the financial markets, evaluating them and setting requirements for capital adequacy - usually at a higher level than international requirements. Also, its duty is to prepare a report about the operation of the sector for the Canadian Ministry of Finance and to present it every year (Pánek, 2012). The issue of regulation and supervision of the Canadian insurance market is addressed by a system of shared responsibility among federal and territorial authorities. As such, insurance companies have to be licensed at federal level, but at the same time they have to apply for a license in each territory where they intend to operate. The mentioned supervisory institution OSFI regulates the largest insurers under the Insurance Companies Act, while the regulation of smaller insurers is in charge in particular of territorial regulators. In the USA, under the IAIS, three institutions are represented - Federal Insurance Office, Fed and National Association of Insurance Commissioners. These institutions will be characterized in the following part of the paper. The events of the financial crisis have opened a discussion about changes and possible implementations of new elements into 263 the regulatory system in the United States. A major reform of the financial sector supervision was signed by the president in 2010, a law called the Dodd-Frank Act, which led to the creation of special institutions (Koba, 2012). This law presents the framework under which the entire financial sector is regulated. It was designed to address unfavorable events related to the financial crisis, as the purpose of this law is to prevent the collapse of other financial institutions. Under this law, the Federal Insurance Office (FIO) was established to collect information on insurance companies to determine which commercial insurers pose a major risk to the entire financial system. According to Koba (2012) the aim of the Dodd-Frank Act is to strengthen the stability of the financial system, in particular by improving transparency and accountability in the financial system. With regard to regulation of insurance industry this area falls under the Head of the Dodd-Frank Act which addresses the issue of the Federal Insurance Office. The regulation of financial market in the United States is governed by Federal Reserve System supervision and by other institutions (FED, 2010). National Association of Insurance Commissioners is an important institution for the insurance market (Pánek, 2012). In the insurance sector each individual state of the USA is dealing with its own legislation, but for a possible co-operation there is a non-governmental National Association of Insurance Commissioners (NAIC) which unifies national regulators of individual states. The mission of this association is to determine the minimum standards of international accounting, valuation, collateral, financial analysis and solvency in relation to the effects and consequences of the financial crisis. Concerning the structure and calibration of solvency requirements, NAIC has introduced a modernization initiative in this area in 2008, inspired by the European Union's solvency conditions (Mesršmíd, 2015). As regards insurance licensing activities, commercial insurance companies in the US have to apply for authorization in all countries where they want to operate their insurance businesses, the principle of a single license to conduct insurance business does not exist for insurance companies in the USA. While US insurance companies have to apply for a license in each country to obtain a national license, the advantage for policyholders is that they are protected in every US state by a guarantee fund in the event of the insolvency of the insurance company (Zweifel, Eisen, 2012). Therefore, if we compare the ways in which insurance market is regulated in Europe and the USA, we will find that they are somewhat different. Insurance companies of the USA are regulated at the state level, NAIC is the regulator. In the European Union, the regulatory framework applies to all member states. Since the end of 2013, efforts have been made in the United States to move towards a federally-regulated system. These efforts began on the basis of the FIO report that was presented in response to the DoddFrank Act call for studies and suggestions on how to modernize and improve the insurance regulation (Hull, 2015). The financial system in Japan was initially characterized by the business and investment banking department, separately, but the convergence of these two areas began in the late 1990s. The regulation of the financial market in Japan falls under the Ministry of Finance and under supervision (Pánek, Valová, 2008). Japan's Financial Services Agency (FSA) is the regulator and supervisor of the insurance industry. This agency has also adjusted the regulatory framework for insurance, in particular in the area of solvency requirements, risk assessment, stricter documentation requirements and control of large insurance companies in shorter time intervals in the context of the financial crisis (Mesršmíd, 2015). The insurance market of Japan should be in line with ICPs. According to the International Monetary Fund, the Japanese FSA has taken the necessary steps to improve the oversight of holding companies in Japan and to conduct market analysis, but it should continue to focus on improving macro-prudential analysis. It should be active in cooperation with international supervisors, strengthen supervisory independence, prepare Early Warning System plans and improve regulation of collateral, especially in case of natural disasters. 264 4 Conclusions Ideas expressed in this paper seek ways how to restore the growth and global competitiveness on the global insurance markets. The financial crisis was triggered by insufficient regulation of insurance markets, which could have been caused by excessive lending, new risky financial products and poor risk assessment. This crisis has revealed serious financial regulation shortcomings, the removal of which requires stronger and longer-term interventions than just focus on immediate action. According to Brokešová, Pastoráková, Ondruška (2014), it is important to pay attention to the insurance industry as it fulfills the function of stabilizing the economy. Regulators' interest in regulating and supervising insurance is increasing as the sector contributes to the economic development and stabilization of economies as a whole over the long-term. The resilience of the insurance industry proved particularly during the financial crisis, when the effects of the financial crisis were overcome in a short time due to the shock resistance of the insurance industry. A number of authors tend to think of the benefits of more prudent regulation, stronger regulation of the insurance industry, but there are also opposing views that not consider a large amount of regulation as optimal. A number of regulatory initiatives could greatly flood the regulatory sector with high costs. Regulation forces high costs with respect to and compliance with solvency capital requirements. As a result, profits and overall growth in insurance may be reduced as well as less interest in the benefits of innovation. According to Ducháčková, Daňhel (2015), today's world is very complicated and in the current development of the new environment it is impossible to apply the principles that applied in the past. Inappropriate emphasis is placed on the over-formalized economy and the implementation of mathematical approaches and market regulation models, which do not include unexpected events, whereby the results of institutions do not take sufficient account of the volatility of economic events and then they are sharply in contrast to the current reality. On the other hand, the Solvency II regulatory concept applied to the European Union's commercial insurers was supposed to contribute to better risk management and consequently lead to better strategic decisions. As part of EU regulation, the European Commission (see Mesršmíd, 2015) introduced a smart regulation concept that would reduce regulatory burdens and simplify existing legislation, while maintaining a high level of market protection. For the time being, however, it is only a goal of regulation in the outlook, not a current reality in regulating of insurance markets. Acknowledgments This paper was initiated as a part of special research project of the Internal Grant Agency of the Faculty of Business and Management of Mendel University in Brno No. PEF_TP_2017001 with the title „Regulation of Financial Markets in the EU after the Financial Crisis“. Concurrently, research behind this paper was supported by the Czech Science Foundation within the project 16-17796S “Affiliation with financial conglomerate as a determinant of performance and risk of banks”. References Baldwin, R., Wyplosz, Ch. (2013). Ekonomie evropské integrace. 4th ed. Prague: Grada Publishing. Brokešová, Z., Pastoráková, E., Ondruška, T. (2014). Determinants of insurance industry development in transition economics: empirical analysis of Visegrad group data. The Geneva papers on risk and insurance - issues and practice, vol. 39, pp. 471-492. Burling, J. M., Lazarus, K. (2011). Research handbook on international insurance law and regulation. 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Insurance Economics. Berlin: Springer-Verlag. 266 Impact of high frequency trading on volatility in short run and long run Juraj Hruška1 1 Masaryk University Faculty of Economics and Administration, Department of Finance Lipová 41a, 60200 Brno, Czech Republic E-mail: 206887@mail.muni.cz Abstract: Computers have overtaken the most of tasks in intraday trading on modern exchanges. From stock picking to deal timing, optimized algorithms are crucial in trading process. This phenomenon is apparent on the spot as well as on derivative markets. In this paper, we consider the effects of high frequency trading on the short term volatility. The aim of the paper is to investigate the links between high frequency trading (HFT) and spot volatility. High frequency with presence of market microstructure noise and also low frequency data from German stock market are considered. We employ Markov switching models to estimate the relationship of dynamics in stock returns and changes in the activities of high frequency traders. Activity of algorithmic traders is estimated by proxy variables based on the average size of trades. The problem of optimal sampling biases is avoided by incorporating Bundi-Russell (2008) test and test of Lagrangian multipliers. Market microstructure noise can cause biasness in the estimates of the empirical volatility measures and models based on such variables. It is mostly caused by bid ask bounce and technical realization of trading on certain exchanges. Most actively traded stocks listed on the German stock exchange (Deutsche Borse) are selected for the empirical analysis. Analyses of optimal sampling suggest that highest frequency without market microstructure noise should be approximately hourly. Results from models confirm the hypotheses of positive impact of high-frequency trading on market volatility. Interesting are conclusions that aggressive trading using market orders have smaller impact on realized volatility than market making using limit orders. Keywords: volatility, high frequency trading, Markov switching model JEL codes: C24, C55, C58, G12 1 Introduction Since the 1980s computer algorithms have helped traders to prepare and execute their investment decisions. Nowadays trading on world exchanges is dominated by computers who are capable to create and execute trades on their own. Securities exchanges are fully electronic and floor trading is not able to keep up with the challenge of automated trading. High frequency algorithmic trading without any doubt changed the basic features of stock and derivative markets. Financial scientists and traders are not able to justify whether this development improves or deteriorates market. Market efficiency and liquidity are increased at a cost of increased volatility and biased prices in turbulent situations (Kendall 2007). Both academics and practitioners cannot agree on the role of high-frequency trading in this new development. On one hand, high frequency traders (HFT) proclaim that their activities as market makers are increasing market liquidity for all traders, narrowing spreads and hence lowering overall transaction costs. On the other hand, there are long term institutional investors and traders used to floor trading who are criticizing HFT traders for scalping their orders (shifting the price of their orders before the orders of institutional investors reaches the market), manipulating the market (using forbidden techniques such as front running, quote stuffing, layering, spoofing, pinging and ignition) and boosting volatility. Academics have provided numerous studies that mostly take the side of HFT advocates, but there are also some studies confirming the HFT fault in volatility outbreaks. Since we are discussing timeframes of several dozen of minutes it is really difficult to analyze behavior of every HFT trader on every asset on 267 every market. That is why several manipulative schemes were overlooked in the past. Few minor financial market crashes, such as the 2010 Flash Clash or the case of Knight Capital, are believed to be at least partially caused by algorithmic trading. After such events regulators are searching for ways and methods to control the HFT market and strictly punishing those guilty of market manipulation. For example, circuit breaks are now implemented on the markets to shut down trading in case of increased volatility to prevent flash crashes. Also risk prevention measures have to be implemented in HFT companies, whose aim is to check the trading algorithms. Several companies were charged fines in millions of dollars for their manipulative practices on market. In this paper, we focus on the impacts of computer driven trading on the volatility of stocks' prices. These are usually considered to be growing with increasing activity of High frequency traders. Our study is dealing with the analyses of the relationship between the high-frequency trading and price volatility both in high frequency (one minute frequency) and low frequency data (one hour frequency). In previous papers we have managed to address the best way to test optimal sampling frequency. Those test were also applied for the date from Deutsche Borse. Hence, one hour frequency was identified. Market microstructure noise ( order book information that reflects variability of prices caused by the technical side of trading and existence of spreads between the bid and ask prices of the analyzed security ) bias the estimations of realized volatility and make standardly used estimators unreliable. The effect of the market microstructure noise is negligible in the long run, where small discrepancies in price movements are overshadowed by much larger fluctuations caused by the variation of supply and demand. However market microstructure noise should be always dealt with in the high frequency data. Relationship between the trading activity and volatility was known since the beginning of modern trading. Karpoff (1987) has proven direct positive link between volume of trading and volatility. Kyle (1985) documented positive relation between volatility and number of trades and order imbalance before algorithmic trading had been introduced. Easley et al. (1997) confirm positive relationship between trade size and price volatility using competitive models. Jones et al. (1994) showed that after the introduction of HFT trading average size of the trade had started to play role in the stock volatility. Before that volatility was more dependent on the number of trades, and their size did not matter. Impact of average trade size on market volatility become more and more evident as shown by Chlistalla et al. (2011). Newer studies suggest that order imbalance, and not a number of trades, initiates an impact of trading volume on volatility. Comparing small and large trades by their effect on volatility, Huang et al. (2003) discover that small trades close to the maximum-guaranteed quoted depth tend to affect the price changes more than big trades. Chan et al. (2000) showed that only a number of trades matters, not volume of trading and trade size, when realized volatility is used as a proxy of market instability instead of volatility measured by absolute returns. Leal et al.(2016) provide evidences that HFT are positively active on volatility generation and are cause of flash crashes on the stock markets. On the other hand some studies suggest that highfrequency traders in general and especially market makers have tendency to decrease market volatility (Kirilenko et al., 2015). This research differs from other existing papers in several aspects. First, we focus on of the European stock market (particularly the German stock market) as oppose majority of studies on the relationship between HFT trading and price volatility which were conducted on the US data. Second, we propose partially new methodology to measure the highfrequency trading based on the volume of trading, trading activity and average trade size and their changes in time. Third, we focus on the problem of appropriate sampling to detect market microstructure noise to analyze its role in this topic. 268 2 Methodology and Data Methodology Volatility of market prices (or returns) as a most common measure of investment risk can be calculated in several ways. For the matter of simplicity the standard deviation of market returns is often proposed. However it is largely dependent on returns of previous observations in the given sampled data. The most frequent data available for our analysis is minute data. Hence, the best choice for estimating the current market volatility is even simpler method. We have used the logarithm of the ratio of the highest and lowest prices during the observed time interval (minute) (Aldridge 2013). -2, log ‡ ˆ~‰ˆ2, Š‹Œ2, • (1) The most accurate measure of high-frequency trading activity is by capturing the number of orders send by HFT traders (both valid and canceled) and compare them to overall message traffic from all traders. But such data are not available mostly. If some exchanges keep such records, they only seldom distinguish between various types of order submission. Hence, it is necessary to create proxy variable to estimate HFT activity based on market and stock specific characteristics, such as number of orders with relatively small size and increased number of orders Hendershott (2011). We measure difference in HFT activity as logarithm of reverse relative change of average trade size (in number of shares) multiplied by relative change in number of trades. ˆŽ 2, ŠH • • ‘y‹Š2, ` y‹Š H a ’’’’’’’ 2, ,“ ” •H2, 1– ‘y‹Š2, ` y‹Š H a ’’’’’’’ 2, ,“ ” •H2, 1– •H2, H’2, ,“– •H2, H’2, ,“– — ˜ (2) where is voli,t volume of trading of share i in time t. It is identified as the sum of volume of market orders (vmi,t), volume of limit sell orders (vai,t) and volume of limit buy orders (vbi,t). Number of orders of share i in time t is denoted as ni,t. It is again given by sum of number of trades (nmi,t), number of limit sell orders (nai,t) and number of limit buy orders (nbi,t). One extra trade (calculated as the mean of average sizes of trades in last h observations) is added to the ratio of the change in average size of trade (or order). This will assure that function will be defined even in cases of complete market inactivity. Average number of trades (again calculated from last h observations) is added to second ratio. Without this change, relative change in number of trades would be higher for lower absolute changes. If change of aggressive HFT activity needs to be calculated only volume of market orders (vmi,t) and number of trades (nmi,t) are used. On the other hand, when changes in defensive HFT activity are needed, it would be calculated only from volume of limit orders (vai,t and vbi,t) and number of limit orders (nai,t and nbi,t). After dealing with the problem with market microstructure noise we move to formulate the model and choose the most appropriate method to analyze the impact of highfrequency trading on stock price volatility. We compare this relationship two data sets wit different frequencies based on the results of optimal sampling tests. The first data set (with presence of noise) with one minute frequency and the second where we run the same models on the data with one hour sampling frequency (chosen by the results of the Bandi-Russel test (2008) and test of Lagrangian multipliers for MMN by Shin, D. W., & Hwang, E. (2015)). The initial model we have chosen to test the relationship of dynamics in HFT activity and market volatility has the following form: 269 -™2, š o2 ™2, š›œ 2, ™2, š B2, ™2,pš/œ2, ™2,ešB2, •2, (3) where RVm,t is estimation of realized market volatility calculated from 30 one-minute returns previous to time t of German stock index DAX 30. /œ2, is a dummy variable indicating observations where without any trades matched during interval and B2, is a volume of trades of stock i during observation t. Control variables were inspired by ones used in Giot et al. (2010). Error in data with high frequency consists of νŸ,Z uŸ,Z ϵŸ,Z, where uŸ,Zis an error term and ϵŸ,Z represents market microstructure noise. For low frequency data, market microstructure noise is considered to be not present. We use three different estimation procedures for the analysis. The first, linear estimation employs the generalized method of moments (GMM) method with Newey-West (1994) Bartlett HAC estimator to treat autocorrelation and heteroscedasticity. This method show no feasible results or did not converge to any results. The second estimation procedure we used was GARCH(1,1) model with intraday adjustments for seasonality. If none of the external regressors were non-significant, we had switched to the exponential GARCH(1,1) model. In this case we use the same model as in the first case. The coefficients for seasonality in the model were not significant in any of cases. Hence, we switched to GARCH(1,1) model, but these model also did not bring any satisfactory outcomes. As last method we have chosen Markov switching model techniques with three levels. More regimes brought any improvement to our results. This method helped us to obtain better explanatory power of the model (even though this is not necessary, as we are more concern by the coefficients for HFT variable) by estimating coefficients for three different levels of explained and explanatory variables. Switching to these levels is random process. Hence, if the coefficients in models will not be consistent, it would be difficult to explain the nature of tested relationship. Reduced-form of model is used in these estimations, because other control variables tended to be insignificant: -™2, š o2 ™2, š›œ 2, ™2, š B2, •2, (4) Data As was described before two different sampling of data were used for testing. The same models were applied on both samples in order to better comparison between of effects in particular samples. Different effects of algorithmic trading on spot volatility can be analyzed under the influence of market microstructure noise and without its presence. For the version with influence of MMN we have chosen one minute data and one hour date for the other one. Most traded stocks on the Deutsche Borse were picked based on the following criteria: minimal volume of trading (at least 10 million shares a month), minimal market capitalization (at least 2 million EURO), and minimal number of active observations (at least 10 000 minute observation where at least one trade occurred). Only primary issues are selected. After excluding stocks with too many missing observations during selected period, 26 stocks fitted the imposed criteria. This might not be the optimal number for generalization of our results, but as we are working with proxy variables, the stocks should fulfill our strict criteria, or otherwise, our analyses would give biased results (many other stocks are less frequently traded). The period selected for our analyses starts at April 15, 2015 and ends at October 19, 2015. Daily observations start at 9:06 a.m. and end at 5:24 p.m. to exclude opening and closing auctions because these periods usually contain negative spreads and increased volatility. Thus, the first and the last daily intraday returns are omitted, which grants that estimates of realized volatility are not biased by intraday jumps. All days with shortened trading time (due to holidays or system breakdowns) were excluded. The average summary statistics for all stocks is provided in Table 1. All data were gathered from Bloomberg. 270 For the analyzed period is typical rather stable slow decline of nearly all larger stocks traded on European markets, which also holds for German market. Standard deviation of returns was close to mean volatility given by the ratio of highest and lowest prices (Formula 1), which confirms that these two estimations of market volatility gives very close values. The average number of trades in observed one-minute is approximately 8. Approximately 155 limit orders were on both side of limit order book, which indicates sufficient activity of HFT traders and market makers for our analysis. Table 1 Average summary statistics for all chosen shares from French market with one minute frequency on the period from April 15, 2015 to October 19, 2015 Variable Mean Maximum Minimum Stand. Dev Price (P) 46.67 54.17 37.4 4.09 Profit (r) (in 0.001) -0.3 22.21 -39.55 0.57 Number of trades (nm) 7.48 156 0 5.95 Number of sell orders (na) 158.02 852.15 14.38 65.41 Number of buy orders (nb) 155.07 794.38 13.19 63.14 Volume of trades (vm) (in ths) 3.37 810.87 0 5.53 Volume of sell orders (va) (in ths) 6.83 816.18 0 6.63 Volume of buy orders (vb) (in ths) 8.84 2718.17 0 19.16 Spread (s) 0.04 0.47 0.01 0.01 Order imbalance (oi) 0.19 0.42 0.06 0.05 Volatility (σ) (in 0.001) 0.47 16.47 0.03 0.36 HFT activity change (hft) 0 1.21 -0.77 0.15 Source: author 3 Results and Discussion Both linear GMM estimation and GARCH(1,1) brought no satisfactory results for enough equities. Intraday seasonality played no role in any case either. With such a limited outcome we were not able to generate any general conclusion. There were full set of results for low-frequency data, but without high-frequency results we were not able to compare them. Most accurate results were for the Markov model with 3 levels of switching. First we provide results for models applied on high-frequency data. In cases, where we use overall changes in activity in submitting market and limit orders, we find the positive relationship between high-frequency trading and market volatility in all cases (Table 2). J-tests confirm validity of the results in all cases, and HFT activity coefficients are significant for every stock. Aggressive trading that is restricted only to submission of market orders has smaller effect on market returns volatility, but is also positive. This is against our initial hypothesis. We have assumed that aggressive traders, who are willing to pay market price would cause more disturbance then total or just defensive trading. Only in one regime was the impact smaller. Defensive market making after all seems to produce the most variability of prices from the chosen measures. As the regimes are not fixed it is difficult to compare them among shares. But in average limit orders tend to produce more volatility then market orders in HFT data. This might be explained by various factors such as the fact that the activity of market orders is smaller, that market makers submit limit orders further from the market price or just by bias caused by using proxy variable instead of direct measurement of HFT activity. 271 Coefficients of determination are quite small in comparison to standard econometrical practice. But while using high frequency data it is difficult to capture all variability hidden in MMN and other distortions. Next we applied the same methodology on data with one hour frequency. This frequency of sampling was chosen after the analysis of tests for presence of MMN. With these data frequency without the presence of MMN all forms of HFT trading seems to have in average stronger influence on variability of prices. Table 2 Markov switching model estimations of the model (4) for HFT and LFT activity (All statistics for coefficients of HFT are scaled to represent impact to one hour volatility) Mean coefficient Standard deviation Max Min Average R^2 High-frequency data - total Regime 1 5.60 4.39 10.49 0.83 0.30 Regime 2 3.05 3.24 7.76 0.44 0.22 Regime 3 2.46 1.70 4.08 0.90 0.23 High-frequency data - aggresive Regime 1 3.96 5.79 10.64 0.35 0.23 Regime 2 3.25 1.38 4.24 1.67 0.25 Regime 3 1.80 2.18 4.31 0.44 0.23 High-frequency data - defensive Regime 1 4.12 2.26 7.73 1.60 0.29 Regime 2 6.44 4.39 10.85 1.59 0.25 Regime 3 3.76 4.38 11.21 0.83 0.23 Low-frequency data - total Regime 1 5.75 4.99 16.25 0.21 0.64 Regime 2 4.84 7.35 27.29 0.35 0.64 Regime 3 6.50 7.51 22.93 -0.19 0.62 Low-frequency data - aggresive Regime 1 1.63 2.64 8.70 -0.30 0.64 Regime 2 3.38 5.94 21.54 -0.21 0.62 Regime 3 5.24 8.90 41.72 -0.09 0.54 Low-frequency data - defensive Regime 1 5.02 5.86 20.01 0.29 0.67 Regime 2 4.02 6.97 27.82 -0.20 0.62 Regime 3 6.35 5.91 22.20 0.19 0.61 Source: author This might be due to aggregation and alternation of market orders in time. These results confirms hypothesis that HFT increases volatility in long run as well as short run. However in long run there seem to be few cases where also negative coefficients occurred, which suggest that HFT may also decrease volatility in certain occasions. Or it might be caused by external factors that we have not taken into account. Significant influence of aggressive algorithmic trading has been also confirmed in low frequency data. But as in previous case the impact of defensive market making is higher after all. The coefficients of determination values confirm that this relationship is valid in all cases also for low-frequency data. The results for overall and defensive HFT activity 272 were very similar which is caused by much lower number of market orders compared to limit orders. 4 Conclusions Lots of popular as well as academic papers have dealt with the pros and cons of algorithmic trading. Many criticized it for its adopting manipulative techniques and aggressive trading strategies used by some high-frequency traders. In this paper, we analyzed the link between HFT trading activities and volatility of stock market prices on the German stock market. We have concluded that the high frequency trading had a positive correlation with the variability of prices in all cases. These effects have also been proven for low frequency data without the presence of market microstructure noise. Effect on volatility is stronger in long run. This suggest that HFT is not part of the market microstructure noise. In our specifications of high frequency trading, the aggressive trading had weaker impact on volatility than the defensive trading, which is in contradiction with preliminary hypothesis. Our study supports some of our previous findings conducted on different European stock markets and contradict others. Anyway the relationship is the same in every market. HFT increases volatility of market prices. Acknowledgments The support of the Masaryk University internal grant MUNI/A/1039/2016 “Modelování volatility na finančních trzích a její aplikace v oblasti řízení rizik a oceňování aktiv” is gratefully acknowledged. References Aldridge I. (2013). High-frequency trading: a practical guide to algorithmic strategies and trading systems. Hoboken, New Jersey: John Wiley & Sons, Inc. Bandi, F. M., Russell, J. R. (2008). Microstructure noise, realized variance, and optimal sampling. The Review of Economic Studies, vol.75(2), pp. 339-369. Chan, K., Fong, W. M. (2000). Trade size, order imbalance, and the volatility–volume relation. Journal of Financial Economics, vol. 57(2), pp. 247-273. Chlistalla, M., Speyer, B., Kaiser, S., Mayer, T. (2011). High-frequency trading. Deutsche Bank Research, vol. 7. Easley, D., Kiefer, N. M., O'Hara, M. (1997). One day in the life of a very common stock. Review of Financial Studies, vol. 10(3), pp. 805-835. Giot, P., Laurent, S., Petitjean, M. (2010). Trading activity, realized volatility and jumps. Journal of Empirical Finance, vol. 17(1), pp. 168-175. Huang, R. D., Masulis, R. W. (2003). Trading activity and stock price volatility: evidence from the London Stock Exchange. Journal of Empirical Finance, vol. 10(3), pp. 249-269. Hendershott, T., Jones, Ch. M., Menkveld, A. J. 2011. Does Algorithmic Trading Improve Liquidity? The Journal of Finance, 66(1), pp. 1-33. Jones, C. M., Kaul, G., Lipson, M. L. (1994). Information, trading, and volatility. Journal of Financial Economics, vol. 36(1), pp. 127-154. Karpoff, J. M. (1987). The relation between price changes and trading volume: A survey. Journal of Financial and Quantitative Analysis, vol. 22(01), pp. 109-126. Kendall, K. (2007). Electronic and Algorithmic Trading Technology, 1st ed. London: Academic Press, pp. 203. Kirilenko, A. A., Kyle, A. S., Samadi, M., Tuzun, T. (2015). The flash crash: The impact of high frequency trading on an electronic market. Available at SSRN 1686004. 273 Kyle, A. S. (1985). Continuous auctions and insider trading. Econometrica: Journal of the Econometric Society, pp. 1315-1335. Leal, S. J., Napoletano, M., Roventini, A., Fagiolo, G. (2016). Rock around the clock: An agent-based model of low-and high-frequency trading. Journal of Evolutionary Economics, 26(1), pp. 49-76. Newey, W. K., West, K. D. (1994). Automatic lag selection in covariance matrix estimation. The Review of Economic Studies, vol. 61(4), pp. 631-653. Shin, D. W., Hwang, E. (2015). A Lagrangian multiplier test for market microstructure noise with applications to sampling interval determination for realized volatilities. Economics Letters, vol.129, pp. 95-99. 274 The Application of Sovereign Bond Spreads and the Development of the Stock Market on GDP Prediction: The Case of Visegrad Group Jana Hvozdenska Masaryk University Faculty of Economics and Administration, Department of Finance Lipova 41a, 602 00 Brno, Czech Republic E-mail: 174974@mail.muni.cz Abstract: The yield curve – specifically the spread between the long term and the short term interest rates is a valuable forecasting tool. It is simple to use and significantly outperforms other financial and macroeconomic indicators in predicting recessions one to six quarters ahead. A rise in the short rate tends to flatten the yield curve as well as to slow down real growth the near term. The relationship between the spread and future GDP activity was proved already before. For better predictions it is good to use other overtaking indicators of economic activity as the development of the stock market. This paper aims to analyze the dependence between the slope of the yield curve, development of the stock market and an economic activity of selected countries between the years 2000 and 2016. The selected countries are the Czech Republic, Hungary, Poland and Slovakia. The natural and probably the most popular measure of economic growth is GDP growth, taken quarterly. We have found out that the bond spreads and stock market development might be used for predicting of the future economic activity, the best lags of bond spreads are 2, 4 or 5 quarters. These findings might be beneficial for investors and provide further evidence of the potential usefulness of the yield curve spreads as indicators of the future economic activity. Keywords: bonds, slope, spread, yield curve JEL codes: E43, E44, E47, G01 1 Introduction Many market observes carefully track the yield curve’s shape, which is typically upward sloping and convex. However when the yield curve becomes flat or slopes downward (the spread between sovereign 10-year and 3-month bond is negative) it may signal GDP decrease (or recession). The spread of 10-year and 3-month government bond is widely used and it is the most common measurement of the yield spread. The yield curve simply plots the yield of the bond against its time to maturity. The yield curve – specifically the spread between long term and short term interest rates is a valuable forecasting tool. It is simple to use and significantly outperform other financial and macroeconomic indicators in predicting recessions four quarters ahead. This paper builds on a wide range of previous researches, but differs in some ways. Bernard and Gerlach (1998) in their paper showed empirically on eight countries that the slope of the yield curve is a good predictor of the real economic activity. Berk and van Bergeijk (2001) examined 12 euro-area countries over the period of 1970-1998 and found that the term spread contains only limited information about future output growth. Their work is based on the previous theoretical researches of Estrella and Hardouvelis (1991), Estrella and Mishkin (1996). There was proven the evidence that the slope of the yield curve and the future GDP activity are related together. However it is necessary to say that this rule was true until the end of 20th century and it mostly disappeared at the beginning of 21st century and appeared again during the financial crisis (from 2008) and later on (De Pace, 2011; Giacomini and Rossi, 2006; Chinn and Kucko, 2010). Most of the studies are focused on the relationship of the yield curve and GDP activity of the United States of America. All the authors used as a spread, which was analysed in their works, the spread of 10-year and 3-month government bonds. This relationship was 275 proved to be the best in the past (Estrella and Hardouvelis, 1991; Estrella and Mishkin, 1996). The aim of this paper is to show if the yield curve and development of the stock market possess the predictive power of the future economic activity in countries of Visegrad group (Czech Republic, Hungary, Poland and Slovakia) and to examine which time lag of the yield spread is the best for prediction of the future GDP. We are going to examine if this relationship has changed after the financial crisis. Based on the literature review we are going to use the yield spread between 10-year and 3-month bonds. This spread has been proved as the best one for predictions. Despite various researches, there is not any comprehensive theory that would prove the correlation between the yield spread and the economic development of the country yet. We often come across the statements that have only theoretical basis without generally valid empirical evidence. Economic models are largely based on the argument that the yield curve tends to be flatter in the situation of the tight monetary policy and the economic slowdown typically occurs with a slight time lag (Szarowska, 2015). Almost perfect tool containing the relevant future data provides the yield spread of the government bonds. The simplest interpretation of the yield spread is through monetary policy of the country. Based on this criterion – relatively low spread reflects the restrictive and tight monetary policy and vice versa – high spread reflects loose monetary policy. We can find the theoretical justification for using of the spread in expectations hypothesis it assumes that a long term rate of return is the average of the current and expected future short term yields. The investor’s decision to invest in short term or long term asset is completely irrelevant (Mishkin, 1990). Dependence of the yield spread and future economic activity can be derived from their connection to the monetary policy of the state. As bond yields react to monetary policy as well as monetary policy is able to respond to the output of the economy, the yield curve assumes overlapping of the policy measures and responses. The yield curve has the ability to reflect future production either directly or indirectly. Indirectly it comes to predicting of the future interest rate and the future monetary policy. It may also reflect the future production directly because the 10-year yields may depend on estimates of the output of the economy in 10 years. The dependence of the development of stock market and the future economic activity has been proved already many times before (Duca, 2007; Levine and Zevros, 1993). A question arises – how many months, quarters, years of future economic activity can be predicted by the yield spread? Based on the study of Bonser-Neal and Morley (1997) as well as Chinn and Kucko (2010) spread has the greatest ability in predicting one-year horizon (four quarters ahead). As it was mentioned above, to prove if the spread has the best predictive power in one-year horizon is one of the aims of this paper. 2 Methodology and Data There are many ways of using the yield curve to predict the future real activity. One common method uses inversions (when short term rates are higher than long term rates) as recession indicators. Obtaining predictions from the yield curve requires a lot of preliminary work. There is the principle which needs to be held: keep the process as simple as possible. A yield curve may be flat, up-sloping, down-sloping or humped. The standard solution uses a spread (difference between two rates). The problem is to choose the spread between the right terms. The most used spread is between 10-year and 3-month bonds. The problem is that there are rarely bonds which mature exactly in 10 years (or 3 months). In that case the best solution is to use the yield curve, which shows the yield of each maturity. Creating and calculating of the yield curve is a rather difficult task 276 because there are many ways how to do it and every country uses a different model of construction. The yield curves are constructed by Bloomberg, therefore the data for spreads were gained from Bloomberg. Quarterly data were used for the spreads because the data of the economic activity growth are taken on quarterly basis as well. The data of real GDP growth can be found at Eurostat, OECD statistics or Bloomberg. The data of real GDP obtained and used in this paper are from OECD statistics. Data relating to the stock market development, which is represented by the year-to-year evolution of the country's chosen index of shares in individual quarters of the specified period was obtained through the Bloomberg terminal. For the Czech Republic we chose index PX, for the Hungary index BUX, for Poland index WIG 20, for Slovakia index SAX. The selected countries are the Czech Republic, Hungary, Poland and Slovakia. Model Specification As a measure of real growth four-quarter percent change in real GDP was used (thus the percent change of the quarter against the last year’s same quarter was calculated, e.g. the change from 1Q2004 and 1Q2003 real GDP was used). GDP is standard measure of aggregate economic activity and the four-quarter horizon answers the frequently asked question – what happens the next year? The sample period starts from 1Q2000 and ends on 4Q2016. This time range covers the period before financial crisis, period of financial crisis and period after financial crisis. The basic model is designed to predict real GDP growth/decrease four quarters into the future based on the current yield spread (Bonser-Neal and Morley, 1997). This was accomplished by running of a series of regressions using real GDP activity and the different spreads lagged from one to six quarters (e.g. if the spread was lagged 4 quarters, then the interest rate spread used for 3Q2001 is actually from 3Q2000). The last step is to find out which bond spread lag is the best for which country and to prove the hypothesis that the lag of four quarters is the best one. To generate the GDP predictions with the different lags and stock market development the regression using the whole sample was run, and later on two divided samples of real GDP and stock market developments and spreads of each selected country (the sample is divided in 4Q2007/1Q2008, because this period preceded financial crisis and should show some changes in prediction of the yield curve spread) were run. Time series data structure and ordinary least squares (OLS) method was used. All calculations were carried out in Gretl software. The coefficients α, β1 and β2 were estimated for each country: ¢q+ £9 ∝ ∗ ¥¦:§&¨ ∗ ¥ ‹1| ©&:|§ q§y§Š‹¦•§H ª (1) Where: ¢q+ H is a prediction of the future real GDP in time t n quarters H is the lag of spread, value of the lag can be 1, 2, 3, 4, 5, 6 ¥¦:§&¨ is a spread between 10-year and 3-month government bonds in time t ¥ ‹1| ©&:|§ q§y§Š‹¦•§H is a year-on-year quarterly growth of the country's selected stock index ª is a white noise 3 Results and Discussion The tests of normality were carried out. For the evaluation of the normality test is probably the easiest to observe the result from graph of the assumed normal distribution 277 in comparison to the actual distribution of residues and analyse p-values of Chi-square test. We test the hypothesis H0: Residuals are normally distributed, against the hypothesis H1: Residuals are not normally distributed, the significance level of α was chosen as 0,01. If the p-value is greater than α then we cannot reject the H0, therefore the residuals are normally distributed. The test contributed that the data have normal distribution. For the testing of heteroscedasticity we chose the White’s test. We test the hypothesis H0: Constant variances of residuals – homoscedasticity, against H1: Heteroscedasticity. The significance level of α was chosen as 0.01. If the p-value is greater than α then we cannot reject H0, therefore it contributes homoscedasticity. Results of Regression – Whole Sample The whole sample of dataset contains the real GDP from 1Q2000 to 4Q2016. A regression of the whole sample was run and we got the results as seen in Table 1. It is necessary to say that we can contribute this model statistically significant for all the countries because of p-value under 1% (***) respectively under 5% (**) or 10% (*). We got the best results of the models for lag 2 (Czech Republic and Slovakia) and for lag 6 (Hungary and Poland). All the models may be used as predictive models. Table 1 Results of All Countries and Whole Sample from OLS Regression 1Q00 – 4Q16 Constant Spread P-value StockM P - value R2 Czech Rep. (n=2) 0,0398 −0,9382 0,0284** 0,01587 0,0371** 0,58133 Hungary (n=6) 0,01998 −0,4435 0,042** 0,0745 0,0052*** 0,46412 0 Poland (n=6) 0,01647 0,27317 0,0015*** 0,0015 0,0011*** 0,56256 9 Slovakia (n=2) 0,050536 −0,5991 0,0915* -0,0875 0,0276** 0,75061 7 Source: author’s own calculations The R2 coefficients (coefficients of determination) show us how many percent of the sample can be explained by these models. In this case we got quite high values of R2 which means that the variables were chosen well. For example we can say that future real GDP of the Czech Republic will be: Real GDPW±²³´ µ²¶·¸¹Ÿ³ Z£ 0,0398 0,9382 ∗ spreadW±²³´ µ²¶·¸¹Ÿ³ Z 0,01587 ∗ stock marketW±²³´ µ²¶·¸¹Ÿ³ Z By this model we can predict future real gross domestic product for the Czech Republic two quarters ahead. We can test the hypothesis that the behavior of the spread, development of the stock market and gross domestic product has changed during the financial crisis, therefore the sample was divided into two samples in order to prove this hypothesis. Results of Regression – Divided Samples The research continued as follows – the whole sample was divided into two samples. The first one is from 1Q2000 to 4Q2007, the second one is from 1Q2008 to 4Q2016 in order to show if there is any change of behavior and dependency between the variables before or after the financial crisis. Regressions of the first sample and the second sample were run. The results for the time span of 1Q2000 – 4Q2007 (first sample) are possible to see in Table 2, the results for the period of 1Q2008 – 4Q2016 (second sample) are in Table 3. 278 In the first period we got the best results of the models for lag 5 (Czech Republic and Hungary) and for lag 2 (Poland) and lag 3 (Slovakia). All the models may be used as predictive models. R2 are lower than in the time period of whole sample – 1Q2000 – 4Q2016, it means that the predictive power of these models in the first period (1Q2000 – 4Q2007) was lowered. Table 2 Results of All Countries and Sample from 1Q2000 to 4Q2007 1Q00 – 4Q07 Constant Spread P-value StockM P - value R2 Czech Rep. (n=5) 0,016579 −0,6382 0,0425** 0,02764 0,0871* 0,35734 Hungary (n=5) 0,018279 −0,6471 0,061* 0,0463 0,0312** 0,39759 Poland (n=2) 0,011872 −0,4891 0,0051*** 0,0015 0,0011*** 0,56256 Slovakia (n=3) 0,030714 −0,4894 0,0845* -0,00577 0,0476** 0,46052 Source: author’s own calculations In the second period (1Q2008 – 4Q2016) the best results were gained for lags mentioned in the Table 3 – lag 4 (Czech Republic, Poland and Slovakia) and lag 5 (Hungary). All models are statistically significant. R2 are higher than in the previous time spans, which is interesting. This change in prediction possibility may be caused by different behavior of financial markets after the financial crisis (after year 2008). The results show that the models have much higher explanatory power after the year 2007. Table 3 Results of All Countries and Sample from 1Q2008 to 1Q2016 1Q08 – 4Q16 Constant Spread P-value StockM P - value R2 Czech Rep. (n=4) 0,040256 −0,4685 0,0084*** 0,02767 0,00795** 0,73824 Hungary (n=5) 0,02597 −0,6435 0,0027*** 0,01579 0,043** 0,59387 Poland (n=4) 0,03856 −0,0015 0,045** 0,067 0,0001*** 0,71259 Slovakia (n=4) 0,04067 −0,876 0,0015*** -0,0967 0,0027** 0,85726 Source: author’s own calculations At the end we can summarize the new theoretical findings according to which lag is the best for predicting of the future GDP growth. We proved that in these selected countries the best lag is a lag of 2, 4 or 5 (we have added all results together and these lags showed up three times in total each). The results showed that dividing of the sample made a difference between pre-crisis and after-crisis period and it showed the different relationship of spreads and the models. The finding that the best lag is lag 2 or 5 is in contradiction with the theoretical background when almost everybody who predicts the future GDP growth by bond spread uses a lag of 4 quarters. However the lag of 4 quarters was mentioned above as well and we can say that this result contributes the theoretical background. We have shown that the stock market development has a predictive power on the future GDP activity as well as the bond spread and it makes the R2 of the models higher. 279 4 Conclusions The 10-year and 3-month spread has substantial predictive power and should provide good forecast of real growth four quarters into the future (this was proved in USA already before). We showed that after the year 2000 the best predictive lags in the Czech Republic, Hungary, Poland and Slovakia are the lags 2, 4 and 5 quarters. The results presented above confirm that these spreads have a significant predictive power for real GDP growth and the behaviour of the models changed during and after the financial crisis. The results show that the dividing of the sample made a difference in use of the best predictive lag. We have shown that the development of the stock market has a predictive power as well and it helps the models to increase the R2 . The simple yield curve growth forecast should not serve as a replacement for the predictions of companies, which deal with predicting of many economic indicators, it however does provide enough information to serve as a useful check on the more sophisticated forecasts. Future research could be extended to a wider examination of the best spreads and other overtaking indicators in more countries around the world and especially in European Union. It would be interesting to see if there is the rule which would prove the hypothesis that the lag of 4 quarters is the best for predicting future GDP growth in the countries of the European Union. Acknowledgments The support of the Masaryk University internal grant MUNI/A/1039/2016 Modelling of volatility in financial markets and its application in risk management and asset pricing is gratefully acknowledged. References Berk, J., van Bergeijik, P. (2001). On the Information Content of the Yield Curve: Lessons for the Eurosystem? Kredit und Kapital, vol. 34.2001, pp. 28–47. Bernard, H. J., Gerlach, S. (1998). Does the Term Structure Predict Recessions? The International Evidence. CEPR Discussion Papers, vol. 1892. Bloomberg (2017). 10-year, 3-month Government Bond Yields. Development of the stock market (PX, BUX, WIG 20, SAX). Retrieved from: Terminal Bloomberg. Bonser-Neal, C., Morley, T. R. (1997). Does the Yield Spread Predict Real Economic Activity? A Multicountry Analysis. Economic Review: Federal Reserve Bank of Kansas City, vol. 5(3), pp. 37-53. Chinn, M., Kucko, K. (2010). The predictive power of the yield curve across countries and time. NBER Working Paper Series. De Pace, P. (2011). GDP Growth Predictions through the Yield Spread: Time – Variation and Structural Break. Euro Area Business Cycle Network. Duca, G. (2007). The relationship between the stock market and the economy: Experience from international financial markets. Bank of Valetta Review, no. 36. Estrella, A., Hardouvelis, G. A. (1991). The Term Structure as a Predictor of Real Economic Activity. Journal of Finance, vol. 46(2), pp. 555-576. Estrella, A., Mishkin, F. S. (1996). The Yield Curve as a Predictor of U.S. Recessions. Current Issues in Economics and Finance, vol. 2(7), pp. 1-6. Giacomini, R., Rossi, B. (2005). How stable is the Forecasting Performance of the Yield Curve for Output Growth. Oxford Bulletin of Economics and Statistics, vol. 68(1), pp. 783-795. 280 Levine, R., Zervos S. (1993). What We Have Learned about Policy and Growth from Cross-Country Regrassions? American Economic Review, vol. 83(2), pp. 426-430. Mishkin, F. (1990). Yield curve. NBER Working Papers Series, vol. 3550. OECD statistics (2017). Quarterly National Accounts: Quarterly Growth Rates of Real GDP, Growth rate compared to the same quarter of previous year. Retrieved from: http://stats.oecd.org/index.aspx?queryid=350. Szarowská, I. (2015). Tax Shift by Economic Functions and Its Effect on Economic Growth in the European Union. Acta Universitatis Agriculturae at Silviculturae Mendelianae Brunensis, vol. 63(6), pp. 2127-2135. 281 Empirical Analysis of Farmers’ Winterkill Risk Perception Monika Kaczała1 1 Poznań Univeristy of Economics and Business Department of Insurance Al. Niepodległości 10, 61-875 Poznań, Poland E-mail: m.kaczala@ue.poznan.pl Abstract: Some studies have shown that personal risk perception influences risk attitudes of farmers as well as the type of risk management strategy undertaken. For this reason, identification of the structure of perceived risk and the formation of farmers’ risk perceptions is crucial both for designing a government risk management policy applicable in the agriculture sector and for suppliers of risk management tools as it affects demand for insurance. The aim of this research is to investigate the perception of winterkill risk among Polish farmers, the factors which have an impact on this perception and the impact of acceptable (normal) and catastrophic event on risk perception. Statistical analysis, logit models were applied for analysing the representative poll taken in March 2012 in Poland (750 respondents) with the use of the CATI methodology. Preliminary results show that farmers’ winterkill risk perception is affected mostly by farmer’s loss experience. The structure of answers on risk perception depends also on the nonacceptable loss level. Keywords: winterkill, risk perception, risk management, agriculture risk, natural hazard JEL codes: Q54, Q12, Q120, G220, D81 1 Introduction Winterkill is one of the most important risk for Polish farmers. Unpredictable weather during winter may cause substantial losses even if the condition of winter grains and rape is sufficient or good. The most dramatic years in the last decade were 2010-2012, when up to 30% of autumn sown cereal and rape had to be ploughed-up (Central Statistic Office, 2012) and 2016. Winterkill is an insurable peril and it is understood as “losses caused by frost killing, soaking, smothering, cold wind burning or planting of the crop in the period from the 1th of December to the 30th of April, that result in partial or total destroying of plants or partial or total yield reduction” (ACAIA). On a farm level, it causes a drop in crop production and, in the case of extreme losses, the necessity of removing the crop by ploughing up, reduce crop quality, and increase production costs by additional agricultural practices. Since it is a systemic risk, winterkill affects the whole sector resulting in wide economic losses on the macro level. According to the data provided by insurance companies the compensation for winterkill losses in 2006-2015 on average account for 38% of all compensation paid from compulsory crop insurance, with the remarkable share of 82% in 2012 (Prime Minister, 2016). One has to mention that the system has covered up to 25% of total arable land so far, so the real losses must have been much higher. Risk management of natural hazard undertaken by a farmer depends, at least partly, on his risk perception (Ullah et al., 2016; Tucker et al., 2010; Birkholz et al., 2014; Li et al., 2017; Arbuckle et al., 2015). For this reason, identification of the structure of perceived risk and the formation of farmers’ risk perceptions is crucial both for designing a government risk management policy applicable in the agriculture sector as well as for suppliers of risk management tools as it affects demand for insurance. The aim of this research is to investigate the perception of winterkill risk among Polish farmers, the factors which have an impact on this perception and the impact of acceptable (normal) and catastrophic event on risk perception. 282 In order to discover how people perceive risk the following approaches have been developed. A classical approach assumes that the risk may be described by two dimensions: probability and potential damages (severity). The theory of expected utility assumes that agents have perfect information on the probabilities and potential damages related to risky events and for this reason their assessment of risk is identical to the objective risk. Hence factors which influence risk perception are objective. They could be divided into two groups: the factors determining farmers’ risk exposure and the determinants of farmers’ risk sensitivity (van Duinen et al., 2015). The observed discrepancy between individual risk assesment and expert risk estimates based on classical approach caused the theory of risk perception to shift towards a psychological approach. It started with the heuristic paradigm (Tversky and Kahneman, 1973, 1979; Tversky et al., 1982). Reasearches based on this paradigm are concentrated on heuristics used by people to evaluate information that could become cognitive biases, e.g. representativeness, availability heuristic or anchoring and adjustment heuristic. Within the psychological approach “the psychometric paradigm” was created as well (Fischhoff et.al., 1978; Slovic et. al, 1984, 1985). The judgement of riskiness for diverse hazards reflecting risk characteristics such as: voluntariness, controllability, newness etc. Factor analysis has revealed that these diverse characteristics are reducible to three factors: the first called 'unknown risk' (it has been composed of scales such as: unknown to those exposed, unknown to science, unfamiliar, and involuntary) the second,called 'dread risk' (has included the characteristics: severity of consequences, dread, and catastrophic potential) and finally the number of people exposed to the risk. Research in psychology, sociology and cultural were combined in the Social Amplification of Risk Framework (SARF). It asserts that individual risk perceptions are susceptible to social norms through interactions within social networks. Communications of risk events pass from the sender through intermediate stations to a receiver and in the process serve to amplify or attenuate perceptions of risk (Kasperson et al., 1988). 2 Methodology and Data Primary data was gathered on the basis of a survey conducted in March 2012 by means of the CATI method, with the use of a structured questionnaire, on a focus group of 750 farmers across Poland who grow crops. A representative sample was selected on the basis of the farm location and size. Answer variants and respondents’ profiles were expressed by means of different qualitative variables: binary variables, polynomial variables – both nominal and ordinal ones. The data about the farmers and the characteristics of their farms was collected. Farm managers assessed 13 perils in the scale from 1 to 7, where 1 denoted a negligible peril, whilst 7 represented a definitely dangerous phenomenon. The list included hail, flood, winterkill, spring frost, drought, hurricane, plant pests and diseases, the farmer’s health problems, increase in agricultural input prices, price volatility on the crop markets, political changes, property damage and sudden changes in agricultural technology. The data on acceptable losses in crops and losses in crops leading to a farm’s bankruptcy were obtained according to declarations made by farmers, as well as data about loss experience and insured perils. A non-parametric Kruskal-Wallis test and a Spearman’s rank correlation analysis was conducted in order to determine the correlation between the perception of winterkill risk and the evaluation of other risks. In order to verify the hypothesis concerning the correlation between the spring frost risk perception and various qualitative features, a number of contingency tables (cross-tabulation) was produced and the Pearson’s test of independence was conducted. As some of the features considered had quite a few variants, a problem appeared with regard to the appropriate sample size in each cell of the contingency table. Therefore, winterkill risk perception was categorised into three classes: 283 • Low level of risk, if it was evaluated 1 or 2, • Medium level of risk, if evaluated 3 to 5, • High level of risk, if evaluated 6 to 7. Cramer’s coefficient, based on chi-squared statistics was used as a measure of strength of this correlation or the non-parametric Kruskal-Wallis test was applied. The potential determinants of risk perception researched were put into two groups. The first one based on the classical, the second on the psychological approach. Within the first gropup the following objective features influencing risk exposure or risk sensivity are included into the analysis: types of crops, province where the farm is located, farm size, dominant soil quality class, dominant production, production purpose, the use and character of additional, non-farming sources of income. The other group consists of the following determinants: sex, age, educational background, the degree of crop loss which does not jeopardise the farm operation, the degree of crop loss leading to bankruptcy, experience related to different perils: the frequency of various adverse occurrences in the previous 10 years and the scope of adverse occurrence affliction, i.e. the evaluation of the influence the adverse phenomenon had on the farm’s income from crops (in the scale of 1 to 4, where 1 denotes lack of influence on the income, and 4 denotes a very big influence). In the last stage of the research three ordered categories logit models were constructed in order to produce a tool to permit the respondents’ classification into one of the three determined risk classes. In the first model the objective variables, in the second model the subjective variables and in the third model all statistically significant variables from the previous two models were assumed. It was assumed that the variables which remained in the model would be significant at the confidence level of 95 percent. GRETL and Statistica10PL software was used for all the calculations. 3 Results and Discussion The structure of winterkill risk assessment The assessment of winterkill risk was on average 5.52. The analysis of figure 1 shows clearly that respondents most often gave medium or high grades to winterkill. The highest grades were given by the people who had insured their crops (6 and 7) and the lowest grades (1, 2, 3 and 4) were given by those who had not insured their crops. In order to confirm the statistical significance of the response distribution an independence test for qualitative features was conducted assuming three classes of risk assessment. The findings confirm a correlation between the risk assessment and a decision to buy a crop insurance policy (Chi sq. = 13.28) at a confidence level of 99.9 percent. This correlation is not very strong (Cramer coeff.= 0.13). Figure 1 Distribution of winterkill risk rating in the whole focus group and in sub-groups of those who had insured their crops and those who had not Source: author’s own calculation 284 It is interesting, that the perception of winterkill is correlated to the assessment of other risks. The results of Spearman’s rank correlation coefficients indicate that the strongest positive correlation is observed to other weather phenomena, i.e. spring frost (0.5608), hurricane (0.2841), hail (0.2768), to plant diseases and pest (0.2648) and also to exogenous risk like political changes relating to agriculture (0.3241) and dramatic changes in cultivation technology (0.2858). The weakness and the only one negative correlation could be observed in the case of flood (0.1590). Factors affecting perception of winterkill Objective features The result of the chi-square independence test clearly points to the fact that winterkill risk perception is not affected by farm size. The correlation between the location of the farm and assessment of the winterkill risk placed on the verge of significance (Chi sq. = 33.66, p-value = 0.053; Cramer coeff.= 0.15). According to the historic statistical data, one could suppose that the sensitivity to winterkill is correlated to the types of crop and soil quality class. Both factors determine the plant condition and therefore its resistance to weather damages. It turns out that according to the results of U-Mann Whitney test the above assumption is valid for some sort of plants, i.e. winter barley, winter triticale and rye. Table 1 shows that this influence is not strong. Similarly the correlation between winterkill risk perception and soil quality class is not strong, despite being statistically significant (Table 2). Table 1 Comparison of plants whose cultivation has the largest influence on winterkill risk perception, in the light of the Chi-square test of independence Type of plant Chi-sq. stat. (p-value) Relationship Winter barley 10.834 (0.004) 40.88 percent of farmers cultivating winter barley gave the highest rating to winterkill risk while 11.95 percent of these farmers gave it the lowest rating. In the cases of farmers who do not cultivate this crop the percentages are 27.75 percent and 17.6 percent respectively. Rye 9.866 (0.007) 32.37 percent of farmers cultivating rye gave the highest rating to winterkill risk while 12.95 percent of these farmers gave it the lowest rating. In the cases of farmers who do not cultivate this crop the percentages are 27.81 percent and 21.52 percent respectively. Winter triticale 11.37 (0.0033) 32.31 percent of farmers cultivating winter triticale gave the highest rating to winterkill risk while 12.75 percent of these farmers gave it the lowest rating. In the cases of farmers who do not cultivate this crop the percentages are 27.8 percent and 22.03 percent respectively. Source: author’s own calculations Table 2 Winterkill rating distribution according to the soil quality class Response distribution for all observations Independence test findingsRisk evaluation Soil quality class I and II Soil quality class III and IV Soil quality class V and VI Not classifie d High 45.83% 26.60% 31.56% 55.88% Chi sq. 18.83 Medium 41.67% 57.03% 18.27% 41.18% p-value 0.00445 285 Low 12.50% 16.37% 9.36% 2.94% C. coeff. 0.1120533 Source: author’s own calculations The factor which significantly affects winterkill perception is the dominant production. As Table 3 indicates, the highest ratings were given relatively more often if the dominant production involved pigs (more than 35 percent of these respondents gave it the highest rating) and crops (35.8 percent respectively). However the correlations are not strong. The production purpose (for their own use or to the market) does not affect the winterkill perception. Table 3 Winterkill rating distribution according to the dominant production Risk evaluation Crops Milk No dominant production Porkers Independence test findings High 35.83% 17.50% 23.08% 35.11% Chi sq. 20.236 Medium 50.80% 65.00% 55.49% 52.13% p-value 0.0025 Low 13.37% 17.50% 21.43% 12.77% C. coeff. 0.1177 Source: author’s own calculations The share of agricultural income in total farm’s income influences significantly, however weakly, the winterkill rating distribution (Table 4). The risk perception is the highest if agricultural activity is the only source of income. The bigger the share of non-farming income, especially if it is available throughout the year and not only seasonally, the more often winterkill risk is assessed as medium or low. Table 4 Winterkill rating distribution according to the source of income Response distribution for all observations Independence test findings Risk evaluation Only farming sources of income Non-farming sources of income - seasonally Non-farming sources of income – all year High 34.63% 32.73% 26.94% Chi sq. 10.15 Medium 47.57% 45.45% 58.55% p-value p=0.0379 Low 17.80% 21.82% 14.51% C. coeff. 0.0822 Source: author’s own calculations Subjective features According to the results of the chi-square independence test personal features of a farmer like sex, age and educational background do not affect the winterkill risk perception. However, the subjective assessment of the degree of acceptable and nonacceptable crop loss influences winterkill rating distribution. The degree of crop loss that is perceived as normal, i.e. loss which does not jeopardise the farm operations, affects the distribution on the verge of significance and is very weak (Chi sq. = 12.20, p-value = 0.05762; Cramer coeff.= 0.09). Nonetheless, the degree of crop loss that according to the opinion of a farmer may lead to bankruptcy of his/her farm, has a significant effect on the winterkill perception, although not a strong one (Table 5). One has to notice that the degree of crop loss leading to bankruptcy increases the likelihood of giving this risk medium or low rating, and not very high. 286 Table 5 Winterkill rating distribution according to the degree of crop loss leading to bankruptcy Response distribution for all observations Independence test findings Risk evaluati on Crop loss from 10% to 30% yield Crop loss from 31% to 50% yield Crop loss from more than 50% yield Hard to say High 31.90% 31.67% 33.14% 21.74% Chi sq. 17.21 Medium 51.72% 55.56% 42.86% 61.96% p-value p=0.00853 Low 16.38% 12.78% 24.00% 16.30% C. coeff. 0.1076 Source: own calculations A strong correlation was noticed between various risk assessments and different variables denoting the farmer’s experience with them. As the respondents were divided into three groups depending on their risk perception, whilst the occurrence frequency was indicated on a ratio scale, a classical analysis of variance and non-parametric Kruskal-Wallis test was conducted in order to identify the significant differences in the frequency of occurrence of particular risk connected with natural phenomena which the three groups had experienced. Table 6 presents the results which indicate that: the higher the given phenomenon’s frequency of occurrence in the last 10 years, the larger the propensity to rate winterkill risk as highly dangerous. The exception to this rule is fire, flood, animal and plant diseases where the correlation is not observed. The differences between average ratings of winterkill risk made by people struck by a given phenomenon and those who had not been struck are especially high in case of spring frost and winterkill. Table 6 Frequency of the phenomena vs. winterkill risk perception – results of the classical analysis of variance and non-parametric Kruskal-Wallis test Phenomenon Statistics F and H p-values Type of relationship/ Average rating of phenomenon-struck farmers/ Average rating of phenomenon -unaffected farmers Drought 8.91 0.000 The more frequently drought occurred, the higher the risk was rated/3.36/ 2.3612.89 0.002 Hail 6.83 0.000 The more frequently hail occurred, the higher the risk was rated/ 1.08/ 0.7411.58 0.003 Spring frost 28.45 0.000 The more frequently spring frost occurred, the higher the risk was rated/ 4.04/ 2.1553.32 0.000 Winterkill 54.13 0.000 The more frequently winterkill occurred, the higher the risk was rated/ 3.8/ 1.465.2 0.000 Hurricane 4.09 0.017 The more frequently hurricane occurred, the higher the risk was rated/ 0.51/ 0.366.26 0.04 Flood, fire, plant and pest diseases Lack of significant relationship Source: author’s own calculations The logit model in respondent classification according to their winterkill risk perception class The fact that it was possible to identify several features of the respondents which affect their winterkill risk perception justifies a decision to evaluate their diagnostic power by means of constructing a logit model for ordered categories. First an objective model 287 (Model 1) was estimated. Subsequently, the model including the subjective features (Model 2) and then the model including objective and subjective factors (Model 3) were estimated. Table 7 presents the significant variables for the models along with their assessment of the parameters. Table 7 Regression results for risk perception models – significant variables and logit model parameter assessments (N=750) Variables of the model Model 1 – objective features Model 2 – subjective features Model 3 –objective and subjective features Coeffici ents Standard deviation Coeffici ents Standard deviation Coeffici ents Standard deviation Rape cultivated 0.53** 0.24 -0.01 0.30 Winter barley cultivated 0.51*** 0.18 0.50** 0.23 Winter triticale cultivated 0.41*** 0.15 -0.19 0.19 Rye cultivated 0.48*** 0.15 0.15 0.19 Plant production dominant 0.59*** 0.16 0.24 0.20 Pork production dominant 0.55** 0.24 -0.09 0.30 Production for market and own purposes -0.36** 0.15 -0.11 0.19 Age 50-60 -0.43** 0.19 -0.41** 0.19 The number of winterkill problems 0.12*** 0.04 0.12*** 0.04 Influence of winterkill on income 2.16*** 0.13 2.16*** 0.14 Influence of hurricanes on income 0.24*** 0.08 0.23*** 0.08 Confidence ratio test Chi-sq.(7) = 160.45 [0.0000] Chi-sq.(4) = 774.21 [0.0000] Chi-sq.(11) = 782.97 [0.0000] **Significant at the 5%level, ***Significant at the 1% level Source: own calculations The set of the significant variables and the parameter signs in model 1 are not surprising, except for the variable describing dedication of the production. The fact that a farmer produces not only for the market but also for his/her own purpose, further decreases the likehood of higher ratings of winterkill risk. In the group of subjective features (Model 2) the experiences with the weather phenomena, especially winterkill, are crucial for risk assessment. In the Model3 only one objective feature i.e. winter barley cultivation and all subjective factors are significant. The hit ratio obtained from both of the models is presented in Table 8. Due to the fact that experiences relating to various adverse occurrences affected winterkill perception to a much larger extent than the objective features, the obtained hit ratio in Model 2 and Model3 were much more accurate. One has to notice that the introduction of subjective factors make it possible to increase the hit ratio for farmers with low and high levels of risk perception to the satisfactory level and slightly decrease the hit ratio for medium rating. 288 Table 8 Classification matrix and hit ratios for Model 1, Model 2 and Model 3 Model 1 Model 2 Model 3 Actual assess ment Classification Hit ratio Classification Hit ratio Classification Hit ratio Low Medi um High Low Medi um High Low Medi um High Low 0 119 4 0% 122 1 0 99% 122 1 0 99% Mediu m 0 380 18 95% 1 343 54 86% 1 346 51 87% High 0 209 20 9% 0 101 128 56% 0 104 125 55% Hit ratio (total) 53% Hit ratio (total) 79.1 % Hit ratio (total) 78.9 % Source: own calculations 4 Conclusions By comparing the hit ratios in models 1 and 2 one can state that subjective features are crucial for identifying people with low and high assessment of winterkill. Introducing subjective factors increases the hit ratio from 0%(!) to 99% by low ratings and from 9% to 56% by high ratings. The objective features as such enable only to recognize people with average winterkill risk assessment. It is similar to another risk of cold weather – spring frost (Kaczała and Wiśniewska, 2015). The analyses carried out indicate that within the subjective features, farmer’s experiences in terms of most natural perils are of primary importance. The essential ones are especially the experiences caused by winterkill. It is worth noticing that the subjective assessment of the level of crop loss that leads to bankruptcy influences the winterkill risk assessment. The level of acceptable loss that does not jeopardize the functioning of a farm is less significant. Model 2 and Model 3 very well identifies the people who rate winterkill risk perception as low or medium but it undervalues these ratings for people in the “high” group. This means that in order to identify the people who rate winterkill as dangerous additional information would have to be introduced into the model. The way of measuring the risk perception used in this research is the simplest one. Additionally one have to be conscious, that the year of the survey followed three years hardly affected by winterkill. It could strengthen the importance of experiences as winterkill risk perception determinant. References ACAIA. The Act on Agricultural Crops and Farm Animals Insurance of 7 July 2005 (with further amendments) consolidated text of 6 June 2016 (J.L. of 2016 item 792). Arbuckle, J. G., Morton, L. W., Hobbs, J. (2015). Understanding Farmer Perspectives on Climate Change Adaptation and Mitigation: The Roles of Trust in Sources of Climate Information, Climate Change Beliefs, and Perceived Risk. Environment and Behavior, vol. 47(2), pp. 205–234. Birkholz, S., Muro, M., Jeffrey, P., Smith, H. M. (2014). Rethinking the relationship between flood risk perception and flood management. Science of the Total Environment, vol. 478, pp. 12–20. Central Statistic Office (2012). Wynikowy szacunek produkcji głównych ziemiopłodów rolnych i ogrodniczych w 2012 r. Retrieved from: http://stat.gov.pl/cps/rde/xbcr/gus/RL_wynikowy_szac_prod_glow_ziemioplod_roln_i_og 289 rod_2012.pdf. Fischhoff, B., Slovic, P., Lichtenstein, S., Read, S., Combs, B. (1978). How safe is safe enough? A psychometric study of attitudes towards technological risks and benefits. Policy Sciences, vol. 9, pp. 127-152. Li, S., Juh Asz-Horv Ath, L., Harrison, P. A., Aszl O Pint Er, L., Rounsevell, M. D. A. (2017). Relating farmer’s perceptions of climate change risk to adaptation behaviour in Hungary. Journal of Environmental Management, vol. 186, pp.21-30. Kaczała, M., Wiśniewska, D. (2015). Polish farmers' perception of spring frost and the use of crop insurance against this phenomenon in Poland. Economic and Business Review, vol. 1 (15), pp. 90-111. Kasperson, R. E., Renn, O., Slovic, P., Brown, H. S., Emel, J., Goble, R., Kasperson, J. X., Ratick, S. (1988). The social amplification of risk: A conceptual framework. Risk analysis, vol. 8(2), pp. 177-187. Slovic, P., Fischhoff, B., Lichtenstein, S. (1984). Behavioral decision theory perspectives on risk and safety. Acta Psychologica, vol. 56(1), pp. 183-203. Slovic, P., Fischhoff, B., Lichtenstein, S. (1985). Characterizing perceived risk. In: Kates, R.W., Hohenemser, C., Kasperson, J.X. ed., Perilous progress: Managing the Hazards of Technology, Westview, pp. 91-123. Tucker, C. M., Eakin, H., Castellanos, E. J. (2010). Perceptions of risk and adaptation: Coffee producers, market shocks, and extreme weather in Central America and Mexico. Global Environmental Change, vol. 20 (1), pp.23–32. Tversky A., Kahneman D. (1973), On the Psychology of Prediction, Psychological review. vol. 80(4), p. 237-251. Tversky A., Kahneman D. (1979). Prospect Theory: An Analysis of Decisions under Risk, Econometrica: Journal of the econometric society, vol. 47, pp. 263-291. Tversky A., Kahneman D., Slovic P. ed. (1982). Judgment under Uncertainty: Heuristics and Biases, Nowy Jork: Cambridge University Press. Ullah, R., Shivakoti, G. P., Kamran, A., Zulfiqar, F. (2016). Farmers versus nature: managing disaster risks at farm level. Natural Hazards, vol. 82(3), pp. 1931-1945. van Duinen, R., Filatova, T., Geurts, P., van der Veen, A. (2015). Empirical Analysis of Farmers’ Drought Risk Perception: Objective Factors, Personal Circumstances, and Social Influence. Risk Analysis, vol. 35(4), pp. 741–755. 290 Negative interest rates – consequence staying in error? (empirical evidence) František Kalouda1 1 Masaryk University Faculty of Economics and Administration, Department of Finance Lipová 41a, 602 00 Brno, Czech Republic E-mail: kalouda@econ.muni.cz Abstract: The paper is focused to further description and analysis the selected aspects of behavior of Czech banking as a cybernetic system. The objective of the paper is to clarify the implications of central bank regulatory interventions in relations of the managing system (regulator – central bank) to managed system (controlled system – commercial banks) as relationships between operational indicator (discount rate) and regulated indicator (commercial rates).The paper draws on data published by the Czech National Bank (CNB) at http://www.cnb.cz. Methodology of the paper is principally based on the economic cybernetics with special focus to the time series methods and trends analysis. The usual description, literary research, comparison and analytic-synthetic methods are used here as well. The main expected results of the paper relate to the linkages between discount rate and the commercial rate (still not explored), which leads to a discussion of negative commercial rates. Conclusions of the paper partly redefine the position of the central bank in the banking system of the Czech Republic. Keywords: banking system, cybernetics, discount rate, negative commercial rate JEL codes: C67, E58, G21, G38 1 Introduction The aim of the paper is to clarify the implications of central bank regulatory interventions in relations of the managing system (regulator – central bank) to managed system (controlled system – commercial banks) as relationships between operational indicator (discount rate) and regulated indicator (commercial rates). It should be noted that this paper is focused exclusively on the conditions in the banking system of the Czech Republic. This applies both to the theory and practice of managing this sphere of the financial system of the Czech Republic, as well as to the analyses and conclusions of the author of the paper. And above all - as conclusions and evidence of empirical character are formulated in the paper - the analyses and all the conclusions and evidence formulated herein are based on data relevant to the environment of the Czech Republic. Any generalizations of the results of the paper to other banking systems (or other territories) are subject to subsequent stages of research in the field. Within the system of operational management of the monetary policy by the central bank (CNB), confidence remains in the possibility of managing capital cost in the commercial sphere (hereinafter referred to as the commercial rate) using three types of interest rates with a special focus on the regulatory potential of the discount rate (Revenda, 1999). The focus of theory on this rate is based on the assumption that this rate is usually considered to be the most important rate in capital cost management processes (Dvořák, 1999). Therefore, this paper focuses mainly on the discussion of this rate in the given context. After all, the virtually invincible confidence of the theory of banking system management in the regulatory potential of the discount rate is not the exclusive domain of domestic theoretical authorities. Very similar or even identical positions can be found, for example, even in the context of the US banking system management theory (Mankiw, 2000). 291 Significant doubts concerning the legitimacy of this confidence in the ability of the CNB to manage the capital cost at the commercial rate level through the discount rate have been previously published by Kalouda (2014a), Kalouda (2014b) and Kalouda (2014c). The above publications show that the commercial rate cannot be perfectly managed using the discount rate and thus the assumption is wrong. However, the aforementioned existing results of research did not follow the wider context of the analyzed issue, they did not show what can result from dwelling in this error. Therefore, this paper focuses on presenting empirical evidence of the fact that the persistent efforts of the CNB to manage commercial rates through the discount rate necessarily lead to a discussion of the need for, or even the necessity of, the introduction of negative interest rates. 2 Methodology and Data The maximum allowable length of the paper makes it virtually impossible to provide a detailed presentation of all methodological procedures that led to the results presented below. Similarly, the thematic focus of the paper, its obvious interdisciplinarity lead to the risk of an increase in the length of the paper beyond an acceptable limit. Therefore, we will limit ourselves to the briefest overview possible of the most important topics discussed, in connection with the relevant sources. Methodology The methodological input is of key importance for this paper. This fact is defined both by the nature of the issues studied and by the researches previously carried out in the relevant field, or by the publications of the results achieved respectively, to which this paper is very closely tied. The methodical basis of the paper is naturally formed by the usual steps ● description, ● analysis, ● comparison, and ● synthesis. In addition, however, the specific topic and the target focus of the paper virtually forced the application of methodological tools which have been less frequently used in the given context. These include, in particular: ● economics, applied mainly in capital cost management (discount rate), represented by Mankiw (2000), Revenda (1999), Soto (2009) and hysteresis in the economic environment (unemployment) (Němec, 2010), ● mathematical economics, here used to answer the question to what extent cybernetic models are usable for solving economic problems, represented by Allen (1971) and Bíza (2014), ● technical cybernetics, explaining the principles of automatic control in the form of feedback systems with a special focus on the existence of hysteresis-type nonlinearities represented by Balátě (2004), Fikar and Mikleš (1999), Houpis and Sheldon (2014), Kubík et al. (1982), Švarc (2003), Švarc et al. (2011) respectively, ● economic cybernetics, transforming the methodology of technical cybernetics into the environment of economic systems, as discussed by Allen (1971), Šerý (2010), Kalouda and Svítil (2009), and Kisačanin and Agarval (2001), 292 ● financial business management in the form of applications of its access to economic cybernetics, as illustrated by Kalouda (2015) and Kalouda (2016), including elements of strategic management, with the primary use of Johnson and Scholes (1989), ● applied statistics, represented by Arlt and Arltová (2009), Arlt and Radkovský (2001), Blatná (2009), Cipra (2008), Hindls et al. (2007), Řezanková and Löster (2013), Swoboda (1977) and Šerý (2010), ● financial markets, primarily using information from Rejnuš (2014) as well as ● behavioral economics, or perhaps more precisely behavioral finance, in the form of Pompian (2006) and Shefrin (2007). Data The paper is primarily based on freely available input data published by the CNB, see http://www.cnb.cz/cs/financni_trhy/penezni_trh/pribor/rok_form.jsp, and http://www.cnb.cz/cnb/STAT.ARADY_PKG.STROM_DRILL?p_strid=0&p_lang=CS, to which (in order to save space) we hereby refer. These data include the time period from 31 January 2004 to 30 September 2013. The values of the discount rate and commercial rate are monitored. The above data are essentially unusable for the given purpose in their original form. The necessary modifications are described and justified in Chapter 3. Results and Discussion. Model Specification In this case, the modelled object is the banking system of the Czech Republic. We model the processes of managing capital cost at the level of businesses rate (commercial rate) by the CNB using the discount rate. The model of this system is the graphical representation of the dependence of the commercial rate on the discount rate. This relatively simple model is, in principle, based on the assumption that the requirement for the linearity of the modelled system is met (Švarc et al., 2011). However, it is able to describe the expected nonlinearities that are typical for the banking system with sufficient accuracy. The relative simplicity of the model used is thus not an obstacle to its use for describing the situation and identifying the problem in order to obtain the reference characteristics of the examined system (Fikar and Mikleš, 1999). 3 Results and Discussion The dependence of the variables monitored is described primarily by the static characteristic, see Švarc (2002), Kubík et al. (1982), Balátě (2004) respectively. In order to identify the desired dependence, it is necessary to approximate the data with a suitable approximation function. The nature of the problem suggests that it should be a linear function. The result obtained using the standard SW support (MS EXCEL) is shown in Figure 1. Data in Figure 1 suggest that, in terms of this concept, the banking system of the Czech Republic could be considered as a linear system only with obvious reservations. It is a positive finding, however, that the core data set tend to cluster, which will be used immediately. Previous results (Kalouda, 2014a, Kalouda 2014b, Kalouda, 2014c) lead more or less to the clear conclusion that the behavior of the banking system of the Czech Republic is different in the case of an increasing discount rate and it is different when the discount rate is decreasing. In addition, it will be necessary to also respect the second condition for the specific construction of the static characteristics, which is the requirement for the use of stabilized data, see, for example, Švarc (2002). 293 Both of these lead to the requirement to filter the data of the core data set so that they meet both conditions. The result will be two isolated approximate static characteristics, one for the increasing of the discount rate and the other for the decrease in the discount rate. Figure 1 Static characteristics of the core data set Source: Prepared by the author using data from ARAD - Time series system - CNB. [on-line] Retrieved from: [Accessed on 26/11/2013]. Consequently, the complex static characteristics will be constructed from the approximated data. These will be static characteristics depicting the dependence of the commercial rate on the discount rate for both increase as well as decrease in the discount rate. In the last step, we will proceed to the approximation of the dependencies identified by straight-line sections in the stylized form of the resulting static characteristics. They will be further used for a model experiment with multiple cycles of increase and decrease of discount rates. The aim is to prove the influence of hysteresis of the banking system of the Czech Republic on the concept of negative interest rates. Primary data filtering The purpose of filtering the input core data set is primarily to ● split the data into two subsets (for the increase and decrease of the discount rate), as well as to ● stabilize data by considering only steady-state data. Dividing data by discount rate growth or decrease is a smooth and unambiguous step. The acquisition of stabilized data is less clear, however, using the preliminary results (Šerý (2010), Kalouda (2014a), Kalouda (2014b), Kalouda (2014c) and mainly Kalouda (2016) together with Artl and Radkovský (2001)) we are concluding that two months are sufficient for the stabilization of the commercial rate. The results of these modifications are captured in Figure 2 and Figure 3. It is apparent from Figure 3 that it is impossible to obtain the perfect linearization of the static characteristic for the discount rate decrease. This leads to the previously deduced hysteresis-type non-linearity which is characteristic for the banking system of the Czech Republic. y = 0,8611x + 2,7068 R² = 0,7506 0 1 2 3 4 5 6 0 0,5 1 1,5 2 2,5 3 commercialrate(%) discount rate (%) 294 Figure 2 Static characteristics – increase in the discount rate (stabilized data) Source: Prepared by the author using data from ARAD-Time series system-CNB. [on-line] Retrieved from: [Accessed on 26/11/2013] Figure 3 Static characteristics – decrease in the discount rate (stabilized data) Source: Prepared by the author using data from ARAD-Time series system-CNB. [on-line] Retrieved from: http://www.cnb.cz/cnb/STAT.ARADY_PKG.STROM_DRILL?p_strid=0&p_lang=CS [Accessed on 26/11/2013]. Complex static characteristic In this case, we also chose a graph to present the results obtained. The reason is simple – an attempt not to overburden the paper with an excess of numerical data (all data can be naturally provided for review by the author of the paper). The result is captured in Figure 4. y = 1,0591x + 2,344 R² = 0,9841 0 1 2 3 4 5 6 0 0,5 1 1,5 2 2,5 3 commercialrate(%) discount rate (%) y = 0,1167x3 - 0,7782x2 + 2,3668x + 2,1528 R² = 0,7974 0 1 2 3 4 5 6 0 0,5 1 1,5 2 2,5 3 commercialrate(%) discount rate (%) 295 Figure 4 Complex static characteristic Source: Prepared by the author using data from ARAD-Time series system-CNB. [on-line] Retrieved from: [Accessed on 26/11/2013]. Approximated static characteristic The approximated static characteristic (see Figure 5) represents the idealized shape of the Figure 4. The decisive (accurate) data are: ● "start" values of the discount rate and the commercial rate [1,00%; 3,40%] ● maximum values of the discount rate and the commercial rate [2,75%; 5,24%] ● coordinates of the remanent commercial rate [1,00%; 3,90%] ● coordinates of the coreccitive discount rate [0,65%; 3,40%] The approximated static characteristic is created from the input data according to Figure 4, i.e. for the basic (first) cycle increase and the subsequent decrease in the discount rate. Figure 5 Approximated static characteristic (basic increase-decrease cycle) Source: Prepared by the author using data from ARAD - Time series system - CNB. [on-line] Available at: http://www.cnb.cz/cnb/STAT.ARADY_PKG.STROM_DRILL?p_strid=0&p_lang=CS [Accessed on 26/11/2013]. y = 1,0591x + 2,344 R² = 0,9841 y = 0,1279x3 - 0,8153x2 + 2,3963x + 2,1486 R² = 0,8459 2 2,5 3 3,5 4 4,5 5 5,5 0 0,5 1 1,5 2 2,5 3 commercialrate(%) discount rate (%) 296 Model experiment (repeated discount rate increase-decrease cycles) The repeated cycles immediately follow each other, i.e. they always start where the previous cycle ended. The first cycle ended with a decrease in the discount rate to its original value of 1,00%. The corresponding value of the commercial rate, however, was not the original 3,40% - it reached a value higher by the remanent commercial rate (0,50%), i.e. 3,90%. The commercial rate will reach the original value of 3,40% only if the commercial rate drops from 1,00% to 0,65%. This represents a decrease in the discount rate by the value of a coreccitive discount rate of 0.35% (see Figure 5). These data are deduced from the complex static characteristic (see Figure 4). Figure 6 Resulting static characteristic Source: Prepared by the author using data from ARAD - Time series system - CNB. [on-line] Retrieved from: http://www.cnb.cz/cnb/STAT.ARADY_PKG.STROM_DRILL?p_strid=0&p_lang=CS [Accessed on 26/11/2013]. The resulting conclusion from the model experiment is, according to the data from Figure 6, obvious: In the long run, the regulatory potential of the discount rate in the definition range of positive numbers is exhausted in the given case after three discount rate growth-decrease cycles. The only option how to apply the discount rate as a rate that controls the commercial rate is to take into account the negative values of the discount rate. This conclusion is in remarkable consistency with the current situation - the discount rate is virtually zero. The same applies to the REPO rate, sometimes considered as an alternative tool for controlling commercial rates. 297 4 Conclusions The conclusions we have reached in this paper are undoubtedly alarming. They quite clearly exceed the horizon of the famous "storms in a glass of water" that may have applied to a small group of banking system management theorists, or a small group of highly privileged "banking sector captains". The introduction of negative interest rates may be, to a certain extent, acceptable to the world of high finance, especially to its sub-group that can financially positively balance even with real economic drops, even at the level of drops of the national economy as a whole. Which in itself is a crazy possibility, completely beyond any morality. Except for business morality, of course. Under no circumstances, however, would negative interest rates be acceptable to the population. Here, on the contrary, with a probability bordering on certainty, citizens would perceive negative interest rates as a punishment. As an absurd punishment, since it would be a punishment for depositing (through savings deposited in commercial banks) their temporarily available funds into the national economy. Which would quite certainly (in this case, all illusions must be put aside) shake up the trust of the citizens in the system. Actually, with easily predicted impacts - even a "man in the street” is financially literate enough to find a satisfactory investment alternative (Greenspan, 2008). It is difficult to precisely determine the impacts of these activities on the stability of the domestic financial system. However, they appear to be scary to the author of this paper. They can be compared with a one-off loss of approximately CZK 600-800 billion associated with the transformation of the Czech economy into a market economy or about CZK 600 billion "leaving" the Czech economy every year due to excessively unilaterally effective considerations which led to the apparent sell-out of the domestic industrial base. This is the moment when the game is over. The moment when "financial alchemy" (King, 2017), however seductive it appears to be, must end. And that is why this paper seeks to be a modest contribution to the effort to bring both the theoretical discussion and practical action back to the level of common sense. References Allen, R. G. (1971). Matematická ekonomie. Praha: ACADEMIA. Arlt, J., Arltová, M. (2009). Ekonomické časové řady. Praha: Professional Publishing. Arlt, J., Radkovský, Š. (2001). ANALÝZA ZPOŽDĚNÍ PŘI MODELOVÁNÍ VZTAHŮ MEZI ČASOVÝMI ŘADAMI. Politická ekonomie 49(1), pp. 58-73. (Rukopis). 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Brno: CERM. 300 Financial Capital Inflows, Current Account Deficit and Economic Growth in Turkey Özcan Karahan1 , Olcay Çolak2 1 Bandirma Onyedi Eylül University Faculty of Economics and Administrative Sciences, Economics Department Yeni Mahalle, No:77, 10200, Balikesir, Turkey E-mail: okarahan@bandirma.edu.tr 2 Usak University Faculty of Economics and Administrative Sciences, Economics Department Ankara İzmir Yolu 8. Km. 1 Eylül Kampüsü, Merkez, Uşak, Turkey E-mail: olcay.colak@usak.edu.tr Abstract: The presence and direction of the relationship between economic growth and current account balance is one of the most fundamental economic matters especially in developing countries. Some economists show that current account deficit arising from increasing import of capital good spurs faster economic growth. Contrarily, some group of researches detect that an increase in the rate of economic growth promotes imports of goods and services, which lead to a deterioration in the current account balance. Some economists also argue that there is a mutual relationship between economic growth and current account balance. This study aims to shed new lights on this controversial issue by examining the Turkish case. Accordingly, using data between 2003 and 2006, we analyse the relationship between economic growth and current account balance financed by capital flows. Empirical findings indicate that there is a bidirectional relationship between economic growth and current account deficit financed by capital inflows. Thus, stability of growth rates in Turkey depend on financing of current account deficit permanently. Keywords: Current Account Deficit, Capital Inflows, Growth JEL Codes: F32, F43 1 Introduction The sources of economic growth and its interactions with other macroeconomic variables have been the most important subject in the literature for a long time. In this framework, the link between current account balance and economic growth draw special attention. Traditionally, based on Keynesian import function, some economists argue that a rise in national income result in current account deficit by stimulating import, which can be called as “growth-led current account deficit” hypothesis. Contrarily, some group of researchers put forward that current account deficit may play a promoting role in the economic growth process of developing countries which supports “current account deficitled growth”. According to this view, increasing the volume of imported intermediate and capital goods causing current account deficit promotes economic growth. Looking at the literature, most of the previous studies have focused on the effect of national income on import or current account deficit. However, along with the improvements in international capital movements, accelerating financial capital inflows to developing countries and its relation with the economic growth in these economies have induced economists to think the macro economic effects of current account deficit. Accordingly, researchers start to concern with current account deficit financed by capital inflows as a significant dynamic of economic growth for developing countries. Consequently, international capital movements make “current account deficit-led growth” hypothesis much more popular against to “growth-led current account deficit” hypothesis. Today, the direction of relationship between current account deficit and national income has been a quite controversial issue in the literature. Some economists argue that a rise 301 in national income results in current account deficit by stimulating import, while other group of economists imply a causal relationship from current account deficit to economic growth. Some economists also indicate a mutual relationship between them. In order to shed new lights on this controversial issue, our study examines the causal relationship between current account deficit and economic growth in Turkey. The paper is organized as follows. Section 2 provides a review of the related literature. Section 3 set out the data, methodology and results. Final section concludes with the key findings and makes policy implications. 2 Literature Review There exist a vast literature aiming to indicate the direction of relationship between current account balance and economic growth. Causal relationship from economic growth to current account deficit called as “growth-led current account deficit” hypothesis while unidirectional causal link from current account deficit to economic growth named as “current account deficit-led growth” hypothesis. Several studies in the literature also find bidirectional causality between economic growth and current account deficit. Supporters of “growth-led current account deficit” hypothesis argue that an increase in national income deteriorates the current account balance by stimulating import of consumption goods. Keynesian import function asserts that national income positively affect the import. It is clear from this point of view that current account balance is negatively affected by the level of national income since economic growth enhance the imports based on well-known Keynesian multiplier model. In line with the above thinking, mainstream studies have tended to rely on econometric analysis focusing particularly on the causal relationship from economic growth to current account deficit. Pacheco-Lopez and Thirlwall (2007) analyze the causality relationship between economic growth and current account balance for the period 1977-2002 in 17 Latin American countries. Panel data analysis concludes that economic growth caused to increase in current account deficit. Lebe et. al. (2009) employ Granger causality and Vector Autoregressive Model on data covering the term of 1991-2005 in Turkey and Romania. Results of analysis show that increasing economic growth rate deteriorates current account balance in both countries. Yalçınkaya and Temelli (2014) investigate the relationship between economic growth and current account deficit in the nine emerging market economies for the period 1992-2013. It is concluded that economic growth has a positive effect on current account balance. Concerning with the studies specially focusing on Turkey, Yılmaz and Akıncı (2011) attempt to investigate the link between current account balance and growth. They use Granger Causality Test and Johansen Cointegration Analysis for the period of 1980-2010. Their findings suggest that direction of relationship operates from economic growth to current account deficit. Finally, Yurdakul and Ucar (2015) examine the relationship between current account deficit and economic growth in Turkey for the period of 1999– 2014. The results of Granger causality and VAR analyses indicate that there is a unidirectional relationship from economic growth rate to current account deficit. Contrary to “growth-led current account deficit” hypothesis, some groups of economists have argued that economic growth can be driven by imported production input in developing countries. It is clear that imported production input like capital and intermediate goods have a significant role in production process in developing countries. Therefore, increased imports play a complementary role in promoting economic growth by enlarging the amount of input in production process. In other words, increasing volume of imports has the potential to play a significant role in promoting economic growth in developing countries that suffer from scarcity of intermediate and capital goods to use in production process. In summary, current account deficit arising from imports has a potential to enhance economic growth for developing countries. 302 Malik et. al. (2010) investigate the relationship between economic growth and current account deficits in Pakistan over the period of 1969-2007. The results of Johansen cointegration test and Error Correction Model show that decreasing in the current account deficit enhance economic growth. Kogid et. al. (2011) examines the causality relationship between imports and economic growth from 1970 to 2004 in Malaysia by using EngleGranger two steps, Johansen and Toda-Yomamoto procedures. They detect that import has a paramount important role in spurring the growth of the economy. Rahman and Shahbaz (2013) analyse the impacts of economic growth on current account balance in Pakistan over the period 1990 to 2010. They apply the structural break autoregressive distributed lag bounds testing approach in order to examine the relationship between the growth and current account deficit. Empirical results confirm that there is a strong causality running from imports to economic growth. Looking at the studies examining the Turkish case, Ugur (2008) examines the causal relationship between import and economic growth by using the Granger causality test and Impulse Response and Variance Decompositions Models for the monthly data set between 1994 and 2005. Findings show that economic growth is significantly influenced by imports which caused to current account deficit. Çiftçi (2014) also investigates the relationship among economic growth and current account deficit between 2001 and 2012 in Turkey. The results of Johansen Cointegration and Granger Causality suggest that current account deficit has positive impact on national income for Turkish economy. It seems that there are numerous studies indicating the validity of “growth-led current account deficit” hypothesis and “current account deficit-led growth” hypothesis. Some studies in the literature also find bidirectional relationship between economic growth and current account deficit. Calderon et. al. (1999) examine the interaction among the economic growth and basic macroeconomic variables in 44 developing countries between 1966 and 1994 by using generalized moment methods. Results of panel data analysis shows that economic growth and current account deficit effect each other. Songur and Yaman (2013) analyse the interaction among the economic growth and current account balance for ten countries between 1981 and 2010. Findings of panel data analysis indicate a bidirectional causality between economic growth and current account deficit. Erdoğan and Acet (2016) investigate the relationship between economic growth and current account deficit by using quarter data between 2003 and 2015 in Turkey. Empirical results of VAR analysis show that there is a mutual interaction between economic growth and current account balance for Turkey. Thus, they indicated that developments in economic growth and current account deficit are dependent each other. In this analytical framework, there has been much attention recently devoted to the role of capital inflows in financing current account deficit. Researchers show that capital inflows also play a significant role in stimulating economic growth by financing current account deficit in developing countries. Accordingly, capital inflows to developing countries finance current account deficit arising from imported capital and intermediate goods. To this respect, capital inflows play crucial role in the production process and thus economic growth. In connection with emerging economies, Akbaş et. al (2014) analyse the relationship between capital flows, current account deficit and economic growth in 20 emerging markets by employing Panel Causality Test over the period of 1990-2011. Unidirectional causality from capital flows to current account deficit and economic growth were strongly determined. Shahbaz and Rahman (2010) explore the role of capital inflows on economic growth in the case of Pakistan by employing ARDL bounds testing approach and Error Correction Model over the period of 1971-2008. Empirical evidence reveals that capital inflows have positive effect on economic growth by financing current account deficit. With regard to the case of emerging Asian economies, Xuan-Vinh Vo (2010) investigates the relationship between capital inflows and economic growth over the period 1980-2001 in South Korea, Indonesia, Malaysia, Thailand and the Philippines. In the framework of Generalized Method of Moments estimation technique, estimation results support the view that increasing capital flows to emerging countries are an 303 important stimulating factor in accelerating economic growth by enhancing import capacity. 3 Data, Methodology and Empirical Results In this section we analyze the relationship between current account deficits (CAD), financial capital inflows (FCI) and Gross Domestic Product (GDP) in Turkish economy. Data set converted into logarithmic form consist of quarterly time series between 2003 and 2016. Data for current account deficits and financial capital flows are sourced from the Electronic Data Distribution System (EVDS) of the Central Bank of Republic of Turkey. Current account deficits cover the imbalances in the trade of goods and services while financial capital flows include movement of portfolio investments and other investments. Empirical examination proceeds in the context of time series analysis. Firstly, we investigate the presence of unit root in the series by performing Zivot-Andrews (1992) unit root test which allows for examining the presence of unit root under structural break. Secondly, since Zivot-Andrews (1992) type unit root test allows for one break in the series, in order to reveal the cointegration relationship among the series we perform Gregory-Hansen (1996) cointegration test. Finally, long-run and short-run relationship among the variables would be investigated by the realm of fully modified ordinary least squares (FMOLS) method proposed by Phillips and Hansen (1990). Unit Root Test Our analysis commences by scrutinizing the stationarity check of the series by performing Zivot-Andrews (1992) unit root test that considers the structural break in the series. Results are displayed in Table 1. Apart from GDP, all variables are stationary at level by rejecting the null hypothesis of unit root with structural break. For GDP series the null hypothesis is not rejected since minimum t-statistics is lower than critical values for each model that allows for structural break in intercept, in slope and in both. However, by first differentiation all series become stationary since the calculated minimum t-statistics exceed the critical values proposed by Zivot and Andrews (1992) for each model hence, series are integrated at I (1). Table 1 Zivot-Andrews Unit Root Test Variables Model Break Date Minimum t-statistics LGDP DLGDP Model A Model A 2007:2 2008:4 -2.921 (2) -8.293* (2) LGDP DLGDP Model B Model B 2008:1 2009:2 -3.063 (2) -7.731* (2) LGDP DLGDP Model C Model C 2008:4 2009:4 -3.322 (2) -8.418* (2) LCAD DLCAD Model A Model A 2005:1 2009:4 -6.379* (0) -9.392* (2) LCAD DLCAD Model B Model B 2012:2 2005:4 -6.342* (0) -8.721* (2) LCAD DLCAD Model C Model C 2010:4 2009:4 -6.488* (0) -9.308* (2) LFCI DLFCI Model A Model A 2010:2 2010:1 -5.646* (0) -10.670* (0) LFCI DLFCI Model B Model B 2013:2 2005:2 -5.612* (0) -10.058* (0) LFCI DLFCI Model C Model C 2010:2 2009:4 -5.692* (0) -10.633* (0) Notes: * indicates the significance level at 1 %. Lag length is selected by Akaike Information Criteria. Corresponding lag lengths are displayed in parenthesis. Model A considers the structural 304 break in intercept, Model B in slope and Model C both in intercept and slope. For critical values for each model see Zivot and Andrews, 1992: 257. Gregory-Hansen Cointegration Test Since Zivot-Andrews type unit root test allows for one break in the series, in order to reveal the cointegration relationship among the series we perform Gregory-Hansen (1996) cointegration test. Gregory and Hansen develop a cointegration test that accommodates single structural endogenous break in underlying cointegration relationship since the coefficients of cointegrated vectors might vary in time. The findings of Gregory-Hansen cointegration analysis are reported in Table 2. The results indicate that Zt and ADF test statistics reject the null hypothesis of no cointegration in Model 2 and Model 4 that underline the structural break in level shift with trend and regime and trend shift at 1 % significance levels. Furthermore, Zt rejects the null of no cointegration in Model 1 and Model 3 at 5% significance levels. However, Za fails to reject the null hypothesis of no cointegration for all models. According to ADF tests statistics breaks are observed in 2006:2 and 2008:4 while it is 2006:4, 2007:3 and 2007:2 for Za statistics. All of these findings indicated that there is cointegration relationship among the national income, current account deficit and financial capital inflows in Turkey. Table 2 Gregory-Hansen Cointegration Test Model ADF Break Date 1 tZ aZ Break Date 2 Lag Model 1 -3.05 2008:4 -5.19** -34.63 2006:4 2 Model 2 -6.84* 2006:2 -6.20* -43.72 2006:4 1 Model 3 -4.94 2006:2 -5.65** -40.80 2007:3 0 Model 4 -7.83* 2008:4 -6.75* -50.23 2007:2 1 Note: *,** indicates the rejection of null hypothesis at 1% and 5% significance levels respectively. Lag length is selected by Akaike Information Criterion for long-run covariance estimation. Model 1: Level Shift, Model 2: Level Shift with Trend, Model 3: Regime Shift where Intercept and Slope Coefficients Change, Model 4: Regime Shift where Intercept, Slope Coefficients and Trend Change. For critical values see (Gregory and Hansen, 1996: 109). FMOLS Estimation Existence of cointegration among the series provided by Gregory-Hansen cointegration test leads us to scrutinize the long-run and short-run mutual relationship between economic growth and current account deficit. In this context, we perform fully modified ordinary least squares (FMOLS) estimation method proposed by Phillips and Hansen (1990). The results are exhibited in Table 3 and Table 4 where upper part shows the long-run coefficients and lower part shows the short-run coefficients based on error correction form. 305 Table 3 FMOLS Estimation Results for Gross Domestic Product (GDP) Note: *, ** indicate the significance level at 1 % and 10% respectively. Section A of Table 3 shows the impact of current account deficit and financial capital inflows on economic growth in the long run. According to the results both current account deficits and financial capital flows positively affect the GDP. Accordingly, current account deficits in Turkey enhance economic growth by promoting imported intermediate and investment goods which in turn leads to increase in production. Financial capital flows also positively affect GDP in the long-run since current account deficits are financed by the capital inflows. In other words, capital flows finance current account deficit which in turn triggers the economic growth. Finally, dummy variable which is represented by D1 in Section A of Table 3 is generated by assigning the value 0 and 1 up to structural break date and afterwards of the structural break date respectively through Gregory-Hansen cointegration test. Here the structural break date is decided to be the last quarter of 2008 according to the results of Gregory-Hansen cointegration test that considers the regime shift where intercept, slope and trend changes. The value of dummy variable is positive and statistically significant which in turn indicate the severity of structural break among the series on affecting GDP. Section B of Table 3 is devoted to estimation results regarding the short-run relationship among the variables, which is based on error correction form. Error correction term is obtained by the residuals of long-run cointegration regression and enters to the regression with a one period lag. All the variables enter into the regression with a first differentiated form. Current account deficits and financial capital flows are positive and statistically significant in the short-run. These findings are in accordance with the longrun estimation results although the magnitudes of the coefficients are less than the coefficients in the long-run. The sign of error correction term is negative and statistically significant as expected. Table 4 FMOLS Estimation Results for Current Account Deficits (CAD) Note: *, ** and *** indicates the significance levels at 1%, 5% and 10% respectively. A- Long-Run Coefficients Dependent Variable: LGDPt Coefficient (Prob.) LCAD t 0.1328 (0.0000)* 0.0826 (0.0883)** 0.3490 (0.0000)* LFCI t D1 B- Short-Run Coefficients (Error Correction Form) Dependent Variable: DLGDPt Coefficient (Prob.) EC t 1 -0.6910 (0.0000)* DLCAD t 0.0189 (0.0572)** DLFCI t 0.0438 (0.0009)* A- Long-Run Coefficients Dependent Variable: LCADt Coefficient (Prob.) LGDPt 0.7101 (0.0000)* LFCIt 0.3291 (0.1287) D1 0.3847 (0.0987)*** B- Short-Run Coefficients (Error Correction Form) Dependent Variable: DLCADt Coefficient (Prob.) EC t 1 -0.4494 (0.0623)*** DLGDPt 0.5443 (0.0326)** DLFCIt 0.2411 (0.1623) 306 By using the same methodology, we also investigate the impact of economic growth and financial capital inflows on current account deficit in Table 4. Findings in Section A of Table 4 show that GDP has a positive effect on current account deficit while financial capital inflows does not have any significant impact current account balance in the long run. Dummy variable (D1) in Table 4 is obtained by the same way in Section A of Table 3. The coefficient of dummy variable is positive and statistically significant which points out that structural break among the series arising from the global financial turmoil in 2008 do have crucial impact on the long-run relationships among variables. These findings are also corroborated by the short-run estimation based on error correction form, which is presented in Section B of Table 4. In the short-run GDP has positive impact on current account deficits in a lesser extent while short-run capital inflows don’t have any significant impact on current account balance. Hence, either in the long-run or in the short-run increases in GDP exacerbates the current account deficits via stimulating imports in terms of demand for consumption goods. Finally, the sign of error correction term is negative and statistically significant through the expectations. In summary, empirical findings indicate that there is a mutual relationship among the current account deficit and economic growth. Therefore, imports can be treats as a main source of economic growth although it deteriorates current account balance in Turkey. Thus, increased imports caused to current account deficit plays a significant role in promoting economic growth via transfer of production input from abroad. Results also indicate that capital inflows are essential in order to determine dynamics of interactions between current account deficit and economic growth in Turkey. 4 Conclusions The direction of relationship between current account deficit and economic growth has been a quite controversial issue in the literature. In order to shed new lights on this controversial issue by examining the Turkish case, we examine the direction of relationship between economic growth and current account deficit financed by capital inflows in Turkish Economy. We employ Gregory-Hansen cointegration test and fully modified ordinary least squares (FMOLS) method on quarterly data set between 2003 and 2016. Empirical findings show that there is a bidirectional relationship between the current account deficit financed by capital inflows and economic growth. This means that developments in economic growth and current account deficit are dependent each other. Thus, accelerating capital inflows to Turkey creates sources for financing imported capital and intermediate goods that will be utilized as inputs for production process, which enhances much more economic growth. In other words, the expansion of current account deficit financed by capital inflows is an integral part of economic growth in Turkey. From the policy perspective, it can be concluded that sustainable growth rates in Turkey need policy framework covering the measures supporting steadily financing of current account deficit. Therefore, outward-looking economic growth policy should advocate the measures supporting permanent finance of current account deficit in Turkey. Thus, we also interpret the result of study as strongly supportive of the role of financial capital inflows in explaining continuous growth in Turkey. Consequently, policy makers should pay intensive attention to attracting capital flows permanently as a significant source for sustainability of current account deficit and economic growth in Turkey. References Akbaş, Y. E., Lebe, F., Uluyol, O. (2014). Analyzing the Relationship among the GDPCurrent Account Deficit and Short term Capital Flows: The Case of Emerging Markets. Yönetim ve Ekonomi, 21 (2), pp. 293-303. 307 Calderon, C., Chong, C., Loayzo, N. (1999). Determinants of Current Account Deficit in developing Countries, Working Papers Central Bank of Chile, No:51. Çiftçi Necati (2014). Relationship between Current Account, Real Exchange Rate, and Economic Growth in Turkey: An Co-Integration Analysis. Anadolu University, Journal of Social Sciences, 14, (1), pp. 129-142. Erdogan S., Acet, H. (2016). The Relationship between Current Account Balance and Economic Grwoth: Turkish Case (2003-2015). International Journal of Social Science, 51, pp. 539-548. Gregory, A. W., Hansen, B. E. (1996). Tests for cointegration in models with regime and trend shifts. Oxford Bulletin of Economics and Statistics, vol. 58(3), pp. 555-560. Kogid, M. D., Mulok, K. S., Ching, J. 2011. Does Import Affect Economic Growth in Malaysia. The Empirical Economics Letters, 10 (3), pp. 297-307. Lebe, F., Kayhan, S., Adıgüzel, U., Yiğit B., (2009). The Empirical Analysis of the Effects of Economic Growth and Exchange Rate on Current Account Deficit: Romania and Turkey Samples. Journal of Applied Quantitative Methods, 4 (1), pp. 69-81. Malik, S., Chaudhry, I. S., Sheikh, M. R., Farooqi, F. S. (2010). Tourism, Economic Growth and Current Account Deficit in Pakistan: Evidence from Co-Integration and Causal Analysis, European Journal of Economics, Finance and Administrative Sciences, 22, pp. 21-31. Pacheco-Lopez, P., Thirlwall, A. P. (2007). Trade Liberalisation and the Trade-off between Growth and the Balance of Payments in Latin America, International Review of Applied Economics, 21 (4), pp. 469-490. Phillips, P. C. B., Hansen, B. E. (1990). Statistical inference in instrumental variables regressions with I (1) processes. Review of Economic Studies, vol. 57(1), pp. 99-125. Rahman M. M., Shahbaz, M. (2013). Do Imports and Foreign Capital Inflows Lead Economic Growth? Cointegration and Causality Analysis in Pakistan. South Asia Economic Journal, 14 (1), pp. 59-81. Shahbaz, M., Rahman M. M. (2010). Foreign Capital Inflows-Growth Nexus and Role of Domestic Financial Sector: An ARDL Cointegration Approach for Pakistan. Journal of Economic Research, 15, pp. 207-231. Songur, M., Yaman, D. (2013). The Relationship between Current Account Balance and Economic Growth in Developing Countries: Panel Data Analysis, Maliye Dergisi, 164, pp. 220-232. Ugur, A. (2008). Import and economic growth in Turkey: Evidence from multivariate VAR analysis. East West Journal of Economics and Business, 11(1-1), pp.54-75. Vo Xuan-Vinh (2010). Net Private Capital Flows and Economic Growth-the case of Emerging Asian Economies. Applied Economics, 42, pp. 3135 - 3146. Yalçınkaya, Ö., Temelli, F. (2014). The Relationship between Economic Growth and Current Account Balance: The BRICS and MINT (1992-2013). Süleyman Demirel University, The Journal of Faculty of Economics and Administrative Sciences, 19 (4), pp. 201-224. Yılmaz, Ö., Merter, A., (2011), “İktisadi Büyüme ile Cari İşlemler Bilançosu Arasındaki İlişki: Türkiye Örneği”, Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 15 (2), pp. 363-377. Yurdakul, F., Uçar, B. (2015). The Relationship between Current Account Deficit and Economic Grwoth: An Empirical Study on Turkey. Procedia Economics and Finance, 26, pp. 1001-108. 308 Zivot, E., Andrews, D. W. K. (1992). Further evidence on the great crash, the oil price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, vol. 10(3), pp. 251-270. 309 The Causal Relationship between Inflation and Interest Rate in Turkey Özcan Karahan1 , Metehan Yılgör2 1 Bandirma Onyedi Eylül University Faculty of Economics and Administrative Sciences, Economics Department Yeni Mahalle, No:77, 10200, Balikesir, Turkey E-mail: okarahan@bandirma.edu.tr 2 Bandirma Onyedi Eylül University Faculty of Economics and Administrative Sciences, Econometrics Department Yeni Mahalle, No:77, 10200, Balikesir, Turkey E-mail: myilgör@ bandirma.edu.tr Abstract: The causal nexus of inflation and interest rate has a significant role in the application of monetary policy. Indeed, interest rate-inflation nexus is important for policy makers to be effective in selection of the monetary policy aims and tools. However, there is no consensus among the economists about the direction of the causality relationship between inflation and interest rate. Some economists, in the framework of Fisher Hypothesis, argue that there is a positive causal relationship from inflation rate to nominal interest rate. Contrarily, other economists assert that any increase in interest rate accelerates inflation rate by raising the cost-push inflation. This paper aims to examine the relationship between inflation and interest rate in the case of Turkey in order to make a new contribution to the discussions about the direction of causality among the interest rate and inflation. We examine the interest rate-inflation nexus for the term 2002-2016 in Turkey by employing the cointegration and causality tests. The empirical results confirm that there exists a unidirectional causality from inflation to interest rate. From the policy perspective, it can be concluded that to provide price stability is essential for managing interest rate efficiently for Turkish economy. Keywords: Inflation, Interest Rate JEL Codes: E31, E43 1 Introduction Inflation and interest rate are two significant macroeconomic variables which are closely related each other. The inflation-interest rate nexus has been the subject of much empirical research in the literature. However, there exist a strong debate in the literature concerning with the causal direction of the relationship between interest rate and inflation. Most of the studies in the literature indicate the causality from inflation to interest rate, which advocate Fisher Hypothesis. Some researchers argue that any change in the interest rate caused to change in the inflation. Thus, how inflation and interest rate are related is one of the significant controversial issues in the literature. Understanding the relation between inflation and interest rate is basic for policy maker in order to perform monetary policy effectively. Existing of causal relationship from interest rate to inflation let policy makers to control inflation rate by using policy interest rate. For example, when the price level increase, policy makers can reduce inflation rate by tighter monetary policies, so they will increase the level of interest rate. Accordingly, higher interest rate encourages people to save more and thus led lower level of consumption in the economy. As a result, prices come down since demand is less than the supply. Thus, it may be the best way to increase interest rate to stabilise the general price level if causal relationship runs from interest rate to inflation. That means directional relationship between interest rate and inflation affect the efficiency of monetary policy in achieving stable price. 310 Given the potency of the monetary policy in achieving stable price indicated above, greater attention has been paid to the nexus between interest rate and prices in recent decades. However, the causal direction of the relationship between inflation and economic growth still remains as one of the most debatable issue in the literature. In order to shed new lights on this controversial issue, this study examines the causal relationship between interest rate and inflation in Turkey. The organisation of the paper is as follows. Second Section briefly reviews the related literature. Third Section describes data and methodology and presents empirical results. Final section offers concluding remarks and makes some policy implications. 2 Literature Review The direction of relationship between inflation and interest rate is closely related to effectiveness of stabilization programs. However, the causality between inflation and interest rate is a quite debatable matter in the literature. It is mostly, in the framework of Fisher Hypothesis, argued that increasing inflation rate results in increasing interest rate. Contrarily, some researchers assert that any change in the interest rate caused to change in the inflation which can be called as “interest rate-led inflation” hypothesis. Supporters of Fisher hypothesis argue that increasing price level raises the level of interest rate by decreasing real money balance. Originally, Fisher (1930) hypothesized that the nominal interest rate is made up of two components: the expected rate of inflation and the real interest rate. Under the assumption of rational expectations, expected inflation rate equals to inflation rate in the end of the term. Consequently, Fisher hypothesis argue one-to-one relationship between the inflation and the nominal interest rate, assuming the real rate of interest rate unaffected. Thus, Fisher hypothesis provides theoretical basis for econometric analysis focusing particularly on the causal relationship from inflation to interest rate. Accordingly, the Fisher effect has been extensively examined and investigated in the literature. Booth and Ciner (2001) analyze the bivariate relationship between Eurocurrency interest rate and inflation for nine European countries and US from 1978 to 1997. Empirical results reveal that there is a one-to-one relationship between Eurocurrency rates and inflation. Thus, findings supports the Fisher Hypothesis that markets participants incorporate a predictable portion of the inflation rate into the nominal interest rate. Saymeh and Orabi (2013) investigate the interactions among interest rate, inflation and economic growth in Jordan over the period 2000-2010. Employing Johansen Cointegration and Granger Causality Test, it is concluded that inflation causes interest rate, which confirms Fisher Hypothesis. Büberkökü (2014) examines the relationship between nominal interest rates and inflation within the framework of Fisher’s hypothesis for seven emerging markets between 2003 and 2013. The cointegrating coefficients estimated according to panel group mean FMOLS and DOLS show that an increase in inflation rate raises nominal interest rate, which advocates Fisher hypothesis. Concerning with the Turkish case, Şimşek and Kadılar (2006) investigates the relationship between nominal interest rate and inflation by using quarterly data from 1987 to 2004. Results of Autoregressive Distributed Lag model show that interest rate is determined by the inflation. Köksel and Destek (2015) analysis the relationship between inflation and interest rate for the period of 2002-2014. The results of Granger causality test show that nominal interest rate is increased by one-for-one in response to an increase in inflation rate. Thus, findings indicate that there is unidirectional causal relationship from inflation to nominal interest rate. İncekara et. al. (2015) test validity of Fisher Hypothesis by employing Johansen Cointegration and Granger Causality Test between 1989 and 2011. It is concluded that in the long run there is causal relationship from inflation to interest rate in Turkey, which advocates Fisher Hypothesis. Finally, Doğan et. al. (2016) examine the causality relation between the interest rates and inflation by using the data series between the 2003 and 2015 period. The results of 311 Johansen cointegration and Granger causality tests reveal that there is a causality relation from inflation to interest rates. Contrary to Fisher hypothesis, some groups of economists have argued that rates of interest play a significant role in determining of inflation level. Concerning with the causal relationship from interest rate to inflation rate, it can be argued that there are two channels through which a variety in interest rate can affect the level of prices. Accordingly, the influence mechanism of interest rate on inflation can be operated in supply and demand channels indicated below (Bhunia, 2016: 20-21). In the demand channel, a change in interest rate affects the individual’s preference to consume out of their income. For example, an increase in the interest rate probably leads to decline in consumption by spurring savings since a rise in the interest rate increases the opportunity cost of consumption today. Thus, individuals save much more part of their income in order to have interest bearing financial assets rather than spending on consumption. This will result in total demand contraction and thus decrease the price level. On the other hand, low interest rate put more borrowing power in the hands of people to consume much more. In this case the economy tends to grow inducing inflation if there is not enough supply to satisfy rising demand for goods and services. That means short fall in supply against to increasing demand causes to inflation. This kind of inflation can be categorized under demand pull inflation. Prices can also be increased based on the impact of interest rate on supply side of economy. Indeed, interest rate plays a pivotal role in not consumption or total demand but also production or total supply. Higher the rate of interest, higher is the cost of finance for investment and production in the economy. To some extent, business may than pass on this higher marginal cost of capital to prices in order to protect their profit margin. In other words, since higher interest rate augments the financing cost of production for firms, producers pass their production cost on to consumers in the form of increased prices, which called as cost-push inflation. On the other hand, a rise in the interest rate increases the cost of credit for entrepreneurs, which would be expected to reduce investment demand. This results in decreasing production and, in turn, total supply of goods and services. In conclusion, rising interest rate increases price level by creating of total supply constriction resulting excessive demand in the goods market. Thus, the interaction between the interest rate and the supply side of the economy provide a clear indication of the impact of the interest rate on price level. Asgharpur (2007) examines the causal relationship between the interest rate inflation in panel of 40 selected Islamic countries using panel causality methodology over the 2002- 2005 period. The results of study show a unidirectional causality from interest rate to inflation in all of the countries. It is also concluded that interest rate must be reduced in order to decrease inflation. Mahdi and Masood (2011) analyze the causality relationship between interest rate and inflation for the period 1989-2007 in Iran. The results of Johansen’s cointegration approach and vector error correction model (VECM) approach concludes that there is a bidirectional relationship between interest rate and inflation. However, the relation from interest rate to inflation is more powerful than inflation to interest rate in Iran. Finally, Amaefula (2016) investigates the causality between interest rate and inflation in Nigeria. He employs Johansen cointegration test and vector error correction model (VECM) of Granger causality on data between 1995 and 2014. The results of analysis show evidence of long run equilibrium relationship between the variables from interest rate to inflation rate. Looking at the Turkish case, Gül and Ekinci (2006) indicated the causal relationship from interest rate to inflation. They analyzed the interactions between nominal interest rates and inflation over the period of 1984-2003 and found that causality exist in only unidirectional relationship from nominal interest rate to inflation. Onur (2008) also examines the relationship between interest rate and inflation between 1980 and 2005 by using Cointegration and Granger Causality tests. Empirical results indicate that interest 312 rate has an impact on inflation. Finally, Bari (2013) researches the impact of interest rate on inflation by using Vector Error Correction Model over the period 2001-2012. According to empirical results, interest rate and output gap are main determinants of inflation in Turkey. In conclusion, there are numerous studies indicating the validity of both Fisher Hypothesis (inflation-led interest rate) and “interest rate-led inflation” hypothesis in the literature. However, it seems that most of the studies confirm Fisher hypothesis along with the increasing application of inflation targeting monetary policy regime over the last decades. Therefore, economists recently devote much attention to analysing the developments of interaction between inflation and interest rate under the inflation targeting regimes in different countries. Turkey has also adopted Inflation Targeting as a monetary regime after 2001 – between 2001 and 2006 implicitly and then explicitly. Thus, economists also analyse the relationship between inflation and interest rate, in particular as Turkey moves to an inflation targeting regime. Findings concerning with the Turkish case suggest that there is a strong Fisher effect under inflation targeting monetary regime in a global trend-compatible manner. Accordingly, Köse et. al. (2012) and Tunalı and Erönal (2016) examine the relationship between inflation and interest rate and both studies indicate that Fisher effect is valid for Turkey since the beginning of the implementation of inflation targeting regime. 3 Data, Methodology and Empirical Results In this section we examine the relationship between inflation (INF) and interest rate (IR) in Turkish economy between 2002 and 2016. We use monthly deposit rates and Consumer Price Index for the variables of interest rate (IR) and inflation (INF), respectively. Both data set sourced from the Electronic Data Distribution System (EVDS) of the Central Bank of Republic of Turkey. Empirical analysis begins by checking the stationary statute of data set. We check whether time series of inflation (INF) and interest rate (IR) are stationary by using Augmented Dickey Fuller (ADF) and Philips Peron (PP) tests. Then, the long run relationship between inflation and interest rate is analysed by using Cointegration Test. In this framework, we perform Engle-Granger (1987) and Johansen (1988) cointegration tests. Finally, we examine the causality relationship between inflation and interest rate by operating Granger (1988) Causality Test. Unit Root Test The first step of the empirical methodology finds the order of integrations of the data. Accordingly, in order to indicate the stationary statute of data set we perform Augmented Dickey Fuller (ADF) and Philips Peron (PP) tests. The results of Unit Root Tests are presented in Table 1. Findings show that all variables are not stationary at level since test statistics is lower than critical values for both methods. However, by first differentiation all series become stationary since the calculated test statistics exceed the critical values. In conclusion, both inflation (INF) and interest rate (IR) series are integrated of the same order at I (1). That means data set is suitable for employing cointegration tests developed by Engle-Granger (1987) and Johansen (1988). 313 Table 1 Results of Unit Root Tests ADF (Augmented Dickey Fuller) PP (Philips Peron) Variables Level First Difference Level First Difference INF (constant) -2,8343 -3,2509** -1,9090 -6,6189* DINF (constatnt+trend) -2,0007 -3,8072** -1,8510 -6,6439* IR (constant) -1,5291 -7,7514* -1,3640 -8,9176* DIR (constant+trend) -2,2919 -8,1113* -2,2967 -9,4555* Not: * and ** indicate the significance level at 1 % and 5 %, respectively. Maximum lag length is specified by considering Akaike Information Criterion (2). Cointegration Tests In this section we employ cointegration tests which are the standard tools in order to investigate the linear combination of time series variables. Thus, this method finds out the presence of number of cointegrating vectors indicating the long run relationship between variables. Accordingly, we perform Engle-Granger and Johansen Cointegration Tests to indicate whether inflation (INF) and interest rate (IR) have a long run relationship or not. In the framework of Engle-Granger test for co-integration, we firstly run a regression of interest rate on inflation and then inflation on interest rate separately and save the residual for both regressions. Whether the residual series of regression equation are stationary or not are checked by Tau-statistic and Z-statistic. Second row of Table 2 shows the stationary statute of residual series of regression of nominal interest rate on inflation while third row of the Table 2 presents the stationary statute of residual series of regression of inflation on nominal interest rate. The tau-statistic and Z-statistic reject the null hypothesis of no cointegration (unit root in the residuals) at the 1% and 5% levels for both regressions. Thus, results clearly suggests that interest rate and inflation are cointegrated, which means that there is a long run relationship between them. Table 2 Engle-Granger Cointegration Tests Results Dependent Variable Tau- Statistic Prob. Z- Statistic Prob. INF -3.4646 0.0467** -69.1130* 0.0000* IR -7.9927 0.0000* -65.9941* 0.0000* Not: * and ** indicate the significance level at 1 % and 5 %, respectively. Presence of the long-run association between inflation (INF) and interest rate (IR) is also checked by performing Johansen cointegration test. This methodology is based on maximum likelihood estimation in which it allows for testing the long-run relationship between the variables by using the maximum eigenvalue and trace statistics. Thus, in the framework of the Johansen Cointegration Analysis, we used two different tests called Trace Statistic and max-Eigen statistic for deciding whether there is a long run relationship among the variables. Accordingly, the results of Johansen Cointegration analysis are divulged in Table 3. The null hypothesis examines the cointegrating vector number which is stated by the rank (r) equals zero of equal less than one. Findings show that the computed value of the both Trace Statistic and max-Eigen statistic are more than the critical value at 5% level of significance. Therefore, the null hypothesis of no cointegrating relationship or no long-run relationship between inflation and interest rate (r = 0) is rejected. That means there exists an equilibrium relationship between the variables. Thus, it can be concluded there is long run relationship between inflation and interest rate in Turkey. 314 Table 3 Johansen Cointegration Tests Results Null Hypothesis Trace Statistics Maximum Eigenvalue Statistics Test Statistic % 5 Critical Value Test Statistic % 5 Critical Value r = 0 54.5116 16.4947 32.6641 14.2646 r ≤ 1 21.8400 3.8414 21.8475 3.8414 Causality Test After indicating the presence of cointegration or long run relationship among the variables, in this part of study we analysed the causality between interest rate and inflation rate. Granger causality test based on the vector autoregression (VAR) model will be used in order to detect the direction of relation between the variables. The results of The Granger-causality tests are presented in Table 4. Findings reveal that causality runs from inflation rate to interest rate while the reverse causation is not confirmed. That means, unidirectional Granger causality is indicated from inflation to interest rate. In other words, the direction of the relationship between inflation and interest rate runs from inflation to interest rate. That means a variety in inflation affects the level of interest rate in Turkey. Table-4 Granger Causality Test Null Hypothesis F- Statistic p- Value Decision IR does not Granger cause INF 0.8956 0.4490 Accept INF does not Granger cause IR 2.8500* 0.0453 Reject Not: * indicates the significance level at 5 %. Maximum lag length is specified by Akaike Information Criterion (2). In conclusion, empirical findings show evidence of long-run equilibrium relationship between interest rate and inflation. Furthermore, there is a unidirectional causality from inflation to interest rate. In short, the empirical results imply that inflation spurs interest rate in Turkey. These findings support Fisher hypothesis defining the inflation as a crucial integral part of nominal interest rate. Thus, empirical findings also give significant insights into monetary policy programmes for being effective in selection of the policy aims and tools. According to this, policy makers can manage the interest rate by controlling general price level in Turkey. In other words, to provide price stability is essential for managing interest rate well for Turkish economy. 4 Conclusions The causality between inflation and interest rate has attracted substantial attention among economists. Accordingly, a lot of studies focusing on the link between inflation and interest rate have been made in the literature. However, the direction of relationship between inflation and interest rate has still been a quite controversial issue among the scholars. Some economists support Fisher hypothesis arguing that inflation is an important factor affecting the level of interest rate. Contrarily, other group of economists asserts that inflation is mainly influenced by the interest rate. This study aims to shed new lights on this controversial issue by examining the Turkish case. For this aim, we analyse the link between inflation and nominal interest rate by using cointegration and causality tests for the term of 2002-2016. 315 The findings from Engle-Granger and Johansen Cointegration tests indicate that there is a long run relationship between inflation and interest rate. Granger causality test result shows unidirectional causality running from inflation to interest rate while no causality from interest rate to inflation. The overall evidence proposes that inflation rate is a main factor affecting the integral part of nominal interest rate, which advocates Fisher Hypothesis. From the policy perspective, it can be concluded that stability of nominal interest rate firstly requires stability of price level in Turkey. Thus, policy makers should pay intensive attention to stability of general price level to control the level of interest rate. In other words, policy makers should follow a policy framework covering the measures supporting stability of price level in order to manage the rate of interest effectively in Turkey. References Amaefula, C. G. (2016). Long-Run Relationship between Interest Rate and Inflation: Evidence from Nigeria, Journal of Economics and Finance, 7 (3), pp. 24-28. Asgharpur, H., Lotfali A. K., Azhdar K. (2007). "The relationships between interest rates and inflation changes: An analysis of long-term interest rate dynamics in developing countries". International Economic Conference on Trade and Industry (IECTI) 2007, 3 - 5 December 2007, Penang, Malaysia. Bari, B. (2013). Main determinants of Inflation in Turkey: A Vector Error Correction Model. International Journal of Economics and Research, 4 (6). pp. 13-19. Bhunia, A. (2016). How Inflation and Interest Rates Are related to Economic Growth? A Case of India. Journal of Finance and Accounting, 4 (1), pp. 20-26. Büberkökü, Ö. (2014). The Relationship between Nominal Interest Rates and Inflation in Emerging Markets: Evidence from Panel Cointegration Tests. Bankacılar Dergisi, 88, pp. 80-86. Booth, G. G., Ciner, Ç. (2001). The Relationship between Nominal Interest Rate and Inflation: International Evidence. Journal of Multinational Financial Management, 11, pp. 269-280. Doğan, B., Eroğlu Ö., Değer, O. (2016). The Causal relationship between Inflation and Interest Rates: The Case of Turkey, Çankırı Karatekin University, Journal of the Faculty of Economics and Administrative Sciences, 6 (1), pp. 405-425. Engle, R. F., Granger, C. W. J. (1987). Cointegration and Error Correction Representation and Testing. Econometrica, vol. 55, pp. 251-276. Fisher, I. (1930). The Theory of Interest, New York, Macmillan. Granger, C. W. (1988). Some recent development in a concept of causality. Journal of Econometrics, 39(1), 199-211. Gül, E., Ekinci, A. (2006). The causal Relationship between Nominal Interest Rates and Inflation: The Case of Turkey. Scientific Journal of Administrative Development, 4, pp. 54-69. İncekara, A., Demez, S., Ustaoğlu, M. (2012). Validity of Fisher Effect for Turkish Economy: Cointegration Analysis. Procedia-Social and Behavioural Sciences, 58, pp. 396- 405. Johansen, S. (1988). Statistical Analysis of Co-Integration Vectors. Journal of Economic Dynamics and Control, 12, pp. 231–254. Köksel B., Destek, M. A. (2015). Testing of Fisher Hypothesis in Turkish Economy: An Empirical Analysis upon 2001-2014 Period. The Journal of International Social Research, 8 (41), pp. 1247-1253. 316 Köse, N., Emirmahmutoğlu, F. Aksoy, S. (2012). The Interest Rate-Inflation Relationship Under AN Inflation Targeting Regime: The Case of Turkey. Journal of Asian Economics, 23, pp. 476-485. Mahdi, S., Soleymani, M. (2011). The Long Run Relationship between Interest Rate and Inflation in Iran: Revisiting Fisher’s Hypothesis. Journal of Economics and International Finance, 3 (14), pp. 705-712. Onur, S. (2008). Türkiye Ekonomisinde Faiz Oranları-Enflasyon ilişkisi Üzerine Bir Model Denemesi, Akademik İncelemeler Dergisi, 3 (2). pp. 69-110. Saymeh, A. A. F., Orabi, M. M. A. (2013). The Effect of Interest Rate, Inflation Rate, GDP on Real Economic Growth Rate in Jordan. Asian Economic and Financial Review, 3 (3), pp. 341-354. Şimşek, M., Kadılar, C. (2006). The Analysis of Fisher Effect Using Turkish Data. Dogus University Journal, 7 (1). Pp. 99-111. Tunalı H., Erönal, Y. Y. (2016). The Relationship between Inflation and Interest Rates: Validity of Fisher Effect in Turkey. Suleyman Demirel University, Journal of Faculty of Economics and Administrative Sciences, 4, pp. 1415-1431. 317 The role of accounting in regard to information concerning the convergence of airline business models Mariusz Karwowski1 1 Warsaw School of Economics The Collegium of Business Administration, Department of Management Accounting al. Niepodległości 162, 02-554 Warsaw, Poland E-mail: mkarwo@sgh.waw.pl Abstract: The objective of the research underlying this article is to present the role of accounting in supplying information relative to the trend toward convergence within the airline industry. The review of publications showed that accounting has only marginal value in providing the requisite data for the analysis of airline business models. The results of the study of the available information derived from financial statements of selected airlines partially confirmed the convergence of airline business models. The contribution of the article to the existing literature lies in its identification of the possible use of accounting in studying the business models of companies operating in the selected industry. Keywords: business model, financial statements, airline industry, full service carrier, low cost carrier JEL codes: L93, M40, M41, N70, R40 1 Introduction The primary business models adopted by airlines are based on full-service or low-cost strategies. The full-service carriers (FSCs) offer services such as flights to major airports and business class service. The low-cost carriers (LCCs) create value by focusing on cost containment and customers with less purchasing power. In recent years, however, a trend toward convergence in these business models has been observed within the airline industry. The objective of the research underlying this article is to present the role of accounting in furnishing information concerning this convergence trend within the airline industry. The article poses the following theses: • Accounting has only marginal value in providing the requisite data for the analysis of airline business models. • The available information derived from financial statements partially confirms the convergence of airline business models. 2 Methodology and Data In order to verify the theses, a review of pertinent literature was conducted as well as an empirical analysis. The aim of the study of relevant publications was to describe the trend toward convergence in the business models within the airline industry and to identify methods of evaluating this convergence. The empirical study was based on financial statements for the year ended in 2014 of selected airlines that follow either the FSC or the LCC business model, namely: 7 FSCs (Air France KLM SA – abbreviation: AIRF, Delta Air Lines INC. – DELT, Lufthansa Group – LUFT, SAS AB – SAS, Singapore Airlines – SING, Turkish Airlines, Inc. – TURK and United Continental Holdings, Inc. – UCH) and 4 LCCs (AirAsia Berhad – AIRA, easyJet Airline Company Limited – EASY, JetBlue Airways Corporation –JETB, and Ryanair Holdings plc – RYAN). Three categories of indices were derived from the financial statements and were used to examine and compare the 11 airlines: profitability, labour and aircraft ratios. Based on accepted methods of evaluating business models and the convergence within the airline 318 industry, (as described in chapter 3), and considering the scope of information typically furnished in financial statements, the following indices were calculated: • profitability ratios (net income to sales revenue and net income to total assets), • labour ratio (employee expenses to total expenses), • aircraft ratios (net value of aircraft to total assets, depreciation expenses to total expenses, lease expenses to total expenses). 3 Results and Discussion Differences between the two primary business models in the airline industry In the 1950s and 1960s most of the world’s air transport services were provided by FSCs. By the 1970s, this was beginning to change. Charter carriers, especially in Europe, were serving a small but increasing share of the travel market, while in the U.S., Southwest Airlines began flying in the early 1970s. Following airline deregulation between 1978 and 1993, further expansion of the LCCs occurred in several countries. Ryanair converted to the LCC model in 1991, WestJet started service in Canada in 1996, and Virgin Blue began operating in Australia in 2000 (Tretheway, 2004). The FSC model is based on a differentiation strategy (Hunter, 2006) enabling an airline to offer a tailored product for each customer segment at an acceptable price. This segmentation of passengers, or prospective passengers, according to the purpose of their air journey resulted in the creation of first, business, and economy class cabins (Flouris and Walker, 2005) and the offer of a variety of fare bundles, which meant diverse levels of service (Fageda et al., 2015). These carriers also introduced revenue management, a sophisticated system allowing them to determine the optimal number of seats on a flight to be sold at high fares with few if any restrictions, versus seats sold at low fares with restrictions (Tretheway, 2004), thereby filling any excess space left after premium demand is met (Wensveen and Leick, 2009). Other characteristics of FSCs are (Hunter, 2006): various aircraft types and moderate capacity utilisation (approximately 60%). In contrast to the FSC, the LCC model is based on cost leadership or cost minimisation (Hunter, 2006), generated by operational efficiencies (Wensveen and Leick, 2009). To offer low fares, the LCCs have pursued simplicity, efficiency, productivity, and high utilization of assets (O’Connell and Williams, 2005). LCCs offer a straightforward pricing structure, online booking, unbundled fares and point-to-point service (Fageda et al., 2015). LCCs use less congested secondary airports (with cheaper landing charges), which helps to increase punctuality and reduce turnaround times. Staff remuneration in LCCs is competitive owing to a lower base salary plus productivity-related compensation. LCC pilots typically have a 60% salary base and are able to receive up to the remaining 40% according to their performance. This creates a highly incentivised workforce, which is further motivated through stock option plans (Flouris and Walker, 2005; Morrison and Mason, 2007). Other characteristics of LCCs are (Hunter, 2006; Morrison and Mason, 2007): a single type of aircraft, high capacity utilisation (approximately 70-80%), and low margins. For the LCC, price decisions are linked to long-term costs, while FSC pricing decisions are based only on short-term costs or sometimes are divorced from cost considerations. The difference lies between revenue maximisation and profit maximisation. Over time, however, these FSC pricing and capacity responses prove to be unprofitable (Tretheway, 2004). The trend toward convergence in the airline industry A full understanding of trends in the airline industry would be helpful in comprehending its current position (Franke, 2007). It should be noted as well that trends do not develop without breaks and that new trends emerge (Wensveen and Leick, 2009). In an industry particularly sensitive to economic cycles, airline management were forced to take crucial 319 strategic decisions as to how to reshape their companies and adapt to emerging trends after the airlines’ stock prices fell in 2008 (Jarach et al., 2009). As a result of the foregoing, a transition toward convergence of business models can now be observed in the airline industry (Daft and Albers, 2013). Business models traditionally adopted by airlines, based on low-cost or full-service strategies, are inadequate in the new market reality (Pereira and Caetano, 2015). As Lohmann and Koo (2013) noted, airlines are no longer easily labeled as either LCCs or FSCs. Faced with competition from the LCCs, the FSCs have attempted to fight back by adopting some of the LCCs’ characteristics: more rapid turnaround times, reduced use of travel agents, and lower commission rates (Hunter, 2006). Meanwhile, signs of a slowdown and limited organic growth forced the LCCs to change key principles of their business model: fare unbundling and point-to-point operations. The archetypical low-cost business model has evolved into an adapted business model that takes a hybrid approach (Fageda et al., 2015). Franke (2007) indicates that a company offering more specialized services at lower prices stimulates additional demand because the enhanced services are not primary in this sector (Pereira and Caetano, 2015). From a network perspective, the distinction between the archetypical LCC business model and the modified LCC business model with a hybrid approach is widening. Differences are also clear between hybrids offering connecting services and hybrids offering fare bundling (Fageda et al., 2015). It seems that the FSCs and the LCCs are converging. For instance, easyJet is determined to be perceived as a legitimate substitute for the FSCs and it is driving its business model in this direction by focusing on core activities while offering differentiated products that add value for customers. These tactics are a clear sign of convergence between the FSCs and the LCCs, and strongly echo Lufthansa’s strategy of offering differentiated products to multiple customers (Jarach et al., 2009). Methods of evaluating business models and their convergence from the perspective of the airline industry The available literature on business models was reviewed to identify methodologies for comparing and evaluating business models from the perspective of the airline industry. The following methods were identified to classify and relate key elements of airline business models (c.f. Nair et al., 2011; Fageda et al., 2015): • the product and organisational architecture (POA) approach of Morrison and Mason (2007) and Mason and Morrison (2008), • the conceptual framework for measuring airline business model convergence of Daft and Albers (2013), • the airline business model spectrum of Lohmann and Koo (2013). Mason and Morrison (2008) demonstrated differences in the business models adopted by different airlines (c.f. Nair et al., 2011). Figure 1 illustrates their general POA approach to defining a firm’s business model and its competitive environment (Mason and Morrison, 2008). Product architecture is separated into three elements of service quality (Morrison and Mason, 2007): • Connectivity implies a choice of network design that distinguishes point-to-point (passenger-supplied) from hub-and-spoke (airline-supplied connectivity) networks; as Flouris and Walker (2005) noted hub-and-spoke systems increase the number of connections between city pairs served by the same number of flights compared to a point-to-point system. • Convenience is linked to airport location, punctuality, flight frequency, and baggage service. 320 • Comfort relates to on-board services, on-board service quality, seating comfort, and airport services. The foregoing 3Cs define the product in relation to consumer preferences, which impact market demand within the competitive environment. The 3Cs, however, also impact the magnitude and avoidability of production costs, which in turn affects pricing flexibility and the airline’s competitive position (Mason and Morrison, 2008). Figure 1 General product and organizational architecture of a firm Source: Mason and Morrison, 2008 Within the organizational architecture the size and composition of the fleet along with the organizational design are elements that follow primarily from the product architecture. In particular, the decision over network structure is key because the complexity of operating a hub-and-spoke network requires certain functions relating to coordination, yield management, etc. which implies a more vertically integrated organization. Similarly, Franke (2007) looked at competition between FSCs and LCCs, and mapped the different business models towards service level and complexity demonstrating that the current hub-and-spoke business model is widespread and entails complexity (c.f. Nair et al., 2011). In contrast, an airline operating a point-to-point network has relatively more opportunities to form a “nexus of contracts” organizational structure in which many functions are contracted out. In terms of carriage, all airlines take advantage of internetbased booking systems, yet even here point-to-point network carriers can offer more simplicity in the process with implications for both cost and benefit drivers (Morrison and Mason, 2007). Based on the above analysis, Mason and Morrison (2008) suggest that the positioning of some airlines to offer increased comfort and convenience in a bid to achieve higher yields is marginally successful and is not as profitable as the pure low cost approach practised by Ryanair, for example. The conceptual framework of Daft and Albers (2013) for measuring airline business models is based on three main components that fully describe an airline’s value creation system: • the corporate core logic as the strategic level, • the configuration of value chain activities as the structural level, • the assets of a firm as its resource level. Table 1 shows an overview of the model consisting of three constituting components and their systematic distinction into 7 dimensions and 16 elements (Daft and Albers, 2013). 321 Corporate core logic is the essence of how a company intends to place itself within its industry. The internal policy choice defines the activities that should be done and by whom: airline’s basic route design, and labour policy choices. The external value network refers to the company’s links to the relevant actors in its environment, namely the customers and the external partners: the airline’s mix of seat classes, whether the airline is involved in an extensive network of external partners or operates most of its activities autonomously (Daft and Albers, 2013). Table 1 The airline business model framework for measuring airline business models Components Corporate core logic Assets Dimensions Internal policy choices External value network Tangible Intangible Elements • Business policy • Labour policy • Target product- market combination • Inter- organizational relations • Fleet structure • Infrastructure • Human capital • Property rights Components Configuration of value chain activities Dimensions Inbound Production Marketing Elements • Procurement design • A/C sourcing • Route network • Cabin product • Ground product • Distribution • Fare structure • Bundling concept Source: Daft and Albers, 2013 The configuration of value chain activities represents a firm’s actual architecture that generates value for customers. Inbound activities cover all elements that refer to the allocation of input factors into the transformation stage. The production process of an airline is primarily determined by the specific route network, as well as the cabin product and the ground product visible to the customer. The marketing dimension is used to describe how the airline engages in the selling and promotion of its product portfolio (Daft and Albers 2013). Assets covers the unique set of resources and capabilities of a firm: fleet structure and the infrastructure, human capital, the airline’s staff and its service orientation and skills, other intangible assets that allow operations (such as slots at primary airports or patents) (Daft and Albers, 2013). Across all 49 Asian airlines, Pearson et al. (2015) identified the top three most important resources of advantage: slots, brand, and product/service reputation. Lohmann and Koo (2013) established an airline business model spectrum to create an instrument that, while incorporating a number of product and operational variables, provides a simple representation of the complex reality of the various airline business models now in play. Figure 2 Airline business model spectrum Source: Lohmann and Koo (2013) 322 Looking along the LCC-FSC spectrum (figure 2), apart from confirming those airlines that are traditionally known as LCCs or FSCs, the spectrum helps define the nature of hybrid airlines that are at neither extreme (Lohmann and Koo, 2013). The study of the convergence of airline business models based on financial statements Table 2 presents profitability indices, a labour index and aircraft indices, showing the convergence of airline business models. These indices are also illustrated in Figures 3-5. Table 2 Ratios which might indicate the convergence of airline business models Airline Type of business model Profitability indices Labour index Aircraft indices net income ÷ sales revenue net income ÷ total assets employee expenses ÷ total expenses net value of aircraft ÷ total assets depreciation expenses ÷ total expenses lease expenses ÷ total expenses AIRF FSC -1% -1% 29% 38% 7% 3% DELT FSC 2% 1% 20% 41% 4% 1% LUFT FSC -5% -4% 16% 24% 2% 0% SAS FSC -2% -2% 24% 26% 4% 5% SING FSC 3% 2% 16% 59% 11% 4% TURK FSC 8% 6% 17% 57% 7% 5% UCH FSC 3% 3% 24% 48% 4% 2% AIRA LCC 2% 0% 13% 60% 13% 4% EASY LCC 10% 10% 12% 56% 2% 3% JETB LCC 7% 5% 24% 80% 6% 2% RYAN LCC 10% 6% 10% 57% 8% 2% Source: own study Figure 3 illustrates the profitability of airlines representing the two business models, from the most to the least profitable companies. Generally LCCs were found to be more profitable than FSCs (c.f. Mason and Morrison, 2008). Figure 3 Profitability of airlines that pursue FSC and LCC business models Source: own study Figure 4 illustrates the labour index and one of the aircraft indices (net value of aircraft to total assets). With the exception of JetBlue, LCCs were rather less labour-intensive than FSCs. Considering the ratio of net value of aircraft to total assets, the ratio was higher among the LCCs. 323 Figure 4 The ratios of employee expenses to total expenses (left figure) and net value of aircraft to total assets (right figure) Source: own study Figure 5 illustrates the two other aircraft indices (depreciation expenses to total expenses and lease expenses to total expenses). No relation between these indices and the type of business models was found, which confirms the convergence of the FSC and the LCC business models. Figure 5 The ratios of depreciation expenses to total expenses (left figure) and lease expenses to total expenses (right figure) Source: own study 4 Conclusions The review of airline industry literature confirmed the existence of the trend toward convergence of two main airline business models: • the FSC, which bears much heavier overheads necessitated by the hub-and-spoke system and generally higher operating costs due to the extra services provided, for which a premium price is charged (Hunter, 2006), and • the LCC, which cut costs significantly by reducing overheads, providing no frills service, and often using secondary airports (Hunter, 2006; Morrison and Mason, 2007). Utilising accepted methods of evaluating business models and given the convergence of the airline industry models, as identified in the relevant literature, it was found that accounting has only marginal importance in providing the data required for the analysis of airline business models (c.f. Karwowski, 2016). In the first method, product architecture includes the service quality elements that define the product relative to consumer preferences, and organizational architecture represents the vertical structure, production and distribution choices of the airline (Mason and Morrison, 2008). In the second method, based on financial statements, it is difficult to describe how the firm is 324 linked to its environment and how it intends to create value in this specific setting. This method also covers a distinction in the distribution of the product and its fare structure. Additionally, the specific bundling concept of the airline is considered, which refers to the approach of selling product elements separately (Daft and Albers, 2013). The third method recognizes that airlines are better represented along a continuum rather than by discrete categories, which enables the positioning of hybrid and regional airlines along an LCC-FSC spectrum (Lohmann and Koo, 2013). The information derived from financial statements, such as profitability, labour and aircraft data, partially confirmed the convergence of the airline business models. Generally, the LCCs were more profitable than the FSCs. With the exception of JetBlue, the LCCs were less labour-intensive than the FSCs, while the ratio of the net value of aircraft to total assets was found to be higher among the LCCs. No relation was observed between two aircraft indices (depreciation expenses to total expenses and lease expenses to total expenses). Only these two last ratios confirmed the convergence trend in the industry. References Daft, J., Albers, S. (2013). A conceptual framework for measuring airline business model convergence. Journal of Air Transport Management, vol. 28, pp. 47-54. Fageda, X., Suau-Sanchez, P., Mason, K. J. (2015). The evolving low-cost business model: Network implications of fare bundling and connecting flights in Europe. Journal of Air Transport Management, vol. 42, pp. 289-296. Franke, M. (2007). Innovation: The winning formula to regain profitability in aviation? Journal of Air Transport Management, vol. 13, pp. 23-30. Hunter, L. (2006). Low cost airlines: business model and employment relations. European Management Journal, vol. 24(5), pp. 315-321. Jarach, D., Zerbini, F., Miniero, G. (2009). When legacy carriers converge with low-cost carriers: Exploring the fusion of European airline business models through a case-based analysis. Air Transport Management, vol. 15, pp. 287-293. Karwowski, M. (2016), The risk in using financial reports in the study of airline business models, Journal of Air Transport Management, 55, pp. 185-192. Lohmann, G., Koo, T. T. R. (2013). The airline business model spectrum. Journal of Air Transport Management, vol. 31, pp. 7-9. Mason, K. J., Morrison, W. G. (2008). Towards a means of consistently comparing airline business models with an application to the ‘low cost’ airline sector. Research in Transportation Economics. The Economics of Low Cost Airlines, vol. 24 Issue 1, pp. 75– 84. Morrison, W. G., Mason, K. J. (2007). What is a low cost carrier? Centre for Transportation Studies Working Paper. Nair, S. K. S., Palacios, M., Ruiz, F. (2011). The Analysis of Airline Business Models in the Development of Possible Future Business Options. World Journal of Management, vol. 3(1), pp. 48-59. O’Connell, J. F., Williams, G. (2005). Passengers’ perceptions of low cost airlines and full service carriers: A case study involving Ryanair, Aer Lingus, Air Asia and Malaysia Airlines. Journal of Air Transport Management, vol. 11, pp. 259-272. Pearson, J., Pitfield, D., Ryley, T. (2015). Intangible resources of competitive advantage: Analysis of 49 Asian airlines across three business models. Journal of Air Transport Management, vol. 47, pp. 179-189. 325 Pereira, B. A., Caetano, M. (2015). A conceptual business model framework applied to air transport. Journal of Air Transport Management, vol. 44-45, pp. 70–76. Wensveen, J. G., Leick, R. (2009). The long-haul low-cost carrier: A unique business model. Journal of Air Transport Management, vol. 15, pp. 127-133. 326 Impact of cost calculation quality on hospital performance Magdalena Kludacz-Alessandri 1 Warsaw University of Technology The College of Economics and Social Sciences Łukasiewicza 17, 09-400 Płock, Poland E-mail: Magdalena.Kludacz@pw.edu.pl Abstract: Management accounting in hospitals has been an issue of growing interest in the management accounting literature in recent years. Managerial decisions should be based on reliable and credible cost information, whose source should be a correct and efficient cost accounting, since it is the quality of cost information that determines the value and effects of management accounting practices. It is worth adding, that management accounting practices require not only the knowledge about actual operating costs of the hospitals and health services performed by them, but also about their structure and reactions on changes in the range of services, or the number of treated patients. The aim of the article is to make an analysis and evaluation of the cost calculation quality and management accounting practices which are applicable hospitals. The article attempts to verify the hypothesis according to which the quality of cost calculation in hospitals foster the usage of cost information in managerial decisions, and it has a positive influence on the hospital performance. The empirical material was obtained from the analysis of legal Polish and English regulations, and empirical studies conducted in European hospitals. The method of the correlation analysis was utilized to analyze the data. The research confirmed that the quality of patient treatment process calculation has a positive influence on the use of cost information by the hospital’s management, which further influence the hospital’s performance. Keywords: cost accounting, cost calculation, hospital performance, management accounting practices JEL codes: M41, M48, I18 1 Introduction Public hospitals meet all the criteria of a non-profit organization, and medical care is of high humanitarian importance, which results in a reluctance to consider it economically. However, also in public hospitals, every action should generate revenue commensurate with the costs incurred. Due to the economic responsibility, hospital managers should take into account the limited resources available to these units. They must therefore be aware of how and where their hospital uses the resources at its disposal and whether the consumption is justified or not. For this purpose, they need tool that will allow them to monitor and evaluate what part of the resource is wasted and what part is used effectively (Stylo, 1999). Such a tool is a cost accounting that provides the information needed to analyze and control the costs of medical care without losing sight of the quality of service provided. Cost accounting is the branch of managerial accounting that deals with the costs analysis. It aims to compute the cost of a product or service (Gentili, 2014). In health care, cost accounting predominantly aims to support managerial decisions and cost information can be useful in a number of areas. These include: management of department-level costs, pricing decisions in negotiations with a payer, strategic planning, profitability analysis, performance measurement, preparation of required external reports, monitor and control the resource consumption with respect to hospital service delivery and setting the range of services provided (Finkler, Ward, 1999). The cost information should therefore support the management of the hospital to improve the various management accounting practices. In addition, they may be the basis for managed care contract negotiation and oversight. However, there are a number of 327 barriers and limitations in hospital cost accounting, including the lack of established indirect cost accounting rules, the problems with valuation of medical procedures, homogeneous patient groups (DRG), or with the cost calculation of the whole patient treatment process. The objective of this paper was to diagnose the degree of hospital cost accounting sophistication, especially concerning the quality of calculation of the process of patient’s treatment, and the degree of using cost information by the management of hospitals and evaluate the impact of solutions used in the area of cost accounting system on hospitals’ performance. In pursuance of achieving the stated objectives, following hypothesis was formulated: the quality of cost calculation fosters better usage of cost information by the hospital’s management, and it has a positive influence on hospital performance. The paper presents the results of empirical research conducted in Polish, English and Slovenian hospitals, concerning the solutions used in the area of the cost accounting, especially including the rules of cost calculation of the process of patient’s treatment. The research encompassed features of patient's treatment cost calculation applied in hospitals and the degree of using cost information by the management of hospitals. Further in this paper it is evaluated how the quality of cost calculation influences the hospital’s performance. 2 Methodology For the needs of evaluation of the quality of hospital cost calculation at the level of hospitals functioning in Poland and other European countries which use the DRG system, empirical research was conducted using the technique of survey, interviews and own observations. The paper presents the introductory results of research conducted by a structured questionnaire in European hospitals in the years 2012-2013. The request for participation in the study was addressed to 100 Polish, 100 English, and 21 Slovenian hospitals. The research sample was 64 returned questionnaires (30 from Poland, 28 from England and 6 from Slovenia). The receiving response rates was almost 29% (KludaczAlessandri, 2016). This research brought data necessary to evaluate the hospitals’ performance and degree of using cost information by the hospitals’ management. The study involved chief accountants regarding the rules of cost accounting and managers of hospitals who responded to questions regarding the use of cost information in management process and evaluation of hospital performance. The objective of the study was therefore to investigate the effects of cost calculation quality on management accounting practices and finally on financial and medical performance of hospitals. The analysis of correlation was used in order to estimate the impact of the quality of cost accounting on management accounting practices and hospital performance. In order to measure the relationship between the variables, the partial aggregation method of individual variables representing multidimensional constructs was used (Bagozzi, Edwards, 1998). This means that each construct was represented by a variable that was calculated as an average of the retained original indicators prepared in the form of questions in the questionnaire. They were based on a review of related literature and consultations with experts. In order to prepare the final elements of the mentioned constructs, a factor analysis was performed to combine the single indicators into constructs. In this way, the constructs were validated. Particular attention has been paid to the multidimensionality of three analysed variables: the quality of cost calculation, the management accounting practices and hospital performance. The elements of the construct “quality of cost calculation” represent the features of a bottom up micro-costing approach. It was measured using a five-item, fivepoint Likert-scaled instrument anchored by (1) ‘‘to strongly disagree’’ and (5) ‘‘to 328 strongly agree’’ (Pavlatos, O., Paggios, I. 2009). Respondents were asked to indicate the features of a bottom up micro-costing approach. Finally, one item was removed from the analysis because it produced the lowest level of factor loading. The construct “management accounting practices” was created on the basis of literature analysis regarding management accounting techniques usage (Guilding et al. 2000). Respondents were asked to indicate the extent of the management accounting practices on five-point Likert-scaled instrument anchored by (1) ‘‘definitely not applicable’’ and (5) ‘‘(definitely applicable)’’. A factor analysis revealed that all items from the questionnaire were finally loaded on two factors; the first factor corresponds to various management accounting techniques while the second has been labeled ‘‘modern costing systems” (KludaczAlessandri, 2017). Finally “hospital performance” was specified as a two-dimensional construct comprising financial and non-financial performance (Chenhall, 2003). In accordance with previous studies, respondents were asked to indicate their hospital’s performance relative to their competitors on a scale ranging from ‘‘1” (below average) to ‘‘5” (above average) (Hoque, James, 2000). The results of factor analysis confirmed that the quality of calculation process should be a four-dimensional construct, management accounting practice should be specified as comprising of two factors and performance as two factors (Kludacz-Alessandri, 2017). 3 Results and Discussion In the first stage of the research conducted within a group of chief accountants, applied solutions in the area of the cost accounting system were analyzed. The answers of respondents regarding the features of patient's treatment cost calculation are presented in Table 1. Table 1 Features of patient's treatment cost calculation Features of cost calculation 1 2 3 4 5 tracing direct material costs (medicines, medical products) to the patients and medical procedures 10% 10% 17% 25% 38% tracing other direct costs (e.g. labour) to the patients and medical procedures 12.5% 16% 5% 31.5% 35% allocation of the costs of medical procedures to the individual patients 9.5% 13% 8% 22.5% 47% allocation of the costs of man-days to the individual patients 12.5% 10% 12.5% 16% 49% allocation of other indirect costs to the individual patients e.g. administration costs 19% 9.5% 9.5% 13% 49% * the scale of assessment: 1 (strongly disagree) – 5 (strongly agree), MV – average Source: author’s own elaboration Most surveyed hospitals, mainly from Poland, attempt to use a bottom up micro-costing approach and allocate some of the costs directly to the patient's treatment process. It involves the identification of all relevant cost components as detailed as possible and combine them in order to arrive at the healthcare service costs (Shuman, Wolfe 1992). Direct costs like drugs, medical materials are traced to the cost objects (e.g. medical procedures, DRG, patients). The hospitals create a detailed list of each component of a patient’s care and calculate their costs separately. Moreover they take into account not only the direct costs traced to the patient treatment process but also the costs of intermediate products such as medical procedures, inpatient days and outpatient visits. The indirect cost pools (nurses, equipment) are allocated with a different cost drivers. This provides substantially greater accuracy than in the top-down gross costing approach, used by around ten per cent of surveyed hospitals, mainly English ones. They do not trace direct costs to the patient's treatment process and do not calculate the costs of each component of a patient’s care, but value each cost component for average cost 329 object. Such a model don’t allow to observe the connection between costs and treatment for specific patients or take into consideration some special patient characteristics (Raulinajtys-Grzybek, 2014). Cost accounting is a tool focusing on cost information that facilitates decision making by managers of the organization. Information from cost accounting systems helps managers in using management accounting practices. The second stage of the research conducted among the hospitals’ management involved making an analysis of management accounting practices, which base upon the use of information about costs of health services. For the needs of verification of hypothesis it was necessary to examine to what extent do the managers of researched hospitals use cost information in different processes. The answers of respondents regarding the evaluation of the use of selected management accounting practices in hospitals are presented in Table 2. Table 2 Evaluation of the use of selected management accounting practices in hospitals Management Accounting Practices 1 2 3 4 5 MV Planning and budgeting - 11,1% 11,1% 16,7% 61,1% 4,28 Cost control - - 11,1% 22,2% 66,7% 4,56 Contract negotiations 5.6% 27,8% 11,1% 11,1% 44,4% 3,61 Profitability Analysis - 16,7% 22,2% 27,8% 33,3% 3,78 Pricing decisions - 11,1% 16,7% 38,9% 33,3% 3,94 Processes improvement 11,1% 11,1% 16,7% 27,8% 33,3% 3,61 Performance assessment 16,7% - 22,2% 22,2% 38,9% 3,67 Change in the structure of services 11,1% 22,2% 22,2% 22,2% 22,2% 3,22 Decisions on the purchase of medical equipment 11,1% - 22,2% 33,3% 33,3% 3,78 External reporting 16,7% 5,6% 22,2% 22,2% 33,3% 3,50 Benchmarking 22,2% 38,9% 11,1% - 27,8% 2,72 Analysis of resource efficiency 11,1% 5,6% 22,2% 33,3% 27,8% 3,61 * the scale of assessment: 1 (definitely not applicable) – 5 (definitely applicable), MV – average Source: author’s own elaboration The answers obtained from respondents suggest that hospital managers use management accounting practices in their daily operations. They use cost information primarily for internal hospital needs, especially in the planning, budgeting and cost control process, as well as for profitability analysis, pricing decisions and decisions on the purchase of medical equipment, performance assessment, and resource efficiency analysis. From the information gathered, managers can make decisions on where to cut costs to improve the hospital’s profitability. Cost accounting information is also used as part of an organization's control systems to measure and monitor hospital performance. It also helps the managers to answer such important questions as: how to allocate resources to achieve their objectives? Respectively, how were used the allocated resources. From the descriptive statistics it became clear that cost information plays the least important role in external processes such as benchmarking or external reporting. It regards mainly Polish hospitals that practically do not make any comparisons using cost information and don't use cost information to negotiate prices with National Health Fund (NHF). This is due to the fact that the NHF doesn't collect and process cost information, and the main issue affecting the value of DRG prices are financial resources of the payer and the prices from previous years (Kludacz-Alessandri, 2016). The best evaluation of external reporting regards mainly English respondents because hospitals operating within 330 the public health system are involved in the cost data provision for the purpose of pricing. It is obligatory for them. Prices are determined based on cost information 2 years in advance, because this time is needed for processing cost information (Epstein, 2006). Respondents were also asked to evaluate the hospital performance based on three indicators. The answers of respondents regarding the hospital’s performance indicators relative to competitors are presented in Table 3. Table 3 Hospital’s performance indicators relative to competitors Indicators 1 2 3 4 5 MV Gross margin on sales 6 11 33 44 6 3,33 Resource efficiency - 5,5 39 39 16,5 3,67 Quality of care - - 22 39 39 4,17 Rating scale: 1 - definitely below average; 2 - below average; 3 - average level; 4 - above average; 5 - definitely above average Source: author’s own elaboration The data provided shows that hospital performance is well evaluated by the managers. All indicators were rated by the respondents higher than the level of competition. The quality of care was best assessed (mean above 4). Another purpose of the experiential research was to evaluate the influence of the quality of cost calculation on management accounting practices and hospital performance and to verify the following research hypotheses: the quality of cost calculation fosters a better usage of cost information by the hospital management and has a positive impact on its’ performance. To verify this hypotheses, the analysis of correlation was used. The results are presented in Table 4. Table 4 Correlation coefficients between the variables Calc MAP Per The quality of calculation– Calc 1 Management accounting practices – MAP 0,42* 1 Hospital performance – Per 0,64* 0,60* 1 * Coefficient is statistically significant at p < 0.05 level Of the 3 relationships reported on in this table, all are positively statistically significant (p < 0.05) and consistent with what was hypothesized. The quality of the calculation has a positive impact on management accounting practices and both these factors have a positive impact on hospital performance. 4 Conclusions In summary, it can be concluded that the quality of cost calculation is significant positively associated with the management accounting practices and both of these factor are positively associated with hospital performance. The research results show that the quality of patient treatment process calculation has a positive impact on the utilization of cost information by the hospital’s management, which further influence the hospital’s performance. Higher quality of the patient treatment cost information encourages managers to use it in administrative process which results in improvement of economic score and the efficiency of the patient treatment process (performance). Information indicating the possibility of cost rationalization without deteriorating the treatment quality enables spending saved funds on improving the quality and standards of the healthcare. The results of the research confirm the findings appearing in the cost system design literature. For instance it was already indicated that the level of cost system functionality is significant positively associated with the extent of the use of cost data (Pavlatos, Paggios, 2009). Moreover, the costing systems are designed according to the needs of 331 the users of information (Nicolaou, 2001). On the other hand, some management accounting practices (e.g. pricing decisions, cost reduction efforts) affect the cost calculation system level (Al-Omiri, Drury, 2007). Also according to Pizzini the usefulness of cost data in the management process is positively correlated with the quality of cost calculation, meaning the extent to which systems can provide greater cost detail (Pizzini, 2006). The results also suggest a strong effect of the quality of cost calculation and the management accounting practices on hospital performance. Although the other findings indicate that costing system alone does not impact directly on organization performance, rather it affects performance via management accounting practices (Uyar, Kuzey, 2016). It suggests that management accounting practices could play a mediating role between calculation system quality and hospital performance, but this conclusion needs further investigations. This study has many options for further continuation, both geographically, from the point of view of research methods and from the point of view of the subject of research. In further research it is possible to link mutual findings to enrich findings for international comparison and use. References Al-Omiri, M., Drury, C. (2007). A survey of factors influencing the choice of product costing systems in UK organizations. Management accounting research. Bagozzi, R. 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Linking balanced scorecard to size and market factors: impact on organizational performance. Journal of management accounting, vol. 12(1). Kludacz-Alessandri, M. (2016). The role of cost accounting in setting prices in Polish and English hospitals, In The 10th International Days of Statistics and Economics. Conference Proceedings, Löster Tomáš, Pavelka Tomas (ed.), Libuše Macáková, MELANDRIUM. Kludacz-Alessandri, M. (2017), Model wyceny świadczeń zdrowotnych dla lecznictwa szpitalnego, Wolters Kluwer SA, Warszawa. Nicolaou, A. I. (2001). Interactive effects of strategic and cost management systems on managerial performance. Advances in Management Accounting, vol. 10. Pavlatos, O., Paggios, I. (2009). A survey of factors influencing the cost system design in hotels. International Journal of Hospitality Management, vol. 28(2). Pizzini, M. J. (2006). The relation between cost-system design, managers’ evaluations of the relevance and usefulness of cost data. Accounting, Organizations, Society, vol. 31(2). Raulinajtys-Grzybek, M. (2014). Cost accounting models used for price-setting of health services: An international review. Health Policy, vol. 118(3). 332 Shuman, J., Wolfe, H. (1991). The origins of hospital microcosting. Journal of the Society for Health Systems, vol. 3(4), pp. 61-74. Stylo, W. (1999). Ile kosztuje wykonywanie poszczególnych świadczeń w zakładach opieki zdrowotnej, Zdrowie i Zarządzanie nr 2. Tan, S. S. (2009). Microcosting in Economic Evaluations: Issues of accuracy, feasibility, consistency and generalisability. Rotterdam: Erasmus Universiteit. Uyar, A., Kuzey, C. (2016). Does management accounting mediate the relationship between cost system design and performance?. Advances in Accounting, vol. 35. 333 Market Concentration and Stability in European Banking Kristína Kočišová1 1 Technical University of Košice Faculty of Economics, Department of Banking and Investments Nemcovej 32, 04001 Košice, Slovakia E-mail: Kristina.Kocisova@tuke.sk Abstract: Using aggregate balance sheet data from banking sectors across the European Union (EU) countries over the period from 2004 to 2014 this paper provides empirical evidence on the testing the links between the concentration and stability. The paper contains a theoretical definition of approaches used to test the relation between these two characteristics of the banking sector. We used selected methods to estimate the concentration and stability and to test the presence of concentration-stability or concentration-fragility paradigm in conditions of EU. The stability was estimated with Banking Stability Index (BSI) and Z-score and the market concentration was evaluated with Herfindahl-Hirschman Index (HHI) and Concentration Ratio (CR5). The presence of the paradigm was verified with the test of the Granger causality for panel data. As the result of our analysis, we can say, that in our study the only one-way relationship running from stability to market concentration was estimated. Keywords: Market concentration, Banking stability, Granger causality test for panel data, European Union countries JEL codes: G21, C12, D40 1 Introduction The banking system, as a part of the financial system, in recent years is affected by significant changes in deregulation, market globalization, innovation and technological progress, but also by the macroeconomic development (Soltes and Gavurova, 2014; Knezevic and Dobromirov, 2016; Miklaszewska and Kil, 2016). Changes mentioned above are the main factors that gradually reduce the cost of capital and significantly affect the performance, stability and competitiveness of the banking sector in the international context (Siddik et al. 2016). Competition (indirectly measured by market concentration) in the banking industry is important for the efficient production of financial services, the quality of financial products and the degree of financial innovations (Claessens and Leaven, 2004). Competition is also important for households and firms to access the financial resources, for proper functioning of the financial sector, for stability of the financial system, efficient management of financial intermediaries, improvement of monetary policy transmission through the interbank market rates, and for overall industrial and economic growth (Amidu and Wolfe, 2013). Another important aspect is the banking stability. The issue of banking stability is quite organically linked with financial stability. Banking stability is a yardstick to determine whether an economy is adequately strong enough to withstand both the internal and external shocks. On the other hand, financial stability is a by-product of stability conditions prevailing in the banking system, financial markets, and the real economy and amongst them, banking stability appears to be a vital ingredient to financial stability. Banking stability in itself depends on the efficacies of the several parameters of individual banks, e.g. asset quality, liquidity, capital adequacy, and profitability, etc. Since banking stability gets affected positively or negatively by the prevailing conditions in the financial market and the real economy; ultimately, it determines as to what extent financial stability is ensured in the economy by its ability to absorb the shocks. As such, banking stability can be treated as a forerunner of financial stability in an economy. While the debate on whether competition influences bank stability continues (e.g. Berger et al. 2009), the question if the stability influences the level of competition is not often 334 discussed. Therefore the aim of this paper is to examine the relation between market concentration and banking stability in the European Union countries. In previous studies, the authors try to analyze this relation using the regression or correlation analysis. In our paper, we try to analyze this relation in term of causation. As the main contribution of the paper can be considered in the application of a panel Granger causality approach, which fills the gap in the existing literature. The aim is to examine the relative complexity of the relation between market concentration and banking stability, that causation running not only from concentration to stability but also from stability to concentration. To fulfil the aim mentioned above the paper is divided into next sections. In section 2 the relation between market concentration and banking stability is defined in form of concentration-stability and concentration-fragility paradigms. Section 3 describes the applied methodology and data set. The main findings and discussion are presented in section 4. The last section brings the main findings in form of conclusion. 2 Literature review In the empirical studies, we can find inconclusive findings on the effect of increasing banking market concentration on banking stability. We can talk about two main group of results, which led to the formulation of two opposite paradigms (i.e. Berger et al., 2009; Uhde and Heimeshoff, 2009; Fiordelisi and Mare, 2014). First one, concentration-stability paradigm, suggests that larger (monopolistic) banks in concentrated banking market may enhance profits and thus reduce financial fragility by providing higher capital buffers that can protect them against external macroeconomic and liquidity shocks (Boyd et al., 2004). Another aspect is, that larger banks tend to be more prudent in credit allocation. They usually provide loans with higher quality, which increase the return on investments and increase financial soundness (Boot and Thakor, 2000). They are also able to diversify loan portfolio risks more efficiently due to a wider network of branches and due to cross-border activities. From the point of view of regulation, the market with a few larger bank may be easier to monitor. Therefore the supervision of banks may be more effective and the risk of a system-wide contagion should be presumably reduced (Allen and Gale, 2000). In contrast, the concentration-fragility paradigm, argue that larger banks are often more likely to receive public guarantees or subsidies, which is discussed as the “too big to fail” doctrine (Mishkin, 1999). As they assume, that government will not let them fall, they perform more risky activities. The negative effect of concentration on stability was defined also by De Nicolo and Lucchetta (2009) who claim that the considerable market power of only a few banks allowed them to raise the interest rate on loans, which can induce adverse selection (risky projects are financed) and moral hazard (risk shifting), with negative impact on the stability of the banking system. In the empirical literature we can find papers aimed at study the relation between competition (or market concentration) and bank stability. Berger et al. (2009) tested the presence of paradigms by regressing measures of loan risk (the ratio of non-performing loans to total loans), bank stability (Z-score), and bank equity capital (the ratio of equity to total assets) on several measures of market power (Lerner index, HHI, log value of total assets), using bank-level data for 23 individual nation. They took account of the endogeneity of market power by employing activity restrictions, banking freedom, and the percent of foreign-owned and state-owned banks as instruments. They result suggested that banks with a higher degree of market power also have less overall risk exposure (in line with concentration-stability paradigm). However, the data also provided some support for one element of concentration-fragility paradigm, that market power did increase loan risk in these nations. Uhde and Heimeshoff (2009) applied regression analysis and used aggregate balance sheet data from banks across the EU-25 over the period from 1997 to 2005 and found that national banking market concentration (CR3, CR5, HHI) had a negative impact on 335 European banks´ financial stability (Z-score) while controlling for macroeconomic, bankspecific, regulatory, and institutional factors. Furthermore, they found that Eastern European banking markets exhibiting a lower level of competitive pressure, fewer diversification opportunities, state-owned banks were more prone to financial fragility, whereas capital regulations had supported financial stability across the entire European Union. Fiordelisi and Mare (2014) assess the relation between concentration (HHI, Lerner index) and stability (Z-score) in the European cooperative banking between 1998 and 2009. They obtained three main results. First, they support the concentration-fragility paradigm, when they found that bank market power negatively Granger-caused stability. Second, they provided evidence of negative impact of the 2007-2009 financial crisis on the individual risk exposure of cooperative banks although it did not change the relation between competition and stability. Third, they have shown that herding behavior affected positively bank stability. 3 Methodology and Data The market concentration of individual banking sectors can be measured by various concentration indices. In literature, but also in the statistics of central banks, the most often used indices are Concentration ratio for 5 largest banks on the market (CR5) and Herfindahl-Hirschman index (HHI), usually from the total assets point of view. The starting point in the calculation of these indices is the calculation of market shares (ri; ∀ i; i=1, 2, …, n) of individual banks operating in the banking market. The CR5 index is then calculated as the sum of market shares (ri) of the 5 largest banks, which are arranged from highest to lowest value of the market share (r1 ≥ r2 ≥ .. ≥ rm ≥ .. ≥rn). The calculation of the CR5 index can be calculated as follows: ∑ = = 5 15 i irCR (1) The second, HHI index take into account market shares of all banks operating in the market. The value of HHI below 0.1 shows a very low concentration, in the range from 0.1 to 0.18 shows a moderate concentration, and value of HHI above 0.18 shows a very high concentration of the banking system, whereas the index value equal to 1 shows a full concentration. The HHI index could be calculated as follow: ∑ = = n i irHHI 1 2 )( (2) The stability of banks can be evaluated by several methods, e.g. Banking Stability Index and Z-score. To approximate stability of banking sectors, the Z-score can be used (as, for instance in Berger et al., 2009; Fiordelisi and Mare, 2014; Capraru et al., 2016). The indicator is estimated as follows: TROA ti ti ti ti TA E ROA scoreZ σ , , . , + =− (3) Where ROAi,t is the return on assets for banking sector i in year t, Ei,t/TAi,t denotes the equity to total assets ratio for banking sector i in year t, and TROAσ is the standard deviation of return on assets over the full sample period (T years). According to the Fiordelisi and Mare (2014), the Z-score provides a measure of bank soundness (in our paper banking sector soundness) as it indicates the number of standard deviations by which returns have to diminish in order to deplete the equity of a bank. A higher Z-score implies a higher degree of solvency and therefore it gives a direct measure of bank stability. 336 Another approach how the banking stability can be evaluated is the construction of Banking Stability Index (BSI). In our paper we used BSI suggested and described in detail in Kočišová (2016). In her paper, she brings the attempt to construct an aggregate BSI taking into account indicators of the financial strength of banks (performance and capital adequacy) and major risks (credit and liquidity risk) affecting banks in the banking system. In order to test the Granger causality relation between variables, we will follow the concept of Granger causality developed by Granger (1981). Since the panel Granger causality model is computed by running bivariate regressions, there can take the following form: ti K k kti k i K k kti k iti xyy ,1 , )( 1 , )( , εβγα +⋅+⋅+= ∑∑ = −= − ti K k kti k i K k kti k iti yxx ,1 , )( 1 , )( , εβγα +⋅+⋅+= ∑∑ = −= − (4) Where i = 1,2,…,N denotes the cross-sectional dimension; t = 1,2,…,T denotes the time period dimension of the panel; α is intercept; k = 1,2,…,K are lags; ε is error term. To test the Granger non-causality from x to y, the null hypothesis is: H0: βi = 0, ∀i = 1, 2, … N (5) The alternative hypothesis states that there is a causality relationship from x to y for at least one cross-unit of the panel: H1: βi = 0, ∀i = 1, 2, … N βi ǂ 0, ∀i = N1 + 1, N1 + 2, …, N; (0 ≤ Á à ≤ 1) (6) Before proceeding with the panel Granger causality estimations, we test the stationarity of the series, using panel unit root tests: Levin, Lin and Chu test and ADF test for panel data (Próchniak and Witkowski, 2016). The optimal number of lags is estimated using Schwarz information criterion. In this paper, we try to test the relation between market concentration and banking stability in the European Union countries using a panel Granger causality approach. The aim is to verify the presence of concentration-stability or concentration-fragility paradigms and verify if causation running only from concentration to stability, or also from stability to concentration. To fulfil the objectives in the first part we analyse the concentration and stability in the European Union banking sector. The concentration is analysed by CR5 index and HHI index according to the value of total assets. To analyse the stability, the Z-score and BSI are used. As the main data source will be used database published on the web page of European Central bank. The annual data on the country level (27 EU banking sectors) will be used during the period from 2004 to 2014. The calculations were done in MS Excel, R programme and in the Eviews. 4 Results and Discussion The aim of this paper is to examine the relation between market concentration and banking stability in the European Union countries. To analyze market concentration, the CR5 index and HHI index were used. The development of these indices (EU average) can be seen in Figure 1. The value of CR5 index demonstrates that through the whole analyzed period the top five banks owned an absolute majority of the assets of the European banking market. At the beginning of analyzed period, the development can be regarded as relatively stable until 2012, when the values of both indices decreased. Since this year there was a significant growth of values for both indices. On the basis of Figure 1, we can see that HHI index showed the same tendency of development as the CR5 index. Based on the classification of HHI can be a market of assets during the analyzed 337 period as a moderately concentrated market. Both indices fell in 2012 and remain well above the pre-crisis levels. According to European Central Bank (2013), the dip in 2012 was mostly driven by large banks´ moves – especially in Germany, France, Belgium and Netherlands – to reduce assets to comply with forthcoming regulations. With regard to individual countries, concentration indices reflected a number of structural factors. Banking systems in larger countries, such as a Germany, France and Italy, were more fragmented and included strong savings and cooperative banking sectors. Banking systems in smaller countries tend to be more concentrated, with the notable exception of Austria and Luxembourg. In the case of Austria, this was on account of a banking sector structure similar to the one characterizing the larger countries, and in the case of Luxembourg, it was due to the presence of a large number of foreign credit institutions. Since 2013 there can be seen an increase, remaining at the pre-crisis levels. This increase was mostly driven by moves in the crisis countries where larger banks acted as consolidators in resolutions of non-viable entities – especially in Cyprus, Greece and Spain. Market concentration continued to increase, reaching a historical maximum at the end of 2014. The increase in market concentration reflected primarily the decline in the number of credit institutions and indicates a decline in quality of the competitive environment. Figure 1 The concentration and stability in the European Union banking sector; EU average; 2004-2014 Source: Prepared by author The banking sectors´ stability was analyzed using Z-score and BSI (Figure 1). The analyzed period (2004-2014) can be divided into two stages. The first stage covers the period from 2004 to 2008, when the stability indicators decreased to their minimal values in 2008. The lowest values in this year mirrored the negative effects of the financial crisis which hit the banking sectors in all EU countries. The second stage covers the period from 2008 until 2014 when can be seen a gradual increase in the average stability. A positive development in banking stability during this period was influenced mainly by the growing demand for rising capital adequacy, which was related to the gradual implementation of Basel III. According to the Olszak et al. (2016), the set of new rules covers substantial increases in regulatory capital ratios and in the quality of bank capital. Focusing on the link between market concentration and stability, the theoretical and empirical literature doesn’t provide a clear-cut conclusion about a direct relation between variables. As can be seen in the literature review there exist many paradigms about this relationship. While the concentration-stability paradigm suggests a positive relation between concentration and stability, the concentration-fragility paradigm favors a negative relation between these two variables. We analyze the link between market concentration and stability in the European Union banking market in a panel Granger causality framework. As we believe that it takes time for the effect of concentration on stability and vice versa to become apparent, we adopt 0,100 0,105 0,110 0,115 58 59 60 61 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 HHI (right axis) CR5 (left axis) 0 4 8 12 0,32 0,36 0,40 0,44 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Z-score (left axis) BSI (right axis) 338 yearly lags. The optimal number of lags is estimated using Schwarz information criterion (SC). As the optimal number of lags were appointed one-year lag (see Table 1). Table 1 Lag order selection criteria 0 1 2 3 4 5 6 7 SC 8.0872 0.2892** 0.5797 0.6725 0.8823 1.4607 2.0333 2.5877 ** significant at 5% level Source: prepared by author Before proceeding with the panel Granger causality test, we test the stationarity of the series, using panel unit root tests: Levin, Lin and Chu test and ADF test for panel data. The first condition is, that the variables must be non-stationary at the level (there is unit root), but when we count into first differences they become stationary (there is no unit root). The null hypothesis in both tests assumes that all series are non-stationary. The results of stationarity analysis display in next table (Table 2) allows us to reject the null hypothesis at the 1st differences. Table 2 Panel unit root test Variable Test Level 1st difference Statistic Probability Statistic Probability HHI Levin, Lin and Chu test -1.4817 0.0692 -8.6937 0.0000 ADF test for panel data 61.9761 0.2130 152.841 0.0000 CR5 Levin, Lin and Chu test -0.6781 0.2489 -7.6643 0.0000 ADF test for panel data 35.1774 0.9779 142.266 0.0000 Z-score Levin, Lin and Chu test -0.5392 0.2949 -13.367 0.0000 ADF test for panel data 43.2171 0.8533 206.914 0.0000 BSI Levin, Lin and Chu test 1.11821 0.8683 -14.652 0.0000 ADF test for panel data 26.4966 0.9994 219.030 0.0000 Source: prepared by author In our panel Granger causality test we used panel ordinary least squares (OLS) estimations. The results are displayed in Table 3, both for the causality running from market concentration to banking stability and for causality running from stability to concentration. We test the null hypothesis that there is not Granger causality running between variables. In order to test the null hypothesis, F statistics is appointed. According to the results in Table 3, we cannot reject the null hypothesis if the probability is higher than 0.05 and rather we accept the null hypothesis. Therefore, we can say that there is no Granger causality running from HHI to Z-score, from Z-score to HHI, from HHI to BSI, from CR5 to Z-score and from CR5 to BSI. On the other hand, if the probability is lower than 0.05 we can reject the null hypothesis and we can accept the alternative hypothesis. Based on the results then we can say, that there exist Granger causality running from BSI to CR5, and at the significant at 10% level there exist Granger causality running from BSI to HHI, from Z-score to CR5. So we can say, that BSI causes CR5 and HHI, and Z-score causes CR5. 339 Table 3 Granger causality test – F statistics Null hypothesis F statistics Probability Result HHI does not Granger Cause Z-score 0.68083 0.4100 Accept H0 Z-score does not Granger Cause HHI 0.36282 0.5475 Accept H0 HHI does not Granger Cause BSI 0.03474 0.8523 Accept H0 BSI does not Granger Cause HHI 2.72632 0.0999 Reject H0* CR5 does not Granger Cause Z-score 0.71378 0.3989 Accept H0 Z-score does not Granger Cause CR5 3.72849 0.0546 Reject H0* CR5 does not Granger Cause BSI 0.06098 0.8051 Accept H0 BSI does not Granger Cause CR5 4.03413 0.0456 Reject H0** ** significant at 5% level; * significant at 10% level Source: prepared by author In our research, we apply the Granger causality in VAR model and we use one-year lag. The result of Granger test and the coefficient of variables can be seen in Table 4. Table 4 Granger causality test – Coefficients HHI CR5 CR5 Intercept 0.0107** 1.8728*** 2.9928*** HHI(-1) 0.9770*** CR5(-1) 0.9782*** 0.9854*** BSI(-1) -0.0207* -5.0371** Z-score (-1) -0.0426** R-squared Adjusted R-squared 0.9742 0.9740 0.9818 0.9817 0.9818 0.9817 No. of observations 270 270 270 *** significant at 1% level; ** significant at 5% level; * significant at 10% level Source: prepared by author The results showed that the stability (measured by Z-score and also by BSI) negatively caused the concentration (measured by HHI and also by CR5). It should indicate that the most stable banking systems were those that were obligated to compete in less concentrated markets. Based on the results we can see that there existed only one-way causality running from stability to concentration. The opposite way was not found, so the concentration could not cause the stability. Based on the R-squared and Adjusted Rsquared values we can conclude that the results are statistically significant. These results are in line with concentration-fragility paradigm, which suggests that there exists a negative relation between concentration and stability. This is in line with findings e.g. Fiordelisi and Mare (2014) or Capraru et al. (2016) who found a negative relation between market power and stability measured by Z-score in the European cooperative banking between 1998 and 2009. Thus we have come to the conclusion that the growth of stability in the banking sector will cause a reduction in the concentration. Conversely, with a fall in stability, there will be a rise in the concentration in the banking sector. As we know that concentration reflects the level of competition in the banking market, we can indirectly say, that the growth of stability in one year will lead to the improvement of the competitive environment in the next period. Conversely, if the stability of banks decreases, it will also lead to a worsening of the competitive environment with an annual delay. Thus, if the banking sector becomes more stable, it will act to increase the level of competition, whether in form of multiple banking entities on the market, a more even distribution of the market shares of existing banking entities operating in the banking market, etc. 340 5 Conclusions Just a gradual process of globalization significantly affects the structure of the financial and banking system, their performance and stability (Sinicakova et al., 2017). It is therefore very important to focus on the examination of bank and banking systems performance and stability, under the purpose of investigation of their structure (the level of concentration), and it is important to follow these issues not only as isolated phenomena but also focus on the investigation of their relations. This paper contributes to the existing literature by analyzing the relation between market concentration and stability in European banking. Different methods were used to measure concentration (CR5, HHI) and stability (Z-score, Banking stability index). The analysis of market concentration showed that at the beginning of analyzed period, the development can be regarded as relatively stable until 2012. Since 2013 there can be seen an increase, remaining at the pre-crisis levels. This increase was mostly driven by moves in the crisis countries where larger banks acted as consolidators in resolutions of non-viable entities. Market concentration continued to increase, reaching a historical maximum at the end of 2014. The increase in market concentration reflected primarily the decline in the number of credit institutions and indicates a decline in quality of the competitive environment. The analysis of stability in EU banking showed that the analyzed period can be divided into two stages. The first stage covers the period from 2004 to 2008, when the stability indicators decreased to their minimal values in 2008. The second stage covers the period from 2008 until 2014 when can be seen a gradual increase in the average stability. The positive development in banking stability during this period was influenced mainly by the growing demand for rising capital adequacy, which was related to the gradual implementation of Basel III. As the main contribution of the paper can be considered the application of a panel Granger causality approach to examine the relative complexity of the relationship between market concentration and banking stability, that causation running not only from concentration to stability but also from stability to concentration. The results showed a one-way negative relation running from stability to concentration. This means that greater stability in banking sector enhances the quality of the competitive environment. Acknowledgments This work was supported by the Slovak Scientific Grant Agency as part of the research project VEGA 1/0446/15. References Allen, F., Gale, D. (2000). 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Kolupaieva Kharkiv Trade and Economics Institute of Kyiv National Trade and Economics University Economic Department Otakara Yarosha Lane, 8 61045, Kharkiv, Ukraine e-mail: volirinawork@gmail.com Abstract: The main purpose of the research is to suggest a cognitive model of factors that influence the regulatory policy and to develop impact scenarios based on the model under question. According to the goal, the algorithm of constructing cognitive models includes relevant stages that have been presented in the paper. The construction of cognitive maps has been based on a certain index-oriented graph (orgraph). The vertex (concepts) of the graph has been studied as components of the three populations. The paper presents a cognitive map of concepts of factors that influence the objectives and leverages of regulatory policy in Ukraine. Based on the research, the level of factors that influence the regulatory policy has been determined. The cognitive map presents all connections between the concepts that are only partly reflected in the given figure. The most active and interacting factors selected have been considered within the scenario modelling. Each of these factor-indicators has been analysed separately. Keywords: regulatory policy, government regulation, cognitive model, scenario modelling JEL Classification: L51, B49, H11 1 Introduction Overcoming the economic and political crisis in the country is a priority task of the state leadership and it is possible only in case of synergistic interaction between business, government and the public. This includes business development and growth of industrial capacity that will ensure the job creation, wage increase, and, as a result, improved standards of living. The catalyst of this chain reaction is establishment of conditions for doing business and stimulating consumption, which is effective demand for entrepreneurs. These tasks are important part of current regulatory policy that requires improvement of existing mechanisms of its functioning and implementation of new methods and management tools. When searching for the best tools, a currently central method is modelling the object of research. The regulatory policy is a subsystem of state regulation of the economy, which is being implemented throughout the country in the macroeconomic dimension so that it is characterized by considerable complexity, which requires appropriate tools for analysis. Complex systems are studied by expert modelling methods which are subdivided into two groups: • methods aimed at enhancing intuition and experience of experts; • and methods of formalized representation of systems. Group 1 includes "brainstorming", expert evaluation, "Delphi" scenarios, morphological analysis, graph relevance tree, etc. Group two is represented by analytical, statistical, semiotic, logical, linguistic, graphical, and other methods. Considering the complexity of the analysis it is reasonable to solve the problem by applying borderline methods between intuition and experience enhancement and formalized approach, one of which is the transition method of gradual formalization of decision-making  cognitive modelling. Among the researchers, who have investigated economic development and economic tasks through the cognitive modelling, there are G. V. Gorelova (2010), V. V. Kulba, D. 343 A. Kononov, S. S. Kovalevskiy (2002), D. I. Makarenko (2008), F. S. Roberts (1986), S. S. Solohin (2009), E. A. Trahtenherts (1998), and others. The purpose of the research The purpose of the research is to suggest a cognitive model of factors that influence the regulatory policy and apply it as a basis to develop impact scenarios. 2 Methodology and Data According to the goal established an algorithm of its accomplishment has been developed. The algorithm of constructing cognitive models includes relevant stages shown in Fig. 1. Figure 1 The algorithm of constructing cognitive models Source: Author’s model based on the conducted analyses The results obtained in the course of the first and second stages of the study are provided in the author’s research papers (Kolupaieva, I.V., 2016). The third stage corresponds directly to the construction of a cognitive model. The algorithm of implementing a cognitive approach can be presented as follows (Vandysheva, T. M., 2010): • studying the problem and determining the goals and objectives of the study; • constructing cognitive maps; • constructing cognitive models and test their adequacy; • analysing the distribution of excitation (pulses) on the graph; • solving the problem of complexity, sensitivity and stability of systems; Stage 1. Construction of general vision of the model. Classification of factors that show the problem under question 1.1. Allocation of target factors, the change of which is the purpose of modelling and control factors that are potential leverage of impact on target 1.2. Allocation of the factorindicators that reflect investigational processes and influence the control and target factors Stage 2. Determination of the type of relations between groups of factors 2.1. Expert evaluation of the expected application of control factors as a result of the emergence of factor-indicators 2.2. Deterination of the type and strength of bounds between the factors. Determination of the nature and strength of impact and interinfluence. Construction of the "interaction / activity" matrix and description of factors. Stage 3. Construction of cognitive map 3.1. Construction of a cognitive map, incident matrix, oriented graph. Verification of model adequacy. 3.2. Development of impulse modelling scenarios for individual factors or their groups. Choosing the most efficient scenarios and development of recommendations. 344 The construction of cognitive maps has been based on certain index oriented graph (orgraph): Фn<, X, F, θ> (1) Formula Фп<, X, F, θ> is finite sequence, Where G = < V, E>, V={vi | vi∈V, i=1, 2,…,k }; E={ei | ei∈E, i=1, 2,…,k }; G – index oriented graph (cognitive map), where: V – vertex set; sets («concepts») Vi∈V, i=1, 2,…, k – elements of the system; Е – set of arcs, arcs еij∈E, i,j=1, 2, …,N reflects the relationship between the vertices Vi and Vj; X:V→θ, X – set of parameters of vertices; X={ )( ivX | )( ivX ∈X, i=1, 2,…,k }; )( ivX ={x(i) g}, g=1,2,…,l. x(i) g – g-parameter of vertex Vi, if g = 1; то x(i) g = xi;θ – space of parameters of vertices. In terms of the case under study Vi on Vj can have positive, negative or no impact. Like control factors the factor-indicators can have positive or negative impact on the target factors of the model, and on other factor-indicators and control factors, or they can have no connections with them, so that in mathematical terms this can be formulated as follows: 1, if Vi increases, then Vj increases == × ijkkijG aaA ,][ -1, if Vi decreases, then Vj increases (2) 0, in case of no connection 3 Results and Discussion To build cognitive maps it is necessary to determine (before determination of indexoriented graph) the vertices (concepts) that are directly analysed within the model. The concepts are analysed as components of the three populations (see Tab. 1) 345 Table 1 Concepts of index-oriented graph Groups of factors Components of the group Factor-indicators V1 – participation of the state in the process of international economic integration; V2 – increased competition between multinational corporations; V3 – influence of certain international regulatory organizations; V4 – membership in international trade organizations; V5 – activities of governments of others countries; V6 – harmonization of tax legislation of Ukraine with the provisions of international law; V7 – impact of globalization in terms of providing access product to new markets, the search of new technologies and equipment; V8 – changes in the commodity structure of the world market to the advantage of high-tech products; V9 – rapid growth in trade of services; V10 – influence of political organizations; V11 – economic policy of government (strengthening of state regulation of economic relations along with market self-regulation); V12 – dominating ideology in society (socialism, liberalism); V13 – existing economic legislation; V14 – priorities of national security; V15 – political stability; V16 – public participation; V17 – activity of trade unions in the country; V18 – the dynamics of the national economy development; V19 – branch structure (growth of knowledge-intensive industries, increase in share of innovative products); V20 – potential of the national export; V21 – level of investment in the economy; V22 – inflation; V23 – share of high-tech innovative products; V24 – level of technical and technological base; V25 – the development of venture capital; V26 – offshore financial centres and "tax heavens"; V27 – economic crisis; V28 – level of unemployment; V29 – asymmetry in the labour market; V30 – level of corruption; V31 – level of social protection of the population; V32 – social tension in society; V33 – interstate and interregional migration; V34 – degree of agenda regulation for the development and adoption of regulations in the field of management; V35 – scientific apparatus of exact sciences (system tools, modelling, etc.); V36 – level of bureaucracy of the state system; V37 – information and automation of the systems for adopting regulatory acts; V38 – the effectiveness of regulatory acts; V39 – level of communication support of regulatory policy; V40 – responsibility and motivation of civil servants; V41 – volume of knowledge, skills and training of the subject of regulatory activities; V42 – the morality of public servants; V43 – level of spiritual education of the population; V44 – cultural level of the population; 346 Controlfactors V45 – licensing; V46 – quotation; V47 – limited transactions (currency operations with securities, etc.); V48 – sanctions by regulators; V49 – tax burden; V50 – write-off and deferral of tax debt; V51 – ratio of local and state taxes; V52 – budget deficit; V53 – government debt; V54 – organization and legal regulation of activity; V55 – guarantee of protection and security; V56 – protection of resources; V57 – staff assurance; V58 – regulation of discount rate; V59 money issue; V60 - public funding; V61 – optimization of number and value of taxes; V62 – international agreements on avoidance of double taxation; V63 – legalization of untaxed income; V64 - tax breaks / changes in terms of tax payment (deferral / instalment) / tax rebate; V65 – accelerated amortization; V66 – tax credit / rebate; V67 – alternative tax regimes (simplified system of taxation, special tax regimes); V68 – introduction of microloan system; V69 – preferential loans; V70 – leasing; V71 – strategic planning, foreign economic activity support; V72 – creation of a favourable legal framework; V73 – adjustment of prices and tariffs; V74 – information openness; V75 – areas / policy / programs monitoring; V76 – information activities; V77 – social standards (minimum pension, minimum wage, living wage); V78 – marketing and advertising activities; V79 – development of image strategies; V80 – grants; V81 – subsidies; V82 – subventions; Target factors V83 – economic growth; V84 – establishment of competitive national production; V85 – establishment of worthy conditions of life and work of citizens, implementation of the social objectives of society; V86 – integration into the EU. Source: authors, based on the conducted analyses The resulting data and values agree with those obtained by experts in government regulation. Each factor is correspondent to an individual symbol of graph vertex. Values in matrix represent the conditions of concept interaction in accordance with the formula (2). It is not possible to present index-oriented graph due to its cumbersomeness. The cognitive map designed on the basis of the research carried out is also very difficult for display as it presents 86 interaction factors. In previous researche (Kolupaieva I. V., 2016) we have determined a number of influential factors including increased competition of multinationals corporation, the influence of certain international regulatory organizations, state economic policy (strengthening of state regulation of economic relations along with market self-regulation and corruption). In particular, the impact of these factors has been given in Fig. 2 as a fragment of cognitive map to illustrate certain features of communication between the concepts of the model. The designed cognitive map and index-oriented graph only statically reflect the interdependence between the concepts of the model. The main advantage of the cognitive approach is the possibility of scenario development, which refers to the definition of level of target factors, reaction of control factors on factor-indicators and possibility of adjusting the model to achieve the goals. Scenarios have been developed on the basis of analysis of impulse processes of the graph, so that transition of the system from a state t-1 to t, t + 1, ... in accordance to the rule changes settings in the vertex graph in time tn+1 if at the time point tn the pulses P are coming into concepts. This process can be formulated as follows: )()(),,()()( 1 1 : 1 + − ∈= ++= ∑+ ni k Eeev nijji tQtPexxftxtx ijj jnini (3) 347 where )( 1+ni tQ – pulse of concept of index-oriented graph in the corresponding periods of time under certain values Х0 and initial vector P0. In order to study the nature of changes in target factors depending on the dynamics of these factor-indicators we need to construct these scenarios. The pulse has been directed to the selected concept of each scenario. (V1 – strengthening of competition of multinational corporations, V2 – the influence of certain international regulatory organizations, V3 –economic policy of the state (strengthening of state regulation of economic relations along with market self-regulation) and V5 – level of corruption. In mathematical terms pulse has been described as a matrix-vector. Figure 2 The cognitive map presenting the concepts of factors, which influence the objectives, and leverages of regulatory policy in Ukraine (fragment) Direct impact Reverse impact Source: Author’s calculations 348 The results of tact modelling calculated as a result of multiplying the adjacency matrix by the index-oriented graph have been graphically presented in Fig. 3. These data reflect the sixth stage of the modelling, as this was the stage when a significant increase in the values of factors was observed. Figure 3 The graph of changes in the values of target factors in the vertex V83, V84, V, V86 in terms of pulse q1=+1 at the vertex V2, V3, V11, V30 Source: Author’s calculations 4 Conclusions Based on the results of the research carried out the degree of influence that factors have on regulatory policy has been determined. It should be noted that the cognitive map presents all connections between above mentioned concepts that are only partly reflected in the given figure. The factors selected as most active and interacting have been analysed within the scenario modelling. Each of these factor-indicators has been studied in isolation. Thus, the factor of economic policy of the government (strengthening of state regulation) has had greatest influence that proves its dominating influence on the system of regulatory policy (in general and in particular) and on its leverage as the most effective components of the mechanism. The target factors of international regulatory organizations have been significantly influenced. Smaller but also significant influence has factor multinational corporations. -105000 -55000 -5000 45000 95000 Economicgrowth establishmentof competitivenational production formingofworthy conditionsoflifeand workofcitizens, implementationofthe socialobjectivesof society integrationintotheEUstrengthening of competition of multinational corporations the impact of certain international regulatory organizations government economic policy (strengthening of state regulation of economic relations along with market self-regulation) level of corruption 349 Significant negative impact under the modelling has been obtained from corruption. Impact of corruption has the same strength as government economic policies, and is threatening results, because the implementation of mechanism of regulatory policy requires correct environment which cannot be created due to significant corruption component. Regarding the level of target factors, the economic growth for the results is similar to the factor of integration into the EU. And the formation of a competitive national production is similar to the formation of decent living and working conditions of citizens. The results reflect the modern trends in the development and implementation of regulatory policy, which will be addressed in further research to develop scenarios of the influence of other factors. References Gorelova, G. V. (2010). Experience in cognitive modelling of complex systems. Cybernetics and Systems 2010, Proceedings of the 20-th European Meeting on Cybernetics and Systems Research, Austria, Vienna, pp. 220-223. Kolupaieva, I. V. (2016, December). Scientific-theoretical generalization of the factors that influence the regulatory policy. Mizhnarodna naukovo-praktychna konferentsiya “Problemy pidvyshennya efektyvnosti ekonomiky ta upravlinnya v suchasnyh umobah, Odessa, pp. 47-50. Kolupaieva, I. V. (2016). An analysis of effectiveness of regulatory policy instruments. Biznes Inform. Vol. 12, pp. 49-54. Kulba, V. V., Kononov, D. A., Kovalevskiy, S. S. (2002). An analysis of the dynamic behaviour scenarios in the socio-economic systems. Moscow. Makarenko, D. I. (2008). Conceptual modelling of the military security of the state. Moscow, Nauka. Roberts, F. S. (1986). Discrete mathematical models with application to social, biological and environmental tasks. Moscow, Nauka. Solohin, S. S. (2009). About cognitive modelling of sustainable development for socioeconomic systems (for instance, tourist-recreational system of the South Russia). Iskustvennyi intelekt, vol. 4, pp. 150-160. Trahtenherts, E. A. (1998). Computer support of decision-making. Moscow, Sinteg. Vandysheva, T. M. (2010). The cognitive approach to risk management of credit institutions. Izvestiya. Informatsionnyye I gumanitarnye technologii v upravlenii ekonomicheskimi I sotsialnymi sistemami. Vol. 4, pp. 51-56. 350 Correlation analysis between macroeconomic indicators and level of ratings in EU countries Tomasz Kopyściański1 ,Tomasz Rólczyński2 , Marzena Franków3 1 WSB University in Wroclaw Faculty of Finance and Accounting, Department of Finance and Accounting 29-31 Fabryczna Street, 53-609 Wrocław, Poland E-mail: tomasz.kopyscianski@wsb.wroclaw.pl 2 WSB University in Wroclaw Faculty of Finance and Accounting, Department of Economics 29-31 Fabryczna Street, 53-609 Wrocław, Poland E-mail: tomasz.rolczynski@wsb.wroclaw.pl 3 WSB University in Wroclaw Faculty of Finance and Accounting, Department of Finance and Accounting 29-31 Fabryczna Street, 53-609 Wrocław, Poland E-mail: marzena.frankow@wsb.wroclaw.pl Abstract: Credit rating agencies have a significant impact on the securities market, facilitating investment decisions on the basis of ready-made benchmarks. In this way they contribute to reducing the asymmetries and costs of obtaining information necessary to make decisions regarding buying or selling financial instruments, taking into account the debtor’s estimated risk of default. For many years now credit rating agencies have been an integral part of the financial market, impacting to an even greater degree the functioning conditions of countries, as their dependency on being able to finance their borrowing needs from external sources is continuously growing. Permanent budget deficits and public debt are inherent elements of contemporary public finances. The increased significance of the assessments made by the rating agencies refers closely to the dynamic development of the debt security market and thus emerging demand on the part of the buyers of those securities for an independent analysis, which would determine the issuer’s financial standing. The assessments which are published using special symbols reflect country’s ability to raise capital and its cost. The results of the analysis published in the previous article (Rólczyński, T., Kopyściański, T. (2016). Economic Condition of the European Union Countries and Level of Rating. European Financial Systems 2016, p. 654.), indicate that the ratings provided by the rating agencies are linked to the countries’ economic condition. This conclusion has been drawn on the basis of Spearman's rank correlation coefficient between rating level and the aggregated synthetic measure of information provided by the individual variables describing economic condition of the country. It implicates another important research question whether there is a correlation between each macroeconomics indicator (which were included in creation synthetic variable) and the level of rating in the EU countries. Therefore the main purpose of the article is to identify which macroeconomic indicator closely refers to the level of rating in the EU countries. Within the framework of the research the macroeconomic data and the ratings issued by main credit rating agencies (Standard & Poor’s, Fitch, Moody's) over the period 2014-2016 will be taken into account. To identify correlation between analyzed factors the tau-Kendall's correlation Coefficient will be used. Keywords: Ratings and Ratings Agencies; Economic Outlook; synthetic measure, Kendall’s tau correlation coefficient JEL codes: G240, E660, C1 351 1 Introduction The contemporary financial market characterized by considerable dynamism and ever greater demand for information raises a necessity to look for universal indicators which would make the calculation, estimation and analysis of investment risk possible. In this respect, one of the more important instruments on the financial market are ratings given by rating agencies. In literature rating is likely to be defined as an objective and independent assessment of credit risk of an entity running up debt (Dziawgo 2012). It determines the ability of a given entity to service payment obligations, thereby providing an assessment of the future investment risk related to the entity – to the issuer and instruments issued by it (Kaczmarek 2002). The assessment performed under the rating procedures is an indicator allowing one to determine the level of trust the entity enjoys. Rating is a dynamic category referring to the future and thus, as any other forecast, is burdened by some dose of uncertainty, which is why the ratings of the same entity may vary depending on the rating agency. Rating agencies, there currently exist over 70 rating agencies worldwide, could be described as investment advice firms which, by conducting credit analysis of issuers and their issues, rate specific debts and standardize risk by bringing it down to a single comparable scale. Along with the financial market development and the popularity of obtaining capital through the issuance of debt securities, the rating agencies have become an integral part of the financial market. The most esteemed and popular are the so called Big Three rating agencies including: Fitch Ratings, Moody’s Corporation and Standard&Poor’s. The increased significance of the rating agencies, in particular, for the financial viability assessment of countries has in recent years been noted at many levels. The market success of public debt securities issuance depends greatly on ratings. Further to that, rating is considered to be an important requirement while seeking external sources of financing in the securities market, which refers particularly to issuers whose position has not been yet well established on the securities market. Moreover, the issuer credit assessment affects significantly the interest rates on public debts securities and consequently the public debt servicing costs. From the economic perspective, rating fulfills also a number of key functions for the participants of the financial market, particularly (Cantor R., Packer F. 1996; McClintock Ekins E., Calabria M. A. 2012): • it enables one to estimate quickly current investment risk and by increasing investment safety it stimulates the financial market growth. • it allows one to standardize investment risk on global financial market, thus supporting a wider application of transactions and their forms. • rating allows entities-investors to reduce the costs of investment thanks to limiting their own analyses and facilitating diversification of their investment portfolio. • from the issuer’s perspective, rating makes it in the first place easier to win capital by issuing debt securities, as a higher rating means the debtor’s greater creditworthiness. • for an issuer having a rating (especially a high rating) represents an advancement on the international stage. The ability to carry out all those functions is closely linked to reliability and credibility of the information included in the rating prepared. The controversies relating to the role of rating agencies in terms of the recent financial crises have given rise to numerous doubts as to to what extent rating corresponds to the country’s current economic situation and to what extent it is the result of a subjective analysis carried out by the rating agencies. However, the findings based on the research presented in literature have more than once 352 confirmed that the assessments carried out by the rating agencies are certainly not formulated in isolation from the country’s current economic condition. Rating based on synthetic variables in econometric modeling takes into account numerous factors describing the economic situation of a particular country (McClintock Ekins E., Calabria M. A. 2012). Considering that for the participants of the financial market the function of the rating is informative, the issue that is being discussed, in theory as well as in practice, is whether the information provided by the ratings reflects the financial credibility of a country, as measured using general macroeconomic indicators (Arnoud Boot W. A. 2006). The question arising in this context is to what extent a rating reproduces the signals sent to the financial market through individual macroeconomic indicators depicting the country’s economic situation. If so, does the value of a synthetic variable built on the basis of individual macroeconomic indicators shows a greater consistency with the level of ratings than the individual indicators themselves. In order to address this issue, the article makes an attempt to compare the ratings, as published by the three biggest rating agencies, of the countries examined with their economic situation within the period 2014-2016, which was adopted for the study. 2 Methodology and Data For the purpose of this analysis, data were collected for the years 2014 – 2016 on the following variables: Table 1 Variables used in the analysis symbol Variables X1 Harmonised index of consumer prices (HICP) X2 Budget outturn as % of GDP X3 Public debt as % PKB X4 the yield of 10-year-government bonds X5 real change in GDP compared to last year, as % X6 Unemployment rate, as % M Synthetic variable – country’s economic situation R1 S&P’s rating R2 Moody’s rating R3 Fitch’s rating Source: own study The macroeconomic variables applied in the study (X1-X6) were chosen by using the expert method on the basis of the parameters most frequently considered by the rating agencies for the assessment of the financial credibility of a particular country. Based on the variables X1-X6, a synthetic variable was constructed describing the economic situation of the EU countries. The values of the M variable were obtained employing the method of standardized sums (Bartosiewicz, 1992), with the variable being constructed based on the assumptions outlined in the authors’ another article (Rólczyński, Kopyściański, 2016). In line with the objective adopted in the study, the relationships between the variables X1-X6 and the variable R1-R3 (rating level), and the synthetic variable M with the variables R1-R3 will be examined with a view to determine which variable is related to the rating level to the largest degree. The analysis will be conducted based on Kendall’s tau correlation coefficient (Kendall, 1938): )1( 2 − = nn S τ , (1) 353 where: S is the sum +1 and -1 obtained from comparing all possible combinations of pairs in the ranking, determined in the following way: if a given pair of ranks for both variables is ordered in the same way (that is if Xi > Xj and Yi > Yj or Xi < Xj and Yi < Yj) then this situation is assigned value +1. If the rank ordering is reverse (that is Xi > Xj and Yi < Yj or Xi < Xj and Yi >Yj), then it has value -1. Kendall’s tau coefficient measures the degree of similarity between two sets of a series of data for the same set of objects (Abdi, 2007). Kendall’s tau coefficient is based on the difference between the probability that two variables have the same ordering and the probability that their ordering is different. Kendall’s tau coefficient assumes the values inside the interval [-1; 1]. If there is a perfect agreement between the two rankings, τ = 1; when it is reverse, then τ =–1. Coefficient τ= 0 means that there is no association between variables examined. In order to check the significance of Kendall’s tau coefficient, a relevant test (Abdi, 2007) will be applied with the following hypotheses: 0:0 =τH 0:0 ≠τH Test statistic for the number of observations exceeding 10 (Abdi, 2007) can be transformed into Z statistic which has a normal distribution3 and is given by: )1(9 )52(2 − + = nn n Z τ (2) On the basis of the determined test statistic Z, p value is read from the normal distribution which is then compared with the level of significance α . If p≤α , then H0 is rejected. The test will be carried out with the significance level 05,0=α . If the null hypothesis is rejected, then the alternative hypothesis is adopted which says that the variables are dependent. 3 Results and Discussion The data serving as the basis for calculation and assessment of the economic situation of the EU countries were derived from the Eurostat data base, and the ratings from www.tradingeconomics.com. Calculations were made using Statistica 12 and Excell software. Based on the data collected, the calculations were carried out in order to determine the level of the synthetic variable M describing the economic situation of the EU countries. The results are illustrated in Table 2. Table 2 The values of the synthetic variable M for the EU countries in 2014-2016 2014 2015 2016 Austria 0,595 0,557 0,614 Belgium 0,655 0,532 0,520 Bulgaria 0,547 0,514 0,655 3 Z statistic has a normal distribution with a mean of 0 and a standard deviation of 1 354 Croatia 0,496 0,428 0,535 Cyprus 0,292 0,309 0,464 Czech Republic 0,783 0,699 0,748 Denmark 0,775 0,669 0,782 Finland 0,586 0,590 0,651 France 0,607 0,598 0,614 Germany 0,726 0,696 0,737 Greece 0,123 0,020 0,191 Hungary 0,718 0,604 0,631 Ireland 0,766 0,879 0,839 Italy 0,552 0,532 0,568 Latvia 0,678 0,638 0,751 Lithuania 0,773 0,555 0,676 Luxembourg 0,774 0,733 0,911 Malta 0,729 0,594 0,787 Netherlands 0,733 0,646 0,784 Poland 0,791 0,538 0,695 Portugal 0,512 0,444 0,468 Romania 0,604 0,575 0,688 Slovakia 0,716 0,591 0,710 Slovenia 0,672 0,502 0,705 Spain 0,486 0,383 0,555 Sweden 0,803 0,617 0,700 United Kingdom 0,648 0,646 0,633 Source: own study Next, the values of the variables X1 – X6 and the synthetic variable M were compared with the rating level recorded for the EU countries at the end of each year and Kendall’s tau correlation coefficient was calculated. The results are presented in Table 3,4 and 5. Table 3 The values of Kendall’s tau rank correlation coefficients between the synthetic variable M and the level of rating in 2014 Asset symbol Kendall’s tau Correlation Coefficients in 2014 Underlined correlation coefficients are significant with p < 0,05 X1 X2 X3 X4 X5 X6 M R1 0,462312 0,014932 -0,216136 0,312668 -0,785724 -0,537433 0,429312 R2 0,456591 -0,048458 -0,258227 0,362387 -0,811869 -0,554066 0,414364 R3 0,477287 -0,021167 -0,230838 0,364830 -0,831603 -0,556198 0,422704 Source: own study 355 Table 4 The values of Kendall’s tau rank correlation coefficients between the synthetic variable M and the level of rating in 2015 Asset symbol Kendall’s tau Correlation Coefficients in 2015 Underlined correlation coefficients are significant with p < 0,05 X1 X2 X3 X4 X5 X6 M R1 0,365915 0,062915 -0,232013 0,252390 -0,771840 -0,471321 0,577059 R2 0,386013 0,018060 -0,289883 0,292818 -0,748409 -0,452552 0,522985 R3 0,396408 0,038947 -0,267708 0,279432 -0,789789 -0,453422 0,559212 Source: own study Table 5 The values of Kendall’s tau rank correlation coefficients between the synthetic variable M and the level of rating in 2016 Asset symbol Kendall’s tau Correlation Coefficients in 2016 Underlined correlation coefficients are significant with p < 0,05 X1 X2 X3 X4 X5 X6 M R1 0,348405 -0,059373 -0,244991 0,112972 -0,815833 -0,373514 0,445707 R2 0,351947 -0,047571 -0,289710 0,125055 -0,778597 -0,415652 0,425697 R3 0,345401 -0,068279 -0,274508 0,118918 -0,821745 -0,370550 0,433900 Source: own study In the light of the analysis a few key patterns emerge. First of all, the synthetic variable estimated is associated significantly with the level of ratings in each of the year under study, with the level of significance 05,0=α . Apart from the synthetic variable, the following individual variables were related to the ratings in each of the years discussed: • harmonized index of consumer prices (HICP) • real change in GDP compared to last year, as % • unemployment rate (X6) It is worth pointing out that real change in GDP compared to last year expressed as % (X5) is associated with the ratings even stronger than the synthetic variable that was constructed. This observation is confirmed by the real actions of the rating agencies which are very likely to make decisions about changing the rating of a particular country in the wake of information on GDP current level and forecast for the next years. In comparison to the rest of the variables, the synthetic variable is associated clearly stronger with the level of rating. An interesting fact is that the role of the budget outturn for the assessment of the country’s credibility, often addressed in the comments made by the rating agencies, shows no significant correlation with the level of rating that was given. This might, to some degree, be explained by the fact that in the analyzed years the situation became much more stable in terms of the countries’ debt and current budget outturn. 4 Conclusions The findings of the study presented here allow one to draw a few important general conclusions. On the one hand, they support the argument that the assessments made by the rating agencies are linked to countries’ economic situation (Rólczyński, Kopyściański, 2016). One should, of course, draw attention to the fact that, within the study’s framework, the influence of numerous other factors, which were not taken into account, and yet may affect the examined issues, was disregarded. The considerations presented in this paper are mainly based on the comparisons of the rating levels with the economic condition measured using the most common macroeconomic measurements. On the 356 other hand, the strong association of the rating with one variable, i.e. the percentage real change in GDP in relation to the previous year found in the study leaves some doubts as to the advisability of constructing synthetic variables for a collective, ratinglike assessment of the country’s financial credibility. Considering the informative role, in particular in terms of the easiness and speed of application, it is much more convenient to use one variable than a synthetic variable constructed on the basis of the comparison of a series of mutually dependent factors. Settling this question would naturally require more in-depth studies based on the data scope covering a longer period of time, while taking into account a broader range of factors, not only quantitative but also qualitative, affecting the rating of the country’s creditworthiness. References Abdi, H. (2007). The Kendall Rank Correlation Coefficient. In: Salkind, N. (Ed.) Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage. Arnoud Boot W. A. (2006). Credit Ratings as Coordination Mechanisms. Rev Financ Stud 19 (1), pp. 81-118. Bartosiewicz, S. (1992). Ekonometria z elementami programowania matematycznego i analizy porównawczej, Akademia Ekonomiczna, wyd. III. Wrocław. Cantor, R., Packer, F. (1996). Determinants and Impact of Sovereign Credit Ratings. The Journal of Fixed Income, Vol. 6, No. 3, pp. 76-91. Dziawgo, E. (1998). Kredyt-rating. Ryzyko i obligacje na międzynarodowym rynku finansowym. Wydawnictwo Naukowe PWN, Warszawa. Kaczmarek, T. (2002). Zarządzanie ryzykiem handlowym, finansowym, produkcyjnym dla praktyków, Ośrodek Doradztwa i Doskonalenia Kadr Sp. z o.o., Gdańsk. Kendall, M. G. (1938). A New Measure of Rank Correlation. In: Biometrika, Vol. 30, No. 1/2 (Jun., 1938), pp. 81-93. McClintock, E., Calabria, M. A. (2012). Regulation, Market Structure, and Role of the Credit Rating Agencies. In: Policy Analysis, nr 704. Rólczyński, T., Kopyściański, T. (2016). Economic Condition of the European Union countries and Level of Rating. In: European Financial Systems 2016. Proceedings of the 13th International Scientific Conference, Brno: Masaryk University, pp. 654-660. 357 Selected aspects of financial literacy of seniors in Slovakia Jaroslav Korečko1 , Ján Vravec2 1 University of Prešov in Prešov Faculty of Management, Department of Accounting and Controlling Konštantínova Street 16, 080 01 Prešov, Slovakia E-mail: jaroslav.korecko@unipo.sk 2 University of Prešov in Prešov Faculty of Management, Department of Accounting and Controlling Konštantínova Street 16, 080 01 Prešov, Slovakia E-mail: jan.vravec@unipo.sk Abstract: The article analyses selected aspects of financial literacy of seniors in Slovakia. Financial literacy is a system of knowledge, skills and experience for effective management of personal financial resources and assets. The nature of financial literacy is to efficiently ensure adequate financial resources and effective asset management. Financial literacy is not a definitive state, but it must evolve and improve over time, and it also applies to a higher age. Knowledge, skills and experience are often conditioned by variable aspects such as age, education, personality, general outlook, culture or intuition. Pensioners with a low level of financial literacy, without basic financial knowledge and skills, cannot make good financial decisions and manage crisis financial situations. As a result of the low level of financial literacy are serious financial and property problems of many senior citizens, which are causing serious problems and crisis phenomena in the economy and across society. Keywords: Financial literacy of seniors, financial education, seniors JEL codes: D14, O15 1 Introduction The evidence of low financial literacy level of the Slovak population is a large number of deceived citizens. Many residents of the Slovak Republic, in particular seniors, believed the unrealistic promises of non-bank entities. Lot of seniors entrusted them their whole lifetime savings and eventually they lost the money. Many seniors unconsciously sign the unfavorable credit contracts with a high interest rate but even with higher penalties for delaying repayments from non-bank institutions. As a result of inability to repay loans or increasing sanctions, is the situation when seniors possibly lose their flats and houses. Seniors are often executed. While in 2014 the number of executed pensioners was 28,000, at the end of 2017, the Social Insurance Agency in Slovakia performed up to 34,000 cases. Over the past three years, the number has increased by 6,000. The reason for this unpleasant situation is mostly the insufficient financial literacy of seniors. Weak financial literacy could be a threat for citizens also in contact with renowned financial institutions, which often take more care of their profits than the security and satisfaction of clients. Some financial institutions and financial advisors offer to seniors so-called "sophisticated" financial products through massive advertising. Lot of seniors do not even understand that products or services very much, but when they are so "beneficial", they convince the clients. So, seniors with low financial literacy increase their debts excessively because they have chosen a loan or even a number of loans that are inconsistent with their income. The aim of the paper is to analyze financial literacy of seniors in Slovakia and its comparison with other groups of inhabitants. 358 2 Theoretical background Defining of financial literacy is relatively tough. Many authors concern with defining the concept of financial literacy. Bowen (2002) says there are three common terms in the world literature that are used as synonyms for financial literacy. These are the concepts of financial knowledge, financial literacy and financial capability. Financial capability is a change in financial behavior through appropriate products and services that promote economic stability and long-term financial health. Individual ability based on knowledge, abilities and access to manage efficient financial resources, understanding financial products, services and concepts. Financial capability helps individuals make informed decisions, avoiding pitfalls, knowing where to go for help, and taking action to improve current and long-term well-being. Figure 1 Relation of financial knowledge, financial literacy and financial capability Source: own processing Because of this relationship, there are many opinions in the world (especially in the US) that governments and educational institutions should not develop financial literacy but financial capability. However, there are also opinions standing opposite to this claim. Financial literacy means „peoples’ ability to process economic information and make informed decisions about financial planning, wealth accumulation, pensions, and debt. Endogenizing financial knowledge has important implications for welfare, and offers insights into programs intended to enhance levels of financial knowledge in the larger population.” (Lusardi, Mitchell, 2013) The definition of financial literacy according to the Ministry of Finance of the Czech Republic reads: "Financial literacy is a set of knowledge, abilities and value attitudes of citizen necessary to financially secure himself and his family in the present society and to actively perform in the market of financial products and services. Financially literate citizen orientates himself in money and pricing issues and is capable to manage personal or family budget, including management of financial assets and financial liabilities in face of changing living circumstances." Financial literacy and financial intelligence have close links to money and financial investment. According to the first version of National Standard of Financial Literacy, (issued by the Ministry of Finance and the Ministry of Education of the Slovak Republic on October 30, 2008) financial literacy is defined as: "ability to use knowledge, skills and experience to effectively manage own financial resources to ensure lifelong financial security of an individual or household". It is a sum of abilities that are conditioned by age, family, culture, place of residence, and so on. Financial literacy is steady 359 development that enables each individual to respond effectively to new personal events and changing economic environment (Pavlík, 2012). Financial literacy is a secret whose understanding and knowledge divide people into rich and poor. The essential principles of financial literacy must be understood as an entire. Its achievement it is not a one-shot act. It is not enough to complete a one-day seminar and you will be perfectly prepared. It is a life-long process that has its own stages of development (Zbojek, 2007). Financial education is a process that improves people's understanding of different financial products and services. Financial literacy is defined as the ability to use individual knowledge and skills that people acquire to effectively manage resources providing financial prosperity (Hung, 2009). Based on above definitions, financial literacy can be summarized into following 5 points: • Ability to understand and evaluate numerical and financial data and information. • Ability to create your own financial strategy, financial goals and budgets. • Ability to know, understand and evaluate situation on financial markets. • Ability to make good decisions and effective allocation of financial resources to assets and other assets (investing). • Knowing, understanding and implementation of valid legislation - accounting, tax and levies system, directives, consumer protection law, etc. For the purpose of survey Atkinson and Messy (2010) had defined financial literacy as „a combination of awareness, knowledge, skills, attitude and behaviours necessary to make sound financial decisions and ultimately achieve individual financial wellbeing”. 3 Methodology and Data Necessary data were obtained in the spring of 2017 by a questionnaire survey which identified: • financial situation of seniors in Slovakia, • level of financial literacy of seniors in Slovakia, • level of financial education. The questionnaire survey was carried out on a sample of 500 respondents, 85% questionnaires (425 respondents) were returned and correctly filled. Questions in the questionnaire were divided into three sets, each set of questions dealt with other issues of the survey. First set of questions was focused on financial situation; second set addressed financial literacy, and the last one concerned financial education. The questions in the survey were organized into four logically following areas: 1. demographic data (8 questions), 2. analysis of financial situation (3 questions), 3. financial literacy analysis (5 questions), 4. analysis of financial education (2 questions). Respondents of the survey were citizens of the Slovak Republic addressed by interviewers directly in the streets. Their job was to fill in a paper questionnaire. Frequency analysis was used to meet the survey objectives. Analysis classes the answers respondents in four basic groups. Respondents were divided into following groups: students, employees, unemployed and seniors. Group of students was made of 103 university students under the age 25, in the second group were 108 employees (economically active population aged between 18 and 62), third group was made of 102 registered unemployed, and the last group were 112 seniors aged 62 and over. 360 4 Analysis of financial situation In the second set of questionnaire survey was analyzed the financial situation of respondents. Four closed questions focused on financial situation of respondents. First question had identified the current financial situation of respondents. There was a choice of selection from five answers. Respondent was able to choose from: very bad, bad, average, good or very good financial situation. Table 1 shows that up to a quarter of Slovak seniors consider their current financial situation very bad, another quarter as bad. A good financial situation has only 18% of seniors and only 8% of them are very good. Worse results were reached only by the unemployed, where 47% of respondents are in very bad financial situation and almost 40% in a bad financial situation. According to the survey employees have the best financial situation, 13% are very good and 25% are in good financial condition. Table 1 Present financial situation of respondents Social group Very bad abs. / per. Bad abs. / per. Average abs. / per. Good abs. / per. Very good abs. / per. Students 10/9,7% 22/21,4% 33/32% 31/30,1 % 7/6,8% Employees 21/19,4% 11/10,2% 35/32,4% 27/25% 14/13% Unemployed 48/47% 40/39,2% 12/11,8% 2/0,02% 0 / 0% Seniors 25/22,3% 31/27,7% 27/24,1% 20/17,9% 9/8% All respondents 104/24,5% 104/24,5% 107/25,2% 80/18,8% 30/7% * abs. - number of respondents in absolute numbers / per. - percentage of respondents Source: result of own questionnaire survey The second question had find out how the financial situation will change for the future. Answer opportunities about expected future were the same. Table 2 Future financial situation of respondents Social group Very bad abs. / per. Bad abs. / per. Average abs. / per. Good abs. / per. Very good abs. / per. Students 0 / 0% 5/4,9% 31/30,1% 42/40,7% 25/24,3% Employees 12/11,1% 17/15,7% 25/23,2% 33/30,6% 21/18,4% Unemployed 36/35,3% 32/31,4% 18/17,7% 10/9,8% 6/5,8% Seniors 31/27,7% 33/29,5% 21/18,7% 17/15,2% 10/8,9% All respondents 79/18,6% 87/20,5% 95/22,3% 102/24% 62/14,6% * abs. - number of respondents in absolute numbers / per. - percentage of respondents Source: result of own questionnaire survey Table 2 shows the expected financial situation of respondents. There are pessimistic expectations from seniors; almost 60% of seniors expect bad or very bad financial conditions for the future. Group of students has the highest expectation, 65% of students expect a very good or good financial situation for the future. The most pessimistic group of respondents represents the unemployed, where almost 67% of them assume a bad and very bad financial situation. The third question had find out how many respondents are able to save monthly money from their disposable income. 361 Table 3 Savings per month Social group Nothing abs. / per. 1 – 100€ abs. / per. 101 – 200€ abs. / per. 201 - 300€ abs. / per. More than 300€ abs. / per. Students 88/85,4% 12/11,7% 3/2,9% 0 / 0% 0 / 0% Employees 38/35,2% 32/29,6% 20/18,5% 11/10,2% 7/6,5% Unemployed 96/94,1% 5/4,9% 1 / 1% 0 / 0% 0 / 0% Seniors 52/46,4% 28/25% 21/18,6% 9 /8,1% 2/1,9% All respondents 274/64,5% 77/18,1% 45/10,6% 20/4,7% 9/2,1% * abs. - number of respondents in absolute numbers / per. - percentage of respondents Source: result of own questionnaire survey Monthly savings of seniors are relatively low, almost half of seniors (46.4%) save nothing. Only every third senior saves up to 100 euros. Over 300 euros savings dispose only 2% of seniors. Best savings have employed respondents. One third of employees save up to 100 euros, 18.5% of employees save from 101 to 200 euros. Up to 94% of people without work do save any money. 5 Analysis of financial literacy In the third set of questions, financial literacy of survey participants was analyzed. The set contained 5 closed questions. First one was about financial literacy level in the opinion of respondents. Answers are summarized in following Table 4. Table 4 Feeling level of financial literacy Social group Very bad abs. / per. Bad abs. / per. Average abs. / per. Good abs. / per. Very good abs. / per. Students 5/4,9% 14/13,6% 35/34% 26/25,2% 23/22,3% Employees 6/5,6% 13/12% 46/42,6% 27/25% 16/14,8% Unemployed 14/13,7% 15/14,7% 42/41,2% 23/22,5% 8/7,8% Seniors 17/15,2% 15/13,4% 54/48,2% 19/17% 7/6,2% All respondents 42/9,9% 57/13,4% 177/41,6% 95/22,4% 54/12,7% * abs. - number of respondents in absolute numbers / per. - percentage of respondents Source: result of own questionnaire survey Next four questions from this set were aimed at identifying real level of financial literacy of respondents. Respondents had to answer questions about interest rates, bank products, investment funds and life insurance. If respondent answered all 4 questions correctly, his literacy was considered very good. Three correct answers meant good financial literacy, two correct answers average financial literacy, one correct answer was result bad and no correct answer meant very bad level. Table 5 Real level of financial literacy Social group Very bad abs. / per. Bad abs. / per. Average abs. / per. Good abs. / per. Very good abs. / per. Students 7/6,8% 16/15,5% 41/39,8% 22/21,4% 17/16,5% Employees 8/7,4% 17/15,7% 48/44,4% 21/19,4% 14/13% Unemployed 26/25,5% 29/28,4% 33/32,4% 13/12,7% 1 / 1% Seniors 29/26% 37/33% 35/31,3% 9 / 8% 3/2,7% All respondents 70/16,5% 99/23,3% 156/36,7% 65/15,3% 35/8,2% * abs. - number of respondents in absolute numbers / per. - percentage of respondents Source: result of own questionnaire survey 362 Based on Tables 4 and 5, we can say there is a huge degree of disproportion between perceived and real level of financial literacy in Slovakia. Disproportion was most pronounced among seniors and unemployed. Only 15.2% of seniors feel that they have very poor financial literacy, but in real up to 26% of them have a very poor financial literacy. Only 13.4% of seniors feel they have poor financial literacy, but in real have a poor literacy up to 33% of them. A good and very good level of financial literacy is assumed by up to 25% of the seniors, but only less than 11% of seniors have a good or very good financial literacy. The smallest disproportion between perceived and real financial literacy degree is in the group of students and economically active inhabitants of Slovakia. In their case, the perceived and real degree of financial literacy differs much less. For example, 18.5% of students think they have a bad and very bad level of financial literacy, but in real it is only 22.3% of students; 41% of employees think that they have average level of financial literacy; actually 32.4% of them belong to average level group. 6 Analysis of financial education Fourth set of questions analyzed respondents’ relationship to financial education. First question was focused on the importance of financial education in human life. The question was closed and the respondents had a choice of five options: certainly not, rather not, rather yes, certainly yes or I do not know. Table 6 Importance of financial education for life Social group Certainly no abs. / per. Rather no abs. / per. Rather yes abs. / per. Certainly yes abs. / per. I don’t know abs. / per. Students 3/2,9% 7/6,8% 27/26,2% 66/64,1% 0 / 0% Employees 0 / 0% 11/10,2% 43/39,8% 52/48,1% 2/1,9% Unemployed 17/16,7% 21/20,6% 25/24,5% 34/33,3% 5/4,9% Seniors 12/10,7% 16/14,3% 28/25% 53/47,3% 3/2,7% All respondents 32/7,5% 55/12,9% 123/28,9% 205/48,2% 10/2,4% * abs. - number of respondents in absolute numbers / per. - percentage of respondents Source: result of own questionnaire survey The objective of second questions was to identify frequency of financial education. How many times did respondents participate in financial education? The question was also closed and respondent had choice of five answers. Table 7 Frequency of financial education during life Social group Never abs. / per. 1 times abs. / per. 2 times abs. / per. 3 times abs. / per. 4 and more times abs. / per. Students 12/11,7% 56/54,4% 26/25,2% 6 /5,8% 3 /2,9% Employees 53/49% 45/41,7% 9/8,3% 1 /0,9% 0 / 0% Unemployed 69/67,6% 28/27,5% 5/4,9% 0 / 0% 0 / 0% Seniors 87/77,7% 19/16,7% 4/3,6% 0 / 0% 2/1,8% All respondents 221/52% 148/34,8% 44/10,4% 7/1,6% 5/1,2% * abs. - number of respondents in absolute numbers / per. - percentage of respondents Source: result of own questionnaire survey 363 All population groups consider financial education to be important. Up to 72.3% of seniors think financial education is important to their lives. The importance of financial education was confirmed by 90.3% of students and 88% of employees. Somewhat surprising are the results for unemployed people where up to 37.3% of them think that financial education is not important for life. Survey results about the frequency of financial education during respondents’ life are alarming. Over 77% of seniors, 67.6% of unemployed, 49% of employees and almost 12% of students have not even once completed some financial education; 16.7% of seniors, 27.5% of unemployed, 42% of employees and almost 55% of students took part in financial education in any form. 7 Results and Discussion Evidence from the whole world, but particularly available data about economic development of the EU States suggest that illiteracy in financial sector is one of the factors contributing to emergence of financial crises. The inability to make qualified financial decisions under current conditions increasingly negatively affects normal life of people. According to an independent agency Focus survey, the average level of financial literacy of Slovaks is 62.5%. We think that the poor level of financial literacy is firstly due to absence of general basic economic and financial education at elementary and secondary schools. As the basic principles of mathematics are taught, basic economic principles and the nature of financial processes should also be taught. Our survey supports these contentions. Inter alia the survey results say, there is a clear trend that residents of Slovakia, including seniors (77%), consider financial education to be important, but only 48% of Slovaks have participated in some financial courses or financial education trainings. This situation is very unfavorable and requires prompt solutions. Seniors in Slovakia could, in our opinion, be educated at least in the following areas: • Money - money development and function, money market, demand for money, money supply, payment system. • Financial planning - financial market institutions, product prices in the financial market, consumer protection in the financial market. • Bank system and bank products - bank system in the Slovak market, commercial banks, types of banking activities, correct selection of bank products, Internet banking, payment by contactless card. • State pension scheme - definition of pension and basic legal conditions for entitlement to old-age pension. • Loans - institutions providing loans, forms of short-term loans, forms of longterm loans, risk of execution. • Savings and investing. • Insurance - insurance of persons and property, life and non-life insurance. • Taxes. 8 Conclusion Identifying the starting points for supporting financial literacy of Slovak citizens is relatively demanding and complicated. Long-term trends show that the complexity and intricacy of financial products and services are steadily increasing. Citizens are offered products and services by a wide range of subjects, especially banks, insurance companies, financial intermediaries and other financial institutions. Under the long-term impact of various factors and aspects, household indebtedness is rising steadily, poverty is rising, and the number of households with economic and existential problems is growing. People are affected by advertising and other marketing and media tools. Increasingly, they do not want to postpone their consumption in the future, so they buy 364 goods and services using mortgage and consumer loans, use hire purchase, financial and operational leasing. If individuals are to avoid financial problems, they need to make qualified financial decisions; witch can have a significant impact on their budgets and financial stability, even across generations. Fast and good financial decisions are usually result of a sufficient level of financial literacy, financial knowledge, and skills of individuals and households. It is not only a chance. Building a society that supports and develops financial literacy of its population (of all ages) is very important and requires a long-term vision and strategy. Acknowledgment The Paper was elaborated within the support the project KEGA 058PU-4/2015. References Apolen, P. (2013). Výsledky prieskumu o finančnej gramotnosti sú katastrofálne. Retrieved from: http://www.investujeme.sk/vysledky-prieskumu-o-financnej- gramotnosti-katastrofalne. Atkinson, A., Messy, F. A. (2010). Assessing financial literacy in 12 countries an OECD pilot exercise. Retrieved from: http://www.cerp.carloalberto.org/wp-content /uploads/2011/03/wp_115.pdf. Bowen, C. F. (2002). Financial Knowledge Of Teens And Their Perents. Financial Counseling and Planning Education. Vol. 13(2), pp. 93-102. FOCUS (2012). 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Košice: TypoPress. 366 Research of Public Financing Efficiency of Social Well-being in the Russian Federations’ Regions Dmitrii Kornilov1 , Nadezhda Yashina2 , Nataliya Pronchatova-Rubtsova3 , Oksana Kashina4 ¹ Nizhny Novgorod State Technical University n.a. R.E. Alekseev Institute of Economics and Management, Management of Innovation activity Department Minin street 24, 603950 Nizhny Novgorod, Russian Federation E-mail: kornilov-d@yandex.ru 2,3,4 Lobachevsky State University of Nizhny Novgorod Institute of Economics and Entrepreneurship, Department of Finance and Credit Bolshaya Pokrovskaya street 37, 603000 Nizhny Novgorod, Russian Federation E-mail: sitnicof@mail.ru, pronat89@mail.ru, oksana_kashina@mail.ru Abstract: In the knowledge-based economy, the main objective of public finance management is becoming the social well-being maximization that is achieved through the implementation of effective financial policy. It is based on the optimal structural distribution of financial resources between the industrial and non-productive sectors, combined with innovative techniques in public financial management. This policy is directed to well-being growth of the population and investing in human capital. So, the aim of the paper is to research the public financing efficiency of social well-being in the Russian Federations’ regions. Author's technique has been developed for determining a consolidated standardized indicator, which assesses the social well-being level of territories. It is based on a specially formed system of indicators. A qualitative social well-being condition of the population is proposed to be quantified by a number of indicators included in the assessment criteria system. It consists of two indicators’ groups: indicators of investing in human capital (health care, education, social protection, spiritual wealth), as a fundamental value of the modern society and indicators of financial provision in the spheres which determine social well-being. The methods of economic, system analysis and mathematical statistics are used in this research. Approbation of proposed technique is carried out on official statistical data for 2015 from open databases of the Federal State Statistics Service and the Federal Treasury of the Russian Federation. As a result, the rating of the regions is constructed on the basis of the developed technique for assessment of social well-being financing efficiency. The consolidated standardized indicator of investing in the human capital and the financial provision of the social well-being spheres allows to carry out a quantitative evaluation of leaders and outsiders among researched regions. It also makes it possible to develop effective administrative decisions in order to achieve the social well-being maximization. Keywords: social well-being, public financing efficiency, investing in human capital, consolidated standardized indicator, rating JEL codes: I31, I38, H5, H75 1 Introduction Systematic formation and development of the knowledge economy is based on the principles of value-oriented approach, where a human being is the main value. So, satisfaction of the total aggregate of human necessities, which is shown in maximization of the population social well-being level, becomes today the main direction of public financial investments. And due to dramatic limitedness and insufficiency of public resources, the relevance of researching and analyzing the efficiency of investing and using budgetary funds grows. Optimal industry specific and territorial distribution of public financial resources shall be based on the principles of result-oriented budgeting. Increase in the efficiency of using funds is also achieved by applying an innovative 367 approach to public finance management, which is expected to foster the population social well-being growth. The basis of the long-term social and economic development of Russia till 2020 is implementation of a public policy oriented at the country’s population social well-being. Thus, the society is formed based on trust to and responsibility of public and private economic institutions. A decrease in social polarization is expected due to social mobility and implementation of the social policy aimed at support of the vulnerable segment of the population. The middle class will comprise more than half of the population, while its significant part will be formed by people engaged in creating a new knowledge economy, technologies and ensuring the development of a human being. The issues of studying social well-being and the efficiency of its financing are resonating widely in the scientific community. These issues are raised by many philosophers, political scientists, economists, psychologists, cultural scholars, healthcare professionals, religious figures, etc. in their works. Population social well-being is determined by the economic development of a country, which directly depends on the level and quality of the human capital development. It explains why one countries are prosperous, while others are poor. As early as in the 18th century, A. Marshall stated that the most valuable of all capitals is the capital of investing in people. Exactly humans may and should foster growth in income by acquiring various skills and accumulating knowledge. Health care and education supplement to a full extent other aspects of progress, such as receiving income and fighting poverty. Human capital includes aggregate accumulation of investments in education, occupational training and health care, that increase people’s productivity (Becker, 1975). At the macro-level, investments in the human capital are ‘growth engine’ (Lucas, 1988). So, education may have an impact on economic growth, while developing the people’ ability to make technological innovations and increasing the speed of technological diffusion (Nelson and Phelps, 1966). Improvement of the human capital will allow people to reach a wider set of opportunities to increase their well-being and welfare. In practice the human capital is typically measured by several indicators: literacy level, primary and secondary schools, life expectancy, higher education institutions, etc. (Clark, 2006). In the conditions of the knowledge economy the A. Marshall’s words turned out to be prophetic, that is why there is no better investing for developing countries than investing in the human capital. While focusing our attention at the well-being concept, let us look at the works dedicated to the subjective well-being of a human (Helliwell, 2003; Cracolici et al., 2012). The key aspect is to reveal the links between social capital, education and well-being. The use of large international samplings of individual respondents’ answers allows to simultaneously reveal well-being determinants at individual and social levels (Helliwell, 2003). The importance of a ‘comparative income’ for individual well-being was noted by Ferrer-i-Carbonell (2005). The indicator of satisfaction with life is used as a measure of individual well-being. People are the more happier, the larger their income is in comparison with the income of a reference group. Financial stringency has an impact on subjective well-being, but the effect is weakened depending on the level of education and availability of a housing (Cracolici et al., 2012). Growth in people’s income depends on the level of their education, which requires long-term and large-scale investments (Giziene, 2012). A study of the efficiency of financing of public expenses for education and health care (Gupta et al., 2002) in 50 countries showed that an increase in public expenses for these fields is related with an improvement of both an access to schools, and their achievements, a decrease in infant and children mortality rates. The model of education regression are stable to various specifications, but the interrelation of expenses for health care and mortality is more weak. 368 An assessment of the efficiency of allocations to education in countries based on the data of UNESCO showed that the correlation of accumulation of the human capital and GDP growth is not high in countries with non-efficient distribution of expenses for education, but it is significant and positive in the countries with a better quality of management of allocations to education (Judson, 1998). Fostering higher education may increase the rates of accumulation of the human capital and mitigate negative consequences of a slow-down in the labor force growth (Annabi et al., 2011). However, this impact depends on the efficiency of public expenses for education. The issues of fostering investments in the human capital and assessing their efficiency attract scholars’ interest and have an ambiguous nature (Ahmed and Krishnasamy, 2013; Kozuń-Cieślak, 2013). An assessment of the efficiency of public investments in the human capital using the Data Envelopment Analysis (DEA) linear programming method was carried out in EU member states (Kozuń-Cieślak, 2013). The results show that among 25 EU member states (except for Cyprus and Malta) there are no significant differences in the efficiency of public expenses for building up the human capital. The DEA method allowed to identify a group consisting of 15 efficient economies, in which the human capital state corresponds to the financial funds allocated for its building up. The assessment using the DEA method showed the degree of conversion of the funds invested into the potential of a society’s health and knowledge. So, ‘the most effective’ country does not necessarily coincide with the country with the highest levels of the health care and education system, and vice versa. This allows to suggest that public funds for health care and education in a country are not used in the best possible way. Gromova and Shaftelskaya (2017) studied the interrelations of the public policy objectives and citizens’ well-being. As a result, it was found out that the efficiency of the fiscal policy, and as a consequence, the improvement of the citizens’ life quality and nation’s well-being depend on performance of state obligations and responsibilities. The author Hlushchenko (2016) suggested a method of assessing and measuring wellbeing stability approved on the basis of data on Ukraine. The well-being financing indicator includes the sum of assets financed out of the public funds for environment protection, health care, spiritual and physical development, education, social protection and financial assets of households. It was found out that there was a correlation between the sum of well-being financing and an average expected life expectancy at birth. Within the framework of the study the social well-being of the territorial population should be understood as such qualitative state of the human capital development, when a society and each individual within the society in the existing social economic and regulatory legal conditions is timely and fully satisfied with the level and quality of his/her life and satisfies his/her basic needs (for support of material welfare, maintaining health, safety, education, cultural enrichment, etc.) or has such an opportunity. The objective of this work is to study the efficiency of public financing of the population social well-being (using the examples of regions of the Russian Federation) by applying a developed author’s method. 2 Methodology and Data Under the circumstances of increase in the efficiency of spending budgetary funds, taking into account the reference points of the public policy implemented both in Russia and in the rest of the world, it is evident that one needs to study and develop methodological approaches to assess the social well-being level of territorial population taking into account a specially formed system of indicators. The methodology shall comply with the ease of use requirement conditioned by the accessibility and reliability of source information. So, it is suggested to use in assessments official data from open databases of the Federal State Statistics Service and the Federal Treasury of the Russian Federation. 369 In the development of the methodology, the issue of selecting the implementation methods and initial set of indicators becomes non-trivial. So, unjustified use of complex methods and excess number of indicators may result in loss of simplicity and unambiguity when interpreting received results, as well as in difficulties related to repeatability of such results. That is why the basic principle is not to multiply beings without necessity. The result shall represent a summarized, unambiguous measurement, index. Depending on the index values one may easily put in order the multitude of the studied regions by the level of efficiency of financing the territorial population social well- being. So, to achieve the study objective a methodology is being developed of determining a consolidated standardized indicator allowing to assess the efficiency level of financing the territorial population social well-being. It is suggested to quantitatively measure the qualitative state of the population social well-being by a number of indicators included in the criteria system used to assess the social well-being. The selection of indicators is based on the principles of the balanced system of indicators (BSC) theoretical concept (Best practices Benchmarking Report, 1999): data reliability; data sufficiency (several dozens of indicators); data significance (use of indicators directly related to population social well-being); sampling diversity; ambiguity (the useful effect is expected to monotonically change along with an indicator value). Two groups of indicators (in total more than 50) have been formed: • indicators characterizing the efficiency of investing in the human capital (healthcare, education, social protection, spiritual wealth) as the fundamental value of the modern society development, • indicators of financial support of the fields determining social well-being. Model Specification Calculated individual indicators allowing to assess social well-being may have various dimension, importance or weight. And carrying out a comprehensive assessment based on the whole set of indicators requires to bring them to a non-dimensional form, to a unified zero reference point, to a unified variation interval. It is suggested to standardize the indicators, which will allow to adjust the value range in accordance with the conversion functions for added convenience when comparing them. The authors use a method based on linear conversion of initial indicators, within which standardized indicators are determined according to the formulas (1) and (2), and their values fall within the specified interval from 0 to 1 (see Table 1). Upon conversion the indicator dimension disappears, but a fine structure of changes in certain indicators remains. Now one has the possibility to compare them and display in a unified coordinate system, as well as to aggregate the calculated indicators in the context of the specified groups (Yashina et al., 2015). Standardization is carried out taking into account the impact on the social well-being level of the territorial population. An increase in value of one indicators results in decrease (‘negative effect’, the lower the coefficient value, the better), and an increase in value of other indicators results in growth (‘positive effect’, the bigger the coefficient value, the better). As an example, a fragment of sampling of the indicators is shown in Table 1. 370 Table 1 Systematization of indicators by direction of impact on the comprehensive assessment of the population social well-being in regions of the Russian Federation Indicator group name / indicators Group 1 ‘Efficiency of Investing in the Human Capital’ ‘Negative effect’ indicators (oppositely directed) ‘Positive effect’ indicators (co- directed) Number of deaths per 1,000 people of the population Number of births per 1,000 people of the population Infant mortality per 1,000 live births Surplus of births over deaths Average number of visiting ambulatory and outpatient institutions per one resident a year Sufficiency of medical personnel for 10,000 residents Number of population with monetary income below the minimum subsistence level (in % of the total number of the population of a constituent entity) Number of theater viewers and visits to museums per 1,000 people Number of children with disabilities under 18 receiving social benefits per one resident in average Library stock per 1,000 people Group 2 ‘Financial Support of Social Well-being’ ‘Negative effect’ indicators ‘Positive effect’ indicators Compulsory payments and various contributions in % of the total volume of monetary income of the population Gross regional product per capita across the constituent entities of the Russian Federation, RUB/person Healthcare related expenses in relation to the number of population, RUB/person Ratio of average wage of doctors and health workers with higher education, %, Expenses for implementation of social support measures aimed at certain categories of citizens, RUB/person Amount of the monthly child allowance established in the constituent entities of the Russian Federation, RUB/month Actual amount of appointed pensions (in % to the previous year) Education related share of expenses in gross regional product, % Source: prepared by the authors Calculation of standardized indicators is carried out according to the formulas (1) and (2): • ‘Negative effect’ indicators: Ä2z ∗ ÅÆÇ ÅÆ ÈÆÉ ÅÆ ÈÊË ÅÆ ÈÆÉ , 0 x Ä2z ∗ x 1. (1). • ‘Positive effect’ indicators: Ä2z ∗ ÅÆ ÈÊË ÅÆÇ ÅÆ ÈÊË ÅÆ ÈÆÉ ,0 x Ä2z ∗ x 1. (2), where Ä2z is the calculated value of the ith coefficient of the system of social well-being level indicators among the population of the jth region, Ä2z ∗ is the standardized indicator of the ith coefficient of the system of social well-being level indicators among the population of the jth region, Ä2 >?* is the highest calculated value of the ith coefficient, Ä2 >29 is the lowest calculated value of the ith coefficient. Actual values of individual indicators may be applied to compare the levels of population social well-being in various regions in the dynamics over a number of years. They can 371 also be used to compare a specific state of a region with similar indicators of other constituent entities of the Russian Federation in the context of directions of investments in the human capital. The statistics of the coefficient data for a long period may be considered as a reference point to form standards when implementing a result-oriented budgeting policy. Subsequently, the state, when the coefficient values included in the criteria system used to assess the population social well-being are within the limits of standard values and are characterized by positive dynamics, is interpreted as a stable state of the population social well-being in a constituent entity of the Russian Federation. The final position of a constituent entity of the Russian Federation is determined by the value of a consolidated standardized indicator allowing to assess the population social well-being level (Ä789• ∗QÌ ). It is calculated as the sum of values of all individual standardized indicators of the assessment describing the efficiency of public financing of the population social well-being (3). Ä789• ∗QÌ is compared with the pre-determined by an expert method threshold values of consolidated standardized indicators for 3 classes of regions based on the most and the least successfully developing regions, as well as based on the scope of variation of the coefficients within the whole set of 85 regions. The constituent entities of the Russian Federation are distinguished, which relate to the classes of efficient, poor and satisfactory management of public financing of social well-being. Ä789• z ∗QÌ ∑ Ä2z ∗ (3), where Ä789• z ∗QÌ is the consolidated standardized indicator allowing to assess the population social well-being level in the jth region of the Russian Federation. Ä789• z ∗QÌ can be represented by integrating the enlarged (by summing up) standardized indicators of the jth region of the Russian Federation in the context of the key areas of the human capital determining the efficiency of public financing of social well-being. The enlarged standardized indicators are formed, of the efficiency of managing public financing of health care (ijÍÎÏ z ∗ÐW ), social protection (ijÍÎÏ z ∗ÑÒ ), education (ijÍÎÏ z ∗ÓÔÕ ), spiritual wealth — culture (ijÍÎÏ z ∗W ) of the jth region (4). Ä789• z ∗QÌ Ä³ÍÎÏ z ∗ÐW ijÍÎÏ z ∗ÑÒ Ä³ÍÎÏ z ∗ÓÔÕ Ä³ÍÎÏ z ∗W (4). The lower the value of a consolidated standardized indicator allowing to assess the social well-being level Ä789• z ∗QÌ , the more efficient public financial policy and the more qualitative management of public investments in the human capital of a specific region of the country is. This methodology allows to build a rating of the constituent entities of the Russian Federation by size of the values Ä789• ∗QÌ taking into account the ‘clipping’ of regions by the classes of efficient, poor and satisfactory management of public financing of social well- being. 3 Results and Discussion Practical implementation of this methodology was implemented based on the sampling of data of 85 regions of Russia in 2015. A comparative rating of regions by increase of the value of a consolidated standardized indicator allowing to assess the social well-being level is shown in Table 2. 372 Table 2 Rating of constituent entities of the Russian Federation based on the results of the methodology approbation, 2015 (a fragment) Constituent entity of the Russian Federation ijÍÎÏ z ∗ÐW ijÍÎÏ z ∗ÑÒ Ä³ÍÎÏ z ∗ÓÔÕ Ä³ÍÎÏ z ∗W Ö×ØÙÚ Û ∗ÜÝ Place 1 Class ‘Efficient Management of Public Investments in the Human Capital’ 23.73 22.42 2.92 3.04 52.09 1 Yamalo-Nenetsk Autonomous District 32.84 25.42 4.67 3.66 66.59 2 Khanty-Mansiysk Autonomous District 33.28 25.01 4.92 4.50 67.71 3 Kamchatka region 32.50 26.34 5.56 3.84 68.24 4 Moscow 39.73 23.76 3.66 1.65 68.79 5 Magadan region 34.62 26.01 5.76 3.70 70.10 6 Saint Petersburg 38.59 29.16 4.65 1.08 73.48 7 … Krasnodar region 41.72 34.67 5.36 4.00 85.75 26 Leningrad region 42.26 33.45 5.74 4.39 85.83 27 Republic of Dagestan 39.68 35.33 6.65 4.49 86.15 28 Perm region 41.46 35.26 5.73 3.75 86.20 29 Novosibirsk region 41.19 35.11 6.31 3.90 86.52 30 2 Class ‘Satisfactory Management of Public Investments in the Human Capital’ 42.55 34.25 6.10 3.86 86.75 31 … Nizhny Novgorod region 44.91 34.89 5.72 3.22 88.74 43 … Altai region 45.02 37.67 6.92 4.09 93.68 76 Kurgan region 44.98 37.69 7.23 4.06 93.95 77 Republic of Mordovia 46.43 37.08 6.69 3.85 94.05 78 … 3 Class ‘Poor Management of Public Investments in the Human Capital’ 50.84 41.46 8.13 4.88 105.32 83 Source: authors’ calculations based on the data of the Federal State Statistics Service and the Federal Treasury of the Russian Federation The leading positions in the 2015 rating are held by the regions where the public financial policy is the most efficient, but no representatives of the first class were revealed. The structural distribution of financial resources in the leading regions among the production and non-production branches is the most rational and close to the optimal variant. Flourishing regions are distinct for implementation of the best result-oriented practices, for managing public financing in the conditions of the knowledge economy. The policy of public financing of the population social well-being in these regions proves to be efficient. This is confirmed by the indicator values: high birthrate and life expectancy, high actual amount of pensions goes along with low mortality rates and large volumes of gross regional product per capita, low number of population with monetary income below the minimum subsistence level. The numerical assessment has unambiguously shown that the first class and lagging regions are not necessarily characterized by all the best and all the worst indicators, respectively, regarding health care, social policy, culture and education. For instance, the Novosibirsk region and the Republic of Dagestan by the majority of the indicators fall within the ‘poor’ class of managing public investments in the human capital. Nevertheless, a comprehensive assessment allowed to determine them within the ‘satisfactory’ class. The Nizhny Novgorod region, according to its indicators in the field of education and cultural field, shall fall within the middle group of efficiency, but it turned out to be not enough to mitigate the adverse effect of other factors: health care and 373 social protection. The received results coincide with conclusions received by other researchers (Kozuń-Cieślak, 2013). The defining moment is not the total amount of investments in the human capital, but the quality of satisfaction of the society’s needs, implementation of socially-oriented programs taking into account the public investments: creation of opportunities for receiving modern education of various specializations and levels; receiving affordable and qualitative health care with the use of innovative methodologies and technologies; ensuring security of the citizens; having an opportunity of cultural development, enrichment, personal fulfillment. Otherwise, a stable progressive development of the country in the conditions of the knowledge economy is not possible. 4 Conclusions The developed consolidated standardized indicator of the efficiency of investing in the human capital and financial support of the fields determining social well-being allows to carry out a numerical assessment of the territorial leaders and outsiders, as well as to develop efficient managerial decisions to achieve the maximization of the social wellbeing. The authors have determined that a critical value of a specific indicator of the assessment system (of an enlarged indicator in separate fields of financing of the human capital) does not pose a threat to the population social well-being as a whole. However, this is a signal to strengthen control over spending funds in the corresponding field. So, competent public authorities shall primarily rely on the following: permanent monitoring of both each separate indicator and the whole system of indicators in their interrelatedness; in-depth analysis of separate economic objects and processes when the criteria dynamics deviate from the normal trajectory; orientation of taken managerial decisions at the value of humans. The analysis of the results of the suggested methodology approbation confirms its consistency in whole. The formed system of indicators to a large extent characterizes the population social well-being in various regions and in the country as a whole. The actual level of the population social well-being in 2015 corresponds to the final rating of regions according to the comprehensive assessment of the efficiency of public financing of the social well-being, while truly depicting the human capital state and trends of nonproduction fields development. References Ahmed, E. M., Krishnasamy, G. (2013). 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In: European Financial Systems 2015, Proceedings of the 12th International Scientific Conference. Brno: Masaryk University, pp. 696-703. 375 The impact of barriers in the access to financial products and services on the financial exclusion of the generation 50+ in Poland Anna Korzeniowska1 , Wojciech Misterek2 1 Maria Curie-Skłodowska University in Lublin Faculty of Economics, Department of Banking Pl. Marii Curie-Skłodowskiej 5, 20-031 Lublin, Poland anna.korzeniowska@umcs.lublin.pl 2 Maria Curie-Skłodowska University in Lublin Faculty of Economics, Department of Banking Pl. Marii Curie-Skłodowskiej 5, 20-031 Lublin, Poland wojciech.misterek@umcs.lublin.pl Abstract: Financial exclusion is defined by the European Committee as a situation in which a person encounters difficulties in both access to as well as the usage of financial products and services at the level required to meet his/her needs. The idea beyond is also to enable them to lead a normal social life. It is necessary to point out that the access to financial products is understood as a possibility to use them under reasonable economic conditions. Among those most endangered by financial exclusion are, inter alia, elderly people because their reluctance to accept new technologies and opportunities provided by financial markets is much bigger than that one of younger generations. There is much evidence for that statement, some of it being the ratio of underbanked elderly people which in Poland for the aged 55-64 and over 65 is 32% and 57% respectively, with the average for the whole country population - approx. 23%. The question arises as for the reasons for such a situation and that is why the aim of this paper is to analyse the existence of barriers in the access to financial products and to verify their influence on the diagnosed higher level of financial exclusion of the generation 50+. To analyse the factors influencing behaviours of senior citizens on the financial markets, the research in the form of questionnaire sent to the group of approx. 380 citizens of the Lublin region in Poland was conducted. The results indicate that, while cooperating with financial institution, elderly people are aware of the barriers they meet such as modern technology, a professional and incomprehensible language, extensive or scarce range of banking services ,etc. However, these barriers do not constitute statistically significant factors in the high underbanked ratio of this generation. Keywords: financial exclusion, usage of financial products, personal finance JEL codes:D14, D83, G2 1 Introduction The concept of financial exclusion was first described by A. Leyshon and N. Thrift in the 1990s in the context of lack of access to financial services due to geographical barriers. Only in subsequent years they began to be applied to describe the investigated phenomenon, also as an effect of a broader spectrum of restrictions on access to the banking sector (Polasik, Piotrowska, 2014). For a profound study of these issues, the definition given in the EU Report (2008) seems to be essential. It goes as follows: ”Financial exclusion refers to a process whereby people encounter difficulties accessing and/or using financial services and products in the mainstream market that are appropriate to their needs and enable them to lead a normal social life in the society in which they belong”. A number of further studies on these issues either refer directly to this definition or largely reproduce it (Buckland, 2012). The above-mentioned Report of the European Commission and the subject literature often refer to narrower areas of financial exclusion. Given the importance of the banking secto,most often indicated is banking exclusion or exclusion from particular banking 376 services, i.e. transactions, credits or savings. Less frequent is the research conducted in the area of exclusion from insurance services (Leeladhar, 2006; Byrne et al., 2005). The subject literature expands and develops the knowledge on financial exclusion also in other areas. There are different internal and external reasons for financial exclusion. Among them are the barriers in the access to financial products. This is the subject matter of this article as its aim is to analyse the existence of barriers in the access to financial products and to verify their influence on the diagnosed higher level of financial exclusion of the generation 50+. 2 Financial exclusion among 55+ in Europe The first problem is identification of factors contributing to financial exclusion. The research in this area brought to the attention, among others, geographical factors and thus geographical access to banking services (Leyshon and Thrift, 1995), low incomes of market participants, and so, inability to use selected financial services (Gloukoviezoff, 2006) as well as the changes in the social area, which affects the lower financial independence mainly the one of the young (Anderloni and Carluccio, 2006).Relevant considerations regarding the subject were systemized in the Report of the European Commission which in a more detailed way distinguishes three groups of factors affecting financial exclusions: demand, supply and social. At the same time, on the basis of the research run in 2007 in 14 countries (The UK, the Netherland, Spain, Slovakia, Poland, Norway, Latvia, Italy, Ireland, Germany, France, Bulgaria, Belgium and Austria), some particular factors were identified and categorized by the number of states where a given cause was noted (Table 1). Table 1. The reasons for financial exclusion in selected EU member states Type of factors Factor Number of countries with identified cause of financial exclusion Socialfactors Demographic changes technological gap 10/14 Labour market changes 8/14 Income inequalities 8/14 Liberalisation of markets - less attention to marginal market segments 6/14 Liberalisation of markets - disappearance institutions targeted to low income 5/14 Social assistance 5/14 Demographic changes overindebted 4/14 Money laundering rules/Identity checks 3/14 Fiscal policy 3/14 Demographic changes - young 2/14 Demographic changes – migrants/minorities 2/14 Cash is common 1/14 Supplyfactors Risk assessment 8/14 Marketing 8/14 Geographical access 7/14 Product design (terms and conditions) 7/14 Service delivery (e.g. internet) 7/14 Complexity of choice 7/14 Price 4/14 Type of product 1/14 m a n d fa ct Concern about costs 8/14 Belief that not for poor / low self esteem 8/14 377 Fear of loss of financial control 7/14 Mistrust of providers 7/14 Preference for alternative providers and cultural factors 4/14 Demand factors Religion 4/14 Opposition to use 4/14 Bad past experience 1/14 Fear of seizures 1/14 Source: Financial Service Provision and Prevention of Financial Exclusion, Report of the European Commission, March 2008, p. 45 The second area identified in the subject literature is the impact of knowledge on the level of exclusion from financial services. The research done in this respect underlines the rapid development of financialization, which, on the one hand, makes the society more dependent on financial industries, and, on the other, aggravates the problem of persons excluded; due to their financial knowledge, they are not able to function properly on the market. The problem is increased by financial institutions themselves as they do not have any policy measures targeting this very group of customers. Consequently, there appears an issue of the rationale for implementing educational programmes aimed at those groups with the highest risk of financial exclusion (c.f. Atkinson and Messy 2012; Buckland 2014; Solarz, 2013; Iwanicz-Drozdowska, 2011). Another area analyzed in the literature is the impact of information technologies on the level of financial exclusion. This research indicates that the arrival of new solutions in access to financial services and their distribution systematically increase the level of financial exclusion, mainly for senior citizens (Salovaara et al., 2010). It should be pointed out, however, that not only technological development contributes to limited access to financial services in this very group. The elderly often feel to be outside the financial services sector also because of their psychophysical limitations or economic knowledge they hold. Equally important are the activities of banks themselves which make such exclusion even more intensified for these kind of persons (Czechowska, 2013). This situation gives rise to carry out profound research on factors determining a lower percentage of the use of financial services by elderly persons, and so, it is the subject of the studies presented in this publication. The level of financial exclusion of persons above 55 years of age in the individual Member States of the European Union The analysis of financial exclusion was carried out on the basis of the results from the Eurobarometer survey of April 2016. The survey was conducted in individual EU countries and indicates the use of the following financial services: current bank account, savings account, mortgage loan, credit card, personal loan, shares or bonds, investment fund, life insurance, car insurance, private health insurance, other insurance products and services. The research will be analyzed separately for respondents aged 55+ and - for the sake of a better comparison - for the whole research sample. The level of financial exclusion will be verified in three areas: the percentage of persons not holding any of the above-mentioned instruments, the percentage of persons holding a maximum one instrument and the percentage of people holding a maximum two instruments. As the data from Figure 1 show, there are noticeable inequalities in the banking penetration ratio for individual EU states, both for the whole research sample and the over 55s. The lowest number of persons aged 55+ ,i.e. those financially excluded in the first area, is in the countries which joined the European Union earlier. The lowest indicators were recorded in Sweden (0%), Finland (1%), Denmark (1%), Belgium (1%), 378 the Netherlands (1%), Luxemburg (3%), Cyprus (4%), France (4%), Spain (5%) and Greece (5%). It should be noted that these are the countries with a generally high banking penetration ratio of the whole populations. It is also important to note the fact that in these countries the percentage of persons over 55 who do not use financial services is similar, and sometimes even higher than compared to the one for the whole population. That means that a problem of financial exclusion of the elderly does not exist there. Figure 1 Percentage of persons claiming not to hold any financial product (the data for the whole researched population and persons 55+) Source: Eurobarometer 2016 The situation is different in the case of countries which joined the European Union after 2000. The level of financial exclusion of the elderly in most of these Member States is well above the EU average which is set at 13%. The highest indicator was characteristic for such countries as Romania (56%), (Bulgaria (39%), Hungary (27%) and Slovakia (21%). Lower rates or similar to the EU average were recorded for Estonia (6%), Slovenia (7%), Latvia (13%) and the Czech Republic (13%). It is worth noting, however, that in both groups the percentage of persons aged 55+ non-users of financial services is far higher than the results for the whole population. Thus, these are those countries where a problem of financial exclusions of the elderly might be diagnosed. An interesting diagnosis was received for Poland, where although the percentage of persons aged 55+ not using financial services is at a high level (18%), it is still slightly lower than the one for the whole population (19%). This fact gives rise to carry out detailed research and to verify two other levels of financial exclusion. 0% 10% 20% 30% 40% 50% 60% ALL + 55 379 Figure 2 Percentage of persons claiming to hold a maximum one financial product (the data for the whole researched population and persons 55+) Source: Eurobarometer 2016 The data in Figure 2 and 3 confirm earlier observations indicating a much lower banking penetration ratio among newly acceded countries to the European Union. It should be pointed out, however, that the analysis of these figures brought to light a problem of financial exclusion of the elderly in a larger group of states. Admittedly, the discrepancies between persons 55+and the whole test sample are much bigger in recently acceded EU states, but this phenomenon has become also apparent in such countries as Spain, Cyprus, Malta, Portugal, Italy, Greece and Austria. The comparative study of the data from respective diagrams indicates that along with the spread of the indicator of financial exclusion, a problem of financial exclusion of the elderly becomes more acute. The best example was provided by the analysis for Poland; at the first level no diagnosis of disparities appeared possible, there was a slight difference at the second level as it was just 3 percentage points, and at the third level -with 7 percentage points- it was quite significant Figure 3 Percentage of persons who claim to hold a maximum of 2 financial products (the data for the whole sample and persons 55+) Source: Eurobarometer 2016 3 Results and Discussion The hypothesis of this paper assumes that elderly people are aware of the barriers they meet in cooperation with financial institutions; these are, e.g. modern technology, professional and incomprehensible language, too wide or too narrow offer, etc. However, 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% UnitedKingdom UE Sweden Spain Slovenia Slovakia Romania RepublicofCyprus Portugal Poland Netherlands Malta Luxembourg Lithuania Latvia Italy Ireland Hungary Greece Germany France Finland Estonia Denmark CzechRepublic Croatia Bulgaria Belgium Austria ALL + 55 0% 20% 40% 60% 80% 100% UnitedKingdom UE Sweden Spain Slovenia Slovakia Romania Republicof… Portugal Poland Netherlands Malta Luxembourg Lithuania Latvia Italy Ireland Hungary Greece Germany France Finland Estonia Denmark CzechRepublic Croatia Bulgaria Belgium Austria ALL + 55 380 these barriers do not constitute statistically significant factors in the high underbanked ratio of this generation. In order to verify this hypothesis there was the research conducted at the area of Lubelskie Voivodeship in Poland in the form of the questionnaire on the group of 388 respondents aged 50 plus, 99 of who was aged between 50 and 55, 148 respondents aged 56 to 65, 86 in the age 66-74 and 55 the over 75s. The research was conducted following the PAPI method and the sample was selected at random. The respondents were interviewed on their usage of financial products, their financial knowledge and financial standing. Other questions referred to barriers they meet in the access to financial products and trust in financial institutions. On the basis of the gathered information the statistical analysis was conducted with the help of IBM SPSS Statistic program. Using the Pearson coefficient it was determined that there is a very low statistically significant correlation between the usage of financial products and the barriers the respondents meet in their cooperation with financial institutions. It excludes the possibility to build a regression model so the simple statistics measures were used. In the majority of analyses presented below the group aged 55 minus was excluded in order to enable comparison of the results gained with those of Eurobarometer. It must be pointed out that the usage of financial products decreases with the age of respondents (Table 2). But only in the case of four products - saving account, investment fund, mortgage credit and credit card - there is a statistically significant low negative correlation (from minus 0,122 to minus 0,192) between the respondents’ age and the usage of this product. The important fact is that in the researched group only 8.5% of respondents declared not holding any financial products which is visibly lower result than calculated by Eurobarometer (see Fig. 1). Although the evidence shows that age is not a significant factor in cooperation between customers and financial institutions, the next stage of this research refers to the barriers met by the surveyed respondents in this cooperation as well as to the assessment of financial knowledge possessed by them in relation to other persons. For this purpose, the respondents were ask to declare their views on the barriers in the six-step scale where 0 meant the barrier never met and 5 – the barrier met very often. In turn, for the knowledge the five-step scale was used where, 1 meant the knowledge far lower and 5 – the knowledge far higher in relation to others. Table 2 The usage of selected financial product by respondents from different age groups Financial product Total Age 56–65 66–74 75+ Private account 70,6% 68,2% 73,3% 72,7% Saving account 29,1% 29,1% 17,4% 10,9% Term deposit 49,3% 49,3% 36,0% 21,8% Brokerage account 2,0% 2,0% 4,7% 1,8% Investment fund 9,5% 9,5% 7,0% 0,0% Individual pension account 6,1% 6,1% 4,7% 3,6% Mortgage credit 7,4% 7,4% 0,0% 1,8% Consumption credit 8,8% 8,8% 5,8% 7,3% Credit card 27,0% 27,0% 8,1% 3,6% Life insurance 49,3% 49,3% 46,5% 40,0% Property insurance 27,0% 27,0% 25,6% 20,0% Source: Korzeniowska and Misterek, 2017 During the analysis it was noted that there is a low statistically significant positive correlation between the declared knowledge and usage of financial instruments in three cases, namely, individual pension account, consumption credit and property insurance. 381 Additionally, a comparison of medians for various age groups was made. The result is to accept H0: which means that age groups in respect of their declared knowledge are quite similar. The average evaluation is between 2,327 and 3,123 and is decreasing over the age (Figure 4). Figure 4 The declared average financial knowledge among the over 55s Source: own calculation Among the barriers, the professional and incomprehensible language of documents as well as too small font appear to be the biggest one for senior citizens (Figure 5). Meeting the requirements given by financial institutions appears to be the smallest problem. Although the rise in average evaluation of barriers corresponds with the rise of respondents age, only two cases showed a statistically significant difference in medians, i.e. a vague offer and lack of technical adjustments in premises with significant 0,002 and 0,003 respectively. Figure 5 The barriers to cooperation with financial institutions (scale from 0 – barrier never met to 5 – barrier met very often) Source: own calculation 4 Conclusions Having analyzed the collected data, one may state that a lower level of using financial products by elderly persons is commonplace. This tendency is also true for Poland. However, based on the data gathered, it would be wrong to conclude that it results exclusively from such technical problems as the inadequacy of banking outlets or small print of bank documents. The analysis of the data collected in Lublin Voivodeship, Poland, 3,123 2,616 2,327 0 0,5 1 1,5 2 2,5 3 3,5 declared knowledge average (aged 75+) average (age 66 to 75) average (age 56 to 65 ) 1,10 2,14 2,18 2,90 2,19 1,50 1,25 1,95 2,71 3,21 1,42 2,36 2,00 2,98 2,13 1,77 1,42 1,64 2,43 3,02 1,87 2,94 2,41 3,41 2,32 1,85 2,28 2,20 2,85 3,46 0 1 2 3 4 failure to meet the requirements vague offer too wide offer too small font long queues in the premises inefficient service technical adjustments of the premises the lack of appropriete offer hidden costs too professional and incomprehensible language average (aged 75+) average (age 66 to 75) average (age 56 to 65 ) 382 showed that the importance of barriers in co-operation with financial institutions grows along with the age of clients. Moreover, there are strong relations between those barriers which confirms the research presented in EU Report (see table 1). 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Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, Finance and Accountancy for Sustainable Development – Sustainable Finance, No. 302/2013, pp. 156-166. 384 The Brief Analysis of the Foreign Trade of the Czech Republic in International Comparison Jaroslav Kovárník1 , Eva Hamplová2 1 University of Hradec Králové Faculty of Informatics and Management, Department of Economics Rokitanského 62, 500 03 Hradec Králové, Czech Republic E-mail: jaroslav.kovarnik@uhk.cz 2 University of Hradec Králové Faculty of Informatics and Management, Department of Economics Rokitanského 62, 500 03 Hradec Králové, Czech Republic E-mail: eva.hamplova@uhk.cz Abstract: The Czech Republic is the country in the Central Europe, it is a member of different international organizations, such as the European Union or Visegrad Four. This country has strong relations with other member states of V4 (namely Poland, Slovakia, and Hungary), where Poland and Slovakia are neighbours of the Czech Republic as well, however, it has the strongest economic relations with other neighbour, namely Germany. As was already mentioned, the Czech Republic, Slovakia, Poland, and Hungary are members of Visegrad Four, and share similar history, geo-political ideas, and economic development. This article deals with the analysis of foreign trade development since 2000, because foreign trade is crucial for sustainable development for almost all countries around the world (it is part of GDP formula in open economy). The aim of this article is to analyse selected aspects of foreign trade in the Czech Republic in comparison with above mentioned countries. The analysis focuses on the development of foreign trade with goods and with services, where have been relatively surprising results (the weakest economy in terms of services has been Germany), but consequently the article analyses relations of the Czech Republic with all other analysed countries. The analysis shows surprising development again, where net balance with these countries has been growing despite the fact that GDP in these countries decreased. Detail description of results is in this article. Keywords: Central Europe, Czech Republic, export, foreign trade, import JEL codes: F43, O11 1 Introduction Foreign trade is very important for every state all around the world because it is, among others, part of macroeconomic Gross Domestic Product (GDP) formula. In other words, it can either improve the GDP level (in case that export is higher than import) or worsen it (in the opposite case). This basic theory has been explained in many books and articles, for example in Andrews, Bernake, & Croushore (2011), or Samuelson & Nordhaus (2010). Nevertheless, foreign trade is important for every country because of other reasons as well. It helps to solve the proportionality problem in almost all countries worldwide, where only few countries have all necessary resources, such as labour force or raw materials, in quantities required for economic development. In other words, for most countries is necessary to ensure some products in the foreign countries for successful development. Foreign trade also has demonstrative effect, where the export program is kind of indicator of the level of economic development and it also helps create an image of developed country. Foreign trade has also other benefits, such as support of peaceful cooperation among partners, reducing of the risk of conflict, growth of education and others. 385 From above mentioned text is clear that foreign trade is very important for every country. The importance of foreign trade has been evaluated in other articles by other authors, for example Baier, Bergrstrand & Feng (2014), Cieslik, Bieganska & SrodaMurawska (2016), Do, Levchenko & Raddatz (2016), Fracasso & Marzetti (2015), Giordano & Zollino (2016), Gladkov (2016), or Vannoorenberghe (2014). Authors themselves already analysed the topic of foreign trade several times, for example in Kovárník & Hamplová (2016). The Czech Republic is a member state of Visegrad Four, and all other member states (namely Poland, Slovakia, and Hungary) are very important business partners for the Czech Republic. Situation in Visegrad Four countries has been also analysed by other authors, for example by Zdražil & Kraftová (2012). However, the most important business partner for the Czech Republic is Germany. The aim of this article is to analyse the selected aspects of foreign trade in the Czech Republic in international comparison with above mentioned countries, namely with Germany, Poland, Hungary, and Slovakia. Firstly, this article analyse the development of GDP in selected countries, because of the importance of foreign trade for this indicator. The development of foreign trade in terms of goods and services in these countries is next topic for analysis. Consequently, the exports of the Czech Republic into all other countries and net balances with these countries are analysed as well. The aim is to verify the hypothesis whether the development of both GDP and foreign trade of these countries is similar or not, and also to verify whether there exists relation between the export into particular country and GDP in this country. 2 Methodology and Data Covered period of time is 2000 – 2016, with one exception of Poland, where there were not available all data in the year 2016, therefore in case of Poland are some data analysed only in 2000 – 2015. The data were obtained in general available database Eurostat and calculated by authors (Eurostat a, 2017, Eurostat b, 2017). Methods of comparison and comparative analysis have been used, moreover, for the verification of above mentioned hypothesis has been used the test of parallelism of regression lines. There exist a lot of different methods for evaluation of relations between two sets of variables, such as correlation, regression, etc., however, because of the limited space of this article has been used only above mentioned test. The authors plan to use other methods in next analysis. The above mentioned test operates with linear regression functions, where it takes parameters b and a of these lines and uses following test criterion (Kubanová, 2003) ( ) ( ) ( ) ( ) ( ) 2 22 2 11 1 2 2 1 2 1 2121 22* 11 4* 21 een i i n i i snsn xxxx nnbb T −+− − + − −+− = ∑∑ == (1) where ( )       −− − = ∑∑∑ === n i ii n i i n i ie yxbyay n s 1 )1( 1 )1( 1 2)1(2 1 2 1 (2) Null hypothesis is about parallelism of regression lines, where alternative hypothesis is about divergence. Critical area is subset of the values of test criterion, where W = {T: |T| ˃ tα, n1 + n2 - 4} (3) 386 Critical value of Student’s probability distribution on 0.05 level of significance and with 30 degrees of volatility is 2.0423 (2.0484 in case of Poland, where are no available data for 2016 and therefore there is only 28 degrees of volatility). If the test criterion in absolute value is within the range or accepted values, the null hypothesis about the parallelism is valid, and vice versa. 3 Results and Discussion The Analysis of GDP Development Based on the fact that the Czech Republic has currently around 10.5 billion of inhabitants, Hungary around 9.8 billion, Slovakia around 5.4 billion, Poland more almost 38 billion, and Germany more than 82 billion, it is quite obvious that the level of GDP in billions of euro is the highest in Germany, second highest in Poland, next in the Czech Republic, in Hungary, and Slovakia is on last position. However, it is better to use the level of GDP per capita for comparison. According to this, the highest level still has Germany, but the Czech Republic is on the second position, Slovakia is the third, Hungary on the fourth place, and Poland is the last. With respect to this information is good to add one interesting fact. Even if the development in the number of inhabitants in each country have not been steady, this number grew in the Czech Republic, Germany, and Slovakia (comparison of the number of inhabitants in the years 2000 and 2016), while in Hungary and Poland it dropped. Deep analysis of GDP development shows that in all analysed countries was significant decrease in this indicator in the year 2009 (both in absolute value and in per capita) as a result of global economic crisis. However, the after crisis development is different. Germany has been growing since 2009 and it managed exceed pre-crisis year already in 2010. The Czech Republic was growing between 2009 – 2011, it was decreasing between 2011 – 2014, and it has been growing again since 2014. Moreover, it managed to exceed pre-crisis year one year later than Germany. Poland was on the last position in 2008, it has been growing since 2009 to 2015, it exceeded pre-crisis year in 2011, but it exceeded Hungary in 2012. However, it has dropped in 2016, where Hungary has exceeded this country again. Hungary has been growing since 2009 with one exception in 2012. It was on the fourth position before crisis and it is on the fourth position again in 2016. Slovakia has been growing, as well as Germany, since 2009, and it also managed to exceed pre-crisis year within one year already in 2010. Following Tab. 1 shows the growth rate in GDP per capita, where this calculation has been made firstly from compared year 2000 to the year 2016, while the second growth rate describes only after-crisis development (2009 – 2016). It can be calculated also for example year-to-year growth rate, nevertheless, the authors have decided to use growth rate for these two periods with the aim to compare the development in the whole period and after economic crisis. Table 1 Growth rate in GDP per capita Country 2000/2016 growth rate 2009/2016 growth rate Czech Republic 154.91% 16.16% Germany 47.99% 27.06% Hungary 128.14% 22.26% Poland 129.59% 34.50% Slovakia 260.44% 25.43% Source: own calculations based on Eurostat a (2017), Eurostat b (2017) Relatively weak position of Germany in overall growth rate described in Tab. 1 is of course result of significantly stronger position of Germany (in other words, of significantly higher amount of GDP per capita in 2000, where Germany had more than 25,750 euro, 387 while second Czech Republic had almost 6,500 euro). It is also reason, why Slovakia has so extremely huge growth rate (it had the lowest GDP per capita in 2000), nevertheless, the fact is that GDP per capita in Slovakia has been growing really significantly. More interesting is the analysis of after-crisis development. The highest growth rate in Poland is again the result of the lowest amount of GDP per capita in Poland in 2009. However, it is obvious that the Czech Republic has the lowest level of GDP per capita growth rate, while Germany has the second highest. It can be explained in that way that in spite of relatively strong position of the Czech Republic before crisis, GDP per capita in this country after crisis has been dealing with problems and the gap between this country and other V4 members has been closing. However, Germany is so strong economy that not only it managed to recover really quickly, but it also has had significant growth rate after crisis and the gap between Germany and V4 countries is getting wider. Foreign Trade Development – Trade with Goods The analysis of foreign trade development shows relatively different results than the GDP development analysis. The development of net balance in terms of goods recalculated per capita is described in following Fig. 1. First interesting fact is that in the first analysed year (2000) all countries except of Germany had negative trade balance, which means they had higher imports than exports. Even if current trade balance (in 2016, in 2015 in Poland) is positive in all analysed countries, the development has been quite irregular. The position of Germany is again, as well as in case of GDP, significantly stronger than in all other analysed countries. Germany had surplus more than 64,000 billion in 2000, and it has surplus more than 276,000 billion in 2016. The second highest surplus in 2016 has the Czech Republic, where this surplus is little bit more than 9,000 billion. For mutual comparison has been surpluses recalculated again per capita, but still has Germany significantly stronger position. It is quite obvious that despite its irregular development, Germany has had the highest net balance from all analysed countries. However, the mutual comparison of V4 countries shows interesting results. The Czech Republic had the worst position in 2000 (the highest deficit per capita, while in absolute amount had Poland worse result), but it has been growing (with few exceptions) and currently the Czech Republic has the second highest surplus (after Germany). Exactly opposite development has been in Slovakia. This country had the best result from V4 countries in 2000 (both in absolute amount and per capita), but it has the worst result in 2016 in absolute amount and the second worst in terms of net balance per capita (in comparison with Poland in 2015). Really interesting fact is that in 2009, during the economic crisis, was net balance decreasing in Germany, while it was increasing in all other analysed countries. 388 Figure 1 Foreign Trade with Goods (Euro per Capita) Source: own calculations based on Eurostat a (2017), Eurostat b (2017) However, the export / GDP ratio shows that Slovakia has currently the highest value of this indicator. This indicator measures the openness of particular economy and it is relatively surprising that even if Slovakia has relatively bad result in terms of net balance with goods, it can be considered as really open economy, where this share is more than 84% in 2016. Another open economies are Hungary (more than 73%) and the Czech Republic (almost 68%), where Poland and Germany are relatively close economies (Poland had this share little bit than 40% in 2015, Germany has little bit more than 38% in 2016). This result is relatively surprising, because Germany is really strong economy with huge GDP growth, but it is relatively close economy. It can be explained in that way that there are other (domestic) parts of GDP formula which have been developing in Germany, while the Czech Republic, Slovakia, and Hungary are dependent on foreign trade. In case of Poland, this country is also relatively close, but because of low value of GDP, other parts of GDP formula are not as great as in Germany in this country. Foreign Trade Development – Trade with Services The analysis of foreign trade with services shows completely different results. Surprisingly, the only country which has been in deficit for all analysed period is Germany. All other V4 countries has been in surplus, with only few exceptions in case of Slovakia. However, the development is quite irregular in all countries, with several increases and decreases. Nevertheless, the deficit in Germany in 2016 is lower than in 200, but on the other hand, surpluses in the Czech Republic and in Slovakia decreased, while surpluses in Poland and in Hungary increased. The development (again recalculated per capita) is described in Fig. 2. Interesting fact is that in 2009, where GDP in all analysed countries dropped, net balance with services increased in Germany (it dropped in case of goods), and dropped in the Czech Republic and Slovakia (where trade balances in terms of goods increased). The weak position of services in Germany is supported also by export / GDP analysis, where this ratio has been growing slowly, but it is still less than 8%. The highest share is in Hungary, where this ratio is almost 19%. The Czech Republic has this share currently around 12.4%, and the other countries less than 10%. However, the development in all analysed countries has been more or less irregular. -1000,00 -500,00 0,00 500,00 1000,00 1500,00 2000,00 2500,00 3000,00 3500,00 4000,00 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Czech Republic Germany Hungary Poland Slovakia 389 Figure 2 Foreign Trade with Services (Euro per Capita) Source: own calculations based on Eurostat a (2017), Eurostat b (2017) The Relation between GDP Development and Foreign Trade Development As was mentioned before, one of the aims of this article is to verify the hypothesis, whether there exists relation between the GDP development and foreign trade development. According to the theory is net balance of foreign trade part of GDP formula, therefore can be expected that increase of net balance of foreign trade can lead into increase of GDP and vice versa. One of the possible way how can be verified this connection is thanks to the test of parallelism of regression lines. The authors are aware that other methods can be used, but these methods will be used in other analysis. Null hypothesis in this case reveal parallelism of regression lines, where alternative hypothesis means no parallelism. Results of this test (calculated based on (1), (2), and (3)) are presented in the following Tab. 2. Table 2 Test of Parallelism of Regression Lines Country Result Test Criterion Null Hypothesis Czech Republic 8.737632 2.0423 Denied Germany 15.63847 2.0423 Denied Hungary 5.956511 2.0423 Denied Poland 13.1138 2.0484 Denied Slovakia 14.69208 2.0423 Denied Source: own calculations based on Eurostat a (2017), Eurostat b (2017) The results of test of parallelism show unexpected conclusion. In all analysed countries are regression lines for GDP and net balance of foreign trade divergent. There exist several possible explanations of this development. First of all, other (domestic) parts of GDP are probably significantly more important in all analysed countries, and therefore development in foreign trade has no influence on GDP. It is also possible that there exists some delay. In other words, the increase or decrease in net balance can have some influence on GDP, but this influence occurs not in the same year, but later. It can be verified for example thanks to Granger’s test, however, it is not part of this article. There can be, of course, other possible explanation of this unexpected development. Nevertheless, in this article is possible to make a partial conclusion that there exists no relation between GDP development and foreign trade development in all analysed countries. -1 000,0 - 800,0 - 600,0 - 400,0 - 200,0 0,0 200,0 400,0 600,0 800,0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Czech Republic Germany Hungary Poland Slovakia 390 The Analysis of Foreign Trade of the Czech Republic with Analysed Countries As was already mentioned, all analysed countries are very important trade partners for the Czech Republic. The most important one is the Germany, where the share of turnover with this country on total turnover of the Czech Republic has been around 30% for the whole analysed period (32.54% in 2016). This share has been growing (with few exceptions) in case of Poland, where it is almost 8%, it has been relatively stable in case of Slovakia (little bit more than 7.6%), and almost 3% with Hungary. Fig. 3 describes the trade balance of the Czech Republic with analysed countries. Figure 3 Foreign Trade Balance of the Czech Republic with Analysed Countries (Euro) Source: own calculations based on Eurostat a (2017), Eurostat b (2017) It is obvious that the trade balance with Germany has been growing since 2006 (with the only exception in 2013) in really fast pace, while the trade balance with Poland has been decreasing with several small exceptions since 2008. That means that Poland is important importer for the Czech economy, while Germany is really important exporting destination. What is really interesting is the fact that in 2009, where GDP in all analysed countries dropped because of the economic crisis, trade balance with Germany significantly increased, while it decreased in case of Slovakia and Poland. Deep analysis shows that both exports and imports of the Czech Republic in this year decreased, but in case of Germany the trade balance increased. Another interesting point is the connection between trade balance with particular country and total trade balance both in the Czech Republic and in that country. Germany was the only country where net balance decreased in 2009 (as well as GDP), while net balance increased in all other analysed countries (where GDP decreased). However, the net balance with Germany increased in 2009. That means that despite the worse situation both in Germany and in the Czech Republic was the decrease of exports of the Czech Republic into this country lower than the decrease of imports from this country, therefore the net balance increased. Moreover, in the year 2010 was the GDP increasing again in all countries, the net balance with Germany as well, but the total net balance of the Czech Republic decreased. That means that in spite of the fact that the trade with Germany was growing in 2010, the trade with the rest of the world decreased and overall trade balance of the Czech Republic decreased in this year (where GDP grew in this year in this country). The authors plan to analyse the connection between GDP development and export of the Czech Republic with statistic tools in the next articles. -6 000 000 000 -4 000 000 000 -2 000 000 000 0 2 000 000 000 4 000 000 000 6 000 000 000 8 000 000 000 10 000 000 000 Jan.-Dec.2000 Jan.-Dec.2001 Jan.-Dec.2002 Jan.-Dec.2003 Jan.-Dec.2004 Jan.-Dec.2005 Jan.-Dec.2006 Jan.-Dec.2007 Jan.-Dec.2008 Jan.-Dec.2009 Jan.-Dec.2010 Jan.-Dec.2011 Jan.-Dec.2012 Jan.-Dec.2013 Jan.-Dec.2014 Jan.-Dec.2015 Jan.-Dec.2016 Germany Hungary Poland Slovakia 391 4 Conclusions The aim of this article was to analyse the GDP development and foreign trade development in the Czech Republic and in other selected traditional business partners of this country, namely in Germany, Hungary, Slovakia, and Poland. The analysis shows that all countries have been growing in terms of GDP per capita, however, after the world economic crisis (since 2009) has been the Czech Republic dealing with serious trouble, the development has been irregular, and the growth rate is the lowest one, while Poland and Slovakia have been growing really fast. It is important to add that the position of Poland is probably the result of the weak position of this country in 2009. However, the Czech Republic still has some lead from previous years before other V4 countries, but this lead is weaker every year because of the slow growth rate, and on the other hand, the gap between V4 and Germany is getting wider, because of the fast development of Germany after crisis (second highest growth rate). The statistical analysis of relation between GDP development and foreign trade development shows relatively unexpected result. Net balance of foreign trade is important part of GDP formula of every economy, therefore it can be assumed that the increase of net balance can lead into increase of GDP, and vice versa. However, this hypothesis has been denied in all analysed countries, the regression lines created from GDP and net balance are not parallel, but divergent. It can be explained in such way that the other parts of GDP formula are more important in every analysed country. Moreover, there can be some delay (the increase of net balance can have influence on GDP in next years, not in the same one), however, this aspect was not analysed in this article. In terms of goods, only Germany had surplus in 2000, while currently all analysed countries have positive net balance. The development has been irregular again, but interesting point is that in Germany in the after-crisis year 2009 the net balance decreased (as well as GDP), but in other V4 countries was increase in net balance. However, in 2010 was net balance growing in Germany again (as well as GDP), where the net balance dropped in V4 countries (in spite of the fact that GDP grew). In terms of services, it is possible to see very surprising results. The weakest country has been Germany, where the net balance has been in deficit for the whole analysed period, it was lower in 2016 than in 2000, but still in negative. V4 countries have been in surplus (with few exceptions of Slovakia), where only Hungary and Poland have higher surplus in 2016 compared with 2000. The analysis of export of the Czech Republic into other countries shows the significant position of Germany, where the net balance with this country grew even in the years when GDP (both in Germany and in the Czech Republic) dropped. Interesting point is that net balance with Poland has been decreasing in last few years, which means that this country is important importer for the Czech Republic, while Germany is important exporting destination. Acknowledgments This study is supported by internal research project No. 2103 “Investment evaluation within concept Industry 4.0”, and by project IGA 2017 No. 1904 “Support of pedagogical work, technical equipment, and communication with experts”, both at Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic. It was prepared in cooperation with students of Ph.D. study at Department of Economics, namely with Ing. Lucie Černá and Ing. Martin Král. References Andrews, A. B., Bernake, B., Croushore, D. D., (2011). Macroeconomics, 6th ed. Boston: Addison-Wesley. 392 Baier, S. L., Bergrstrand, J. H., Feng, M., (2014). Economic integration agreements and the margins of international trade. Journal of International Economics, vol. 93(2), pp. 339-350. Cieslik, E., Bieganska, J., Sroda-Murawska, S. (2016). The Intensification of Foreign Trade in Post-Socialist Countries and Their Role in Global Value Chain. Acta Oeconomica, vol. 66(3), pp. 465-487. Do, Q. T., Levchenko, A. A., Raddatz, C. (2016). Comparative advantage, international trade, and fertility. Journal of Development Economics, vol. 119(1), pp. 148-166. Eurostat a (2017). International Trade of EU, the Euro Area and the Member States by SITC Product Group. Retrieved from: http://ec.europa.eu/eurostat/web/international- trade/data/database. Eurostat b (2017). GDP and Main Components. Retrieved from: http://ec.europa.eu/eurostat/web/national-accounts/data/database. Fracasso, A., Marzetti, G. V. (2015). International trade and R&D spill overs. Journal of International Economics, vol. 96(1), pp. 138-149. Giordano, C., Zollino, F. (2016). Shedding Light on Price- and Non-price-competitiveness Determinants of Foreign Trade in the Four Largest Euro-area Countries. Review of International Economics, vol. 24(3), pp. 604-634. Gladkov, I. S. (2016). European Union in Modern International Trade. Contemporary Europe-Sovremennaya Evropa, vol. 1(1), pp. 85-94. Kovárník, J., Hamplová, E. (2016). The Analysis of Foreign Trade Development in the Countries of Visegrad Four. In Klímová,V., Žítek, V. (eds.) 19th International Colloquium on Regional Sciences. Brno: Masaryk University, pp. 240-246. Kubanová, J. (2003). Statistické metody pro ekonomickou a technickou praxi, 1st ed. Pardubice: University of Pardubice. Samuelson, P. A., Nordhaus, W. D. (2010). Economics, 19th ed. Boston: McGraw- Hill/Irwin. Vannoorenberghe, G. (2014). International trade, risk taking and welfare. Journal of International Economics, vol. 92(2), pp. 363-374. Zdražil, P., Kraftová, I. (2012). Konvergují regiony zemí V4? In Klímová,V., Žítek, V. (eds.) 15th International Colloquium on Regional Sciences. Brno: Masaryk University, pp. 48-58. 393 Determinants for the development of supplementary pension schemes Patrycja Kowalczyk-Rólczyńska1 , Tomasz Rólczyński2 1 Wroclaw University of Economics Faculty of Management, Information Systems and Finance, Department of Insurance 118-120 Komandorska Street, 53-345 Wrocław, Poland E-mail: patrycja.kowalczyk@ue.wroc.pl 2 WSB University in Wroclaw Faculty of Finance and Accounting, Department of Economics 29-31 Fabryczna Street, 53-609 Wrocław, Poland E-mail: tomasz.rolczynski@wsb.wroclaw.pl Abstract: The accumulation of voluntary pension savings by households is a vital element of not only the household’s finances, but also of the social security system. Being informed of the factors which are conducive to the accumulation of capital for oldage security is not only crucial for entities offering various pension insurance contracts, but also for the state, which often supports the development of these contracts. The aim of the article is to identify and evaluate the impact of the factors determining the development of the additional forms of pension provision. In the study, correlation analysis and factor analysis were employed to assess the influence of the selected factors (economic and demographic) on the development of some of the pension insurance contracts. The study’s findings show clearly that the increasing life expectancy and the level of the household’s income are important determinants in terms of the development of voluntary pension insurance contracts. Keywords: pension schemes, pension insurance, correlation analysis, factor analysis JEL codes: H550, J320, C1, C3 1 Introduction Pension schemes are one of the elements of social security. In most of the countries of the world, mandatory and voluntary schemes coexist. The mandatory part of the pension system (the so-called base part) is directly related to the state’s obligation to provide apposite material support for persons past the age of retirement. The base part is typically complemented by the supplementary pension schemes, involving individual or collective accumulation of resources (both financial and material) for the duration of gainful employment, to be later used as a source of supplementary income at retirement. The supplementary pension schemes as generators of additional pension benefits are a crucial element of the social security coverage (Pieńkowska–Kamieniecka, Walczak, 2016). At present, pension benefits from the base part of the pension system – in a decisive majority of pension systems in use globally – are not sufficient to provide their participants with pension benefits whose value would be equal to the level of earnings received prior to retirement. This negative trend can be attested by the net pension replacement rates. In 2014 the lowest net pension replacement rates (below the 50% threshold) were recorded in Ireland and the United Kingdom. Conversely, the highest net pension replacement rates (above the 80% threshold) were found in: Austria, Hungary, Luxemburg, Portugal, Spain, and Turkey (OECD, 2016). It must be noted that, as a result of the continued decline of demographic trends in many European countries, the net pension replacement rates will surely decrease in the years to come. The most important determinants of the present demographic challenge are the increased life expectancy rates and low fertility rates (OECD, 2015; European Commission, 2014). Another important factor is the present structure of migration patterns (mainly emigration) in the working-age population, particularly in those countries which place at 394 the bottom end of the average income spectrum. Those factors will surely contribute to the continued ageing of their respective populations. The expected development of the negative demographic trends seems to elevate the role of additional pension schemes as sources of substantive addition to the existing pension benefits from the base part of the pension system. The objective of this paper is to identify factors that determine the development of supplementary pension schemes and to evaluate their impact on the process under study. The evaluation of the impact of selected factors (both economic and demographic) upon the development of pension insurance contracts was conducted based on the use of correlation analysis and factor analysis. 2 Supplementary pension schemes The demographic processes currently observed in the European economies have raised the need for reforming the existing pension systems. The changes introduced in most of the European countries have followed the pillar-based approach, with base (mandatory) pillar of the pension system supported by supplementary (voluntary) forms of pension plans (cf. Lannoo et al., 2014). In many European countries pension systems have been divided into three pillars: pillar one: public social security system, pillar two: mandatory/voluntary occupational schemes and pillar three: voluntary private schemes (cf. Eichhorst et al., 2011; Rocher and Stierle, 2015). Moreover, Holzmann and Hinz (2005) indicate a fourth pillar of the pension system such as family support, access to health care and housing. It is worth mentioning that not only European countries reformed their pension systems. Asian countries did so too. (see more: Hur, 2010) The mandatory part of the pension system should, in theory, provide adequate support to ensure the satisfaction of basic needs and living standards for persons past the age of retirement. The supplementary part, on the other hand, serves as a source of additional benefits to complement the pension payouts received from the base pillar (Casey and Dostal, 2013). The lower value of pension benefits from the base part of pension system, the higher the demand for supplementary forms of pension security, as they offer the prospect of improved standard of living past retirement. Therefore, it is essential for the supplementary forms of pension security to be construed in such a way as to ensure their utility – in the long-time perspective – as important sources of household economy (Garman et al., 1985). For supplementary pension schemes to yield adequate returns, the system must be based on systematic saving schemes, continued preferably for the whole duration of gainful employment. The supplementary part of the pension system is typically construed around capitalbased solutions. Schemes of this type involve accumulation of additional voluntary pension contributions by a designated financial institution, to be invested on the financial market or on the alternative investment markets (including real-estate investment). Supplementary contributions can be held individually (e.g. in the form of an individual life insurance policy linked to capital investment funds) or collectively (for example, in the form of a collective pension scheme offered to all employees). In the case of occupational retirement provision the contribution is usually paid by the employer – as a defined absolute amount or as a (defined) percentage of the employee's remuneration or other relevant metrics such as the company's profit (Brauninger, 2014). Individual (or private) pension plans should be designed in such a way as to obligate the participants to contribute on a regular basis. Personal savings assigned to support the basic pension plan can be managed by the participants individually, or by a dedicated and authorised financial institution of choice (insurance companies, investment funds). One example is Unit-linked insurance where the net premiums are invested based on the choice of the policyholder. This implies that throughout the entire period of savings the policyholder bears investment risk. Thus, in times of financial crisis the investment 395 risk may materialize with policyholders experiencing financial losses (see more: Ciumas et al., 2012). High potential profitability characterizes this type of insurance because the profit is based on capital investments (see more: Schrager, Pelsser, 2004). Such products like unit-linked insurance are among a group of products offered under a defined contribution pension plan, in which future pension benefits are not set out in advance, being dependent on a variety of factors. These include, in particular, the amount of the premium paid, risk aversion of the insured, lifetime expectancy, portfolio composition (see more: Konicz, Mulvey, 2015). It should be pointed out that making wrong investment decisions by the policyholders (in particular, among people whose education with respect to finances is rather poor) may result in the lack of sufficient retirement income (Blanc, 2011). Two important aspects should be noted that distinguish voluntary private pension savings from general savings, namely (Lannoo et al., 2014): first - governments provide financial incentives to promote pension savings, second - withdrawal from voluntary pension savings is only possible after a certain age is reached, otherwise a penalty is charged. Within the framework of broadly defined pension security, a natural person may also choose to invest in various types of assets to serve as sources of supplementary income past retirement. For example, they may invest in the purchase of a residential-type property at some point of their gainful employment period, to be later used as the basis for a reverse mortgage (or home equity conversion mortgage) agreement with a financial institution of choice. The reverse mortgage loan offers the conversion of capital held in the property into liquid financial assets. Solutions based on the above approach can be found not only in many European countries, but also in Australia (for more, see: Bridge et al., 2010) and the United States (for more, see: Bishop and Shan, 2008). 3 Identification of factors of impact upon the development of supplementary forms of pension scheme Many factors influence the development of supplementary forms of pension scheme, with the most notable category being demographic factors. The steady increase in life expectancy (if not accompanied by a significant adjustment of the existing retirement thresholds) will have the effect of gradually diminishing the income from the base pillar of the pension system. The concurrent effects of waning fertility rates and the increase in the number of households with no child or one child will remove or largely reduce the potential for future financial support from the descendant generation. On the other hand, the growing share of old-age persons across the general population will have the effect of stimulating the competition on the market of individual pension plans offered by financial institutions (due to the relative shrinkage of the target group). Next group of factors is connected with description of households; this may include: • the number of persons per household – the more children (or other persons under custody), the greater share of the budget is allocated to cover the running cost of consumption, • marital status (married couple or single), • the region of residence (according to Stinglhamber et al., “the region of residence has a significant influence on participation in the third pillar systems”), • household income and expenditures which affect the level of household wealth, • savings and assets held by the household, including the immovable property intended for the residential needs of the household members, • the household capacity to make independent financial decisions and the prevailing risk attitude (see more: Blanc, 2011). Households characterised by strong aversion to risk will make distinctly different financial decisions (also with respect to savings and investment) than those with a more lenient risk attitude. The development of supplementary forms of pension scheme is also strongly influenced by the household’s knowledge of financial matters. Financial education of households has 396 the effect of improving the quality of their financial decisions (Clark et al., 2006). Therefore, it seems in the best interest of the state to support the broad dissemination of practical financial knowledge among its citizens. Another important factor to impact the development of supplementary pension schemes is the maturity of the local financial market, particularly the availability of financial products related to pension-type savings. The process is also strongly influenced by the potential to invest in various types of material assets other than the liquid financial assets (such as investment in gold, silver, wine, whisky, art, etc.), either directly or indirectly, e.g. in the form of redeemable securities (units) issued by investment funds as part of investment portfolios based on specific types of material assets (KowalczykRólczyńska and Rólczyński, 2016). The provision of supplementary pension schemes is also largely determined by the involvement of state structures and companies in the formation of flexible and transparent products designed to serve the purpose of additional pension security. In this context, it is also important for the state to provide tax incentives (Lannoo et al., 2014), both to the institutions that offer supplementary pension schemes and to individual participants of such schemes. The growing state involvement in this area will have the effect of augmenting the structure of the pension system and inducing social motivation to participate in the supplementary pension schemes (Whitehouse, 1999). The last category of major determinants is represented by economic factors, including, among other things: the level and the pace of economic development, the labour market situation and trends, the structure of state budget revenues, the interest rates and inflation. 4 Methodology and Data The study was designed to examine the impact of the selected determinants upon the development of additional forms of pension scheme based on their measurable influence on the total value of assets held in the form of additional pension schemes. For the purpose of this study, the examination was conducted of assets held in the form of pension insurance contracts4 . The study involved the examination of the following determinants deemed important for the development of this particular form of pension security: household disposable income, unemployment, level of economic development, household expenditure, household savings, and selected demographic factors5 . Variables associated with the above factors are presented in Table 1. Table 1 Variables representing selected factors of impact upon the development of supplementary forms of pension security. Symbol of variable Variable Y Pension insurance contracts' assets in US dollars X1 Average annual wages in US dollars X2 Unemployment rate X3 GDP, in constant prices, in US dollars X4 Household saving rate (in %) X5 Household final consumption expenditure (as % of GDP) X6 Household disposable income per capita in US dollars 4 An insurance contract that specifies pension plan contributions to an insurance undertaking in exchange for which the pension plan benefits will be paid when the members reach a specified retirement age or on earlier exit of members from the plan (based on: https://stats.oecd.org/glossary/detail.asp?ID=6290). 5 Apart from purely substantive reasons, the selection of these factors was strongly influenced by the availability of relevant data for the period under study. 397 X7 Old age dependency ratio (in %) X8 Fertility rate X9 Life expectancy at birth Source: own study (elaboration) The study was conducted based on data for the period of 2001-2014 (with the exception of variables X8 and X9, which were only available for the period of 2001-2013), as published in the available reports of OECD and the Eurostat, for the five European countries: Denmark, Finland, Italy, Poland, and Spain. An objective of the study was to verify whether the same factors significantly affect the value of assets accumulated in the same form of pension security in the different countries. Of the selected countries, the highest average household income was registered in Denmark, and the lowest – in Poland. Furthermore, two of the countries under examination placed above the OECD annual wage average (namely: Denmark and Finland); with Italy, Poland, and Spain scoring below the threshold. Selected statistical information for the countries under examination is presented in Table 2. Table 2 Selected statistical information for selected European countries in 2014 Denmark Finland Italy Poland Spain OECD Average worker earnings (in US dollars) 64,654 51,965 36,891 11,978 31,683 40,007 Public pension spending (% of GDP) 6.2 10.3 15.8 10.8 10.5 7.9 Life expectancy at birth 79.3 80.5 82.3 76.3 82.0 80 Population over age 65 (% of population) 18.6 20.4 21.7 15.3 18.3 16.2 Source: own study based on OECD (2015) The first phase of the research involved calculations of coefficients of correlation between pension insurance contracts’ assets and the variables presented in Table 1. This was followed by tests for the significance of correlation coefficients, with the purpose of determining the statistical significance of the findings. The next step of the study involved the application of the factor analysis with the aim to reduce the number of variables describing the phenomenon. The analysis also allows one to explore and specify the relationships between a large number of variables (Cattell, 1965). However, the factors isolated on the basis of this analysis have a different content-related interpretation while retaining most of the information contained in the original variables. In the literature, there is a two-fold approach to the factor analysis in that the factor analysis and the analysis of principal components are distinguished as two separate methods (Górniak, 1998), or they are regarded as alternatives of the same method (Walesiak and Bąk, 1977). The factor analysis was used in the study with the factors being singled out by the application of the principal component method. The assumptions of the method and the procedure are described in detail in the above publications. In the case of the analysis carried out in this paper, an attempt was made to distinguish the factors from the variables indicated above for the countries in question. This makes it possible to group variables in various objects, that is to verify whether the same variables in different countries can be considered to represent comparable groups of factors. 398 5 Results and Discussion The result of the first phase of the research are presented in Table 3. Table 3 Values calculated for coefficients of correlation between variables X1- X9 and the pension insurance contracts' assets (Y), for selected European countries. Country Symbol of variable X1 X2 X3 X4 X5 X6 X7 X8 X9 Denmark 0.932 0.640 0.667 -0.085 0.898 0.977 0.954 0.113 0.980 Finland 0.908 -0.689 0.880 -0.284 0.649 0.890 0.675 0.783 0.833 Italy 0.019 0.721 -0.512 -0.880 0.849 0.797 0.993 0.797 0.926 Poland 0.843 -0.684 0.897 -0.717 -0.762 0.921 0.919 0.391 0.882 Spain 0.545 0.609 0.767 -0.114 -0.502 0.916 0.585 0.662 0.842 The underlined values of correlation coefficient are significant at p < 0.05 Source: own study based on data from OECD and Eurostat, developed using the Statistica software. The calculation results suggest that, for all the countries under study, the most significant impact upon the total level of assets held under pension insurance contracts can be observed with respect to the following determinants: household disposable income per capita, unemployment rate, old age dependency ratio, and life expectancy at birth. The remaining variables – depending on the country – were found to be either significant or non-significant. It should be noted that: • fertility rate is found to significantly affect the value of pension insurance contracts' assets in Finland, Italy and Spain, • household final consumption expenditure is significantly correlated with the level of pension insurance contracts' assets in all the countries under examination, with the exception of Spain, • household saving rate is significantly correlated with the level of pension insurance contracts' assets only in Poland and Italy, • both the GDP and the average annual wages were found to be significantly correlated with levels of pension insurance contracts' assets in all the countries under examination, with the exception of Italy; furthermore, negative correlation between the values of pension insurance contracts' assets vice versa GDP was only observed for Italy. The results of the factor analysis are demonstrates in table 4. In particular, it was illustrated which variables produce “new” factors in the individual countries. The basis for the calculation were X1–X9 variables described in Table 1. After conducting the calculations based on the factor analysis, the variables were grouped into two factors for Denmark, Italy and Poland, while for Finland and Spain into three factors. The number of factors was separated according to Kaiser’s criterion, that is, only those factors were retained for which eigenvalues exceeded 1[Kaiser, 1960]. The factor loadings indicated in Table 4 were greater than 0,7. One can observe that variable X6 (household disposable income per capita in US dollars) and variable X9 (life expectancy at birth) is included in the first factor in all the countries under study, while variables X1 (average annual wages in US dollars), X5 (household final consumption expenditure (as % of GDP) and X7 (old age dependency ratio (in %) in the four countries (Denmark, Finland, Italy, Poland). One can thus suggest that the first factor is the factor associated with revenue (income) and demographics and this factor is present in all the analyzed countries. Variable X2 (unemployment rate) is part of the second factor for three countries (Finland, Italy, Spain); variable X3 (GDP in constant prices, in US dollars) is included in the second factor for Denmark and Italy, whereas variable X8 (fertility rate) is part of the second factor for Denmark and Poland. 399 Taking this into consideration as well as the other information contained in Table 4, we can define the second factor differently, depending on the country. For Finland and Italy, the second factor was defined as economic factor; in the case of Denmark and Spain, this factor was named an economic and demographic factor, while for Poland, the second factor is a demographic factor. The third factor was distinguished only for Finland and Spain and it is made up of only one variable X4 (household saving rate (in%)), thus for those countries the third factor is a factor related to household savings. In addition to that, it should be noted that in line with the theoretical approach to the factor analysis every next factor will account for an increasingly smaller part of the total variance. Therefore, the first factor explains 53% of the variance for Spain and up to 78% for Poland. The second factor explains 13% for Poland and up to 29,9% for Italy. The study demonstrated that the variables, being part of the individual factors, can be regarded as similar in the analyzed countries. An attempt to extend the study to other countries is limited by access to data. Table 4 The results of the factor analysis for the countries under study Factor 1 Factor 2 Factor 3 DEN FIN ITA POL ESP DEN FIN ITA POL ESP D E N FIN I T A P O L ESP X1 X X X X X X2 X X X X X X3 X X X X X X4 X X X X X X5 X X X X X X6 X X X X X X7 X X X X X X8 X X X X X9 X X X X X Explained variation 5.49 4904 5.77 6089 5.59 5579 7.03 6544 4.79 3114 2.12 5558 1.91 7535 2.69 5909 1.17 3604 2.53 9318 1.09 4698 1.35 3514 Share 0.61 0545 0.64 1788 0.62 1731 0.78 1838 0.53 2568 0.23 6173 0.21 3059 0.29 9545 0.13 0400 0.28 2146 0.12 1633 0.15 0390 Source: own study, based on the results from Statistica software 6 Conclusions The above findings suggest that the majority of the variables under study do have a significant impact upon the total value of assets held under a supplementary pension scheme, in this case: pension insurance contracts. However, depending on the country, the force of such impact may vary. Moreover, the application of the factor analysis in this study allowed one to single out the principal groups of factors which affect the value of the accumulated pension savings in a pension insurance contract. Therefore, it seems valid to conclude that the processes involved in the formulation of new solutions with respect to supplementary pension plans should place the focus upon those determinants that are found to be most statistically significant. It must be remembered that only some of such factors (e.g. unemployment, household income average) can be influenced through economic or political initiatives. The remaining determinants, most notably the demographic processes will – on the one hand – necessitate further savings under the regime of supplementary pension plans, and – on the other hand – stimulate the competitiveness of supplementary pension products on the market due to the steady decrease in the number of gainfully employed persons active on the labour market. References Bishop, T. B., Shan, H. (2008). Reverse Mortgages: A Closer Look at HECM Loans, http://www.nber.org/programs/ag/rrc/08-Q2%20Bishop,%20Shan%20FINAL.pdf. 400 Blanc, J. (2011). The third pillar in Europe: institutional factors and individual decisions, Discussion Paper, Series 1: Economic Studies, No 09/2011, Deutsche Bundesbank. Brauninger, D. (2014). Institutions for occupational retirement provision in Europe: ongoing challenges, EU Monitor, May 9. Bridge, C., Adams, T., Phibbs, P., Mathews, M., Kendig, H. (2010). Reverse mortgages and older people: growth factors and implications for retirement decisions. AHURI Final Report No. 146. Melbourne: Australian Housing and Urban Research Institute. Casey, B. H., Dostal, J. M. (2013). Voluntary Pension Saving for Old Age: Are the Objectives of Self-responsibility and Security Compatible?, Social Policy & Administration, Vol. 47, No. 3, pp. 287-309. Cattell, R. (1965). Factor analysis: an introduction to essentials I. The purpose and underlying models. In: Biometrics, Vol. 21, No. 1, pp. 190-215. Ciumas, C, Chris, D. M., Mircea B. H. (2012). Global financial crisis and unit-linked insurance markets efficiency: empirical evidence from Central and Eastern European countries, Annals of the University of Oradea, Economic Science Series, Vol. 21 Issue 2, pp. 443-448. Clark, R. L., D'ambrosio, M. B., Mcdermed, A. A., Sawant K.. (2006). Retirement plans and saving decisions: the role of information and education, Journal of Pension Economics and Finance, 5, pp. 45-67. Eichhorst, W., Gerard, M., Kendzia, M. J., Mayrhuber, C., Nielsen, C., Runstler, G., Url, T. (2011). Pension systems in the EU – contingent liabilities and assets in the public and private sector, European Parliament, Retrieved from: http://www.europarl.europa.eu/activities/committees/studies.do?language=EN. European Commission (2014). The 2015 Ageing Report, Retrieved from: http://ec.europa.eu/economy_finance/publications/. Garman, E. T., Eckert, S. W., Forgue, R. E. (1985). Personal finance, Houghton Mifflin Company, Boston. Górniak, J. (1988). Analiza czynnikowa i analiza głównych składowych, In: ASK, pp. 83- 102. Hur, M. H. (2010). A comparative study of the relationship between pension plans and individual savings in Asian countries from an institutional point of view, International Journal of Social Welfare, vol. 19, pp. 379-389. Holzmann, R., Hinz, R. (2005). Old-Age Income Support in the Twenty-first Century: An International Perspective on Pension Systems and Reform, Washington, DC, World Bank. Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, vol. 20, pp. 141-151. Konicz, A. K., Mulvey, J. M. (2015). Optimal savings management for individuals with defined contribution pension plans, European Journal of Operational Research, vol. 243, pp. 233-247. Kowalczyk-Rólczyńska P., Rólczyński T. (2016). Alternative Investments in Voluntary Pension Security, In: European Financial Systems 2016. Proceedings of the 13th International Scientific Conference, Brno: Masaryk University, 2016, pp. 373-379 Lannoo, K., Barslund, M., Chmelar, A., Werder, M. (2014). Pension Schemes, European Union, Retrieved from: http://www.europarl.europa.eu/RegData/etudes/STUD/2014/536281/IPOL_STU%282014 %29536281_EN.pdf. 401 OECD (2015). Pensions at a Glance 2015: OECD and G20 indicators, OECD Publishing, Paris. OECD (2016). Net pension replacement rates (indicator). Pieńkowska–Kamieniecka, S., Walczak, D. (2016). Willingness of Polish Households to Save for Retirement. In: European Financial Systems 2016. Proceedings of the 13th International Scientific Conference, Brno: Masaryk University, pp. 588-595. Rocher, S., Stierle, M.H. (2015). Household saving rates in the EU: Why do they differ so much?, Discussion Paper 005, European Commission. Schrager, D. F., Pelsser, A. A. J. (2004). Pricing Rate of Return Guarantees in Regular Premium Unit Linked Insurance. In: Insurance: Mathematics and Economics 35, pp. 369- 398. Stinglhamber, P., Zachary, M. D., Wuyts, G., Valenduc Ch. (2007). The determinants of savings in the third pension pillar, National Bank of Belgium, Economic Review. Walesiak, M., Bąk, A. (1997). Wykorzystanie analizy czynnikowej w badaniach marketingowych, in: Badania operacyjne i decyzje, no 1, pp. 75-87. Whitehouse, E. (1999). The tax treatment of funded pensions, Retrieved from: https://search.oecd.org/finance/private-pensions/2391559.pdf. 402 New trends in banking sector Jan Krajíček Masaryk University Faculty of Economics and Administration, Department of Finance Lipová 41a, 602 00 Brno, Czech Republic E-mail: krajicek@econ.muni.cz Abstract: The aim of this paper is to analyze the policy of banks in relation to their clients. Long-term systematic increase in deposits and a decline in interest rates in the economy bring new problems and questions how to cope with the increase. With the increase is linked to the management of assets and liabilities. It plays an important role and expected developments in key interest rates. The paper mainly deals with the impact of interbank PRIBOR 3M, which is considered crucial interest rate. Banks derive mainly from the interest rate loans to clients. Deposit growth not only in the banking sector, and has their subsequent rebound in banking policy and the creation of new banking products, with consequent impacts on the economy of the bank. Impacts vary according to the size of banks and their client segmentation. The basis is literature review, which is connected to the analytical part (using modeling Gretl), using data available in the system ARAD (Czech National Bank).In conclusion, it is assessed recent developments, future outlook and anticipated impacts of the financial sector. Keywords: Bank, deposits, revenue, liabilities, profit JEL codes: C15, E37, G20 1 Introduction Results management assets and liabilities are reflected in the overall results of banks. It plays a significant role and expected developments in key interest rates. The paper mainly deals with the impact of interbank PRIBOR 3M, which is considered crucial interest rate. Banks derive mainly from the interest rate of loans provided to clients Krajíček, J. (2016). It is also possible objection that this rate is not important. Theoretically it is possible to consider the rate announced by the central bank (the discount rate repo rate), which have a decisive impact mainly on bond prices. Rate announced by the central bank are reflected in the rates of the interbank market. Therefore, in these calculations considered an interest rate of PRIBOR 3M. For the decisive it is necessary to consider that this is a market rate, which is announced on the basis of quotations of reference banks. It is also possible objection that banks may have quotation manipulated according to your needs, but given that the system in the Czech Republic involved six banks (Czech spořitelna, CSOB, Komerčni banka, UniCredit Bank, Raiffeisenbank, and Expobank) they would have to handle agreed by all banks and it can be eliminated. 2 Methodology and Data Bank Management For the stable development of the bank is necessary qualified asset and liability management, which form the focus of bank management and is an essential part of the financial management of the bank. It's a method of management the balance sheet structure of the bank, which is an overview of the bank's assets and sources of its funding, with the aim of maximizing profits. To bank management also reflect the macroeconomic effects of regulation of the banking sector in particular, as currently applicable Basel II and Basel III, banks are implanted Slovik and Cournede (2011). 403 The paper is mainly used data analysis, which is available in the system ARAD CNB (2016). The analysis is focused exclusively on the Czech Republic for which data are available over a sufficiently long time series, which allows the adoption of conclusions. Use of a literature search is limited primarily to the basic literature, which is dedicated to banks and their evaluation. The decisive is considered primarily an analysis of profits in relation to the development of particularly interest rates, interest rates and risks in the banking sector. For purposes of evaluating risk development are again used data from ARAD CNB (2016). The crucial focus is not on how to analyze the current situation in the banking sector, especially the excess of liabilities reflected in the financial results of individual bank groups. As the basis for the analysis used data ARAD CNB (2016). Their classification is according to the size of banks on small, medium and large banks. Analysis impacts interest rates For the modeling and the analysis of data are used, which are available in the ARAD of CNB (2016). Evaluation is focused exclusively on the Czech Republic for which data are available over a sufficiently long time series, which allows the adoption of conclusions. Use of a literature search is limited primarily to the basic literature, which is dedicated to banks and their evaluation. The decisive is considered primarily an analysis of profits in relation to the development of particularly interest rates, balance sheet and risks in the banking sector. Figure 1 Development interest rates PRIBOR 3M Source: Own processing on basis data ARAD CNB (2016) Constantly falling interest rates. Figure 1 demonstrates how they have significant impacts on financial results in the banking sector. Together with the decline in interest rates significantly growing volume of client deposits. The sharpest increase in client deposits show large banks. Interesting is the decline in deposits at central banks at the end of 2015, which is due to their efforts to minimize clients' interest by depositing funds with them. Conversely, large banks have relatively stable growth in client deposits, which are increasing. It is primarily due to clients who they are looking for certainty and track clients, especially when large clients who have their accounts at major banks have imposed on them a large volume of funds. Development of client deposits for individual categories of banks clearly documents the following graphical representation. 404 Figure 2 Development Client Deposits Source: Own processing on basis data ARAD CNB (2016) Development of volume of customer deposits is one of the decisive criteria and has an impact on the overall orientation of the bank and its subsequent economic results. With the development of interbank interest rates and client deposits is linked to the overall evolution of loans granted to clients. This development is not only affected client deposits, but also other resources that banks receive. The decisive influence, however, are primarily client deposits that banks are cheapest. Crucial to the development orientation of the bank interest rates on loans provided to clients. Especially small and medium-sized banks, which are trying to increase their revenues, mainly focus on the most profitable client segments of the market. The following chart illustrates the evolution of interest rates on consumers and commercial loans. 0 500000 1e+006 1,5e+006 2e+006 2,5e+006 3e+006 3,5e+006 4e+006 4,5e+006 2008 2009 2010 2011 2012 2013 2014 2015 ClientDepositsinMillionCZK Year Banks Total Large Banks Mid Banks Small Banks 405 Figure 3 Interest rate developments consumers and commercial loans Source: Own processing on basis data ARAD CNB (2016) From the Figure 3 it is clear that the average bank interest rates for consumer loans are considerably higher than that of Commercial Loans. In particular, large banks have in its portfolio of large volumes of Commercial Loans, in contrast to the medium and small banks. Especially small banks focus on retail clients, for which achieve significantly higher margins. Figure 4 Developments of ROE Source: Own processing on basis data ARAD CNB (2016) The figure shows how gradually progressive rapprochement with the ROE, thus improving the results for small banks. 0 2 4 6 8 10 12 14 16 2004 2006 2008 2010 2012 2014 2016 InterestRatein% Year PRIBOR 3M Consumer Loans Commercial Loans 0 500000 1e+006 1,5e+006 2e+006 2,5e+006 3e+006 3,5e+006 4e+006 4,5e+006 2008 2009 2010 2011 2012 2013 2014 2015 ClientDepositsinMillionCZK Year Banks Total Large Banks Mid Banks Small Banks 406 Especially large and medium-sized banks are approximately the same results, although the observed increase in ROE for medium-sized banks. The decisive influence here is mainly oriented banks, where banks of medium size, which is focusing more and retail clients in practically the same results as the big banks. But what is crucial for assessing the impact of interest rate developments, which is associated with an increase in deposits in the banking sector, the decline in aversion to risk. In general we can say that especially for large banks growing risk exposure. Small and medium-sized banks show a relatively stable development of risk exposures, while large banks can be traced growing trend, as shown in the following figure 5. The increase in risk exposure of large banks may raise concerns about their future development. These are systemically important banks to tighter regulation. Deserves attention particularly significant decline in risk exposure in medium-sized banks by the end of 2015, which might affect both their credit policy and track clients, the banks provided new loans that did not exhibit such a high risk. The end of 2015 is associated with the stabilization of the risk exposures of large banks can be expected to impact the gradual application of Basel III into practice and the interest of banks to improve their results. Figure 5 Development of Risk Exposure Source: Own processing on basis data ARAD CNB (2016) 3 Results and Discussion Management of assets and liabilities form the crux of bank management and is an essential part of the financial management of the bank. It's a way to control the structure of bank balance sheets, which are an overview of the Bank's assets and the sources of its funding. Financial institutions must manage its balance especially in relation to the defined needs and goals. Uncontrolled development of total assets affected mainly by client deposits has resulted in the need for the resulting cash resources to invest. Critical opportunities for banks are their investment through loans and credits. The lack of quality options for lending has as its consequence the deteriorating quality of their loan and credit portfolios. It is also the basis for discussion, to which the contribution is going. With the development of interest rates depending on the size of banks is associated with the development of the share of interest expenses in assets. The share of interest costs 0 200000 400000 600000 800000 1e+006 1,2e+006 1,4e+006 1,6e+006 1,8e+006 2e+006 2,2e+006 2008 2009 2010 2011 2012 2013 2014 2015 RiskExposureinMillionCZK Year Banks Total Large Banks Mid Banks Small Banks 407 on assets showed a decline in recent years, but the best results are achieved by mediumsized banks. In accordance with the direction of small banks is among them to better results, which are in line with the development for the entire banking sector. 4 Conclusions Management of assets and liabilities and the related management form the main focus of bank management and is an essential part of the financial management of the bank. It's a way of managing the balance sheet structure of the bank, which is an overview of the bank's assets and sources of its financing. Financial institutions must manage their balance, especially in relation to the defined needs and goals. Uncontrolled development in total assets mainly influenced by client deposits and the decline in interbank interest rates, has resulted in the need for the resulting cash resources to invest. Crucial options for banks are their investment through loans and advances. The lack of quality options for lending has as its corollary the deteriorating quality of their loan portfolios. The impact of the deteriorating quality of loans can be expected then, as will be repaid. Especially consumer loans are usually provided to maturity of five years. The same trend can be expected in the future, especially at 100 percentages of mortgages and commercial loans. References ARAD CNB (2016). The time series Database. Retrieved from: http://www.cnb.cz/cnb/STAT.ARADY_PKG.STROM_DRILL?p_strid=0&p_lang=CS. Heffernan, S. (2005), Modern banking. 1.rst. ed. Chichester: John Wiley & Sons. Krajicek, J. (2016). Economics and Management of Banks. 5rd upgraded and revised edition. Brno. Masaryk University. Faculty of Science. Department of Finance - Study texts. Slovik, P., Cournede, B. (2011) Macroeconomic Impact of Basel III, OECD Economics Department Working Papers, vol. 844. 408 Possibility of Accrual Accounting Application in Case of Active Reserve Forces of the Czech Army Miroslav Krč1 , Vladimír Golik2 Aleš Olejníček3 Vladan Holcner4 1 University of Defence Faculty of Military Leadership, Department of Economics Kounicova 65, 662 10 Brno, Czech Republic E-mail: miroslav.krc@unob.cz 2 University of Defence Faculty of Military Leadership, Department of Economics Kounicova 65, 662 10 Brno, Czech Republic E-mail: vladimir.golik@unob.cz 3 University of Defence Faculty of Military Leadership, Department of Economics Kounicova 65, 662 10 Brno, Czech Republic E-mail: ales.olejnicek@unob.cz 4 University of Defence Faculty of Military Leadership, Department of Economics Kounicova 65, 662 10 Brno, Czech Republic E-mail: vladan.holcner@unob.cz Abstract: The article „Possibility of Accrual Accounting Application in case of Active Reserve Forces of the Czech Army” analyses the application of the accrual accounting by way of Active Reserve Forces example. Accrual accounting is considered as a potential tool of resource management system effectiveness increase in the Czech Army. The Active Reserve Force is monitored as a cost centre that is managed by actual and predetermined costs. The three training situation are simulated. The analysis takes into account only activities directly connected with training of the Active Reserve unit influenced by training type and military vehicles involvement. The data are displayed by synthetic and analytical accounts. The training price of separate Active Reserve soldiers is generated through the synthetic and subsidiary accounts under accrual accounting system. Each Active Reserve serviceman have assigned own analytical cost account. This account have displayed all cost items associated with his training. The closing balance of all analytical accounts provides the sum of total costs that were connected with Active Reserve member training. Keywords: accrual accounting, active reserve forces, cost centre, analytical accounts, synthetics accounts JEL codes: M41, M480, M490 1 Introduction Despite of worsened security conditions the national armies have to face a long term goal – continuous improvement of defence resource management with accent on effective allocation of scarce resources. (NATO, 2014) Provision of defence and national territory integrity are high-priority government responsibility. Defence is a public good and social need with the specific qualities of production and consumption resulting very often in tendency to an inefficient use of resources by department of defence and armed forces. (Olejníček, 2003) However, the governments have to spend considerable quantity of human, material and financial resources for preservation of action capacity and functionality of armed forces. Simultaneously, there is growing significance of the effective resource allocation, optimizing its usage and reduction of the production inefficiency or savings rise. 409 The process of continual groping towards more effective resource management is of great value. (Vodáková, 2012; Vodáková, Sglundová, 2016) Important is that economically scarce resources intended for defence is not possible to use without limits. The unsuitable exploitation of resources often results in wasting, and furthermore it is very influential on public opinion and armed forces economic performance assessment. (Krč, Golik, Vodáková, 2016) It has a logical implication that public expects value for many in the case defence and armed forces, too. (Olejníček, Kunc, 2012; Dušek, Horák, 2004) The general public is very interested in what is public funds treatment and effectiveness of its use. The problem of resource allocation in the army is understand as the area with best potential for improvement. (Krč, Golik, 2015) Further, it is evident that command and control system reorganization and use of proper economic instruments may have positive effect. It is essential to analyse the possible uses of accrual accounting for economic performance improvement within resource management system of the Czech Army. (Olejníček, Kunc, 2012) The potential benefits of accrual accounting use as an economic tool is examined and explained by way of example of military training of Active Reserve Forces of the Czech Army. To explore the serviceability of accrual accounting as an economic tool these methods were chosen: description, explanation and economic modelling. Nowadays, the Czech Army has 16 troops of Active Reserve Forces available. However, there is a plan to extend the number of the Active Reserve Forces to 59 in 2025. It comprises the increase from today's 1250 servicemen to 5000 during next decade. This quantitative change creates ample scope for the effective application of the accrual accounting within the troops of Active Reserve Forces. (MoD, 2017; RMH,2017) Using simple analysis of the Active Reserve Forces activities, only operations will be included that directly relate with training of the Active Reserve units influenced by training type and military vehicles involvement. The analysis do not take short term activities of the troops into account. The short term activities as funeral ceremony and publicity campaign which are formally considered as the training activities are excluded from analysis on the grounds of little influence on the preparedness and fighting efficiency of the units. The analysis takes into consideration only military training exercise that contribute to improvement of serviceman competences. The hypothesis is following: „Accrual accounting is suitable economic tool for rationalization of the Active Reserve Force training.” The Active Reserve is part of Reserves of Armed Forces of the Czech Republic according to the new legislation which has come into force last year (Law No. 585/2004 Coll, Law No. 45/2016 Coll). The Active Reserve members voluntarily undergo a military training lasting several weeks after that they continue to work for their civilian employers. Thus, they combine two careers: military and civilian. The Active Reserve is used for strengthening the Armed Forces active force under a state of emergency, state of war, and when non-military crisis situations occur in order to protect the lives and property of people in the aftermath of natural disasters, to include mitigation of their consequences. The present concept of the Armed forces of the Czech Republic is based on the principle of the possible smallest size of the army that is adequate for ensuring the defence of the Czech Republic. A non-crossable personal size of armed forces is determined by the government decision. For the Czech Army, the Active Reserve Force is practical way to ensure reserves in case of emergency. Increasing reserve forces importance is hot discussion topics in all NATO member countries including the Czech Republic. (NATO, 2015) There are several reasons for the reserve forces formation. In the train of failing security environment, states want to increase the numbers of troops and their armies’ abilities. However, only professional soldier’s investment there is not possible way. As a consequence of this situation, the countries create reserves forces which are activated and financed only during part of the year. The Active Reserve Forces are important part of the Czech army. There is growing demand for well-trained soldiers capable to fulfill required function with increasing threat 410 of war. Keeping Active Reserve Force members during several weeks per year for active service (the members are drafted for military training exercise), it is necessary to monitor training costs these servicemen. The questionable is namely situation of increasing value more trained soldiers. For the time spent at the exercises or deployment, the member receives an additional compensation what is a percentage of his or her normal daily income in the civilian sphere, and moreover, they are paid on a daily basis according to their rank. Their civilian employers receive also remuneration depending on type and duration of an exercise or active deployment. To create the monitoring system of the real price of each member of Active Reserve Force, the accrual accounting is possible to use. 2 Methodology and Data The fundamental micro form of the accrual accounting will be simulate by operation model of the Infantry Company of Active Reserve Forces of the Czech army. The data for our research were gained from real training plan of Active Reserve Forces provided by Regional Military Headquarters. The cost data were extracted from financial reports provided by Financial Agency of the Czech Armed forces. The accrual accounting is viewed as an accounting methodology that provided better overview of the current situation in the accounting (e.g. more complete and objective information about cost centre expenses and revenues). Modelling of Active Reserve Forces Unit Activities – Starting Points The model will be define by these characteristics: a) the troop at about strength of 86 persons, b) the troop takes part in military training exercise for duration of 21 days at the maximum, c) plan of military training exercises include three main exercise • winter survival (three days duration in attendance of 25 persons), • major military exercise which take place in the military training area (nine days duration in attendance of 86 persons), • military exercise out of the military training area (small exercise with attendance of 50 persons), d) the troop has three heavy trucks and one off-road light vehicle available. In the article there is worked on the presumption that model troop of Active Reserve Forces will act as an independent cost center that is managed by actual and predetermined costs. A crucial prerequisites for creation of such cost center are: • determination of its economic structure and internal relations, • description of realized activities, • determination of outputs passed mutually on internal element of cost centre, • allocation of expenses and methods of calculation. In this model of the cost centre, an approach to cost monitoring should be based on responsibility accounting and intercompany structure. Simultaneously, each serviceman should have budget of the suggestible costs available that are subjected to check. The cost control should be based on comparison the real costs with in advance planned costs converted into actual output of the cost centre. Subsequently, the causes of costing differences should be found (causes of planed and actual data difference). During cost basis allocation on the real volume of activity, the subject of the cost distribution should be only variable costs per unit of measurable activity (1 member of Active Reserve Forces), the fixed costs should be accepted on the predefined limit. The economic interest of the troop members should be bound by real cost savings in comparison with planned costs. This approach has some limitation. The more accurate is 411 expenses determination, the harder is reaching the cost savings. As logical consequence we could begin to face the costs overrun. The costs allocation would lie in the costs assignment to a relevant element. This element should be an object of the control. The main aim of cost allocation should be specification of the costs development of the selected element. The costs allocation could be possible to use in following situations: • decision-making about the use of economic resources, • calculation of the expenses related to output, • budget negotiation and costs rationalization, • retrieval of information about sources limitedness. A crucial question is how does the costs allocation interconnect to object of calculation? To solve the problem it is recommended that causality principle was used. The causality principle means that each output should be burdened with only causally produced costs. Accounting Entries within Operating Model of Active Reserve Forces Unit of the Czech Army Causally Related with Military Training Case 1 - The winter survival exercise The winter survival exercise used to be located in mountain terrain. The 25 soldiers were called up to the exercise. The soldiers and equipment were transported by bus to the mountain training area. The food was not secured – it was pay in the refund of expenses. All next cost resulted from the winter survival exercise: • the hire charge for bus (12 000 CZK), • the refund of food (7 125 CZK), • the additional soldiers compensation as a percentage of their normal daily income (67 933 CZK), • the basic pay for instructors (49 406 CZK). Case 2 - The major military exercise The major military exercise has taken place in the military training area. It lasted nine days in attendance of 86 soldiers. The soldiers and equipment were transported by trucks to military training area. The soldier´s meals were provided by field kitchen. All following cost resulted from the major military exercise: • the ammunition costs (138 420 CZK), • the pay for the provided food (49 872 CZK), • the additional soldiers compensation as a percentage of their normal daily income (570 195 CZK), • the basic pay for instructors (324 254 CZK), • the fuel costs (14 023 CZK). Case 3 - The small exercise There were planned 50 soldiers for small military exercise. The soldiers and equipment were transported by trucks to military training area. The soldier´s meals were provided by field kitchen. All next costs resulted from the small military exercise: • the ammunition costs (138 420 CZK), • the pay for the provided food (23 570 CZK), • the additional soldiers compensation as a percentage of their normal daily income (234 069 CZK), • the basic pay for instructors (131 061 CZK), • the fuel costs (35 549 CZK). 412 All military exercises activities and induced consumption of the resources are expressed by accounting transaction. Table 1 describes all account assignments of the realized exercises. Table 1 The account assignments incurred during the military exercises period No. Description of Account Transactions Amount (CZK) DS CS 1. Fuel costs 12 000 2. Refund of food 7 125 3. Additional soldiers compensation 67 933 4. Basic pay for instructors 49 406,4 5a. Purchase order of ammunition, type 1 64 000 5b. Purchase order of ammunition, type 2 30 000 5c. Purchase order of ammunition, type 3 25 000 5d. Purchase order of ammunition, type 4 520 5e. Purchase order of ammunition, type 5 18 000 5f. Purchase order of ammunition, type 6 900 6a. Transfer in powder magazine, type 1 64 000 6b. Transfer in powder magazine, type, 2 30 000 6c. Transfer in powder magazine, type 3 25 000 6d. Transfer in powder magazine, type 4 520 6e. Transfer in powder magazine, type 5 18 000 6f. Transfer in powder magazine, type 6 900 7a. Distribution of ammunition, type 1 64 000 7b. Distribution of ammunition, type 2 30 000 7c. Distribution of ammunition, type 3 25 000 7d. Distribution of ammunition, type 4 520 7e. Distribution of ammunition, type 5 18 000 7f. Distribution of ammunition, type 6 900 8. Refund of food 49 875 9. Additional soldiers compensation 570 195 10. Basic pay for instructors 324 254 11 Fuel costs 14 023,32 12. Refund of food 23750 13. Additional soldiers compensation 234 069 14. Basic pay for instructors 131 061,2 15. Fuel costs 35 439,84 Source: own processing 3 Results and Discussion This chapter summarizes main findings concerning implementation of cost and managerial accounting on accrual basis within conditions of the active reserve forces cost centre. The first part of chapter describes synthetic accounts of cost centre. The second part devotes attention to analytical accounts usage. Synthetic Accounts Following Figure 1 shows the entering of the account assignments in the synthetic accounts as consequence of the military training of Active Reserve Forces Unit. 413 Figure 1 Displaying of the synthetic accounts of Active Reserve Forces Unit Source: Own processing Above displayed accountant assignments are not extraordinary in privete sector. However, these transactions are significant for managerial purposes in the area of the armed forces too. Via synthetic accounts, it is possible to gain the comprehensive and complete overview of the resources allocation, regarding fuel, food or wage funds and so on. Subsidiary Accounts The subsidiary accounts usage enable us rather accurately to determine a price of military training of each individual serviceman of Active Reserve Force. The price determination is possible by application of the subsidiary accounts, when each member of the Active reserve forces unit should assign own analytical cost account. These 414 accounts should display the single cost items that are causally related to the troops training. Subsequently, a closing balance of the each analytical cost account would suggest an overall costs that have been spent on training of the individual serviceman to date. Next Figure 2 displays six subsidiary accounts of the active reserve unit members. Figure 2 Displaying of the subsidiary accounts of Active Reserve Forces Unit Source: Own processing The selected subsidiary accounts, displayed above, give basic information about cost allocation against realized military exercises and participating soldiers (account No. 1 – 6). These accounts provide us not only with information about separate soldier consumption, but cost-efficiency of soldier (on condition that we are able evaluate the soldier performance). The subsidiary accounts analysis is able to offer the crucial information for troop commander and economic decision-making process in general. The reasons are following: • Expensiveness – an elementary analysis is able to point out the most expensive or the most inexpensive soldier. The commander or economic officer of the unit can use such information effectively. It is irrational to have “very expensive soldiers” (well-educated, skilled or capable) on position of rifleman. To the contrary it is crucial to use the potential of such unit members (language skills, driving licence, previous experience from electrical or mechanical engineering) for 415 qualification demanding positions (functions), preferentially for officer's staff. From economic and managerial point of view, it is necessary to transfer such soldiers to more suitable position. This measure is desirable on the grounds of the soldier’s compensation system (the more educated soldier the more expensive soldier). This approach provide us better cost/benefit ratio in connection with optimal operation of active reserve force unit. • Benchmarking - the sum of separate analytical cost accounts uncovers overall costs, at the same time allows to make comparison of the separate cost items. • Planning process - it is possible to keep overview of the cost type consumption effectively. For example, this evidence allows to plan the demand for ammunition depending on the actual troop size. If the active reserve forces unit should dispose of it´s budget, fully decision autonomy, it should be able to secure a sufficient amount of ammunition and to correct the demand for future resource allocation in further period. In case of centrally fixed amount of sources (e.g. ammunition, vehicles etc.) and increasing number of new troops, the quality of training could be decreasing logically (e.g. decreasing number of ammunition per soldier). 4 Conclusions The article points out the potential usefulness of the accrual accounting introduction into economic system of the Czech army. In the light of intended increase of the number of the Active Reserve Force units of the Czech army, it is essential to look for more effective way of their control and composition. The above mentioned simplified case study, on the active reserve forces cost centre, shows the possibility of transformation relevant financial accounting data by way of appropriate managerial tool (analytical accounts) to internal databases organized according to their specific needs. In addition to other things the former research (Olejníček, 2015; Olejníček, Kunc, 2012; Olejníček, 2011) has confirmed that they can also serve to monitoring of effectiveness, economy and efficiency. The real knowledge of the costs per solider (unit costs), the costs per military exercise (costs per output), the variance between estimated and actual value of costs and so on sets the scene for effective economic management including such specific area like the armed forces. The presented operating model of Active Reserve Forces unit showed the possibility of the analytical accounts usage. The provided information by model can be used for both resource management and planning and decision-making process. So by means of the accrual, cost and managerial accounting can be achieved the optimal structure and composition of the Active Reserve Forces units of the Czech army not only from militaryoperational but also from cost-benefit and managerial point of view. Acknowledgments This paper was supported by University of Defence, Faculty of Military Leadership (FVL) project “Applied economy in the Ministry of Defence”. References Dušek, J., Horák, R. (2004). Nový způsob rozpočtování a účetnictví v rezortu obrany Velké Británie. In Proceeding from 3rd international conference. The Economics of the army. (Theoretical and practical foundation). Retrieved from: http://rschwarz.uvadi.cz/texty/EkonACR2.pdf. Krč, M., Golik, V. (2015) Resource accounting and budgeting for manning personnel cost ant the amount of capital expenditures in the UK armed forces. European Financial 416 Systems 2015. Proceedings of the 12th International Scientific Conference. Brno: Masarykova univerzita, pp. 320-325. 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(2012). Ekonomické řízení v podmínkách Armády ČR. 1st ed. Uherské Hradiště: L.V. Print. Olejníček, A. (2011). Economic resource management of cost centre of the Czech Army – model design. In: Nauczyciele i mistrozwie ekonomii i logistyki. Warszawa: Wydawnictwo Akademii Obrony narodowej. pp. 442-448. Olejníček, A. (2003). Proč resort obrany směřuje k neefektivnosti? In Sborník Vojenské akademie v Brně. pp. 27-43. Regional Military Headquarters – KVV. (2017). Retrieved from: http://www.army.cz/scripts/detail.php?id=5812. Vodaková, J., Sglundová, N. (2016). Expenses and revenue classifications for managerial purposes in the Czech state administration units. In: European Financial Systems 2016. Proceedings of the 13th International Scientific Conference. Brno: Masaryk University. pp. 848-855. Vodáková, J.(2012). Akruální účetnictví ve veřejném sektoru. Praha: Wolters Kluwer ČR. 417 Mergers of agricultural enterprises in the Czech Republic Michaela Krejčová, Jana Gláserová1 , Milena Otavová2 , Sabina Mašová 1 Mendel University in Brno Faculty of Business and Economics, 1 Department of Accounting and Taxes Zemědělská 1, 613 00 Brno, Czech Republic E-mail: jana.glaserova@mendelu.cz 2 Mendel University in Brno Faculty of Business and Economics, 1 Department of Accounting and Taxes Zemědělská 1, 613 00 Brno, Czech Republic E-mail: milena.otavova@mendelu.cz Abstract: One of the conspicuous manifestations of the globalization of economic activities is mutual operations between enterprises whose aim is to achieve a higher valuation of them. These are transformations of companies and cooperatives whose use is steadily rising among businesses. Mergers occur throughout the entire economy, including agriculture. Agriculture is an important component of the economy although its share of gross domestic product shows a decreasing trend. In agriculture, the most common motivation for mergers is the simplification of the structure of a group of companies, as suggested by the study of merger projects. To evaluate the success and efficiency of mergers selected ratios are used and the measured period is three years before and three years after the merger. The study involves agricultural enterprises in the Czech Republic that were merged in the years 2010 – 2012. Also, a relationship between the number of transformations in the form of mergers in agriculture and the development of GDP in the years 2006 – 2016 is examined. Keywords: mergers, company transformation, agriculture, efficiency JEL codes: G34, K20, M21 1 Introduction The term “mergers” is now one of the most popular words. The reason is that with the ever-growing globalization of economy there is a trend towards consolidation in different production areas and in different markets. This trend is evident in all business areas, even in agriculture. Agriculture in the Czech Republic is a production sector with a long tradition. Together with fishing and forestry it belongs to Section A according to the classification of economic activities CZ-NACE. This group, together with mining and quarrying, is referred to as the primary sector, since producers are in direct contact with nature and when growing crops, they interact with natural influences (Homolka 2010). Agriculture has productive and non-productive functions (Boháčková, 2014). The nonproductive function of agriculture is often supported by various subsidies from national and European funds (Synek, 2010). In 2015, agricultural land accounted for approximately 53% of the total area of the Czech Republic. About 47,000 agricultural producers and processors of agricultural raw materials are currently engaged in this sector of economy, managing approximately 3.5 million hectares, of which 2.5 million hectares is arable land (71% of farmland). About 84,000 workers are employed in agriculture (eAgri, 2016). At present, agriculture, forestry, and fishing constitute less than 3% of GDP and it is the tenth-smallest sector in the Czech Republic (Kučera, 2014). Agricultural production has some special characteristics which are described by various authors (Boháčková and Landová, 2014; Homolka, 2010; Dvořáková, 2012; Kučera, 2002), who concur that the basic differences are predominantly the biological character of agricultural production. Another specific feature is the seasonal character of agricultural production, dependence of the production cycle on natural conditions, high 418 demands for specialized equipment, large-area production, production of main and secondary products and many others. Agriculture is an indispensable part of the national economy even though its share of GDP is still decreasing. As in other sectors, also here there is a tendency towards concentration of businesses whose aim is to make production more efficient, to save costs in the hope of improving the market position, and to increase the competitiveness of the company, so mergers of companies and cooperatives occur quite often even here. In the Czech Republic mergers are most often implemented with the aim of improving efficiency and the company’s position in the market (Konkolski, 2011). A study of the motives for mergers in agriculture was conducted by Šuráň (2016), and the survey showed that the most common motive for mergers in agriculture is the simplification of the group structure (organizational administrative, personnel and logistics structures). In the Czech Republic, the transformation of companies and cooperatives are mainly governed by Act No. 125/2008 Coll. on the transformation of companies and cooperatives, as amended. Besides mergers, which are the main topic of this paper, transformations are also divisions of companies, the transfer of assets to a shareholder and the change of the legal structure. The merger itself is defined in §§ 61– 242 of the Act. A merger is an operation in which at least one company disappears and the assets of the disappearing companies are transferred to a successor company which already exists or will be newly created for this purpose. By form, mergers can be divided into mergers by acquisition and mergers by the formation of a new company. The most common form is a merger by acquisition, because according to Salachová (2014) it is associated with less administrative and legal burden, so the costs of implementation are also lower. Nevertheless, it is an act which requires careful preparation of all documents, crucial decisions regarding the record date, and also a time management plan as this process will take several months. As it is necessary to harmonize all legal, accounting and tax areas of this issue, the process makes a lot of demands on all stakeholders. This means that it is essential to respect more laws than just the aforementioned Act No. 125/2008 Coll. on transformation of companies and cooperatives, as amended, but also, for example, Act No. 563/1991 Coll. on accounting, Act No. 586/1992 Coll. on income taxes, Act No. 304/2013 Coll. on public registers of legal and natural persons, and Act No. 143/2001 Coll. on the protection of competition and amendments to certain acts, as amended, and others. Despite the fulfilment of all legal requirements and recommendations, the success of the merger is not guaranteed and the increase in economic profit, which is often referred to as the main motive of mergers, or increased competitiveness need not be achieved. According to Kislingerová (2001) and Synek (2007), a merger is often beneficial to a disappearing company, whereas the successor company can be successful only if it is a strong company, the expected synergy is as big as possible and the size of merging companies is adequate – it is good if the size of the disappearing company is about onethird to one-half of the successor company. In such a case, the merger is expected to be the least problematic. 2 Methodology and Data The aim of the paper is to evaluate effectiveness of the mergers of selected agricultural enterprises. These included agricultural enterprises in the Czech Republic where the merger took place in the years 2010–2012. The agricultural enterprises were divided into five categories on the basis of the financial volume of the mergers, as shown in Graph 1, and one representative was chosen for each category. Because the merger worth more than 1,000 million CZK took place only in the year 2016, it could not be included in the survey. For this reason, two representatives were selected for the category of 100–500 million CZK. This was the category in which the largest number of mergers went through 419 in the years 2010–2012. Thus, it was possible to assess selected ratios over the 3 years before the merger and 3 years after the merger. This time interval is determined on the basis of existing studies, e.g. Synek (2010), Martynova, Rennebook (2008). Data on completed mergers in agriculture were sourced from the Commercial Register, the Commercial Bulletin and from the website justice.cz. Effectiveness of mergers was assessed by ratios, which are preferred predominantly by investors because of their high explanatory power. The calculations were based on the financial statements, namely balance sheets and profit and loss statements of selected companies. The following indicators were examined: • Return on equity (ROE), which measures the return on investment: Equity IncomeNet ROE = (1) • Return on sales (ROS), calculated as follows: Equity IncomeNet ROS = (2) • Total debt ratio: AssetsTotal sLiabilitieTotal RatioDebt = (3) • As mergers may impact the number of employees, it is good to include sales per employee: EmployeesTotal Slales EmployeeperSales = (4) The paper also examined the relationship between the number of mergers and GDP development in 2006–2016. The number of mergers in relation to the macroeconomic environment is determined by correlation analysis. In addition to the basic scientific methods and correlation analysis, methods based on the principles of logical thinking, especially the method of deduction, were used. The results are given in tables and graphs. The synthesis method is used to draw conclusions 3 Results and Discussion Development of mergers in agriculture in the years 2006–2016 From 2006 to 2016, a total of 191 mergers of different sizes were made. Graph 1 shows the development of the number of mergers categorized by size. Most mergers in this period amounted from 10 million CZK to 100 million CZK. The category of mergers worth more than 1 billion was represented only by one merger that took place in the year 2016. 420 Figure 1 Development of the number of mergers in agriculture by financial volume Source: Own processing based on justice.cz The number of mergers in agriculture out of the total number of mergers in the Czech Republic Table 1 below gives the overview of all mergers in the Czech Republic between 2006 and 2016, and also the number of mergers in agriculture. Unlike Poland and Serbia, as reported by Luty (2016), the number of mergers in the Czech Republic was not affected by the financial crisis, as evident from the table. The table shows that mergers in agriculture account for approximately 5%, whereas in the year 2015 they were less than 4% out of the total number of completed mergers. In the year 2016 they constituted 6%, which is due to the overall lower number of all mergers. The number of mergers in agriculture was almost the same as in the year 2015. The table also shows that the number of mergers in agriculture is growing, but it is moderate growth, only in the year 2010 there was an increase in the number of registered mergers. The total number of mergers was 30, however the average number of mergers is 17 per year. In the reporting period, the total number of mergers was 191, of which only 4 were mergers by the formation of a new company, the others were mergers by acquisition. Table 1 Development of the number of mergers in agriculture out of the total number of mergers in the Czech Republic Member of mengers 2006 2007 2008 2009 2010 2011 agriculture 18 13 16 19 30 15 economy 256 214 342 335 359 399 2012 2013 2014 2015 2016 ∑ agriculture 16 15 14 17 18 191 economy 346 444 321 438 293 3747 Source: Own processing based on justice.cz Legal structures of merging companies As for the legal structures of companies that went through mergers in agriculture, Graph 2 shows that in the year 2016 the most common type was a merger between two limited 0 5 10 15 20 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 less than 10 mill 10 -100 mill 100-500 mill 500-1000 mill over 1000 mill 421 liability companies, followed by a merger between a limited liability company and a joint stock company. The joint stock company becomes a successor company and the limited liability company is a disappearing company. This year there was no merger in which at least one cooperative would be a merging company. Figure 2 Legal structures of merging companies in the year 2016 Source: Own processing based on justice.cz The number of mergers in relation to the macroeconomic environment Graph 3 clearly shows the connections between the number of mergers in agriculture and the macroeconomic environment, represented by one macroeconomic quantity – gross domestic product. There are two number series, one of them shows the values of the number of mergers in agriculture in individual years and the other the volume of GDP in the years 2006–2016. These number series were created using the formula X_t-X_(t-1). The number series thus shows changes compared with the previous year. When the pattern of these two curves is compared, there is no similar pattern evident until 2008. In the other periods, there is a delay in the number of mergers compared to GDP changes. A decrease in GDP is evident in 2009, but a decline in the number of mergers occurs in 2011, the pattern of curves is very similar but with approximately a two-year shift. Figure 3 A relationship between changes in GDP and the number of mergers Source: Own processing 0 1 2 3 4 5 6 7 8 9 Plc→Ltd Ltd→Plc Ltd→Ltd Plc→Plc Number of merger -20 -10 0 10 20 30 40 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Number of mergers GDP changes (in bill. CZK) 422 Assessment of merger efficiency An assessment of merger efficiency was made in agricultural companies which merged in the years 2010–2012 using selected ratios. Return on equity The first indicator which was examined in the companies was return on equity (ROE). A positive development is documented by the increasing value of this indicator. In the companies under study, this indicator showed a declining trend after the merger, only in the third year after the merger the value of this indicator started to increase gradually. Figure 4 Development of return on equity Source: Own processing Return on sales Figure 5 reveals that return on sales does not change much in the companies after the merger, only Company 4 shows larger fluctuations in the value of this indicator. In the other companies the value changes only slightly in both directions. So, it cannot be concluded that the merger contributed to increased return on sales, which can be described as a positive development. Figure 5 Development of return on sales ratio Source: Own processing -50 -30 -10 10 30 3 2 1 Merger 1 2 3 less than 10 mill 10 -100 mill 100-500 mill 500-1000 mill over 1000 mill -60 -40 -20 0 20 40 60 less than 10 mill 10 -100 mill 100-500 mill 500-1000 mill over 1000 mill 423 Company’s indebtedness After the merger, there was a reduction in the debt ratio of the companies, which indicates an increase in the company’s financial self-sufficiency, only in one company debt was on the rise. From this viewpoint it is possible to say that for the companies that show a decline in this indicator the merger was beneficial. Figure 6 Development of companies’ indebtedness Source: Own processing Sales per employee The study revealed a decreasing trend in the development of sales per employee, which is undesirable from the viewpoint of the company. In the companies under study first of all there was an increase in the number of employees and in turnover (immediately after the merger), which was followed by an evident decrease in both indicators. Figure 7 Development of sales per employee Source: Own processing 4 Conclusions According to a merger project survey, it was found that the most common motive for mergers in agriculture was the simplification of the group structure (organizational, administrative, personnel and logistics structures). This emerged from more than half of the merger projects under examination. Other often cited reasons were increasing 0 10 20 30 40 50 60 70 80 3 2 1 Merger 1 2 3 less than 10 mill 10 -100 mill 100-500 mill 500-1000 mill over 1000 mill -4000 1000 6000 11000 16000 21000 3 2 1 Merger 1 2 3 less than 10 mill 10 -100 mill 100-500 mill 500-1000 mill over 1000 mill 424 business efficiency and subsequently strengthening market position and also concentration of business activities. In every tenth merger, the motivation was an attempt to simplify interrelationships between the participating companies, which is a frequent reason for vertical mergers where suppliers merge with customers. The analysis of the mergers in agriculture indicated that also in this sector companies merge and of the total number of mergers in the Czech Republic mergers in agriculture accounted for approximately 5%. The correlation analysis of the dependence of the number of mergers and GDP over the years 2006 to 2016 showed that these two variables were positively correlated, but with about a two-year delay. The results of ratios suggest that their values are not very favourable, which supports the view of many authors that the merger may not always be beneficial for companies. Favourable development can only be seen in the company’s debt ratio, which suggests higher financial self-sufficiency. The company’s performance is, however, influenced by many factors and it is not clear from the company’s statements whether variations in financial statement items are due to the effects and consequences of the merger or other factors affecting business. As already mentioned in the introduction to this paper, agriculture is, moreover, a specific sector which is influenced by economic factors and also by factors typical of agriculture. Acknowledgments This article was prepared as a result of an internal grant project IGA PEF_TP_2017004 Mendel University. References Boháčková, I., Landová, P. (2014). Ekonomika agrárního sektoru. V Praze: Česká zemědělská univerzita, Provozně ekonomická fakulta. Hlaváč, J. (2010). Fúze a akvizice: proces nákupu a prodeje firem. Vyd. 1. Praha: Oeconomica. Homolka, J., PletichováL, D. a Mach, J. (2008). Zemědělská ekonomika. V Praze: Česká zemědělská univerzita, Provozně ekonomická fakulta. Dvořáková, D. (2012). Specifika účetnictví a oceňování v zemědělství. Praha: Wolters Kluwer Česká republika. Kislingerová, E. (2010). Manažerské finance. 3. vyd. V Praze: C. H. Beck, xxxviii. Konkolski, S. (2011). Strukturalizace a analýza faktorů ovlivňujících efektivnost fúzí a akvizic. Acta academica karviniensia, pp. 104-124. Kučera, Z. (2002). Vybrané kapitoly ekonomiky odvětví zemědělské výroby. České Budějovice: Jihočeská univerzita. Kučera, L. (2014). Postavení primárního sektoru v ekonomice ČR. Český statistický úřad. Retrieved from: https://www.czso.cz/csu/czso/cri/postaveni-primarniho-sektoru-v- ekonomice-cr-2014-v0jolr7bmu. Luty, P. (2016). THE ANALYSIS OF MERGERS AND ACQUISITIONS IN SERBIA AND POLAND IN THE PERIOD 2005 - 2014: A SECTORAL APPROACH. Retrieved from: http://portal.finiz.singidunum.ac.rs/Media/files/2016/14-25.pdf. Martynova, M., Rennebook, L. (2008). The Performance of the European Market for Corporate Control: Evidence from the 5th Takeover Wave, Sheffield University: Management School Retrieved from: http://ssrn.com/abstract=941731. 425 Salachová, B., Vítek, B., Gláserová, J., Otavová, M. (2014). Obchodní korporace a jejich přeměny: (právní, účetní a daňové aspekty). Vydání první. Ostrava: Key Publishing. Sedláček, J. (2013). Proces fúzí obchodních společností v právních, účetních a daňových souvislostech. Vyd. 1. Brno: Masarykova univerzita. Skálová, J. (2015). Účetní a daňové souvislosti přeměn obchodních společností. 2., aktualiz. vyd. Praha: Wolters Kluwer. Šuraň, A. (2016). Fúze a akvizice v českém zemědělství. Brno. Diplomová práce. Masarykova univerzita. Vedoucí práce doc. Ing. Jaroslav Sedláček, CSc. Vítek, B., Salachová, B. (2014). Právní prostředí podnikatele. Vyd. 1. Ostrava: Key Publishing. Zákon č. 125 ze dne 19. března 2008 o přeměnách obchodních společností a družstev. Retrieved from: https://portal.gov.cz/app/zakony/zakonPar.jsp?idBiblio=66974&nr=125~2F2008&rpp=15 #local-content. Zákon č. 563 ze dne 12. prosince 1991 o účetnictví. Retrieved from: https://portal.gov.cz/app/zakony/zakonInfo.jsp?idBiblio=39611&nr=563~2F1991&rpp=1 5#local-content Zákon č. 586 ze dne 20. listopadu 1992 o daních z příjmů. Retrieved from: https://portal.gov.cz/app/zakony/zakonInfo.jsp?idBiblio=40374&nr=586~2F1992&rpp=1 5#local-content 426 Reporting of Goodwill in Mergers & Acquisitions Zuzana Křížová Masaryk University Faculty of Economics and Administration, Department of Finance Lipová 41a 602 00 Brno, Czech Republic E-mail: krizovaz@econ.muni.cz Abstract: In the new economy, intangible assets have become the main value creators for a large number of companies and economic sector. Recent growth of the service sector and of the information technology-related business, along with the dramatic increase in the number and size of mergers and acquisitions, has made accounting for intangible assets very significant, especially in the field of reporting of goodwill. Comparative analysis is focused on the differences between IFRS, US GAAP accounting procedures and Czech accounting legislation. Main areas of analysis and synthesis are the identification of mehods for goodwill recognition and reporting. The result is to recommend more broadly voluntary disclosure in the reported financial statements of companies. Data disclosed and published on a voluntary basis are Keywords: goodwill, intangible assets, mergers and acquisitions, reporting JEL codes: F36, L21, M41 1 Introduction In the new economy, intangible assets have become the main value creators for a large number of companies and economic sectors. However, the valuation of these assets within the accounting framework raises several problems with regard to their identification, measurement, and control (Zeghal, D., Maaloul, A., 2011). The relatively recent growth of the service sector and of the information technologyrelated business, along with the dramatic increase in the number and size of international mergers and acquisitions, has made accounting for intangible assets (IA) very significant (Lev, 2001). Context determines the value given to tangible assets and makes an even higher impact on the valuation of intangible assets. It is important to bear in mind that context affects the valuator's perception and that individuals assess intangible components subjectively (Axtle-Ortiz, M.A., 2012). In view of their importance, the investor is made aware of the intangibles during various meetings with the entrepreneur (Smith, Cordina, 2014). At present the variability of recommendations for reporting elements that are not part of financial statements continues to grow. There is growing evidence that narrative disclosure is superior under a mandatory regime (Aerts et al., 2013, Beattie et al., 2008 and Li, 2010). According to Kang (2011) are few comprehensive guidelines for corporations in either International Financial Reporting Standards (IFRS) or in United States Generally Accepted Accounting Principles (US GAAP) on how to report IA, other than for purchased goodwill and some development costs, in company financial statements. There is in the accoutancy of intangibles sometimes-large difference between accountig rules and guidelines implemented in different parts of the world. Although accounting rules of different countries are converging, differences still remain also with regard to the treatment of intangibles. The most important progress made in the last years is that the intangible assets to be accounted for have been described extensively togeher with procedures for their impairment and amortisation. In order to calculate or valuate reliably a monetary value for particular intangibles different methods and procedures are followed. Another major obstacle is formed by the fuzzy character of 427 goodwill (Seetharanam et al., 2006), which is often described as an amount of money paid in excess of the goods acquired. Given that goodwill is usually a significant part of the total purchase price and often the largest component, it is right that stakeholders should be told what it represents but two doubts remain as to whether accounting standards go far enough. Is a qualitative description, along the lines required in the revised standards, adequate for stakeholders’ needs? And secondly, what happens if companies fail even to comply with this simple disclosure requirement? This article analyses in more depth what can be done in analysing goodwill. 2 Methodology and Data Goodwill as a important part of intangible assets has been more and more interesting not only for researchers but mainly for company managers and owners. This article deals with differences in accouting treatments and reporting in financial statements pursuant to the IFRS in comparison to the US GAAP and the Czech accounting legislation. The rewiew also lists current trends, terms and definitions used in dealing with term goodwill in publications on intangible assets and mergers and acquisitions. Our results are going to be a part of pilot project, its aim is to analyse accounting practices in reporting goodwill, identify the differences in procedures, and to evaluate their influence on the reported situation concerning equities and capital of the participating companies and investments of partners. This article is based on analysis and comparisons of relevant literature resources, mainly articles and conference papers, but also legislative acts and monographs. 3 Results and Discussion Zanoni (2009) is providing a precise and accurate definition of goodwill, which allows to differentiate between goodwill emerging from a business combination recognized by standards and internally generated goodwill. From a theoretical perspective, the goingconcern goodwill is the present value of abnormal earnings flows expected by the firm. According to Reporting Goodwill Internationaly (2008) the recent issue of revised accounting standards for business combinations under US GAAP (SFAS14R) and IFRS (IFRS3R) has been a significant step down the road to convergence. One of the most interesting changes has been the adoption by the Financial Accounting Standards Board, in the US, of the IFRS requirement for acquiring companies to disclose and explain the nature of the goodwill arising from the purchase price allocation. SFAS141R will require acquirers to disclose “a qualitative description of the factors that make up the goodwill recognized. It goes on to suggest that these might include expected synergies and intangible assets that do not qualify for separate recognition. The new US GAAP requirement remains noticeably weaker than the IFRS3 equivalent which is for “a description of the factors that contributed to a cost that results in the recognition of goodwill, a description of each intangible asset that was not recognised separately from goodwill and an explanation of why the intangible asset’s fair value could not be measured reliably or a description of the nature of the excess recognised in profit or loss.” In the Czech Republic application of the International Financial Reporting Standards is compulsory for all accounting units issuing securities registered on the regulated securities markets of the EU Member States. The intangible asset area is settled in the Czech accounting legislation in Standard no 013 Long-Term Intangible and Tangible Assets (Křížová, 2008). This standard contains definition and pricing of long-term intangible and tangible assets, depreciation principles, procedure of account recognition on acquisition, technical 428 valuation and asset write off. Like the other standards the Czech Accounting Standard no 013 is governed by Act no 563/1991 Coll., on Accounting, and Decree no 500/2002 Coll., executing some provisions of the Accounting Act. The standard contains the list of assets defined as long-term intangible assets. These include establishment costs, intangible results of research and development, software, rights of determinable value and goodwill. The condition for classification as long-term intangible asset is usable life of more than one year and the asset value higher than the valuation limit determined by the accounting unit. This class also includes other long-term intangible assets, long-term intangible work in progress, and advances provided for this type of assets. According to Czech accounting legislation in Standard no 013 goodwill is a positive or negative difference between the valuation of the entity, or its part in the sense of the Commercial Code, acquired by purchase, deposit or asset and liability appreciation in the context of company transformation, except for change of legal status of the company, and the sum of the individually revaluated asset items reduced by taken over liabilities. According to Zanoni (2009) six components of goodwill emerging from a business combination are identified. Similar to other, this author breaks down the goodwill emerging from a business combination in overpayment, synergies between the target and the acquiring firm, reevaluation, newly identified intangible assets, and internally generated goodwill. One of the reasons often given to why goodwill has not been described is that it cannot be done or is too difficult. The following are examples of what goodwill is comprised of and how to place a value on it and describe it. There are unlikely to be intangible assets which do not meet the recognition criteria stipulated in FASB 141 and IFRS 3. However, if there are such assets they should be allocated to goodwill. The standards require them to be disclosed and reasons given why they have not been valued. The standards prohibit the valuation of workforce as an identifiable intangible asset, therefore if one has any value it should be included within goodwill. Table 1 Reasons mentioned in purchase price allocated to goodwill Justification of goodwill Frequency mentioned Rights-related reasons Technology related reasons 9 Customer-related reasons 20 Contract-related reasons 2 Cost-savings-related reasons 21 Expertise-related reasons 24 Other reasons 13 Source: Boekestein, B. (2009) p. 394 The value of a business is often greater than the sum of the individual components. This value increase is quantifiable and should be allocated to goodwill and described. Of course the reverse is true as well. Our research has seen no such analysis describing such underlying value attributed to goodwill. The application of the breakdown approaches using the example of a business combination between two Italian banks (Unicredit and Capitalia) presents Zanoni (2009): Real goodwill, terminal goodwill, current and growth goodwill, businesses goodwill, as well as positional and system goodwill are identified. One of the reasons often given to why goodwill has not been described is that it cannot be done or is too difficult. The following are examples of what goodwill is comprised of and how to place a value on it and describe it. There are unlikely to be intangible assets which do not meet the recognition criteria stipulated in FASB 141 and IFRS 3 (R). However, if there are such assets they should be allocated to goodwill. The standards require them to be disclosed and reasons given why they have not been valued. The 429 standards prohibit the valuation of workforce as an identifiable intangible asset, therefore if one has any value it should be included within goodwill. Economy of scale creates significant cost synergies when businesses combine. This can be rigorously quantified and is often a key motivation for the acquisition (Sedláček at al., 2011). For example, a saving of annual head office costs might be quantified at £5m a year, equating to a capital value of £50m. Unit costs can also be greatly reduced through increased purchasing power, creating further cost synergies. According to Goodwill Reporting Internationaly cross-selling opportunities create sales synergies which can be quantified. For example, Aviva’s acquisition of the RAC enabled Aviva to sell RAC services to its existing customers as well as being able to sell Aviva’s products and services to RAC’s existing customers. The portfolio effect can also be created where two powerful portfolios combine, facilitating an increase in overall sales because of their combined attraction. Examples of goodwill qualitative descriptions In its 2006 annual report Mittal Steel Company discloses details of the acquisition of Arcelor for €29 billion, of which €6 billion was allocated to goodwill. In addition Mittal completed two earlier acquisitions in 2005 for a total of €8 billion, including €1 billion of goodwill. One note covers all three acquisitions and states that “Goodwill recorded in connection with the above acquisitions is primarily attributable to the assembled workforces of the acquired businesses and the synergies expected to arise after the Company’s acquisition of those businesses. (3) Granted these acquired businesses are similar, as they are all steel producers, but is a generic disclosure that does little more than repeat the wording in IFRS3 really adequate to explain to stakeholders what €7 billion was spent on. It is depressing how many large companies decide that the present, decidedly unchallenging, requirements of IFRS3 for disclosure of the components of goodwill are all too much, and choose to remain silent, with the acquiescence of their auditors. For example, Goodwill Reporting Internationaly states, that the acquisition of Allied Domecq by Pernod Ricard in July 2005 cost around €15 billion of which €3 billion was allocated to goodwill. There are numerous other examples of a complete disregard of the requirement to disclose the components of goodwill. Alcatel allocated €8 billion to goodwill on the acquisition of Lucent in April 2006. In the year to September 2007, Siemens completed two major acquisitions at a combined cost of €7 billion, of which close to €5 billion was allocated to goodwill. It may be too much to hope that an arcane accounting disclosure requirement might protect stakeholders from poor company managements but stronger disclosure requirements and a requirement to quantify, at least in broad terms, and have audited, the components of goodwill would bring improvements in information for stakeholders to assess for themselves significant business investments. 4 Conclusions The following conclusion can be drawn from this study: discussions about the role and reliability of information of the basic financial accounting statements continue to be evoked by bankruptcies of large companies or by the past global financial crisis. Questions are often asked what causes the profound differences between the accounting and the market value of companies. Analyses and research results in this area often speak about very different ways of human capital management, differences in customer relationships, use of information technologies, employee knowledge or specific corporate organisational cultures. Due to the shift in the way investors and other stakeholders consider emerging economies and their companies, most of the international attention is 430 now on a select group of high-flying and top-performing emerging market companies (Smith et al., 2003). It may be naïve to assert that total transparency regarding IA would automatically enhance the quality of corporate information being distributed to external stakeholders. Quantification and qualitative descriptions of goodwill brings may be a discipline to the acquisition process and management, knowing that their estimates would be subject to audit scrutiny, might be dissuaded from repeating some of the excesses of the past. Such a process would be effective even if the quantification of the components of goodwill did not have to be disclosed in the annual report itself. Data disclosed and published on a voluntary basis are typically provided by listed companies which also attached a business report to their financial statements, in theircase, the financial statements contain far more items than the required minimum. It is unlikely that the investor becomes aware of the intangible assets whilst analysing the financial statements. In view of their importance, the investor is made aware of the intangibles during various meetings with the entrepreneur. At present the variability of recommendations for reporting elements that are not part of financial statements continues to grow. There is growing evidence that narrative disclosure is superior under a mandatory régime. References Axtle-Ortiz, M. A. (2012). Perceiving the value of intangible assets in context Journal of Busines Research, Vol. 66(3) pp. 417-424. Aerts, W., Cheng, P., Tarca, A. (2013). Management's earnings justification and earnings management under different institutional regimes. Corporate Governance: An International Review, vol. 21 (1) pp. 93–115. Boekestein, B. (2009). Acqusitions reveal the hidden intellectual capital of pharmaceutical companies, Journal of Intellectual Capital, Vol. 10 No.3, pp. 389-400. Goodwill Reporting Internationally. Retrieved from: http://www.intangiblebusiness.com/Reports/Goodwill-Reporting- Internationally~1071.html. Kang, H. H., Gray, S. J. (2011). Reporting intangible assets: Voluntary disclosure practices of top emerging market companies, The International Journal of Accounting, Vol. 46, Iss. 4, pp. 402 – 423. Kovács, Z. I. (2015). Immaterial Assets in the Hungarian Accounting System and Financial Statements. Public Finance Quarterly (0031-496X). Vol. 60(2), pp. 226-237. Křížová, Z. (2009). Why Are the Intangible Assets so Interesting for Revenue Authorities? In Global Conference on Business and Finance Proceesings, Volume 4, No. 3 Atlantic City, USA. Křížová, Z. (2008). Many Changes in Recognition and Depreciation of Intangibles. In 4th Workshop on Visualising, Measuring and Managing Intangibles and intellectual Capital. Hasselt, Belgium: EIASM, pp. 101-111. Lev, B. (2001). Intangibles: Management, Measurement, and Reporting. Washington, DC: The Bookings Institution. Sedláček, J., Křížová, Z., Hýblová, E. (2011). Development of mergers and acquisitions in the conditions of the Czech economy 2001–2010. Acta univ. agric. et silvic. Mendel. Brun., LIX, No. 7. Seetharanam, A. (2006). Managing impairment of goodwill, Journal of Intellectual Capital, Vol. 7 No.3, pp. 338-53. 431 Smith, G. (2003). Emerging Might: Champions in China, Russia, and Elsewhere Lure. https://www.businessweek.com/magazine/content/03_28/b3841025_mz047.htm Smith, J., Cordina, R. (2014). The role of accounting in high-technology investments. The British accounting review Vol.46, Issue 3. Zanoni, A. B. (2009). Accounting for Goodwill, Routledge Taylor and Francis, New York. Zeghal, D., Maaloul, A. (2011). The accounting treatments on intangibles – a critical review of the literature. Accounting Forum, 35 (4), pp. 262–274. 432 Analysis of Insurance Distribution on the Czech Insurance Market Martina Krügerová VŠB – Technical University of Ostrava Faculty of Economics, Department of Law Sokolská třída 33, 702 00 Ostrava, Czech Republic E-mail: martina.krugerova@vsb.cz Abstract: In the insurance market, either internal or external sales method can be selected to distribute an insurance coverage. The external distribution channel (also called the intermediary channel) is represented by insurance intermediaries; the internal distribution channel (also called the employee channel) by employees of insurance companies. The new/forthcoming regulation of insurance distribution brings several changes, and alters a number of important fundamentals of the regulation of insurance distribution, in consequence insurance intermediaries, too. One of the goals of a comprehensive amendment, the introduction of a new categorization of insurance intermediaries and removal of differences in the external and internal distribution of insurance. This article is focused on the analysis of insurance distribution aiming insurance intermediaries in the context of insurance market development as well as the changes in regulation of insurance distribution as it comes to both the Czech and European law. The aim is to analyze the groups with the impact of the new regulation. Keywords: distribution of insurance, insurance intermediaries, insurance markets regulations JEL codes: G30, G22, F36 1 Introduction In the insurance market, various distribution channels can be selected to distribute an insurance coverage. The selection of those channels depend on many factors and specific criteria. The marketing strategy of insurance company and evaluation of distribution channel advantages serve as the basis when selecting the products distribution. Economic assessment and legal aspects of considered distribution channel are being evaluated. Further, particular criteria such as insurance company´s status in the market, organizational structure, territorial scope, competitive environment, costs, flexibility effectiveness, capacity and preparedness of the selected distribution channel are considered as well. The specifics of insurance company and offered insurance products becomes the important aspect of the selection – i.e. selection by the type of insurance company or customer (retail clients, entrepreneurs, public administration etc.), further by type of risk or e.g. by insurance product. In the literature, distribution channels are divided into internal or external sales (Ducháčková, 2015, Mesršmíd, 2016). The external distribution channel (also called the intermediary channel) is represented by insurance intermediaries; the internal distribution channel (also called the employee channel) by employees of insurance companies. Insurance intermediaries are the entrepreneurs, either a natural or legal person, who, for payment, provide insurance intermediary activities on the basis of Act No. 38/2004 Coll., on Insurance Intermediaries and Independent Loss Adjusters and on amendment to the Trade Licensing Act (hereinafter the "Act on Insurance Intermediaries"). Employees of insurance companies are under employment of insurance companies and their own network offering insurance products. Employees were exempt from the Act (section 2) of the Act on Insurance Intermediaries, entry into effect of amendment 261/2014 Coll. The reason for employees’ exemption is based on this Act, which regulated business activities, while employees are bound by employment relationship and thus the employer bears the responsibility for their actions. Insurance and reinsurance 433 companies were able to provide their employees with a training program to make them experts. Amendment of Act began to unify the regulation of internal and external sales of insurance products. New Insurance Distribution Directive (in text IDD) changes the regulation of insurance distributors in the EU as well as insurance intermediaries. The IDD enters into force on 23 February 2016. Member States have 24 months to transpose its provisions into national law. By 23 February 2018, insurance distributors will be required to comply with the new rules. The IDD directive aims to improve the legislative modification of the retail insurance market in effective way. Its focus is to ensure fair conditions for all participants in the sales of insurance products and to strengthen the customer protection. The paper focuses on analysis of concept insurance distribution, in more detail insurance intermediaries, in the context of developments in the insurance market and changes in the insurance distribution regulation in Czech and European law. The aim is to analyze the groups with the impact of the new regulation. The article therefore provides with a point of new categorization of insurance intermediaries. 2 Methodology and Data Insurance distribution means to sell, propose the sale, give advice or carry out different activities in order to prepare an insurance contract. It includes dealing with claims after an insurance event. The Insurance Distribution Directive (IDD) regulates the activities of insurance intermediaries, insurance companies, their employees, ancillary insurance intermediaries as well as online distribution. Distribution channels being used when selling the insurance are similar in particular countries; however, specifics exist as well. All EU members are obliged to deal with new regulation regarding the insurance distribution and transpose it into their laws. Some countries though express their objections the regulation as they have their own specifics when using the distribution channels mainly agents and brokers. For example, Ramharter (2016) examines a critical analysis of information requirements and rules on advice pursuant to the new Directive (EU) 2016/97 on insurance distribution (IDD). In his articles, Mesršmíd evaluates and analyses optimal options of transposing the regulation into the Czech law. Insurance intermediaries in the Czech Republic are regulated by the Act No. 38/2004 Coll., on Insurance Intermediaries and Independent Loss Adjusters and on amendment to the Trade Licensing Act (hereinafter the "Act on Insurance Intermediaries"). The categorization of insurance intermediaries should be amended by the draft law Act on insurance and reinsurance distribution. Rewarding of intermediaries is discussed area, too. Šindelář (2015) evaluates distribution and remuneration in life insurance. Browne, Ju, Tu (2014) offer a different view of contingent commissions, which are payments made by an insurer to brokers based on the volume and profitability of insurance placed with the insurer. Carson, Dumm, Hoyt (2007) review use of contingent commission compensation and the economic rationale for contingent commissions for independent agents. Marano, Rokas, Kochenburger (2016) deal with new option of online sale of insurance. They focus on insurance sales, consumer protection, cyber risks and privacy, as well as dispute resolution. Data used in this article are collected from annual reports provided by the institution of the Czech insurance market mainly Czech National Bank, Czech Insurance Association. For comparison, data from the OECD or the European Insurance and Reinsurance Federation (Insurance Europe) are used. These institutions publish an analysis of the insurance market from different views. Further, data of annual reports provided by 434 significant insurance companies operating in the Czech insurance market mainly used distribution channels are added. 3 Results and Discussion Gross premiums increased worldwide in the life insurance and in the non-life insurance. (OECD Insurance Statistics). Insurance companies realize very clearly that the distribution is the key factor of their success. To sell their insurance products, they use a combination of different channels showed in Table 1. Table 1 Insurance distribution channels Internal channels External channels Alternative channels Employees (own business service) Insurance intermediaries (not included in the internal distribution) Direct internet marketing Exclusive insurance agents Brooker pool Telcassurance Intermediaries being controlled by insurance company Cooperative Partners including Bancassurance, Cross-selling Source: own processing In Figure 1 show model distribution channels from the perspective of direct distribution or intermediated distribution. Figure 1 Direct or intermediated channels in insurance Source: own processing Internal distribution channels Among traditional internal distribution channels, employees of branches nets of insurance companies (own business service) and net of exclusive insurance agents together with insurance intermediaries being controlled by particular insurance company are. Governmental bill as for distribution of insurance in accordance with the regulation influences also own business services of insurance companies. As the consequence of Directchannels Intermediated channels Customer Exclusive insurance agents Cooperative partners Broker pool Intermediary Ownsalesservice E-sales:internet,teleassuranceatc. Insurer 435 those changes, some insurance companies made the changes in internal distribution model. For example, the Česká pojišťovna created 100 % subsidiary company ČP Distribuce s.r.o., into which internal distribution of the Česká pojišťovna will be transferred within 2017. (Česká pojišťovna, 2016). Together with the aim to ensure continual business services, it changes the way of rewarding as well as adds different benefits. Table 2 Number of employees of insurance company and insurance intermediaries operating in the name and on the account of one insurer only (Czech Republic) 2009 2010 2011 2012 2013 2014 2015 Total employees of insurance company 13985 13678 13314 13763 13397 13173 13018 - of which: staff underwriting insurance 3362 3035 2803 2811 2782 2668 2545 Total insurance intermediaries operating in the name and on the account of one insurer only 17994 18980 18788 16866 15232 14194 13264 - of which: exclusive insurance agents 11751 12606 12827 11443 11583 10657 9813 - tied insurance intermediaries 4768 4808 4216 3693 2763 2432 2150 Source: Czech Insurance Association, Annual report External distribution channels Among external distribution, insurance intermediaries belong being not included into own net of insurance company. The most often, insurance agents and insurance brokers are considered. The current legislation dealing with insurance intermediaries allows an operation in the following six categories: a tied insurance intermediary, a subordinate insurance intermediary, a fixed insurance agent, an insurance agent, an insurance broker (see Table 3) and an insurance intermediary, whose home member state is other than the Czech Republic. (see Adolt, Suchánek, 2005). Table 3 Categories insurance intermediaries (Czech Republic) Source: Czech National Bank, Statistics The established categorization of insurance intermediaries is to be changed by the government bill dealing with distribution of insurance and reinsurance. Instead of existing five categories of insurance intermediaries, there will only be two new categories: independent intermediaries and tied agents. They differ from each other by the fact whether the person concerned is responsible for his/her actions, or whether this 2009 2010 2011 2012 2013 2014 2015 2016 Insurance intermediary 91219 109971 132543 141809 150418 157245 162956 167826 - broker 643 689 728 760 782 790 780 783 - agent 1193 1286 1367 1412 1452 1442 1393 1379 - fixed agent 23260 27970 32325 34565 36780 38709 40014 41448 - tied intermediary 11008 11822 12278 12421 12676 12751 12917 12823 - subordinate intermediary 55115 68204 85845 92651 98728 103553 107852 111393 - foreign intermediary 3741 4055 4704 4937 5570 5691 5802 5998 436 responsibility will be assumed by another entity. This division based on the principle of accountability reflects already established and proven regulation on the capital market. The insurance intermediary will be able to register only in one position; a concurrence of both categories in a single entity will not be conceivable, which is the case under the current legislation. The category of insurance intermediary, whose home member state other than the Czech Republic, will remain unchanged (Foreign intermediary). Gradually it shows how important it is to follow importance of brokers and agents in the distribution of insurance products. (see Eckardt, Räthke-Döppner, 2010). The commissions-to-premiums ratio provides information on the relative importance of brokers and agents in the distribution of insurance products (relative to direct sales by insurance companies themselves). Kwon, Wolfrom, 2016. It is obvious that it is necessary to search for an optimal set of rewarding system to reward the employees of business sale as well as intermediaries themselves. For example, rewarding the sales over long-term retention or suitability of the products can result in unsuitable sales or the unnecessary replacement of life insurance and savings products. Therefore, some regulatory adjustments are made. The aim is to avoid the conflict of interests and always recommend such product being the most suitable for the client not considering the amount of commission or other reward of the salesman. (eg empirical analysis by Browne, Ju, Tu, 2014 or analysis in life insurance by Šindelář, 2015) Among other options of external sale the sale by partners is - leasing companies, banks, savings and loan associations, car sale companies, real estates, travel agencies, the Czech Post, commercial chains etc. Also cross-sale is used. Those partners are also included into the Act on insurance intermediaries. Also these persons have to fulfill the requirements and conditions of the act and have to be registered in some of categories, also by the government bill. Alternative distribution channels Insurance companies are increasingly using modern information technologies. That means insurance arranged on-line or by phone (by sending sms or call center) (see Marano, Rokas, Kochenburger, 2016). Only some determined products can be arranged on-line or by-phone; offers of insurance companies differs across the insurance market. Insurance companies add the alternative ways of sale by modern ones such as cashless payments, digital signature or mobile payment terminals. For example, as it comes to non-life retail products, Insurance Company Kooperativa intends to adjust insurance conditions, agreements and legislative aspects so that the agreements could be allowed to be accepted by payment receive. Client though would pay the insurance amount electronically and would not be obliged to sign the agreement. All needed documents will be sent to him by email. (Kooperativa, 2016). Distibution channels According to the Insurance Europe (2016) is bancassurance the main life distribution channel in many European countries. The most products were sold via bancassurance in Italy (79% of gross written premiums) and France (64%), while in the UK8 and Germany most life products were sold by agents and brokers (83% and 73% respectively). The market in which agents and brokers were most dominant was Bulgaria (85%). Agents alone were the main distribution channel in Slovenia (82%) and Slovakia (63%). (see Figure 2). (Insurance Europe, 2016). 437 Figure 2 Life distribution channels (% of GWP), 2014 Source: Insurance Europe, European Insurance in Figures, 2015 data Non-life insurance policies are mainly distributed through agents and brokers. Agents predominate in Slovakia (80%), Italy (79%), Turkey (66%), Slovenia (62%), Germany (58%) and Portugal (56%). Meanwhile, brokers account for 61% of non-life premiums in Belgium and 50% in Bulgaria. (see Figure 3). However, direct sales through employees or distance-selling are less developed in life than in non-life insurance. (Insurance Europe, 2016). Figure 3 Non-life distribution channels (% of GWP), 2014 Source: Insurance Europe. European Insurance in Figures, 2015 data Monitoring of the distribution method is not interest of the supervisory authority, therefore it is not possible to create objective time series for the Czech Republic. 438 According Czech Insurance Association the distribution of non-life policies in Czech Republic is mainly through intermediaries and direct sales by employees and distance- selling. 4 Conclusions Insurance is inseparable part of the risk management of companies as well as financial portfolio of most of Czech homes. Insurance market is developing continuously, is innovated, new products occur as well as new risks. Distribution of insurance ensures significant competitiveness in the market so insurance companies permanently develop their distribution channels. The paper focuses on analysis of concept insurance distribution. Among internal distribution, intermediaries belong being controlled by insurance company and insurance intermediaries operating on behalf of and on the account of insurance company. Intermediaries not included into the own net of insurance company belong to the external channels. As distribution channels have evolved over time, their financial compensation mechanisms change too. The new European rules for insurance distribution will come into force on 23 February 2016. The bill on insurance distribution intended to transpose the European Insurance Distribution Directive (IDD) will be submitted in the Czech Republic. The bill to fully supersede the existing Act No 38/2004 on Insurance Intermediaries and Independent Loss Adjusters. New changes will be of contribution for insurance market aiming an improvement of market transparency, removal of differences in external and internal distribution of insurance as well as strengthening of credibility of insurance distributor´s profession (both external and internal one). In terms of the changes in regulation, insurance companies have to adjust their distribution channels. Development of information technologies has its significant impact on it so insurance companies will search for more effective and newer approaches. However, personal sale or sale with advising will hold one of the most significant positions. In case the product is being sold without counseling, clients can be surprised by the result because the product could not match their requirements. Insurance companies monitor quality indicators – the way of the sale versus the result (expectation) for client (relation of determined sale and real duration of agreement). Evaluation of quantitative parameters of the salesmen (not volume of production) becomes the advantage – e.g. volume of cancellations, the results of clients satisfaction measuring, the number of rightful claims etc. The emphasis will be put to the knowledge, skills and experience of advisors and sales staff. Both employees and insurance intermediaries have to possess of present knowledge about the products, sale practices and rules regarding the consumer protection. Otherwise, there exists the risk of improper sale. Insurance companies and intermediaries have to set internal controlling mechanism in order to manage the quality of distribution net. Distribution channels being used when selling the insurance are similar in particular countries; however, specifics exist as well. According to the Insurance Europe (2016) is bancassurance the main life distribution channel in many European countries. Non-life insurance policies are mainly distributed through agents and brokers. However, direct sales through employees or distance-selling are less developed in life than in non-life insurance. In Czech Republic the distribution of non-life policies is mainly through intermediaries and direct sales by employees and distance-selling. Insurance fraud as well as cyber-attacks are of consideration when choosing the distribution channel. Distribution channels can be of help in the fight against insurance fraud; advisors are the first and the most important defensive line. Customers are reliant 439 on advisors´ information, instructions and advice; it does not have to be stressed that advisors have to fulfil their duties in ethical way. Acknowledgments This paper was supported under the Operational Programme of Education for Competitiveness – Project no. CZ.1.07/2.3.00/20.0296 References Adolt, J., Suchánek, P. (2005). Pojišťovací zprostředkovatelé. Praha: ASPI Publishing. Browne, M., Ju, L., Tu, Z. (2014). Monitoring of Premium Adequacy: The Role of Contingent Commissions. Applied Economics, Vol. 46, No. 20, pp. 2375–2386. Carson, J. M., Dumm, R. E., Hoyt, R. E. (2007). Incentive Compensation and the Use of Contingent Commissions: The Case of Smaller Distribution Channel Members. Journal of Insurance Regulation. Vol. 79, No. 1, p. 53 – 67. Czech Insurance Association, Annual report. Retrieved from: http://cap.cz/o-nas/vyrocni- zpravy. Czech National Bank, Statistics. Retrieved from: http://www.cnb.cz/cs/dohled_financni_ trh/seznamy/jerrs.html. Česká pojišťovna (2016). Annual report 2016. Retrieved from: https://www.ceskapojistovna.cz/vyrocni-zpravy#2016. Directive (EU) 2016/97 of the European Parliament and of the Council of 20 January 2016 on insurance distribution (IDD). Ducháčková, E. (2015). Pojištění a pojišťovnictví. Praha: Ekopress. Eckardt, M., Räthke-Döppner, S. (2010). The Quality of Insurance Intermediary Services—Empirical Evidence for Germany. The Journal of Risk and Insurance. Vol. 77, No. 3, pp. 667 – 701. Insurance Europe (2016). European Insurance in Figures, 2015 data. Retrieved from: https://www.insuranceeurope.eu/sites/default/files/attachments/European%20Insurance %20in%20Figures%20-%202015%20data.pdf. Kooperativa pojišťovna, a.s., Vienna Insurance Group (2016). Annual report 2016. Retrieved from: https://www.koop.cz/preview/format;html/assets;a14726?field=data. Marano, P., Rokas, I., Kochenburger, P. (Eds.). (2016). The "Dematerialized" Insurance: Distance Selling and Cyber Risks from an International Perspective (1st ed.). Switzerland: Springer. Mesršmíd, J. (2016). Marketing v pojišťovnictví, Praha: Kamil Mařík - Professional Publishing. Kwon, W. J., Wolfrom, L. (2016) Analytical Tools for the Insurance Market and MacroPrudential Surveillance. OECD Journal: Financial Market Trends. Volume 2016/1. OECD Insurance Statistics (2017) Retrieved from: http://stats.oecd.org/ Index. aspx?DatasetCode=INSIND. Ramharter, M. (2016). Recent and Future Matters af Life Insurance under Civil and Supervisory Law – Information and Advice Pursuant Directive (EU) 2016/97 on Insurance Distribution. Zeitschrift für die gesamte Versicherungswissenschaft. Volume 105, Issue 3, pp 221 – 254. 440 Šindelář, J. (2015). Mýty a fakta o distribuci životního pojištění. Unie společosti finnančního zprostředkování a poradenství. Retrieved from: http://www.usfcr.cz/wp- content/usf-publikace/myty-a-fakta-o-distribuci-zivotniho-pojisteni.pdf. 441 The Governmental Policy of Budget Balancing in Ukraine Petro Krush1 , Dmytro Mastiuk2 , Petr Valouch3 1 National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Faculty of Management and Marketing, Department of Economics and Entrepreneurship Peremohy Ave. 37, building 7, 03056, Kyiv, Ukraine E-mail: kafedra-ep@yandex.ua 2 National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Faculty of Management and Marketing, Department of Economics and Entrepreneurship Peremohy Ave. 37, building 7, 03056, Kyiv, Ukraine E-mail: dimamastiuk92@gmail.com 3 Masaryk University Faculty of Economics and Administration, Department of Finance Lipova 41a, 602 00 Brno, Czech Republic E-mail: petr.valouch@mail.muni.cz Abstract: The main aim of the paper is to identify distinctive features of the state Budget balancing policy realization, to define the internal and external factors that influence the State’s Finance and to determine sources of the supplement and balancing of the Ukrainian State Budget. As the result, the paper provides crucial contradictions of the state policy implementation of the State Budget balancing in Ukraine. In the article, we group main influential factors, depends on the nature of the influence (external and internal), that define the negative state and misbalance the Ukrainian Budget. Herein, we offer the scientific approach for Ukrainian Budget’s replenishment and balancing with main accent on activation of the internally related financial potential sources. Introduced approach will ensure the equalization of the expenditures and income, which will set up the basis for a long-term stable growth and resolving of the socio-economic issues. The results of the research are very important and can be applied for the anti-crisis plan development in order to ensure the additional incomes to the State Budget using the potential financial sources accumulation, as the basis for effective long-term execution of the Ukrainian state policy of the Budget Balancing. Keywords: Budget, state finance, state policy, Budget deficit, state debt JEL codes: E02, E42, E52 1 Introduction The primary policy of most developed countries during the deployment of the global financial crisis has become the strategy, which aims to balance the public finances. In practice, such a strategy determines not only the extent of social problems solving, but also stabilizes the economic fluctuations, primarily related to accumulation and redistribution of financial resources and helps to search for reserves to ensure the stabilization of the economic development. The issues of the stabilization and balancing of the State Budget, which are connected with worldwide crisis tendencies and local negative factors, have arisen in front of the Ukrainian government, and lead to unbalancing of the financial system. Thus, the question of the State finances replenishment is particularly relevant in the theoretical and practical investigations. Taking into consideration the papers related to the public finance, we indicate that the issues of the formation and development of the financial system, the deficit of the State Budget and its balance, problems of monetary policy and the Government debt were revealed in the significant number of the Ukrainian scientific works. However, the rapid onset of the second wave of the financial crisis has strongly risen the issue of reducing the Budget deficit and public debt as the basis of balancing the State Budget. In this 442 context, the matter of determining the sources of the State Budget Balancing, as well as clarification, under what conditions they can be effective, is remain unsolved. The questions of the State Budget Balancing, developing of the financial system, Budget deficit and monetary and debt issues have been revealed in numerous work of local and foreign scientists. Moreover, researches related to acquiring of the external financial sources and formation of the excessive debt leverage, as one of the reasons of financial environment worsening of developing countries are revealed by foreign scientists, such as Samuelson (1954), Rogoff (2003), Krugman (2012), Calvo (2014) and Reinhart (2015) etc. The main aim of the paper is to identify distinctive features of the state Budget balancing policy realization, to define the internal and external factors that influence the State’s Finance and to determine sources of the supplement and balancing of the Ukrainian State Budget. Therefore, this should be the key to identification of the replenishment sources and balance the State Budget of Ukraine. 2 Methodology and Data In 2012 individual Western economies entered the phase of recession. On that moment, the main factors of the crisis deployment became the problems and imbalances in the systems of the public finance. Therefore, the current wave of economic crisis is based on disbalances of a State Budget. The income shrinking, the simultaneous rise of expenditures and financing of large-scale stimulus programs have generated significant deficits and forced Western Nations to expand the debt involvement. Nevertheless, such a policy in the face of further worsening of the global financial and economic crisis has led to rising the public debt servicing, the access difficulties to loans and the limitation of opportunities for the socio-economic arrangements financing. Under these circumstances, the need to balance the system of public finances had been declared as a priority of economic policy, particularly by reducing the Budget deficit and national debt that forced the Governments of Western countries to cut spending and increase tax burden, adversely affecting the economic dynamics. Even though the Ukrainian economy has little to do with the developed countries of the West, it had also experienced the crisis trends due to the fact that the Government, on the one hand, carried out a similar policy by funding the anti-crisis programs with the sharp rise in the deficit of the State Budget, and on the other, inefficient use of existing financial resources. Ineffective management of public finances and their impact on the socio-economic dynamics of the national economy development has a threatening character. This character can be approved by analytical and statistical data. Thus, regarding the expenditures of the State Budget, according to reports the Accounting Chamber every third UAH has been spent ineffectively (PER, 2010-2014; Fedosov, 2009). Therefore, especially important for the stabilization of the national economy appeared the balancing of public finance and its effective usage (Moldovan, 2012; Rogoff, 1985; Rogoff 2003). The Government must define the priorities of the system of public finance improvement. The deepening problems in this area can lead to much more negative influence on economic stability than external factors (Calvo, 2014; Reinhart, 2015). Imbalances in the public finances are dangerous as they produce negative macroeconomic trends, which, in turn, lead to further proliferation of the deficit and difficulties of access to the financial resources. The need to improve the balance of the State Budget of Ukraine, as a main priority for improving the efficiency of State regulation of the socio-economic processes, has been indicated in the presidential Program of economic reforms for 2010 – 2014, which eventually transferred to the program of current President of Ukraine. It's a question of 443 countless debates through the national action plan for 2012, and moreover for 2014- 2018. Nowadays, the National Action Plan for 2016-2020 states the unbiased and urgent need to solve the issues of balancing the State Budget as a basis for building an effective system for managing public finances and its orientation to the socio-economic problems (RadaGovUa, 2016). Particularly, there are tasks directed on reducing the deficit and the growth of the public debt, increasing the efficiency and the flexibility of fiscal expenditure and optimizing the management of public finances (PER, 2014; RadaGovUa, 2013; RadaGovUa, 2016). However, despite the existence of regulatory, the issue of balancing public finances solving runs very slowly. In our opinion, this is due to the influence of external and own internal problems and contradictions. The internal reasons we include, firstly, a fairly high level of cumulative state deficit, which Ukraine faces from year to year and could not overcome with defined toolkits. Analysis of indicators of the deficit of the State Budget over the past nine years proves its growth (Figure 1) in the structure of GDP. Figure 1 Changes in Ukrainian Budget deficit in 2008-2016 in millions of UAH (L) and in percentage (R) Source: introduced based on Ministry of Finance data (2017) Talking about general debt, at the end of 2015 the governmental and state-guaranteed debt reached the point of 79% to GDP, which much higher than European level of 60% that creates problems for its maintenance. The average Debt Stability indicator for developing countries in 2015 was 39,4%, while Ukraine in January 2016 reached 79,4% that doubled the average level. To put it differently, the accumulated governmental debt not only reached the healthy defined level, but exceeded it, which influenced the Debt Stability level that leads to increasing the expenditures for the debt maintenance (Yakibchuk, 2016; MinFin, 2016). 3 Results and Discussion It was not the exception, and last 2016 year we can observe the growth of Budget deficits month-to-month (Figure 2). 20 08 20 09 20 10 20 11 20 12 20 13 20 14 20 15 20 16 Income 2E+02E+02E+03E+03E+03E+04E+05E+06E+0 Outcome 2E+02E+03E+03E+04E+04E+04E+06E+07E+0 Deficit -125-355-642-235-534-647-780-451-701 -200000 0 200000 400000 600000 800000 Value,mln.UAH YearIncome Outcome Deficit -10,00% 0,00% 10,00% 20,00% 30,00% 40,00% 2008 2010 2012 2014 2016 %toGDP Year Income % to GDP Outcome % to GDP Deficit % to GDP 444 Figure 2 Changes in Ukrainian Budget deficit in 2016 in millions of UAH Source: introduced based on Ministry of Finance data (2017) It should be noted that the growth of the Budget deficit can lead to a sharp decline in investment activity, increase of the indices of inflation, compounding the imbalances in the structure of national economy (Fedosov, 2009; Krugman, 2012). Similarly, saddled with a commitment to service and responsible public debt (MinFin, 2017). Thus, over the past five years, the national debt increased 5.8 times in UAH (with 88.7 billion to 515.4 billion) and in 3.7 times in dollar equivalent (17.6 billion to 64.5 billion). The current year in the performance of the Budget of the planned indexes as part of State loans and guarantees to the Government public debt could reach 645 billion at the end of the year, will be 41% of the official GDP forecast for 2017 and more than 45% of GDP – in case of use of more real forecasts of GDP and the exchange rate. If we talk about the risks that arise in this case, for the country, the excessive debt levels could undermine the long-term fundamentals of the economic development and increase the vulnerability of the economy to reverse the movement of foreign capital, sharp changes in exchange rates. Moreover, according to experts (Bohdan, 2016) in such circumstances, substantially increases the probability of debt crisis in Ukraine, which can trigger the promotion of Helix financial destabilization and exacerbation of the crisis in the real sector of the economy. The impetus it could become like a narrowing of the potential to attract new loans, and the insufficiency of budgetary resources for the implementation of already accumulated debt. In addition, it is still open and remains the problem of imbalance of local Budgets, pension fund, NJSC "Naftogaz of Ukraine" (Moldovan, 2012). The reformation of the Pension Fund is extremely important for the financial system stabilization as approximately 11% of the GDP goes to the Pension Fund where only half of it is covered by pension payments, and the rest of it financed by budget. The pension reform is important not only for budget stabilization, but for increase of the social protection. Almost 70% of the retirees live under the level of poorness. According to the State Budget in 2016, the expected deficit of the Pension Fund (together with State contributions) was at a level of almost 57 – 58 billion. Individual experts estimate that the real Budget deficit of the Pension Fund had a fold 68 – 70 billion USD (Rozenko, 2015; Yakibchuk, 2016). Negative expectations largely justified. According to the Professor of the National Academy of public administration under the President of Ukraine Puhkalo, in 2017, the Government will have to subsidize these pension fund at 83 billion (Yakibchuk, 2016). Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Income 29637 70585 129159 171948 219227 265632 301300 361180 407551 463566 543427 616275 Outcome 42892 72921 140159 193673 245599 300410 349996 403156 470766 523363 588883 684743 Deficit 2452,1 -2404 -10567 -22191 -26447 -35089 -49945 -42936 -63402 -60303 -45543 -70130 -200000 0 200000 400000 600000 800000 Value,mlnUAH Month Income Outcome Deficit 445 Moreover, one of the most important reforms for the Budget stabilization is the reformation of the healthcare system, which is currently is considered as extremely not effective. Ukraine spends around 4% of the GDP on the healthcare – more than any of the countries with similar level of income – but the quality of the healthcare system remains very low (RadaGovUa, 2016). We agree that the enhance of socio-economic standards of society is the main task of the State, so the economy (including Ukrainian) should work to ensure a higher level of wellbeing of the population. Nonetheless, this level of well-being is directly correlated with the level of economic development and currently and we can observe the misunderstanding of the dependencies between them. If we talk generally about the factors ensuring socio-economic growth in part of balancing public finances, we will consider the external and internal factors. Firstly, to the external factors that affect the stabilization of public finance, it is necessary to include the increase of the risk of another wave of financial crisis. Low investment image of Ukraine and high risks of a loss of foreign capital, recession of most Western countries and, as a result, the deterioration of their economic and financial situation. These factors significantly complicate the access of Ukraine to the financial markets and block the international loans. Introduced by the authorities, several legislative and institutional rules and regulations do not protect fully the rights of property and do not create an attractive for domestic and foreign capital investment environment. The existing imperfect system of protection of the rights of the lender, depositor and investor together with increasing the level of credit risk, as well as the lack of efficient mechanism of attracting investments, including international, are indicated in the governmental documents (PER, 2014) and international ratings. Here, it is worthwhile to state the fact that the desire to solve the problem of balancing the country's Budget, primarily due to external sources (borrowing) can lead to rapid disruption of macroeconomic financial stability and the lead to the "trap" of long-term insolvency. While the hope for the internal factors and high rate of economy growth of Ukraine, and the replenishment of the revenues of the State Budget are illusory, however, from our point of view with the situation that has developed in the Ukraine, it is required from the State to concentrate on internal factors. Now we can watch realistic predictions about the significant slowdown in the growth of the economy, and, respectively, and Budget revenues. Moreover, real GDP growth in 2016 amounted to just 0.2%, which does not match even the most pessimistic predictions. As a result, the World Bank significantly – from 3.5% to 2%, lowered its forecast for GDP growth in Ukraine in 2017 on the background of the general lowering of macroeconomic activity. Consolidated data (KreditProm, 2017) of foreign and domestic experts predict growth and Budget deficit within the limits of more than 3% (table 1). Therefore, we can talk about what the background bitmap improvements in certain areas of the overall dynamics of macroeconomic growth does not contain under a real thoughtful public policy and support. 446 Table 1 Macroeconomic forecast for 2017 year Organization GDP Inflation Budget deficit % to GDP CASE Ukraine 2.50% 8% 3% NBU 3% 9.10% 3.40% IMF 2.50% 15.10% 3% Fitch 2.50% 15.00% 3% World Bank 2% 10% 3.10% EBRD 2% 10% 3.00% Consensus-forecast 2.10% 8.90% 2% Source: based on Ministry of Finance data (2017) and KreditProm (2017) In this situation, identified the problem of imbalance of public finances can become active factors in the decline in economic activity and lead to a dramatic strengthening of the fiscal pressure on the business through the returns of VAT and mobilization of advanced payments for taxes, "crowding out" the liquidity of the domestic market and, as a consequence, freezing lending; an uncontrolled devaluation; a sharp narrowing of the domestic demand; the fall of the investment; social depression (Moldovan, 2012), other negative socio-economic phenomena and processes. The probability of such a deployment script for Ukrainian economy became apparent in 2012-2016 and 2017 is still quite high. Therefore, in our view, particularly important to the possible negative consequences of the crisis is to prepare effective Government plan that would have provided additional Budget revenues due to the accumulation of internal potential financial resources (in the background simultaneous arrangement policy the use of external sources). Thus, an important source of replenishment of the State Budget may be the shadow economy, the scale of which is estimated at more than 50% of official GDP. Just organize illegal import and circulation of excisable goods can bring tens of billions of UAH. Perhaps knowing these problems, the President of Ukraine in 2012, has put the task in front of the State Customs Service (now the Ministry of revenue and fees) on the need for an additional increase in taxes to the State Budget. However, in 2013, the shadow economy not only holds the positions, but reinforces them more. Going further to 2016, according to the Ministry of finance by the end of the year, the Budget again lost 3 billion of excise only oil, and together with VAT and ecological tax losses will reach 7 billion (Ivanchenko, 2016). The following source is public procurement, which every year is growing constantly. They have, in fact, hidden huge financial opportunities. And given the domestic practice of spending and the size of the "sliding" tariffs, can with a high probability estimate that financial resources can be found in this area. Another area of activity of the Government, which could bring the State coffers for more billions of UAH, are establishing an adequate rent for operation of national natural resources. In Ukraine, according to Ukrainian scholars and foreign experts, the level of taxation of the mining industry in the 8-10 times lower than in European countries, which leads not only to large losses, but also to the irrational exploitation of domestic bowels. In addition, the Ukraine does not receive rent for mining and industrial development of iron ore, manganese and uranium ore, coking coal, nonferrous metal ores and other minerals that are the property of the Ukrainian people. Therefore, the extension of the list of the above-mentioned minerals, extraction and realization of which charged rent significantly increase to the amount of Budget revenues. An important source of replenishment of the State Budget is a considerable financial resources, which are hidden in the existing system of social benefits. According to 447 estimates of the World Bank on social benefits in this country spent about 21% of GDP and Ukraine according to this indicator takes the fourth place in the world (Rozenko, 2015). In this case, as evidenced by the statistics, processes that take place in this area in many cases are opaque not created a single register of beneficiaries, there is no targeting of receiving social assistance, and public funds that are allocated to cover the social programs, irrationally. According to estimates, only 23% of all social benefits in Ukraine get needy part of the society (Sokolovskyi, 2012). It is the President of Ukraine was speaking about the need to improve social standards, stressed the need to restore fairness and giving benefits and pay subsidies only to those who truly deserves it. Direction of governmental action that may bring additional revenue to the State Budget, is the introduction of a tax on the wealthy (income, real estate, etc.), and the abolition of special tax regimes offshore areas. An additional source of replenishment of the State Budget can also be tools that will get the Government from undoing its decision about reducing rates of VAT from 20% to 17% and lowering the tax rate to 16%. In the first case the VAT tax rate cuts does not reduce the fiscal pressure on the business and will not lead to an automatic reduction of the retail prices. What regression to improve income, then it would be to introduce a reduction of tax rates is not the entire commodity group, and only the social goods, on which the majority of the population spends almost 60% of their income (Basova, Pokotylova, 2016). On the standards of income tax rate cuts, it does not mean that the company immediately redirects released funds for investment in economic activity, and therefore more effective would be the introduction of a reduced rate of tax on the part of the profit the company reinvests in its development. Review of government programs and plans shows that the authorities are aware of the many challenges of balancing public finances. Among the main issues, it is worth to indicate the possibility of establishing a tax on wealth and the extraction of minerals, amendments to legislation on offshore areas. But the Government's plans and above potential sources of replenishment of the State Budget is closely interrelated with the interests of the subjects of big business and shadow schemes of redistribution of the State Budget. These factors can block government action to improve State finances. Therefore, for the successful implementation of this problem requires political will in making such an economic policy that would be based not on short-term electoral purposes, and the national interest. Not the last role in the implementation of sound economic policies of these positions can be created by the Ministry of revenue and the meeting is intended to streamline customs and tax policy. 4 Conclusions Taking into account the political, economic and institutional features of the Ukrainian governmental system we state that it can be put into the group of developing economies, institutional unbalanced and natural resources oriented economies. One of the main reasons of transformational processes failures and constant socio-economic crisis is the availability in the government of the business groups that using their capital determine and influence the governmental policy, and lobby their interests. To stabilize the economy of the country the first priority of a government has to be the development of the healthy and balanced of public finances structure. To do this, the primary direction of State policy should be oriented to generation of the domestic financial sources, through the preparation and real implementation of Government effective anti-crisis plan with the foundation of the national interests, which could provide additional Budget revenues. The sources of replenishment of the State Budget can be the next the shadow sector, public procurement, the establishment of adequate rent for mining, objectivity policy provision of social benefits, a tax on wealth, the abolition of the preferential taxation of offshore zones, cancellation of the decision on reduced rates of VAT and income tax rates, the total tax recovery of the economy, etc. 448 Currently, we state that external financial sources of the Government Budget balancing should have a supportive character to minimize the risk of insolvency, long-term state of the macroeconomic distress and financial dependence. Therefore, the identified sources and directions of the State Budget Balancing can be used as the basis for further theoretical researches and practical implementations. Acknowledgments The article is written in accordance with the scientific project MUNI/A/0823/2016 – Behavioural, knowledge-based and economic aspects of valuation and trading of financial and other assets of Masaryk University in Brno, Faculty of Economics and Administration, Department of Finance, the scientific project 0116U004578 – Economic diagnostic in the management system of enterprises in market environment of National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Faculty of Management and Marketing, Department of Economics and Entrepreneurship, and the scientific project 0116U004579 - Modern problems of the economy and management of an enterprise, and their solutions. References Basova, L. O., Pokotylova, V. I. (2016). Bidnist v Ukraini: tendentsii, faktory, shliakhy podolannia [Poverty in Ukraine: Trends, factors Cures]. Ekonomika ta derzhava, no. 1, pp. 18-21. Bohdan, T. (2016). 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Rozenko, P. (2015) Protses chy rezultat [The process or result]. Dzerkalo tyzhnia. Ukraina, no. 5 (53), pp. 8-9. Samuelson, Paul A (1954). "The Pure Theory of Public Expenditure". Review of Economics and Statistics. 36 (4), pp. 387–89. Sokolovskyi, Yu. (2012). Utopii dlia utoplenykiv [Utopia for drowned]. Dzerkalo tyzhnia. Ukraina, no. 7 (55) (2012), pp. 7-15. Yakibchuk, M. (2016). Yak podolaty bidnist [How to overcome poverty]. Retrieved from: http://www.epravda.com.ua/columns/2013/03/18/366256/view_print/. 450 IPO Non-Financial Factors Influencing the Investors Decisions Andrea Krýslová University of Technology Faculty of Business and Management, Department of Economy Kolejní 2906/4, 612 00 Brno, Czech Republic E-mail: kryslova@fbm.vutbr.cz Abstract: This article describes the impact of non-financial factors on investor decisionmaking. The range of economic capital needs a funding system that is tailored to the requirements of the new context. The IPO decision is an essential strategic decision that needs to be seen from several angles of view. This article brings new point of view through non-financial factors. The entry into the stock exchange establishes the relationship between the company and a number of institutional and minor shareholders and here is the time to consider meaning of non-financial factors. The relationship between companies and investors need to be solve with the appropriate sense and knowledge about factors, that play the key role in the investors decisions. With the everincreasing range of global investment opportunities, companies need to focus on building long-term investor relations based on trust and regular communication. Non-financial factors are thus one of the central points of this demanding process. High standards of the company and its quality management will significantly contribute to its long-term performance. The goal of the article is to confirm, that companies that develop their skills in non-financial areas, such as value creation, management enhancement, environmental care, and those who can demonstrate their performance through comprehensive reporting, will be much more attractive to investors than companies that are currently learning or have not yet done so. This article is focused in detail on the factors that influence investor decision-making and its inference is, that impeccable analysis of nonfinancial factors and its proper implementation can move company into the next successful phase of building its financial future. Keywords: Corporate Governance, investor decisions, IPO, non-financial factors JEL codes: G11, G02, G34 1 Introduction For many companies, IPO is an important business objective. For growing companies, entry to the stock market is more than just share offer. It is a signal to the business environment that they have reached a significant business goal. IPO does not only provide access to capital and liquidity for companies, but provides an unofficial approval stamp of a successful company in the public market (Marta Maretich, 2014). Registered capital increased by successful public offer strengthens the ability to expand to the new markets. This can help the company attract new business partners and, last but not least, strengthen its "prestige" factor, which not only serves but also pledges. At a stage when a company chooses to enter the stock market, it is no longer responsible for itself but also for its investors. They must be able to demonstrate that they are acting in the interest of their investors and the investors should demonstrate that they are acting in the interest of the company. This non-normative nature of a flexible and robust governance framework will provide the company with good prerequisites for a successful IPO. Quality standards of the company are a central precondition for attracting investors. Therefore, the goal of the company should be to create and to maintain a flexible, effective and efficient company framework that provides added value for investors (McMorrow 2012). IPO is more than just an important milestone for the company. The entry into the stock exchange establishes the relationship between the company and a number of institutional 451 and minor investors. A public offer imposes a number of regulatory obligations and responsibilities between a broad investors group and exposes the company to unprecedented public control. All these aspects create a number of problems that need to be solved with the appropriate sense and knowledge. The relationship between companies and investors become increasingly important, not only with regard to financial factors, but also in particular with regard to non-financial factors that will be further specified. With the increasing range of global investment opportunities, companies need to focus on building long-term investor relations based on trust and regular communication. Nonfinancial factors are thus one of the central points of this demanding process. High standards of the company and its quality management will significantly contribute to its long-term performance. By properly preserving the issue of the seriousness of nonfinancial aspects and their regular development, it can be ensured that a company better applies its strategy and manages its growth regardless of macroeconomic conditions (McMorrow, 2012). Non-financial factors and non-financial reporting of the companies are now gaining in their importance much more than they did in the past and across all industrial sectors. The trend of balancing non-financial and financial factors is becoming increasingly widespread and promoted (Padraig and Murhphy, 2010). This means that companies that develop their skills in non-financial areas, such as value creation, management enhancement, environmental care, and those who can demonstrate their performance through comprehensive reporting, will be much more attractive to investors than companies that they are currently learning or have not yet done so. Perfect analysis of non-financial factors and its proper implementation can move the company into the next successful phase of building its financial future. 2 Methodology and Data This article is based on the analysis of the literature, articles and is based on the theory and previous empirical research. All non-financial IPO indicators analysed in this article have sufficient support in the professional domestic and foreign literature. The survey provides information of the most frequent non-financial factors that influence investor decision-making when buying shares and to what kind of criteria they give more weight. For the primary research, a written controlled interview was used. When compiling the questionnaire survey, care was taken to determine the exact survey goal. The questionnaire included logically structured, comprehensible and non-suggestive questions, the evaluation of which was used to determine a comprehensive statistical set that was further processed using quantitative statistical methods. In addition, data files from the AMADEUS central database were used for primary research. To answer the research questions, what are the non-financial factors which influence the investor decision, a questionnaire survey was produced, the results of which were presented. 35 respondents were interviewed by the structured questionnaire where they attributed corresponding percentage weight to each factor. These respondents were institutional investors, including portfolio managers, stock analysts and executives from the Czech Republic. Questionnaire was send and evaluated between October 2016 and February 2017. 3 Results and Discussion According to the research, the non-financial factors have a major impact on investor's decision-making. These are, for example, the credibility, transparency, experience, the quality of the company's strategy, the market position and, last but not least, the quality of management and trust in management, which is cited as the most critical nonfinancial factor of IPO success. 452 Investors are increasingly focusing on a combination of financial and non-financial factors in their decision-making. Non-financial factors usually dominate the factors of credibility in management, especially their experience. This article is focused in detail on the factors that influence investor decision-making, respectively. To their sub-factors and activities, which, according to the author, are closely related to each other and are fully supported by factors that are superior to them. The IPO requires a disciplined focus on the most influential key indicators. Creating systems through which employees across the company can reliably grasp IPO issues and understand its characteristics is an essential criterion for a successful IPO. This article is focused on the following individual factors and sub-factors that, according to the author, affect the investors decisions the most. To these factors investors placed a percentage of importance and results of the survey included detailed description of the factors, that play the most important role of the investors decisions, are given below. Trust in management and its sub-factors Transparency – one of the most important corporate governance adjustments should be compliance with disclosure legislation. Transparency in the IPO mechanism can have significant impacts on investor participation, IPO pricing and returns. Nowadays, when companies try to break the globalization of personalization and become more localized, transparency is another important aspect of marketing. Achieving goals – focusing on strategic and long-term goals. Vision and Strategy – the strategy tells how company will strive to achieve the goals set. Its mission is to show the direction that a company must take to achieve top performance in all areas. The company must be prepared to respond to unexpected successes and unexpected failures. This will prevent a strategic surprise. The company must be able to respond to changing needs and market requirements with existing sources. The strategy must be constantly subjected to periodic scrutiny. This research concerns its functionality in the framework of existing paradigms and the review of existing paradigms. The challenge of the strategy is therefore to constantly increase the value of the company. Therefore, the best employees available to company must be involved in the strategy. These knowledge workers need to create extremely good working conditions to build their loyalty to the company. (Kaplan & Norton, 1992) Quality Team – company must use the latest knowledge of all disciplines. Qualified top management is aware that, due to technology mergers across different disciplines, unexpected emergence and exploitation of huge opportunities where no one expected them. There is also known from experience that the interdisciplinary teams, composed of workers with a wide range of knowledge, work most effectively. Soul of the company – only talented, capable and ambitious people can create prosperous businesses. They are key components at any IPO stage. Employee retention and development is the most effective way to secure long-term sustainable success. Recruiting new employees may not always lead to greater productivity. Having the right team is considered to be one of the key factors of IPO success. They are always people who stand by the fall or rise of large societies (McMorrow, 2014). Building a strong management team with the right experience, skills, discipline and the art of focusing on long-term visions is one of the most important aspects of every business. If we make the right strategic decision, but the employees will not be properly motivated and misunderstood the vision of the company, the desirable results will certainly not occur and IPO implementation will be more demanding. 453 Table 1 Trust in management Source: own elaboration Brand strength and market position and its sub-factors It follows from Kotler's definition that the view of the brand value is twofold. On the part of the consumer, it is primarily about brand awareness stored in memory associated with the association. The owner of the brand then sees its value above all in a better differentiation from competition, higher customer fidelity, less vulnerability to crises and competition, higher margins, and the ability to extend the brand to other products. This is about brand awareness, loyalty to the brand, emotional association and financial value. Awareness is a core component of brand value, "it is important to realize that high brand value reflects high brand awareness." (David A.Aaker, 2003) Story – investors becomes more influential and less manageable. It requires personal attitude, and the degree of responsiveness no longer rests in mass advertising, which draws on its prowess, but when the company reveals its true stories (remember, for example, Apple's story). Honesty and openness to the business environment will strengthen the competitive edge. Individual Behaviour and Motivation - employees who are motivated by, for example, the acquisition of company shares can achieve greater engagement in the company and willingness to participate in further growth and development. The beginnings of implementing some strategies can be painful, especially if company is to make a fundamental turn in orientation. Employees typically have large victims in these cases, often leaving a comfortable and busy way of working and starting to behave completely differently. These impacts are demotivating, and it is therefore important for the vision to highlight the benefits. Table 2 Brand strength and market position Source: own elaboration Average [%] Deviation [%] Soul of the company 10,7 5,3 Transparency 41,4 9,0 Vision and strategy 12,9 5,7 Quality team 23,6 4,8 Achieving goals 11,4 3,8 Average [%] Deviation [%] Diversity and enhancement of creativity 10,7 5,3 Individual behaviour and motivation 13,6 9,0 Story 23,6 6,3 Positive image 35,0 6,5 Visions development 17,1 9,9 454 Corporate Governance and its sub-factors Timely and high-quality reporting – early identification of information that may affect the running of processes and company plans is required at any time. Regular evaluation of the functioning of key core elements of organizational structure and processes, with respect to the company's vision and strategy, are key success factors. Creating the future value of a company – the prospect of the company's future success is its sleekness, networking and broad knowledge base. Communication between CG and shareholders – well-executed corporate governance increases ownership protection and reduces risk to owners. It also ensures proper communication between the views of the owners and the management of the company. Promoting investors' interests and transferring these interests to the company. Operating and behavioural standards – operating management is manifested by the efforts of managers to lead workers to achieve the desired result in optimal time for the managed object and desirable levels in a competitive environment. It is based on a management strategy that can be defined differently. What matters is the role played by ethics in the goals of managers, or in the methods they are going to achieve. The goal is for managers to formulate specific real goals and ethical methods to achieve them. While respecting not only personal interests, but also social needs and standards that are related to the high quality of their business plan. Setting up an IPO thinking – the company that decides for IPO must adopt a new context of responsibility and thinking because it brings with it a new set of obligations that were not needed in a period when the company was operating in the private sphere. It must be more flexible to respond to rapid changes. The company must be sure that all employees understand the difference between the private and the public sectors and that all employees understand all aspects of the company on the stock market. Table 3 Corporate Governance Source: own elaboration Quality of the strategy and its sub-factors Structure and Process – a common goal of the company should be to optimize the organizational structure and processes, aligning the company's strategic goals with the roles, responsibilities and responsibilities of individual organizational units and employees. This is also directly related to the introduction of a system for monitoring organization performance and determining how working groups work together and mechanisms to increase their performance. Flexible and Effective Management – quality management of companies knows how to get the necessary resources including capital and allocate them where they are best used. Quality management at the same time acts ethically, is open to new ideas and ideas, is transparent, focused on a longer period, and is also able to take a cautious risk. (Michael Mouboussin, 2016) Average [%] Deviation [%] Setting up IPO thinking 23,6 8,0 Operating and behavioural standards 17,9 2,7 Communication between CG and investors 30,7 12,1 Future value of a company 15,0 2,9 High quality reporting 12,9 3,9 455 Ability to maintain and acquire new talent – this ability must be based on a participatory principle. Employee engagement is a prerequisite for adapting rating systems and remuneration systems to the desired behaviour. Employees work hard only on what they are valued and rewarded for. Rating and reward systems can positively influence business culture and contribute to its focus on collaboration and knowledge sharing, reducing employee fluctuations. Common vision – company must be aware of what is important to it, where it is heading, what it wants to achieve, and what needs to be done. It has the rules and follows them. Employees identify with them. On the other hand, unsuccessful companies are characterized by internal fluctuations, partial interests prevail over fundamental goals, power ambitions over customer needs, individual prestige over strategic interests. It follows that the inner atmosphere of the subject that is typical for it has a large share in its success. The inner atmosphere should be in the symbiosis of shared opinions, attitudes, expectations, beliefs and suppositions that are not determined by anyone but affect the way people behave in an organization to achieve common goals. Employees must know every day how they contribute to the organization's goals. Coordinated Intelligence – company levels should be intertwined to understand what they are doing and why. As mentioned above, everyone is unique in their understanding, and their recognition of the essential contexts can be various. That is why a coordinated intelligence is needed so the company can form a functional and, most of all, a coherent unit. Insufficient awareness and the existence of blurred goals and vague visions, whether due to poor information or worse, because of the unwillingness to familiarize their employees with the deeper meanings of their tasks, will lead to imperfect work. If each employee is to give maximum performance, he / she should identify with the nature of his / her work and know the reasons why he or she was entrusted or delegated to it. Table 4 Quality of the strategy Source: own elaboration 4 Conclusions If a company wants to be attractive to the new investors, it is imperative that it be in line with its principles or at least be able to properly argue for any deviations between itself. Building a positive image in public will boost initial sales efforts and maintain investors interest in market shares. The company needs to create and strengthen this position towards those who are potential investors. Creating an image can include creating a business story and maintaining a positive external communication with investors. The key non-financial factors as trust in management, brand strength and market position, corporate governance and quality of the strategy determinate successful operation and will make flexible, efficient and effective company. The company should Average [%] Deviation [%] Coordinated Intelligence 13,6 5,6 Common vision 18,6 9,0 Maintain and acquire new talents 15,0 8,2 Flexible and Effective Management 35,0 9,1 Structure and process 17,9 4,9 456 establish the most appropriate management methods based on its corporate culture, size and business complexity with respect to non-financial key factors that influence investor decision-making. Company must be clear about how it intends to meet its goals by using non-financial factors as the key tool of investors decision making and which direction it wants to evolve. It should be clear who is responsible for managing the company and who controls the achievement of the key non-financial factors. All the experts which were asked thought the questionnaire have agreed on a clear rule that the roles of company management must be clearly specified and determined. Achieving efficient and effective management is certainly a very costly business. It must be offset by increasing the value of the company in its quality of management (Padraig, Corin 2010) There should be a common vision in the company to what kind of non-financial factors they should pay the most attention. Also, what wants the company to achieve by using them and what steps will be taken to meet all goals across all levels of the company. These visions must have a well-thought out story and form of communication, both internal and external. The needs and objectives of the investors must be in communication between the board of directors and investors and must be set up in such a way that the two sides understand their needs and comply with their views. Particular interests should not be against the common goals. The company must maintain a full and open dialogue with the investors. Another effective non-financial prerequisite for IPO success is an efficient management team. The investment community wants to be confident that there are good people in the company's leadership with experience and coherence that can work in the long run to develop the company's vision and achieve common goals. The strength of the brand is among the key criteria for investor's decision-making. Brand perception and positive company presentation to the public is a key aspect for future development. Brand strength is a set of associations and behaviour of brand, distributor, and company customers that enable the brand to retain lasting and distinct competitive advantages. All the non-financial factors and sub-factors, which are influencing investors decisions from this article will be further tested on a larger sample to confirm the results and their importance. References Aaker, D. A. (2003). Brand building, Brno: Computer Press. Anand, S. (2008). Essentials of corporate governance. Hoboken, N.J.: John Wiley & Sons. Bainbridge, S. (2008). The new corporate governance in theory and practice. New York: Oxford University Press. Corporate Governance (2005). Správa a řízení společností. Retrieved from: https://www.mpo.cz/dokument2566.html. Elliott, R. H., Percy, L., Pervan, S. (2015). 3rd ed. Strategic brand management. Oxford: Oxford University Press. Freidank, M. (2008). Corporate Governance und Interne Revision: Handbuch fu r die Neuausrichtung des Internal Auditings. Berlin: E. Schmidt. IPO trends (2014). Initial Public Offering trends. Retrieved from http://www.ey.com/gl/en/services/strategic-growth-markets/sgm_ipo_overview. Keller, K., Kotler, P. (2012). Marketing management. 14th ed. Upper Saddle River: Hall. Martin, H. (2010). Integrierte Corporate Governance: Ein neues Konzept der wirksamen Unternehmens-Fu hrung und -Aufsicht. Berlin: Heidelberg. 457 Stout, L. A. (2012). The shareholder value myth: How putting shareholders first harms investors, corporations, and the public. San Francisco: Berrett-Koehler. Strange, R., Jackson, G. (2008). Corporate governance and international business: strategy, performance and institutional change. New York: Macmillan. 458 Some Considerations on the Green Bonds Market Development Anetta Kuna-Marszałek1 , Jakub Marszałek2 1 University of Lodz Faculty of Economics and Sociology, Department of International Trade 3/5 POW Street, 90-255 Lodz, Poland E-mail: akuna@uni.lodz.pl 2 University of Lodz Faculty of Management, Department of Finance and Strategic Management 22/26 Matejki Street, 90-237 Lodz, Poland E-mail: jmarszalek@uni.lodz.pl Abstract: The article discusses current trends in the global bond market. Particular attention was paid to green bonds. The purpose of the article is to identify the conditions for green bonds issues in the context of changes in the environment and financial market conditions. More than 600 issues of such securities throughout the world were analyzed over the years 2010-2016. The analysis included the issuer's nationality structure, bond’s maturity and yield to maturity, the currency the bonds were denominated in and bond’s risk assessment. We also analyzed changes in NOx and greenhouse gas emissions (GHG) and energy consumption. Data were obtained from the World Bank and Thomson Reuters’s Eikon databases. Statistical study was supplemented with literature studies of green bonds reports. Using descriptive statistics analysis (arithmetic mean, median, standard deviation) and significance test of differences it has been shown that green bonds are primarily used to finance investments in highly developed countries. This means that their direct impact on the environment is difficult to be identified. Beneficiaries are mainly companies developing new technologies. In turn, issues from poorer regions are also targeted to the developed capital markets to reduce the cost of financing. This means that the “green nature” of these bonds is highly debated, and the environmental aspect seems to be only a lure for investors. Keywords: green bonds, eco-investments, global market, climate change, ecology JEL codes: F21, G15, O13, O16, Q50 1 Introduction Green bonds should be seen in the context of climate change and other sustainability challenges that have emerged in recent years. Transition to green, low-carbon economy requires shifting capital from traditional to sustainable investments. The UNEP Report adopted at the Earth Summit in Rio de Janeiro in 2012 stated that it was essential to invest 2% of global GDP in greening central sectors of the economy in order to shift development and unleash public and private capital flows into a low-carbon, resourceefficient path (UNEP, 2011). The Paris Agreement also highlighted the importance of green investments as a key to solve both climate change and other sustainability challenges (United Nations, 2015). Transition towards a climate resilient, resourceefficient and more broadly known green economy is now a motion and it seems unavoidable because of many international agreements in this regard. However, there is a lot of doubts, how to finance these sustainability targets. In order to reach some environmental challenges new types of financial instruments related to climate change can be used, e.g., green bonds (Green Bonds Climate Capital..., 2015). They are bonds whose proceeds are invested in an environmentally friendly way (Ehlers and Packer, 2016) with projects producing sustainable benefits. A “green” bond differentiates from a “conventional” bond by its label, which signifies a commitment to exclusively use the funds raised to finance or re-finance in part or in full new and/or existing eligible green projects or business activities (ICMA, 2015). Since 2007 a market for green bonds has been growing rapidly, its integrity so far remains 459 robust (Ceres, 2015). The geography of the green bond market is expanding and diversifying (OECD, 2015), over the last years we can observe increasing numbers of specialized green bond funds as well as institutional investors and financial institutions, which have intended to increase green bond holdings. Green bonds, nevertheless, account for a small fraction of the overall bond market (Cochu et.al., 2016; Shishlov, Morel and Cochran, 2016), they represented about 1.4% of global debt capital markets issuances in 2016, and according to economists it was well below the scale needed to play a significant role in the transition to a green economy (Record year for..., 2017). According to van Renssen (2014) and Clapp (2014) the main factor that drives the increasing demand for green bonds is that many organizations are also under intense pressure to be investing green and that future legislation is likely to benefit green investments. If a green bond offers the same yield as a conventional one, investors might choose the green alternative. Green bonds ensure that proceeds raised from the issuance will be spent on projects that bring environmental benefits with the issuer agreeing to publicly report on the use of proceeds. This commitment distinguishes green bonds from the non-green financial instruments. Analyses of the benefits offered by the projects a green bond finances must be combined with the assessment of their contributions to a low-carbon economy (Investing for a low..., 2013, p. 78; Climate Bonds Initiative, 2016). This financial instrument could be tied to mitigating the effects of climate change (e.g. climate bonds) or to a specific environmental issue or technology, such as wind and solar energy efficiency projects, energy retrofits and clean transportation (e.g. renewable energy bonds, energy efficiency bonds and green transportation bonds) (Green bonds: victory ...., 2013). Such initiatives also include renewable energy, construction of energy efficient buildings, reforestation, sustainable waste management, sustainable land use, biodiversity conservation, and other investments (Ahuja and Mackay, 2016; Chiang, 2017). According to ICMA (The International Capital Market Association) there are currently four types of green bonds, in turn OECD has categorized green bonds into six distinct forms that can be issued as different structures or “types” (OECD, 2015). It means that additional types may emerge as the market will develop and grow. To promote green finance some principles and standards, as well as several green bond indexes were introduced (Green Bonds. Climate Capital..., 2015). Nowadays, green bonds face two key challenges: 1). they need to ensure environmental integrity in order to reduce reputational (green-washing) and legal risks of the market; 2). the number of climatefriendly projects needs to be expanded (Shishlov, Morel and Cochran, 2016). Green bonds are an instrument to shift capital for green investments, but the question is whether green bonds really can increase and accelerate green investments. Certainly green bonds can be profitable for an issuer, because they can provide: 1). access to a broader range of investors, especially those focused on environmental, social and governance performance; 2). possibility to enhance his reputation by demonstrating issuer’s green credentials and showing his commitment to the environment; 3). possibility to develop closer relationships between finance and sustainability professionals; 4). confirmation of his sustainability commitments; 5). more pricing benefits (i.e., lower yields) over traditional bonds (KPMG, 2015; Kim, 2015; Shishlov, Morel and Cochran, 2016; Cochu et.al., 2016). The green bond market can offer several important benefits for green investment. They include e.g.: providing an additional source of green financing; enabling more long-term green financing by addressing maturity mismatch; facilitating the “greening” of traditionally brown sectors; making new green financial products available to responsible and long-term investors (Ross, 2015; Green bonds: country experiences ..., 2016). Green bond market is based on voluntary guidelines and standards, as well as more recently on rules and regulations in some jurisdictions such as China, India and France (Green bonds: country experiences ..., 2016). It means that the process for labelling a bond as green is rather unregulated, there is no formal approval or vetting process (An 460 Introduction to green bonds ..., 2016). Issuers must include only a declaration statement in documents indicating that the proceeds raised will be allocated to green projects. The impact of green bonds on the environment is not easy to estimate, as it largely depends on the quality and performance of the underlying projects that are financed by the proceeds (www.unep.org, retrieved from 30.05.2017). The World Bank provides some data, e.g., two energy saving projects in China expect to reduce 12.6 million tons of CO2 equivalent annually through USD 400 million of financing from green bonds (www.unep.org, retrieved from 30.05.2017). In turn Kfw Group presents data that evaluate specific environmental and social impact of its green bonds issued in 2014. KfW granted EUR 3.8 billion under its “Renewable Energies – Standard” programme and cofinanced projects summing up to EUR 5.9 billion. With an investment of EUR 1 million, e.g., 1,271 tons of CO2 equivalent in greenhouse gas emissions are saved per year and the costs of energy imports to Germany and that of fossil fuels are reduced by EUR 67,155 per year (www.kfw.de, retrieved from 30.05.2017). Many similar examples could be found in the World Bank Report (Green bond. Impact report, 2016). This means that, in many cases, the objectives pursued are achievable for the benefit of the environment. Taking the above into consideration, the goal of our article is to identify conditions for green bonds issues in the context of changes in the environment and financial market circumstances. Despite the short time of the presence of these securities on the financial market, we will make an attempt to assess the impact of green bond issues on the reduction of environmental pollution. In addition, green bonds will be compared to other investment alternatives of similar risk. Such an approach will capture both the environmental and financial circumstances of the green bond market’s development. 2 Methodology and Data The analysis of determinants of green bonds market development was based on Thomson Reuters Eikon database. At the end of 2016, there were 625 green bonds registered in the market throughout the period 2010-2016, which value-wise represented USD 122.87 billion. The group of 288 green bonds was listed on almost all the major stock exchanges in the world. Only 173 were guaranteed. Almost half of the instruments (291) had an investment rating and 156 were rated as high-yield bonds. Other bonds were not given a rating. The value and number of issuances with the distinction of major domestic issuers is presented in Table 1. Table 1 Value and number of green bond issuances with the distinction of major domestic issuers Specification 2010 2011 2012 2013 2014 2015 2016 Value of issuances (in mln USD) 1 124 77 1 564 10 940 28 773 41 152 38 233 number of issuances 20 10 17 40 117 212 196 Including: Eurobonds 20 8 13 30 66 82 79 United States - 2 1 6 15 96 42 Sweden - - - 2 14 13 26 France - - 3 - 4 5 15 China - - - - 2 - 31 Source: own elaboration based on Thompson Reuters Eikon Database Analyzing data in Table 1, we can see how green bond market developed both in terms of the amount of emissions and the value of instruments placed. According to the type of issuer the market is dominated by offers made by the private entities (over 20% of the issue) and international organizations (about 38% of the issue), although only the international organizations bonds have been issued since the beginning of the period 461 considered (mainly the World Bank, the African Development Bank, the Asian Development Bank, and the European Bank for Reconstruction and Development). In the group of companies the leading players were entities from the financial sector, especially banks. In terms of nationality, we can distinguish first of all: international consortiums placing bonds on the Euromarket and the US entities. The Euromarket choice is simply due to limited possibilities of raising capital in the domestic market (Schalatek et al., 2015). It is particularly important for issuers in developing countries because instruments offered in this way are more credible. Apart from numerous American projects, high activity of Swedish and French issuers is also remarkable. Last year, a large supply of green bonds was also made by Chinese entities. However all issues on the Euromarket were denominated mainly in local currencies, which limits their currency risk. Over one third of the issues were in USD. The study of green bond issue determinants was divided into two steps. The first one was focused on analyzing changes in selected parameters of environmental pollution and energy consumption. It is difficult to expect an immediate and direct impact of greenfunded investments on the environment, but it is worth asking a question about this link. A major difficulty in such analyses is the short presence of green bonds on financial markets and long delays in publishing environmental data by statistical offices. The article compares emissions of two major environmental pollutants - NOx and greenhouse gas emissions (as CO2 equivalent). The period of green bonds’ presence in the financial market was taken into account. The study was conducted only for selected countries. However, both developed countries (especially France and Sweden, due to their active participation in green bonds market) and developing countries are considered. The vast majority of green bonds issues were aimed at financing investments related to the acquisition or conversion of energy. Energy is a key development factor for both technologically advanced and developed economies, as well as for developing ones, based on less innovative industrial processing. Therefore, in the next stage of the study, changes in per capita energy use and changes in the share of renewable energy in energy consumption were analyzed. Than the investment attractiveness of green bond was assessed by comparing them to similar debt instruments. The return rate (YTM) of all instruments issued so far has been analyzed. Statistical analysis was conducted using tests to assess the significance of the difference (t-tests). The selection of tests was not unequivocal, as the main assumption for the test was to analyse the results for pairs of bonds (green or non-green) with the same ratings. Hence, despite the fact that these are different subjects in physical terms, we used paired difference tests. Student’s t-test is the basic test for comparing the two paired populations. In order to perform it, we needed differences between paired measurements, which represent normal distribution. The assumption was not always met in the case of examined companies, and in such cases we used the non-parametric equivalent of t-Student’s test for paired samples, i.e. the Wilcoxon signed rank test. For both tests the null hypothesis assumes a lack of differences between both types of bonds (measured with the expected value of random variable for the t-Student test or the distribution function for the Wilcoxon test), and thus the alternative hypothesis is: there are differences. It was decided that the variable in comparable populations of bonds is statistically significant if the probability in the test, p, was below the assumed level of significance (α=0.05). Calculations were made in IBM SPSS Statistics 22.0. 3 Results and Discussion Developing countries generally increased their emissions during the period considered (Table 2). However, the results were not so clear for Sweden and for the European Union. They managed to reduce average emissions of NOx and GHG, but this change was not particularly visible. A decrease in the average annual reduction of GHG emissions was observed. However, we have to remember that only US entities are fully identifiable 462 during the period. Other countries practically did not issue green bonds or they did so through international organizations. In the group of developing countries some doubts are raised about GHG emissions in Brazil since 2010. It explains the very bad situation of the country in the field of environmental protection in previous years. These doubts do not affect the overall assessment of the results, which indicate a reduction in NOx and GHG emissions, especially since 2010. It must be stressed, however, that this is true regardless of the green bond issue. Therefore it is difficult to indicate their role in pollution reduction. Table 2 Average annual change in Nitrous oxide (NOx) and greenhouse gas emissions (GHG) in selected countries in 1990-2013 (in %). Country NOx GHG 1990-2013 2010-2013 1990-2013 2010-2013 Brazil 1.724 4.133 8.013 0.001 China 2.562 3.627 5.521 5.489 European Union -2.247 -2.361 -0.858 -0.387 France -3.001 -4.879 -0.457 -1.796 India 1.619 1.913 3.612 5.014 Sweden -1.288 -1.550 -0.643 -0.581 United States -0.614 -1.758 0.187 -1.313 Source: own elaboration based on the World Bank Database. The results of the study of electricity consumption changes are not surprising and close to pollution analysis (Table 3). Worldwide, a reduction in energy consumption is observed, especially in China, India and Brazil last years. What distinguishes between developed and developing economies is the use of renewable energy sources. Only these countries report drop in the share of renewable energy in energy consumption. In developed countries this share is growing. It is done without a clear connection to the issue of green bonds. Often it is the result of ecological policy of countries aimed at replacing traditional energy sources with renewable alternatives and reducing energy consumption. Similar trends are observed in developed countries (Germany, Canada, Japan) that do not issue significant amounts of green bonds. Again, Sweden (like other Scandinavian countries) records a different change. The share of renewable energy consumption is growing but with this non-renewable. Table 3 Average annual change in energy consumption per capita and share of renewable energy in energy consumption in selected countries in 1990-2015 (in %). Country Energy use Renewable electricity 1990-2015 2010-2015 1990-2015 2010-2015 Brazil -2.134 -13.484 -0.660 -3.881 China 0.467 -12.726 -2.936 -2.440 European Union -0.257 -0.410 4.057 5.827 France -0.109 -0.931 1.291 3.703 India -1.571 -14.077 -1.898 -1.982 Sweden -0.226 0.869 1.776 2.047 United States -0.470 -0.633 3.697 4.910 Source: own elaboration based on the World Bank Database. Denomination of bonds in the currency of the country involved in a pro-ecological project is linked to the country's financial risk. Hence, there is a noticeable difference in both the bonds coupon and their YTM. Changes in the average value of yield depend on the share of bonds denominated in exotic currencies, which leads to assumptions about the key role of the currency as a determinant of yields of climate bonds. This is indicated by a significant excess of the arithmetic average over the median, which means that the observation significantly exceeds the average level of YTM (Table 4). This is particularly 463 evident in 2014 when many emissions from developing countries were placed. Generally, it is easy to see that green bonds’ YTMs are low and close to yields of corporate bonds, as the major issuers are just companies. However, many of the issues are guaranteed by the state which slightly lowers their YTMs. Table 4 Yield to maturity (YTM) of green bonds in 2010-2016 (in %). Specification 2010 2011 2012 2013 2014 2015 2016 Average 4.972 1.367 2.426 3.692 10.815 4.057 6.681 Median 3.043 1.761 2.027 1.108 1.445 3.253 5.027 Standard deviation 4.438 1.080 2.493 5.410 39.187 3.146 1.494 Source: own elaboration based on Thompson Reuters Eikon Database. Comparative analysis of US green bonds and corresponding US corporate bonds with the same risk indicates that green bonds are attractive financial instruments for investors. The bonds are paired up to have the same investment rating, similar issuer type and moment of issuance (Table 5). The observed differences show that, despite so many similarities, they are different, although not significantly. Green bonds have higher YTM, coupon and shorter payback period than their non-green counterparts. This means, despite a similar risk assessment, they can provide higher returns or cash flows to investor. Shorter payback period allows time commitment for a shorter time and more flexible management. However, it should be noted that higher investment attractiveness for investors means higher cost of capital for the issuer but if the risk of projects financed by green bonds is taken into account, this cost does not seem excessive. Table 5 Selected features of American green and corporate bonds of the same risk in 2011-2016. Specification Type of bond n Arithmetic average Median Standard deviation p YTM (%) Green 128 5.831 6.502 11.118 0.017** Non-green 186 5.824 6.443 9.394 Coupon (%) Green 128 3.451 3.527 2.009 <0.001** Non-green 186 3.298 3.401 2.632 Payback period (days) Green 128 2 485 2 193 780 0.063* Non-green 186 3 068 2 556 1 485 The significance of differences was assessed using Mann-Whitney’s or t test. * - statistically significant differences at α = 0,05; ** - statistically significant differences at α = 0,10 Source: own elaboration based on Thompson Reuters Eikon Database. 4 Conclusions More interest in green bonds among investors is a fact. Greater appetite for environmentally-aware investment products certainly increases the awareness of climate change or adverse effects of human impact on natural environment. Observing green bonds market development we may have a question whether it is accompanied by environmental improvements and whether it is due to issuing green bonds. It is difficult to provide clear answers to these questions because of the short time of observation. The study did not show this effect. If ecological effect of investment is immeasurable and remains only in the sphere of plans it is difficult to take it seriously in financial terms because it precludes accurate financial projections. Even more uncertainty arises from the analysis of issuers by nationality. In addition to international projects, issuances from US entities and several European Union countries dominate. Raised funds go mainly to private companies. However, when analysing the prospectuses of these bonds, it can be seen that the green nature of bonds is fairly 464 contractual. More than two thirds of issuers only declare their environmental goals. However, it is not specified how much “ecological” a project is. The impression is that many entities, especially corporations, use green bonds status to improve the attractiveness of their issues. Beneficiaries of such practice are mainly companies developing new technologies. This is confirmed by the results from the US market. Analysed green bonds have comparable investment rating as the corresponding nongreen bonds. Relatively higher returns and noticed share of high-yield bonds indicate a higher investment risk typical for high-tech companies that often have problems with low-cost financing. Issues from poorer regions also target developed capital markets to reduce the cost of financing. They therefore support the development of mature financial markets, often linked to regions with little environmental impact. It is also worth mentioning that private issuers often have direct or indirect government guarantees, which further increases the investment attractiveness of the instruments offered. This means that the “green nature” of these bonds is highly debated, and the environmental aspect seems to be only a lure for investors. In fact, this capital is invested in countries relatively little affected by environmental degradation and a growing market of "green" debt does not substantially affect pollution reduction in the global perspective. Their impact on the environment, however, seems difficult to prove. Effects of investment, even if they do have "green" consequences, relate to advanced technology development rather than to anything else. Their impact on the environment is therefore a side effect. Of course, green investments are pro-ecological, but they do not stand out in terms of risk, as evidenced by the YTM differences. On the other hand, investments with higher YTM (even high yield ones) are considered to be less risky due to the international support and government-imposed constraints. It can therefore be presumed that the recently observed popularity of green bond issues will last as long as investors can be persuaded to support green investments. In developed countries this is already the case, in developing countries, especially the poor ones, it will still be necessary to wait. The growing need for environmentally efficient and clean energy and technologies may help drive the green bonds market forward. On the other hand, there is a great need for funding environmental projects and governments lack financial resources to meet current and future requirements. The best solution is to attract private sector investment into environmental initiatives. Green bonds will need long term incentives that may provide targeted public support schemes to reduce the cost of capital. These measures may include various forms of subsidies, tax incentives, changes in prudential regulation and public guarantees. These factors may prejudice investment green bonds attractiveness and drive market development. References Ahuja, P. S., Mackay, J. (2016). Fixed impact: Green bond primer, Wealth Management Investment Resources. An Introduction to Green Bonds (2016). Alternative Investment Analyst Review, vol. 6, Quarter 2, NEPC’s Committee. Ceres (2015). A Statement of Investor Expectations for the Green Bond Market. Chiang, J. (2017). Growing the US green bond market. California State Treasurer, California. Clapp, C. (2014). Climate finance: capitalising on green investment trends. In: de Coninck, H., Lorch R., Sagar, A. D. (es.). The Way Forward in International Climate Policy, pp. 44-48. Climate Bonds Initiative. Bonds and Climate Change: The State of the Market in 2016 (2016). Retrieved from: www.climatebonds.net/files/files/ CBI%20State%20of%20the%20 Market%202016%20A4.pdf. 465 Cochu, A. (2016). Study on the potential of green bond finance for resource-efficient investments. European Commission. Luxemburg. Ehlers, T., Packer, F. (2016). Green Bonds – certification, shades of green and environmental risks. Bank for International Settlements. Green bond. Impact report (2016). World Bank. Green Bonds. Climate Capital- Global green bonds Primer (2015). Bank of America, Meryll Lynch. Green bonds: country experiences, barriers and options (2016). G20 Green Finance Study Group. Green bonds: victory bonds for the environment. Special report. (2013). TD Economics. ICMA (2015). Green Bond Principles: Voluntary Process Guidelines for Issuing Green Bond. Investing for a low carbon economy (2013). Special issue COP21. Kim, M. T. (2015). Going green. Consideration for green bond issuers. Government Finance Review. KPMG (2015). Sustainable Insight. Gearing up for green bonds. Key considerations for bond issuers. Retrieved from: www.kpmg.com/sustainbility. OECD (2015). Green bonds: mobilising the debt capital markets for a low-carbon transition. Policy perspectives. OECD Publications. Record year for green bonds likely to be eclipsed again in 2017 (2017). Moodys’ investor sector. Ross, U. (2015). Green bond drivers. HSBC. London. Schalatek, L., Nakhooda, S., Watson, C. (2015). The Green Climate Fund. The Climate Finance Fundamentals 11. Retrieved from: www.climatefundsupdate.org. Shishlov, I., Morel, R., Cochran, I. (2016). Beyond transparency: unlocking the full potential of green bonds. I4CE. UNEP (2011). Towards a Green Economy: Pathways to Sustainable Development and Poverty Eradication - A Synthesis for Policy Makers. UNEP. United Nations (2015). Adoption of the Paris Agreement. van Renssen, S. (2014). Investors take charge on climate policy. Nature Climate Change, vol. 4(4), pp. 241-242. 466 The dynamic relationship between aggregate fund flows and share market returns: Empirical evidence from BRIC Oleksandra Lemeshko Masaryk University Faculty of Economics and Administration, Department of Finance Lipová 5a, 601 00 Brno, Czech Republic E-mail: lemeshko@mail.muni.cz Abstract: Already for more than twenty years investigation of dynamic relationship between aggregate fund flows and share returns represents considerable interest for both practitioners and academicians. The former may use such investigation as useful aid for volatility timing for their investment portfolios; the latter may use such evidence as a proof of efficient market hypothesis violation, which, if found, will have far-reaching implications for the theory of finance. In this paper we aim to investigate the dynamic bidirectional interaction between aggregate fund flows and excess share market returns in a group of emerging BRIC economies. Particularly, we investigate the possibility of a causality mechanism through which aggregate domestic equity fund flows may affect local excess share market returns and vice versa in short-term and long-term period by means of Engle-Granger causality test and VECM. Key words: equity fund flows, excess share market returns, Granger-causality, BRIC JEL codes: G23 1 Introduction Although studies on fund flows are ample, most of them focus either on fund flowperformance relationship or use fund flows as convenient instrument to investigate herding behavior of retail investors. And only small fraction of all existing studies deal with relation which exist between aggregate fund flows and share market returns. To large extent this situation is caused by the lack of global data on individual fund flows and/or short available time-series. The rare existing studies, dealing with share market returns and investment flows, in most cases investigate the role of different macroeconomic indicators including share market returns as determinants of investment flows, mainly in form of FDI and FPI. These studies provide evidence of positive relation between domestic financial conditions, given by size, liquidity and turnover of domestic share markets and domestic FDI (Portes and Rey, 2005). The studies which investigate the impact of various macroeconomic variables on FPI provide positive evidence that information asymmetries and cultural-institutional proximity are important for bilateral FPI (Mishra, 2007). Finally, the studies which deal directly with aggregate fund flows and share market returns provide evidence of existence of a short-term unidirectional causality between fund flows and share returns: share returns cause equity fund flows to rise or fall, indicating herding behavior (Alexakis et al, 2005; Cao et al, 2008; Watson and Wickramanayake, 2012). However, except for evidence from Greece, the mentioned studies fail to find any evidence that excess share returns are caused by changes in aggregate fund flows. All these studies are based on data from developed markets (USA, Australia, Greece, and South Korea). As for studies which work with data from emerging markets, so they provide evidence that higher share market returns as well as better past fund performance have positive effect current fund flows (Pavabutr and Sirodom, 2010; Narayan et al, 2014; Lemeshko and Koncikova, 2015). Here it is important to note that except last three mentioned studies all other studies are structured upon data mainly from the United States and several other developed markets (Australia, Greece and South Korea), thus there is a question, if obtained evidence is 467 equally valid for less developed markets or their groups. Thus in light of stated above in this paper we aim to investigate the dynamic bi-directional interaction between aggregate fund flows and excess share market returns in a group of emerging BRIC economies. Particularly, we investigate the possibility of a causality mechanism through which aggregate domestic fund flows may affect local share market returns and vice versa both in short-term and long-term period. Following established practice, dynamic relationship between aggregate fund flows and excess share market returns in a group of emerging BRIC economies is tested by means of Engle-Granger causality test and VECM using end-of-month data for BRIC economies gathered from Bloomberg for the period from 2005 till 2015. 2 Data and Methodology Being the leading energy producers and world crude oil exporters, the seats of manufacturing and heavy industry already for quarter of a century BRIC economies power the emerging markets economic growth. In spite of slight decline in group’s overall growth rate due to persisting recent recessions in Brazil and Russia, in comparison with other advanced and emerging economies BRIC economies are still pumping out growth and provide nice opportunities for international portfolio diversification and extra returns (Huij and Post, 2011). The modern BRIC’s mutual fund industry is characterized by high concentration, imposing lower competition and higher operational costs and management fees, as well as highly volatile fund flows, leading to higher risks and frequent abnormal returns. The same as ten years ago currently the largest number of active equity funds is domiciled in India (241 funds) and the smallest number – in China (59 funds) (Table 1). Although in the recent years Russia and Brazil are experiencing recession and decline of foreign trade, nevertheless last ten years they hold top positions by level of their AUM’s NAV - USD 18 164 and USD 4 761 in Russia and Brazil against USD 86 and USD 2 in India and China respectively. After last decline at the beginning of 2015 currently group’s equity fund industry is experiencing growth in fund inflows, however based on evidence from previous group’s fund flow cycles this growth is expected to finish by 2017 with sharp drop putting the beginning of the new cycle. (Deloitte, 2015) Constantly the main driver of fund flows is Russia (nearly 60% of total flows), followed by Brazil (35%), remaining 5% is splitted between India and China (Table 2). To conclude, since 2012 there is a growing competition between domestic equity funds from BRIC resulting in lower NAV and high fund flows which leads to higher risk-reward relation for BRICfocused investors. Table 1 Number of active and nonactive equity funds and their average NAV in BRIC economies during 2005-2015 Q1/Y2005 Q1/Y2008 Q4/Y2015 N of funds NAV N of funds NAV N of funds NAV Panel A. Active funds Brazil 114 12 614.19 150 9 828.39 150 4 760.67 Russia 32 8 838.92 151 16 736.01 159 18 164.48 India 123 20.24 241 37.00 241 86.41 China 14 0.79 59 1.69 59 2.31 TOTAL 283 5 368.53 601 6 650.77 609 5 753.47 468 Panel B. Nonactive funds Brazil 9 1 324.76 12 3 321.70 NA NA Russia 14 2 146.35 60 3 362.33 NA NA India 5 14.62 13 27.305 NA NA China 0 0 0 0 NA NA TOTAL 28 871.43 85 1 677.83 NA NA Source: own computations based on data from Bloomberg The paper aims to investigate the existence of a positive feedback process between managed aggregate equity managed fund flows and excess share market returns in short-term and long-term period using monthly data from BRIC economies for the time span from January 2005 to December 2015 and to perform subsequently international comparison of the obtained results with existing evidence from other regions or groups of advanced and emerging economies of the world. In total, initially data on 5 596 openend active and non-active equity funds operating in BRIC economies has been collected for chosen time span via Bloomberg terminal. Further this sample shrank to 1 488 funds, which are domiciled in the chosen countries, are actively managed (i.e. non-index funds) and invest domestically. Further this sample was filtered for minimum required number of available observations: initial requirement was presence of any quantity of monthly observations for the chosen period but then this filtering criterion was narrowed by requirement of availability of at least 112 observations. As a result of all this filtering manipulations our initial database of 5 596 equity funds shrank to final 592 equity funds. Using this dataset we created one four TNA- weighted portfolios for country analysis. Table 2 Equity fund flows in BRIC economies during 2006-2015 Y06 Y07 Y08 Y09 Y10 Brazil 57.04 192.69 -330.76 251.49 8.62 Russia 314.29 -233.94 -362.79 445.76 211.22 India -0.02 1.27 -1.61 1.11 0.58 China 0.032 0.09 -0.11 0.07 0.01 TOTAL 371.34 -39.88 -695.26 698.43 220.42 Y11 Y12 Y13 Y14 Y15 Brazil -130.44 73.94 -123.41 -109.84 -11.17 Russia -196.48 -226.76 210.93 -56.36 31.01 India -0.71 0.78 0.17 1.69 0.17 China -0.04 -0.01 0.02 0.02 0.05 TOTAL -327.65 -152.06 87.69 -164.49 20.05 Source: own computations based on data from Bloomberg Following Watson and Wickramanayake (2012) monthly equity managed fund flows were measured as follows: Flowsit = NAVit – NAVit-1 x (1+Rit) (1) where: Flowsit – flow in equity fund i at the end of the month t; NAVit and NAVit-1 – net asset value of equity fund i at the end of the month t and at the end of the month t-1 respectively; Rit – returns of fund i at the end of the month t. As proxies for share market returns we took times-series of returns of local MSCI indices and national stock indices (adjusted for dividends, share splits, bonus issues, etc.) - 469 MXBR and IBOV for Brazil, MXRU and INDEXCF (MICEX) for Russia, MXIN and NIFTY for India, and MXCN and SHCOMP for China - so we could compare the relationship between local fund flows and both local and international proxies for share market returns. Following Remolona et al. (1997) the excess share market returns are calculated as the changes in the logarithms of the end-of-month chosen share market indices and defined as follows: Returnsjt = (log Pjt – log Pjt-1) (2) where: Returnsjt – share market return for index j at the end of the month t; Pjt and Pjt-1 – closing market price for index j at the end of the month t and the month t-1 respectively. Table 3 Summary statistics for aggregate equity managed fund flows, excess MSCI and local index returns in BRIC economies during 2005-2015 Mean Median St.dev. Min Max Panel A. Flows Brazil 10 686 11 182 635 090 -2 874 2 3 439 3 Russia 27 483 117 120 936 450 -3 793 7 2 999 8 India 280 339 2 069 -7 067 5 407 China 148 26 145 -417 528 Panel B. 1. MSCI returns MXBR (Brazil) 1.0048 1.0079 0.0957 0.6764 1.2426 MXRU (Russia) 1.0038 1.0057 0.1019 0.6472 1.3044 MXIN (India) 1.0124 1.0118 0.0706 0.7522 1.2866 MXCN (China) 1.0098 1.0215 0.0778 0.7709 1.1889 Panel B.2. Local index returns IBOV (Brazil) 1.0065 1.0059 0.0647 0.7520 1.1556 INDEXCF (Russia) 1.0118 1.0207 0.0797 0.7122 1.2206 NIFTY (India) 1.0128 1.0106 0.0697 0.7359 1.2807 SHCOMP (China) 1.0123 1.0145 0.0883 0.7536 1.2745 Source: own computations based on data from Bloomberg Summary statistics on managed aggregate equity managed fund flows and excess share market returns for global sample and country samples is reported in Table 3. Based on evidence provided by Alexakis et al (2005)., Cao et al (2008), Watson and Wickramanayake (2012) we expect that aggregate equity managed fund flows and excess share market returns are in equilibrium with each other, moving to restore it in case of shocks or disturbances such as recent global financial crisis and subsequent recession. In light of this in this paper we will test two pairs of hypotheses: First pair of hypothesis: H0 states, that excess share market returns do not Grangercause changes in aggregate equity managed fund flows in contrary to Ha, which states that excess share market returns do Granger-cause aggregate equity managed fund flows. Second pair of hypothesis: H0 states, that changes in aggregate equity managed fund flows do not Granger-cause excess share market returns in contrary to Ha, which states that aggregate equity managed fund flows do Granger-cause changes in excess share market returns. 470 Testing for existence of bi-directional relationship between managed aggregate equity managed fund flows and excess share market returns in short-term and long-term period will include two stages: following established practice, first of all, Engle-Granger causality test will be used to check the existence of lead-lag causal relationship to test both pairs of hypotheses; and, in case of any, then we will run VECM to identify the speed at which fund flows and stock market returns move back to equilibrium after changes in each other. Engle-Granger causality test examines the causality between two variables. X is said to Granger-cause Y if Y can be better predicted using the histories of both X and Y than it can by using the history of Y alone (Toda and Yamamoto, 1995). In context of our paper we will test for the absence of bi-directional relationship between managed aggregate equity managed fund flows and excess share market returns by estimating the following Engle-Granger causality test: Flowsmt = α1t + β1i Flowsmt-1 + ..... + β1p Flowsmt-p + γ1i Returnsjt-1 + ..... + γp Returnsjt-p+ e1t (3) Returnsmt = α2t + β2i Flowsmt-1 + ..... + β2p Flowsmt-p + γ2i Returnsjt-1 + ..... + γp Returnsjt-p+ e2t (4) where: Flowsmt and Returnsmt – aggregate equity fund flows and excess share market returns in country m at the end of the month t respectively; Flowsmt-1 and Flowsmt-p – aggregate equity fund flows in country m at the end of the month t-1 and t-p respectively; Returnsjt-1 and Returnsjt-p – excess share market returns for index j at the end of the month t and t-p respectively; e1t and e2t – error terms for both equations at the end of the month t. For each equitation a rejection of the null implies the existence of Granger causality. 3 Results and Discussion Before running the cointegration test for aggregate equity fund flows and excess share market returns first we need to be sure that both pairs of time-series (fund flows - local MSCI returns, and fund flows – local share index returns) are stationary. To be sure that time-series are stationary we run ADF test for our samples of 131 observations to check null hypothesis, that time-series contain a unit root (Table 4). Based on obtained pvalues we reject null hypothesis and conclude that our time-series are stationary. Table 4 p-values for augmented ADF tests for time-series of aggregate equity managed fund flows, excess MSCI and local index returns in BRIC economies during 2005-2015 Brazil Russia India China Equity fund flows 0.01651 0.00707 0.00001 0.00099 MSCI return 0.00657 0.00077 0.00006 0.00147 Local index return 0.00135 0.00061 0.00002 0.00728 Source: own computations based on data from Bloomberg Having stationary time-series we run Engle-Granger cointegration regressions using aggregate equity fund flows as dependent variable for each pair of time-series – FlowMXBR and Flow-IBOV for Brazil, Flow-MXRU and Flow-INDEXCF for Russia, Flow-MXIN and Flow-NIFTY for India, and Flow- MXCN and Flow- SHCOMP for China. Then we perform ADF test for residuals to check if time-series contain a unit root and, thus, are not cointegrated (Table 5). 471 Table 5 test statistic and p-values from ADF test for residuals from Engle-Granger cointegration tests for aggregate equity managed fund flows, excess MSCI and local index returns in BRIC economies during 2005-2015 Brazil Russia India China Panel A. test statistic MSCI return 10.3529 4.22431 5.80191 3.99308 Local index return 3.82328 3.62547 3.78566 2.72829 Panel B. p-values MSCI return 0.00006 0.00235 0.00001 0.00728 Local index return 0.00001 0.00001 0.00741 0.00005 Source: own computations based on data from Bloomberg Based on obtained test statistic the null unit-root hypothesis is rejected for all pairs of cointegrating regressions. We conclude that aggregate equity fund flows and excess share market returns are cointegrated of I(1). This means that there is at least shortterm causal relation between aggregate equity fund flows and excess share market returns in BRIC economies. For testing whether this relationship holds in the long-term period we write it in errorcorrection form. It will help to estimate the speed at which aggregate equity fund flows and excess share market returns restore their relation in case of disequilibrium in case of shocks and disturbances of each other. To run VECM we, first of all, create a time-series of residuals from cointegrating regressions and further test them for stationarity by means of ADF test (Table 6). Based on obtained p-values for residuals from cointegrating regressions we conclude about their stationarity and run VECM model using the obtained residuals as error correction term and using number of lags suggested by BIC information criterion from lag selection model (Table 7). Table 6 p-values for augmented ADF tests for residuals from cointegrating regressions for aggregate equity managed fund flows, excess MSCI and local index returns in BRIC economies during 2005-2015 Brazil Russia India China uhat_MSCI 0.00004 0.00031 0.00001 0.00571 uhat_Local_index 0.00001 0.00001 0.00001 0.00001 Source: own computations based on data from Bloomberg Error correction term is statistically significant in all VECM where time-series of aggregate fund flows serve as dependent variable and, conversely, where dependent variable is given by excess share market returns (regardless if it is MSCI or local index) the error correction term turns to be statistically insignificant in all country samples (Table 8). This means that aggregate equity fund flows do move to restore the equilibrium with excess share market returns, while excess share market returns behave contrarily – they appear to be weakly exogenous, thus, they do not move to restore the disturbed equilibrium. The obtained results are consistent with evidence provided by Cao et al (2008) and Watson and Wickramanayake (2012), that share returns cause equity fund flows to rise or fall, but not vise versa. In particular, for aggregate equity fund flows in Brazil it takes six months to correct 72% of disequilibrium caused by uprise of excess MXBR return and eight months to correct 58% of disequilibrium caused by uprise of excess IBOV return; for aggregate equity fund flows in Russia it takes ten months to correct 49% of disequilibrium caused by uprise of excess MXRU return and twelve months to correct 55% of disequilibrium caused by uprise of excess INDEXCF (MICEX) return; for aggregate equity fund flows in India it takes eight months to correct 82% of disequilibrium caused by uprise of excess MXIN return and eleven 472 Table 7 Number of lags suggested by BIC under VAR system (maximum lag order 24) for aggregate equity managed fund flows, excess MSCI and local index returns in BRIC economies during 2005-2015 Brazil Russia India China MSCI return 12.501 (6) *** 14.712 (10) ** 0.3393 (8) *** -3.4144 (10) *** Local index return 11.727 (8) ** 14.042 (12) *** 0.5395 (11) ** -4.2640 (12) ** Source: own computations based on data from Bloomberg Table 8 test statistic and p-values for ADF tests for residuals from Engle-Granger cointegration tests for aggregate equity managed fund flows, excess MSCI and local index returns in BRIC economies during 2005-2015 Brazil Russia India China Panel A. Fund flows as dependent variable (flow-MSCI relation) MSCI_return_BIC_lag -0.1505 -0.1691 -0.1045 -0.1026 e_BIC_lag -0.6050 *** -0.2468 *** -0.8901 *** -0.3256 *** Flow_BIC_lag -0.7273 *** -0.4945 *** -0.8287 *** -0.9401 *** Panel B. Stock returns as dependent variable (flow-MSCI relation) MSCI_return_BIC_lag -0.2361 -0.1033 -0.0014 -0.1029 e_BIC_lag -0.1569 -0.2388 -0.0051 -0.0654 Flow_BIC_lag -0.5033 *** -0.4519 *** -0.4681 *** -0.3767 *** Panel C. Fund flows as dependent variable (flow-local index relation) Local_index_ return_BIC_lag -0.0608 -0.2908 -0.0583 -0.1703 e_BIC_lag -0.1061 *** -0.175 *** -0.1801 *** -0.3535 *** Flow_BIC_lag -0.5830 *** -0.5521 *** -0.9012 *** -0.9504 *** Panel D. Stock returns as dependent variable (flow-local index relation) Local_index return_BIC_lag -0.2474 -0.8067 -0.0026 -0.0782 e_BIC_lag -0.1512 -0.9649 -0.0067 -0.0659 Flow_BIC_lag -0.4111 *** -0.4846 *** -0.5341 *** -0.5362 *** Source: own computations based on data from Bloomberg. BIC lag stands for corresponding lag suggested by BIC from lag selection model (Table 4) months to correct 90% of disequilibrium caused by uprise of excess NIFTY return; for aggregate equity fund flows in China it takes ten months to correct 94% of disequilibrium caused by uprise in excess MXCN return and twelve months to correct 95% of disequilibrium caused by uprise in excess SHCOMP return. Thus, on average, it takes nine months for aggregate equity fund flows from BRIC economies to correct 75% of disequilibrium between caused by uprise of excess MSCI return and twelve months to correct 75% of disequilibrium caused by uprise in local index return. Hence we can conclude that, on average, 75% of disequilibrium is overcome within one year period and the remaining 25% is overcome in the subsequent period, implying presence of both short-term and long-term uni-directional Granger causality between aggregate equity fund flows and excess share market returns in emerging BRIC economies. The obtained results lead us to the rejection of first stated hypothesis – excess share market returns do Granger-cause aggregate equity fund flows - and confirmation of the second stated hypothesis – aggregate equity fund flows do not Granger-cause excess 473 share market returns. These findings are consistent with existing evidence for the US, Australian, and South Korean equity fund markets. 4 Conclusions Being an innovative vehicle for international portfolio investments, for already half a century managed mutual funds flows represent considerable interest for both practitioners and academicians. Although studies on fund flows are ample, most of them focus either on fund flow-performance relationship or use fund flows as convenient instrument to investigate herding behavior of retail investors. And only small fraction of all existing studies deal with relation, which exists between aggregate fund flows and share market returns and, even then only for the United States and several other developed fund markets. Thus there is a question, if obtained evidence is equally valid for less developed markets or their groups. On general, the obtained results provide positive evidence of existence of short-term and long-term uni-directional relation between aggregate managed equity fund flows and excess share market returns in BRIC economies. The obtained results are consistent with earlier findings from developed economies. These results might be extended by inclusion of other fund types and analysis of impact of changes in country allocations on local share market returns. Acknowledgement The support of the Masaryk University internal grant MUNI/A/1039/2016 Modeling of volatility at the financial markets and its implications for risk management and asset evaluation is gratefully acknowledged. References Portes, R., Rey, H. (2005). The determinants of cross-border equity flows. Journal of International Economics, vol. 65, pp. 269–296. Mishra, P. K. (2007). Dynamics of the Relationship between Mutual Funds Investment Flow and Stock Market Returns in India. The Journal of Business Perspective, vol. 15, 2011, pp. 131-40. Alexakis, C., Patra, N., Niarchos, T., Poshakwale, S. (2005). The dynamics between stock returns and mutual fund flows: Empirical evidence from the Greek market. International Review of Financial Analysis, vol. 14, pp. 559–569. Cao, C., Chang, E. C., Wang, Y. (2008). An empirical analysis of the dynamic relationship between mutual fund flow and market return volatility. Journal of Banking & Finance, vol. 32, pp. 2111–2123. Watson, J., Wickramanayake, J. (2012). The relationship between aggregate managed fund flows and share market returns in Australia. International Fin Markets, Institution and Money, vol. 22, pp. 451– 472. Pavabutr, P., Sirodom, K. (2010). Mutual Fund Flows in Asian Equity Markets. Capital Market Research Institute, CMRI Working Paper 01/2010. Narayan, P. K. Narayan, S, Prabheesh, K. P. (2014). Stock returns, mutual fund flows and spillover shocks. Pacific-Basin Finance Journal, vol. 29, pp. 146–162. Lemeshko, O., Koncikova, V. (2015) Application of Gravity Framework to Bilateral Mutual Fund Flows in the Asia Pacific Region. In Kajurova, V Krajicek, J.. European Financial Systems 2015: Proceedings of the 12th International Scientific Conference. Masaryk University, pp. 349-358. Huij, J., Post, J. (2011). On the performance of emerging market equity mutual funds. Emerging Markets Review, vol. 12, pp. 238-249. 474 Toda, H. Y., Yamamoto, T. (1995). Statistical inference in vector auto regressions with possibly integrated processes. Journal of Econometrics, vol. 66, pp. 225–250. 475 Environmental insurance in Poland – The notion, scope and legal determinants Malwina Lemkowska Poznań University of Economics and Business Faculty of Economics, Insurance Department Al. Niepodległości 10, 61-875 Poznań, Poland Abstract: The potential insuring parties in Poland have recently started to identify the environmental risk. The environmental insurance products are relatively new as the part of insurers’ offer. The article refers to the legal determinants of environmental insurance market’s development. Firstly the author will explicate the term “environmental insurance” (taking into account Polish and international conditions as the background of analyses) and secondly, explain the influence, that the Polish insurance and environmental legal system have on the development of the market. The main purpose of the researcher is to answer two research questions: what attributes the insurance products qualified in Poland to the category “environmental insurance” have and how the structure of Polish legal system determines their scope. Preliminary results of the research indicate that all environmental insurance products can be divided into three groups (liability, property and personal environmental insurance). The identification and assessment of the environmental risk during the underwriting process are the criteria, which cause that the insurance product is classified as an environmental one. There are only few offers of stand-alone environmental liability products in Poland. Besides, there are environmental clauses in some general civil liability insurance. We expect the development of property and personal environmental insurance in future. They are predicted to fill the gaps in the liability regime. The regime of both administrative and civil liability law is enough to determine the scope of environmental liability insurance products. The property and personal insurance are influenced by the legal scope of freedom of contracting (as defined in Polish civil code), the definition of property and personal interests, which (according to law) can be the subject of insurance and the boarders created by the insurance classification (the list of non-life and life insurance classes). Keywords: environmental insurance, environmental law, polish insurance market JEL codes: A12, G22 1 Introduction The present article focuses on analyses concerning legal determinants of the environmental insurance market development. The subject of this paper is firstly, the explication of the notion of environmental insurance; secondly, explanation of the impact of legal regulations on its development. Environmental risk has been identified and named by potential buyers of insurance cover for a relatively short time. At the same time, environmental insurance products constitute a rather new element of the insurance market offer as well as a new subject of research analyses. Therefore it is indispensable to define precisely the scope of the research and then to clarify the impact that the evolution of legal norms has on the research subject. The aim of the analysis is to answer two questions: what are the attributes of insurance products which belong into the category of environmental insurance in Poland and how the architecture of the Polish legal system determines its scope. 2 Methodology and data Research findings presented in the paper are derived from the analysis of reference materials on the history of environmental law development (especially in the field of 476 environmental liability). For the research problem to be solved, it was indispensable to review the possible interpretations of the notion of environmental insurance in the Polish and foreign subject literature. The analysis of environmental law and the regulations of Polish civil code led to identification of legal incentives for development of environmental insurance products offer as well as its potential limitations. The analysis of literary reference material and legal acts was conducted on the basis of inference as a method of logical reasoning. This enabled explication (narrowing a precise scope) and explanation (clarification) of the influence legal regulations exert on the spectrum of insurance products. 3 Results and Discussion The scope of the notion of environmental insurance – a review of Polish and foreign literature Analysis of the Polish subject literature presents at least three interpretations of the notion of environmental insurance. The first is based on the broad concept of environmental risk, the source of which may be seen in the whole of the interaction between a company and the environment. Therefore, the elements of cover against the consequences of environmental risk materialisation may be noticed in the design of almost all available products on the insurance market (Doś, 2011; limited scope: Borys, 2002). A moderate approach to defining environmental insurance leads to distinguishing between insurance against environmental damage liability and property insurance protecting the possessions of the insured against the impact of pollution (Maśniak, 2003; Rosiek, 2006; Żebrowska, 1997). This group also encompasses the idea of broadening the above to include personal insurance against pollution (Wójcik, 1998; Panasiewicz, 2008). The most limited perspective of environmental insurance narrows it down to insurance against ecological damage liability (Fiedor, 2003) and it is in line with the present economic practice in Poland. Foreign literature, first of all, points at insurance against various types of legal liability – (civil, statutory, regulatory liability). They may result from the adverse effect on the elements of the environment due to the operations of the subject of the risk (the “polluter pays” rule) or from the mere ownership rights to the polluted area (Firms may face…, 2013; Minoli, Bell, 2002; Forrest, Wesley, 2008; Boyer, Porrini, 2008). Broderick, Lavoie and Perel (2000) have also rated financial insurance among environmental insurance. It guarantees protection when the entity fails to repay the loan secured by the mortgage on property where pollution which can be re-cultivated or remedied is identified in the course of validity of the mortgage. A broad product-wise approach is presented in the United Nations Environment Programme (UNEP). The products counted among environmental insurance are the ones which are linked to a particular, potentially polluted area – in terms of the costs of remedial actions as well as insurance against third party liability resulting from pollution; also, insurance products linked to transport of hazardous substances. Environmental insurance also refers, according to the UNEP concept, to some types of financial insurance which offer a cover for the costs incurred in the course of or after terminating the operations dangerous to the environment. It also encompasses products which protect against the costs exceeding the project targets referring to re-cultivation or insurance for service providers (including environmental consultants) against third party liability for environmental damage (UNEP 2007). The vast scope of the presented approach concerns only insurance products; with regard to the subject matter, all the mentioned products refer to insurance against environmental damage liability. OECD also uses the name “environmental insurance” (undefined as such) only to refer to insurance against broadly understood environmental damage liability (OECD 2003). A much more precise approach is presented in the European Commission documents where the notion of “environmental insurance market” refers 477 exclusively to insurance against liability resulting from the environmental liability directive (Directive 2004/35/EC; Bio Intelligence Service, Stevens&Bolton LLP, 2009). The number of interpretations of the term originates from the evolving tendency to recognise the risk of environmental damage in insurance market practice. The above trend, in turn, does not only result from the traits of events leading to risk materialisation and the scope of its impact, but also from the large number and changeability of the regulations which determine liability of the entities affecting the environment (Szot, Maxwell, 2011). Until early seventies of the 20th century (the USA) and a decade later in Europe this risk was included in civil liability insurance without major exclusions. Similarly, property insurance did not exclude damages resulting from pollution. Since 1972, when the Clean Water Act was passed in the USA (Forrest, Wesley, 2008), regulatory activity with reference to environmental damage has been conducted to this day. Also, ever greater payables of insurers for environmental damages have given rise to a tendency to limit the scope of insurance. Originally, the process only took place with respect to cumulative damage (gradual pollution); next, other occurrences connected with entities’ adverse effect on the environment were added. Due to the lack of experience in underwriting and quoting of environmental risk the environmental insurance market shrank and companies suffered because of numerous loopholes in the protection. This situation appeared to be conducive to development of specialised products. The first special environmental insurance was offered by Lloyd’s of London in 1979 (UNEP, 2003). The problem of financial security of environmental risk has repeatedly generated legislative work. The Lugano Convention on Civil Liability (1993), in articles 13-16 compels the parties to ensure – whenever necessary – participation of entities in guarantee systems or their purchasing appropriate preventive financial instruments (Hinteregger, 2008). Subsequently, the European institutions’ activities resulted in a proposal for a directive on civil liability of entities for damages caused by waste management (Proposal for a Council Directive on Civil Liability for Damage caused by Waste, COM(89)282; Proposal for a Council Directive on the Landfill of Waste, COM(93)275), which provides for risk-based liability, a requirement for financial security and the actio directa rule (Coulson, Dixon 1995). Although the above directive never took effect, it inspired legislative interest in environmental damage. As a result, the environmental directive was passed in 2004 (Directive 2004/35/WE). Financial security is addressed there as desirable, although not obligatory (art. 14). The Commission reserves a directive-related right to introduce a system of obligatory security if this proves necessary. Summing up the legal and scientific achievements, environmental insurance must be defined as an economic instrument by means of which an insurer undertakes to pay out a certain provision in the event of one or more of the following occurrences: • materialisation of liability (civil or administrative) for impact on the environment (environmental liability insurance); • damage to insured’s property other than leading to materialisation of liability, caused by pollution or another factor deteriorating the quality of the environment; • violation of the insured’s personal rights due to pollution or another cause deteriorating the quality of the environment. The recommended universality of liability insurance would profoundly reduce the importance of products mentioned in points 2 and 3. They will remain relevant only if the insured is the entity causing adverse impact on the environment. In numerous other cases the insurer will have a right of recourse towards the polluter. Market practice shows that except for legal liability insurance there are no specialised environmental insurance products in Poland; in fact, the Polish market offer of the former type of insurance is meagre and environmental damages are usually covered by additional clauses of general insurance contracts to a very limited extent. In property and 478 personal insurance environmental damage is usually excluded from coverage under “all risks” insurance and it is not found as named risk in other types of products. Hence, the environmental traits can be found both in products which do not disclaim environmental risk from the catalogue of the causes of risk materialisation (e.g. critical disease insurance where the insurer does not identify the causes of the disease) as well as in the products which by naming the risk or due to a clearly specified clause make it possible to cover this risk. For research purposes, it seems reasonable to narrow down the notion of environmental insurance only to the latter category. Identification of the environmental peril by the insurer in the course of developing the insurance product determines the importance of this peril in the completion of the underwriting process. Hence, one can assume that the criterion of the underwriting scope should be decisive when it comes to assigning the product to environmental insurance category. With this kind of approach, the future scope of this category is hard to predict. One should expect a broader market offer, including personal insurance and other financial products (connected with e.g. issuing securities linked to materialisation of environmental risk). Environmental damage liability in Polish law There are two terms referring to environmental damage in the Polish law de lege lata, pursuant to two legal acts: the act of 27 April 2001 – The Law on Environmental Protection, The Law Gazette of 2016, item 672 with further amendments, and the act of 13 April 2007 on environmental damage prevention and remedying, The Law Gazette No 75, item 493 with further amendments (the Act on Environmental Damage). The term “damage inflicted by the impact on the environment“ is used, but not defined in the Law on Environmental Protection. It is a vast notion and therefore it is often treated as a synonymous to “environmental damage”. It is because the Act assumes implicitly that the damage is caused by an influence of an entity (a natural person, an entrepreneur) on all the elements of the environment, as well as on their interaction and impact on each other. Thus understood, damage does not have to be caused by adverse changes in the environment. It is always, however, the effect of unlawful influence on its elements. Damage may be of two-fold nature: on the one hand, it may violate personal rights or property of a given injured party, while on the other it may refer to the environment as common good. Damages to the environment treated as common good are exclusively regulated by the latter of the aforementioned acts. The term has been defined in article 6 par. 11 of the Act on environmental damage. According to the approved definition, the Act limits its scope to the regulation of damages which materialise in three essential elements of the environment (protected species and habitats, land and water). Damage thus understood may be caused by an entrepreneur, a non-entrepreneurial organisational entity and a natural person (but only if she uses the environment in a manner which requires a permit). Both acts present the entitlement to making claims against the entity using the environment both in connection with damage materialisation and merely with a threat thereof. However, in the case of the Law on environmental protection, the obligation to remedy the damage or eliminate its causes arises as soon as a claim is made by the eligible entities (individual ones, ecological organisations or the State Treasury), while the general rule of the Act on environmental damage includes ex lege the obligation for immediate restoration of the previous condition. Only if statutory action has not been taken by the entity using the environment, does the appropriate public administration body issue a decision which imposes the obligation to prevent or remedy the damage. Environmental insurance vs. legal approach to damage and liability The broadly understood regulations of environmental law (lex specialis) complemented by the regulations of the civil code (lex generali) are the basis for shaping the scope of 479 protective measures in civil or administrative liability insurance. A violation, pursuant to these regulations, of property interest of an entity which uses the environment may become the subject matter of insurance (Orlicki, Pokrzywniak, 2008; Ogiegło, 2011). The prerequisite of providing insurance cover is for the occurrence violating the property interest to meet the criteria for a random event. For this reason, the costs incurred by the entity using the environment, regardless of the damage or its threat materialisation, should not be covered by insurance (Molęda 2008). The character of the above regulations is suitable for liability insurance products which are ancillary towards causative liability. These regulations, however, are not exhaustive in the context of the contractual freedom in other types of insurance. For property insurance, this freedom is restricted by articles 3531 , 821, 829 of the Civil Code and a list of insurance groups in Division I, added to the Act of 11 September 2015 on insurance and reinsurance activities (Law Gazette 2015, item 1844). According to the indicated regulations, the content and purpose of the insurance contract must not be against the nature of the contractual relationship, the law and the principles of social coexistence. The property interest – the subject matter of property insurance – must be additionally eligible for pecuniary valuation (Kubiak 2012). The acceptable subjective scope of personal insurance is not finite - according to the code regulations. Indeed, the legislator indicates that personal insurance may in particular refer to life, body injury or health impairment (art. 829 of the Civil Code). The doctrine emphasises the infinite subjective catalogue of personal insurance products, which makes it open to innovation and facilitates market development (Kęszycka 2010). It may be included in the category of personal rights which are also listed in an infinite way in art. 23 of the Civil Code. However, the problem may lie in alignment of insurance products with particular groups of personal insurance. This is because the statutory list encompasses only personal insurance in the form of life, annuity, accident and terminal disease insurance. Designing a first party insurance product on the one hand requires identification of the possible extension of the cover to include situations when the law does not refer to causative liability. On the other hand, though, an analysis must be conducted of cases in which – due to the economic nature of the insurance relationship – the potential scope of the first party insurance will be smaller than the scope of the legal causative liability. The first area of differences between first party insurance and liability insurance is that the former plays a complementary role in in the damage remedy system. Shortcomings and loopholes in the principles of legal causative liability may be mitigated by the system of private insurance, as insurance makes it possible to offer compensation e.g. in the event of unidentifiable author of the damage, the author’s insolvency or the author’s lack of financial security as well as when the author has not fulfilled the premises of legal liability for the act causing damage. Lack of financial security especially occurs when no insurance contract has been signed against the author’s liability. It may also result from imperfection of the insurer’s liability which is ancillary to causative liability. The former is often limited by contractual statements, including, among other things - despite the lack of imperative objection from the legislator - the exclusion of the author’s deliberate fault. Lack of full cover of the injured party may also be connected with a limited application of the risk-based liability system. Besides, the Polish system of civil liability is considered to be conservative as it is lacking in the cover of the so called pure economic loss i.e. financial loss the cause of which is not connected directly with the violation of the injured party’s material goods or personal rights. Therefore, it may be essential to offer compensation in the system of first party insurance, like e.g. in the case of an oil spillage on the surface of the sea, which resulted in plummeting profits of the hotels located on the contaminated coast (Mikołajewicz, 2010; Kwiecień 2009) or damages materialised only in the entity’s financial liabilities. The latter example seems to be meaningless to contemporary insurance practice of covering secondary environmental damage. Still, one might possibly assume that in the course of fostering the process of seeking various forms of financing the consequences of environmental risk materialisation (also those 480 unrelated to insurance) an element will emerge in the insured’s financial liabilities, the level of which will be contingent on the materialisation of environmental risk and which will become, on its own, the subject matter of environmental insurance. The basis for limitation of the scope of first party insurance will be sought in the nature of the insurance relationship. The economic character of an insurance contract is perfectly described in the literature. Nevertheless, the problem of risk insurability is tackled anew every now and then. Along with development of new methods of data storage as well as modern techniques of risk assessment and control, the range of unbiased insurability of risk is growing all the time. It is difficult, therefore, to point at the precise list of occurrences which might limit the contractual freedom. One of the unquestionable examples of a discrepancy within the nature of insurance relationship is the existence of speculative elements in connection with insurance contract. It seems that personal insurance concerning other personal rights than human life or health is particularly susceptible to this risk. There is no obligation in this case to act under the principle of indemnification which protects insurance against a violation of its nature in terms of property-related consequences of damage (Warkałło, 1975); also there is no natural psychological inhibition connected with an intrinsic need to protect life and health. In this context a question arises about legal (with reference to art. 3531 of the Civil Code) basis at least for insurance against deterioration of living standards as a result of the change in the condition of the environment. The potential possibility for appearance of both the speculative element as well as the moral hazard and other elements of contracts which may violate the rules of unbiased insurability of risk has an effect (pursuant to law and considering the economic conditions) on narrowing down the first party insurance offer with reference to scope of protection in liability insurance which is ancillary to causative liability6 . 4 Conclusions The subject of environmental risk and environmental insurance constitutes a very dynamic combination of research areas. This dynamics results from a continuous evolution in terms of identification and perception of environmental perils both in terms of legal regulations and economic practices. Changeability creates an obligation for researchers to evaluate this subject with the view to future developments and to distance themselves from the past concepts of environmental insurance. The criterion of environmental underwriting, being the distinctive feature of environmental insurance on the market, seems to suit the dynamics of this phenomenon. Therefore, it is possible to extend the scope of the term according to the changes in regulations and economy. Defining the notion of environmental damage (primary and secondary) and the object of this damage from the point of view of general law and insurance as well as determining the principles of legal liability for the damage constitute the basis for developing environmental insurance products. These encompass third party liability insurance along with first party insurance, with regard to protection of both property and personal interest. A precise definition of the scope of legal liability for environmental damage is not only a condition for building effective insurance protection for authors of damages but also a factor which determines due consideration for the interest of the injured parties. Moreover, the clear definition of liability for environmental damage contributes to financing the remedy of that damage, emphasising the absolute (according to the call for sustainable development) necessity for doing so (on condition of the fulfilment of completeness and universality of private insurance). Limitations of causative liability, 6 Let us quote the example of the cover referring to an occurrence resulting in deterioration of living standards. Although in terms of first party insurance its legal insurability is rather questionable, it is still covered within the causative liability system, and consequently, within the liability insurance, which by its nature, tends to eliminate the speculative element on the side of the injured party. 481 besides the nature of the insurance relationship and the list of insurance groups, should be, on the other hand, the basis for development of first party insurance products. The factors, presented in the article, which affect the process of designing environmental insurance products, are not the only essential determinants. Apart from the legal factors one must not forget about economic or technological impact on providing insurance services. These factors cause that insurance industry is not willing to independently cover all the types of environmental risks. This declaration concerns both liability insurance and first party cover. References Bio Intelligence Service, Stevens&Bolton LLP (2009). Study on implementation effectiveness of the environmental liability directive (ELD) and related financial security issues. European Commission. Borys, G. (2002). Ryzyko ekologiczne i jego systematyka na gruncie teorii ubezpieczeniowej. In: Czaja, S., ed., Instrumenty rynkowe w ochronie środowiska. Wrocław: I-Bis. Boyer, M., Porrini, D. (2008). The Efficient Liability Sharing Factor for Environmental Disasters: Lessons for Optimal Insurance Regulation. 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Mixed-frequency analysis of the agricultural market drivers Matěj Liberda Masaryk University Faculty of Economics and Administration, Department of Finance Lipová 41a, 602 00 Brno, Czech Republic E-mail: matej.liberda@klubinvestoru.com Abstract: The importance of agricultural commodities markets is growing and their dynamics have changed substantially recently, especially in the period after the financial crisis of 2008. The aim of the paper is to explain agricultural commodities price movements by assessing an impact of multiple economic and financial factors. We study 6 agricultural commodities, 3 representatives of the grain market and 3 soft commodities, in a time span of 20 years ranging from 01/01/1997 to 31/12/2016. We identify 9 macroeconomic and financial drivers. The data are collected from Bloomberg. We use mixed-data-sampling methodology that enables us to study drivers of various frequencies (daily, weekly and monthly) simultaneously in a single linear model. We do not find a link between energy market and agricultural commodities markets suggested by previous papers and thus we conclude that the link is not linear. In addition, results show prevailing impact of the financial variables over the impact of the macroeconomic factors, which is in line with the hypothesis of the commodity market financialization. We also break down the studied period into two fractions divided by the 2008 crisis and we find that the financialization occurred after the crisis of 2008. Keywords: agricultural commodities, mixed-data sampling, market drivers, financialization JEL codes: Q02, G15 1 Introduction The importance of agricultural commodity markets is growing rapidly due to rising demand from the developing countries caused by their rising population. Increasing demand for biofuels even nourishes this trend. Additionally, substantial number of investors and speculators have entered the commodity market recently increasing both its liquidity and volatility. Clayton (2016, p.7) mentions that the interest in the markets by institutional investors jumped from $15 bn. to $200 bn. between 2003 and 2008. Moreover, the development of agricultural commodity markets became substantially turbulent after the food crisis of 2006. As a result, we choose the agricultural commodity markets as the subject of our research to shed more light into understanding of their dynamics. As commodities lack an intrinsic value, investors willing to invest in them cannot conduct a fundamental analysis to determine in which direction the price will move. How should they decide where and when to invest their money in the commodity market then? There are numerous factors that drive the commodity prices linked with both their demand and supply. Moreover, due to the recent financialization of the commodity market (see e.g. Clayton, 2016 or Büyüksahin and Robe, 2014), commodity prices are driven by macroeconomic as well as financial factors. If we knew in which way each driver impacts the commodity prices, we would be able to base our predictions of the commodity markets development on the prediction of their drivers. We look for potential drivers and intend to measure their impact to make such predictions possible for the investors. Although the literature attempts to cover this newly increased variance in the agricultural commodity markets, papers on the agricultural market focus almost exclusively on interdependencies with the energy market and its financialization. Koirala et al. (2015) 485 finds that agricultural commodity and energy prices are highly correlated and exhibit positive and significant relationship using the innovative copula econometric model. In compliance with these results, De Nicola et al. (2016) discover that the price returns of energy and agricultural commodities are highly correlated and that this co-movement increased in recent years. They also conclude that the stock market volatility is positively associated with the co-movement of price returns across markets (especially after 2007). In addition, Nazlioglu et al. (2013) find that oil market volatility spills on the agricultural markets (except for sugar), but only after the food price crisis of 2006–2008. Nazlioglu (2011) studies linear and non-linear relationship between the world oil and prices of corn, soybeans and wheat. He finds non-linear feedbacks between the oil and the agricultural prices and a persistent unidirectional nonlinear causality running from the oil prices to the corn and soybean prices. Wang et al. (2014) argue that whereas oil shocks can explain a minor fraction of the agricultural commodities prices variations before the food crisis in 2006–2008, the proportion of the variations explained by oil shocks after the crisis becomes much higher. Concerning the financialization of the agricultural commodities markets, Jebabli et al. (2014) analyse shock transmission between international food, energy, and financial markets, using time varying parameter VAR (TVP-VAR). They discover that volatility spillovers between the three markets increase during crisis. Baldi et al. (2016) studied the commodity financialization and integration between commodity and financial markets arising from it. Their main finding is that volatility spillover from stock markets to agricultural markets increased significantly after the 2008 financial crises. To complete the literature review on agricultural commodities markets with several miscellaneous remarks, Dimpfl et al. (2017) concludes that the prices of studied agricultural commodities are almost uniquely formed in the spot market. Contrary to this, Huchet and Fam (2016) confirm a positive impact of speculation in futures markets on returns of the underlying agricultural commodities. Finally, Nazlioglu and Soytas (2012) find evidence on the positive effect of a weak dollar on agricultural prices, besides other conclusions. We address the topic in more complex way. The aim of the paper is to explain agricultural commodities price movements by assessing an impact of multiple economic and financial factors. We consider 4 macroeconomic and 5 financial variables. While macroeconomic variables are typically observed at monthly or quarterly frequencies, data about financial variables can be collected at much higher frequencies (for example daily stock index returns). We employ recently developed mixed-data-sampling (MIDAS) methodology that enables us to incorporate the mixed-frequency variables into a single regression model. Thus, we complement the existing literature with the assessment of as many potential drivers as possible in a single linear model. The drivers were predominantly investigated separately so far. Furthermore, impact of the variable of the commitment of traders was not studied before at all, to the best of our knowledge. Our usage of MIDAS model is also unprecedented for such purpose. We also break down the studied period into two fractions divided by the 2008 crisis to compare the results of the two sub-periods and to study a change that this crisis caused. The rest of the paper is organised as follows. Section 2 describes the MIDAS framework and the data used in the research. Section 3 presents obtained results and discusses them with the literature to date. Section 4 concludes. 2 Methodology and Data Main challenge that we face when studying various drivers of agricultural commodities prices is their mixed frequencies. Most of the financial variables are sampled daily, the commitment of traders reports are issued weekly and macroeconomic variables are observed predominantly monthly. Ghysels, Santa Clara, and Valkanov (2004) developed a framework of mixed-data sampling (MIDAS) that deals with such situation. 486 Mixed-data sampling Normally, a researcher must time-aggregate the higher-frequency data to the sampling rate of the lower-frequency data, when he wants to study a variable sampled at a low frequency (e.g. quarterly) with an independent variable observed at a higher frequency (e.g. monthly). This can be achieved by simple or weighted averaging. However, such approach naturally leads to a considerable loss of information during the time aggregation. On the other hand, the MIDAS methodology incorporates all the information from the data in the model without a need of the aggregation of the higher frequency variable. This is achieved by so called frequency alignment procedure. Frequency alignment transforms the time series of the high-frequency variable to a matrix formed by vectors of a length corresponding to the length of the low-frequency variable time series. Consequently, the number of vectors forming the high-frequency matrix is equal to the number of high-frequency observations falling into one lowfrequency period. Coefficients for each vector of the matrix are then estimated by a regression. Reverse unrestricted MIDAS So far, we presented only basic features of MIDAS methodology considering a situation where the low-frequency dependent variable is explained by high-frequency independent variable (like it was originally developed by (Ghysels, Santa Clara, and Valkanov, 2004). However, the problem at hand in this paper is to explain daily observed agricultural commodity returns with variables of either same or lower frequency (weekly or monthly). Foroni et al. (2015) introduced a modified version of MIDAS, so called reverse unrestricted MIDAS, that enables one to incorporate a high-frequency variable as the dependent one. The frequency of the dependent variable is here aligned with the lower frequency of the explanatory variable as well. Nevertheless, a system of equations is subsequently formed, where each vector of the dependent variable matrix (representing each part of the dependent variable data sample for respective part of the low-frequency period) is explained with the data on the low-frequency explanatory variable. It is particularly beneficial when investigating drivers of the agricultural commodities returns. We can choose which particular values of the high frequency variable we would like to explain (say, first, sixth and twenty-first day – values in the beginning of the month, after one week and at the end of the month) or examine only part of the period (say, from the first to the fifth day – the first week of the month). Model specification MIDAS model can be left either unrestricted or functional constraints can be employed. Leaving the model unrestricted can lead to substantially high number of parameters to be estimated. During the frequency alignment procedure, we divide the high frequency variable into | vectors, as was described before, and we need to estimate a coefficient for each of them. When we align a daily variable with a monthly one, the number of coefficients accounts for 21. Their number even rises when we consider additional lags of the variables (with contemporary month and the preceding one we need to estimate 42 coefficients). To reduce the number of parameters that are estimated, several ways of restriction were developed in e.g. Ghysels, Santa-Clara, and Valkanov (2005, 2006) or Andreou, Ghysels, and Kourtellos (2010). Even though these functional constraints propose some useful properties, the model loses its linearity. On contrary, the approach of leaving the model unrestricted is greatly beneficial when forecasting a high-frequency variable, since the model stays. In addition, more explanatory variables of both low and high frequencies can be added to the model without any additional modifications (Foroni et al. 2015). Furthermore, we determine how many lags of the high-frequency explanatory variable are truly relevant using 487 autocorrelation tests to avoid the situation when we need to estimate too many parameters in the model. To sum up, we align the frequencies of all variables to the lowest, monthly, one. We form the system of equations, each corresponding to a specific day in the month, in line with the RU-MIDAS framework. As the length of the paper is limited, we present only the results of the first equation in the paper. In other words, we only compute the impact of the drivers on the returns of the first day in a month. First, we include all 9 drivers in the model as it is shown in the following equation: :?, o ∑ n(n, ª ; & ∈ 〈1; 2; … ; 6〉, ' ∈ 〈1; 2; … ; 9〉, (1) where a corresponds to each of the 6 examined commodities and ' identifies individual drivers. Note that there might be additional lags of drivers included as well. Consequently, we reduce the model from insignificant variables in a standard manner (one after each unless the adjusted coefficient of determination is rising). Since the models are linear, we use standard OLS estimation. The time series are transformed to avoid their stationarity. In other words, log-differences are computed where necessary (for stock index returns etc.). Finally, F-statistic is computed for each model and they are tested for multicollinearity using variance inflation factor. Data Data are collected across the period of 01/01/1997–31/12/2016, which accounts for 240 monthly, 960 weekly and 5,040 daily observations. Data for all variables (commodities as well as drivers) are collected from Bloomberg. The econometric analysis is conducted in the R interface using following packages: midasr, lmtest and fmsb. To cover various classes of agricultural commodities, we consider three commodities from the group of grains (corn, wheat and soybean) and three from the soft agricultural commodities (coffee, sugar and cotton). Daily data on corn, wheat, soybeans, coffee, sugar and cotton are measured in dollars per bushel. We use the data of the spot market indices constructed by the Chicago Board of Trade (CBOT) for the grain commodities and by the Intercontinental Exchange (ICE) of the New York Stock Exchange for the soft commodities. We compute daily returns from the commodities prices using their log difference. We consider 10 macroeconomic and financial variables in total as drivers that potentially have an impact on the commodities returns. Macroeconomic variables comprise inflation, industrial production, real GDP growth, and interest rate. Financial market characteristics include exchange rate, stock index, volatility index, crude oil prices, and commitment of traders. All macroeconomic variables correspond to the U.S. economy since it is considered world centre of the commodity trade. Inflation and industrial production are sampled monthly. GDP growth data are collected quarterly. However, if we included quarterly variable in the models, they would become considerably less feasible, the results less precise and their interpretation more complicated. Hence, we assume that if a certain year-over-year growth rate is registered for the quarter, the equal year-over-year growth rate holds for each month of this quarter. The commitment of traders reports presenting a breakdown of each Tuesday’s (resulting in weekly frequency) open interest for markets in which 20 or more traders hold position equal to or above the reporting levels established by U.S. Commodity Futures and Trading Commission (CFTC). They are issued by CFTC and they reflect investors’ activity in the futures markets. The rest of the variables is of daily frequency. As a final point, interest rate is represented by the federal funds rate targeted by the Federal Reserve’s Federal Open Market Committee (FOMC) as part of its monetary policy. 488 3 Results and Discussion Autocorrelation analysis revealed that we should include the first lag of the stock market returns and first two lags of the VIX returns in the analysis. Furthermore, we include the first lag of the commitment of traders for wheat and its 5 lags for cotton. We observe several interesting facts in the results for the overall time span presented in the Table 1. First, strong influence of the stock market index returns on the agricultural commodities returns is evident, with positive coefficients significant at the level of 1 % in 5 out of the 6 cases (with sugar being the exception). The link between the agricultural market and the stock market is also confirmed by the 5% significance of the VIX returns for half of the commodities. Second, the macroeconomic drivers are not found significant for most of the commodities. Only the sugar is significantly driven by industrial production (1% significance) in negative direction and by GDP growth in positive direction (however, it is significant only at 10 %). Third, apart from these variables, exchange rate positively drives the soybean returns and the commitment of traders negatively drive the wheat returns (both significant at 5 %). Table 1 Agricultural commodities drivers 1997–2016 Corn Wheat Soybean Coffee Sugar Cotton Constant –0.003 (–1.39) –0.001 (–0.75) –0.005* (–1.94) 0.001 (0.70) –0.011* (–1.87) 0.002 (0.87) Inflation 1.2e–03 (1.27) – 1.3e–03 (1.47) – 2.6e–03 (1.27) –1.4e–03 (–1.37) Industrial production – –3.6e–04 (–1.21) –6.4e–03 (–1.54) – –2.4e–03*** (–2.60) –3.0e–04 (–1.04) GDP growth – – 1.1e–03 (1.14) – 3.9e–03* (1.79) – Exchange rate – 0.256 (1.50) 0.292** (2.04) – –0.483 (–1.53) – Stock index 0.359*** (3.47) 0.353*** (2.99) 0.229*** (2.65) 0.346*** (2.85) – 0.287*** (3.03) Stock index (–1) – – – 0.303** (2.42) 0.256 (1.35) – VIX 0.026 (1.24) 0.053** (2.28) – – – – VIX (–1) – 0.024 (1.24) – 0.028 (1.07) 0.101** (2.54) – VIX (–2) 0.022 (1.29) – 0.023 (1.47) –0.045** (–2.00) – – Crude oil – 0.104* (1.88) – – 0.173* (1.78) – CoT – 0.014 (1.08) – – – – CoT (–1) – –0.072** (–2.14) – – – – CoT (–3) – – – – – 5.3e–03* (1.96) F–statistic 4.08** 3.82*** 3.44*** 4.85*** 2.37** 4.28*** R2 0.065 0.118 0.082 0.077 0.067 0.068 Source: Own elaboration based on analysis in R and data from Bloomberg Note: Interest rate was removed from all models during their reduction. Results for the period before the financial crisis are substantially weak. The only case where we observe a significance of the driver higher than 10 % is inflation positively driving sugar returns at the level of 1 %. Similarly to the overall results, the most obvious result for the after-crisis period is the strong positive impact of the stock market returns. In addition, coefficient for wheat is over 0.7, implying considerably close relationship between the two markets. Moreover, 489 positive influence of the contemporary stock market returns on the soybean and cotton market are supported by highly significant positive impact of the its first lag as well. Concerning macroeconomic drivers, inflation significantly drives sugar like in the period before the crisis, nonetheless the direction of the impact is opposite. Industrial production negatively influences the soybean returns. As a final point, exchange rate drives negatively sugar returns. Table 2 Agricultural commodities drivers 1997–2008 Corn Wheat Soybean Coffee Sugar Cotton Constant –0.001 (–2.20) 0.004 (1.34) 0.005 (1.51) 0.002 (0.67) –0.038*** (–3.69) –0.003 (–1.37) Inflation 1.4e–03 (0.97) – – – 0.013*** (3.94) – Industrial production – – – – 1.6e–03 (1.59) 5.7e–04 (1.19) Exchange rate 0.178 (0.83) 0.219 (1.01) 0.377* (1.85) – – – Interest rate –5.7e–04 (–0.71) –1.2e–03 (–1.52) –8.3e–04 (–1.08) – – – Stock index 0.152 (1.04) –0.170 (–1.16) – 0.310 (1.22) – – Stock index (–1) – – – 0.277 (1.64) 0.318 (1.20) –0.132 (–1.13) VIX – – – –0.050 (–1.17) – –0.038 (–1.30) VIX (–1) – 0.032 (1.29) – – 0.091 (1.57) – VIX (–2) 0.024 (1.05) 0.027 (1.18) 0.036* (1.70) –0.050 (–1.48) – 0.033 (1.41) Crude oil – – – – 0.152 (1.11) – CoT (–1) – –0.026 (–0.72) – – – – CoT (–3) – – – – – 5.1e–03* (1.91) F–statistic 0.86 1.45** 2.52* 2.18* 4.22*** 2.72** R2 0.031 0.061 0.053 0.061 0.136 0.093 Source: Own elaboration based on analysis in R and data from Bloomberg Note: GDP growth and contemporary commitment of traders were removed from all models during their reduction. Table 3 Agricultural commodities drivers 2008–2016 Corn Wheat Soybean Coffee Sugar Cotton Constant –6.6e–04 (–0.30) 0.004 (1.35) –0.009** (–2.34) 0.001 (0.50) 0.014*** (2.85) 0.006* (1.96) Inflation – –3.3e–03* (–1.86) 2.7e–03 (1.54) – –0.010*** (–4.10) –3.0e–03* (–1.72) Industrial production – – –1.4e–03** (–2.04) – – – GDP growth – – 2.3e–03 (1.42) – – –1.1e–03 (–1.03) Exchange rate – – – – –1.076** (–2.46) –0.305 (–1.12) Stock index 0.431*** (3.21) 0.739*** (4.85) 0.366*** (3.47) 0.267* (1.85) 0.536** (2.42) 0.499*** (3.06) Stock index (–1) – – 0.446** (3.02) 0.457** (2.44) 0.328 (1.16) 0.505*** (3.14) VIX – 0.062* (1.91) – 0.036 (1.11) – 0.046 (1.57) VIX (–1) – – 0.051* (1.96) 0.065** (2.01) 0.052 (1.11) – 490 VIX (–2) – – 0.033 (1.50) –0.044 (–1.55) –0.063 (–1.49) – Crude oil – – – – – –0.087 (–1.07) CoT –0.132 (–1.35) 0.049 (1.13) –0.051 (–1.06) – 0.103 (1.07) –0.051 (–1.06) CoT (–1) – –0.118* (–1.80) – 0.080* (1.69) – – CoT (–2) – – – – – –0.078 (–1.18) CoT (–3) – – – – – –0.062 (–1.24) CoT (–4) – – – – – –0.078 (–1.61) F–statistic 5.61*** 6.54*** 3.14*** 3.49*** 3.74*** 2.64*** R2 0.106 0.264 0.222 0.191 0.227 0.257 Source: Own elaboration based on analysis in R and data from Bloomberg Note: Interest rate was removed from all models during their reduction. Discussion Opposed to Koirala et al. (2015), De Nicola et al. (2016), Nazlioglu (2011), and Wang et al. (2014), we do not find any evidence on the impact of the crude oil returns on agricultural commodities markets. Since we used a linear framework unlike the aforementioned papers, we can imply that the linkage is non-linear. Furthermore, we find that agricultural commodities markets were financialized in the period after the crisis of 2008 and that they are strongly linked with the returns in the stock market. This is in line with findings of Clayton (2016), Büyüksahin and Robe (2014), Jebabli et al. (2014) and Baldi et al. (2016). Furthermore, we may draw several partial conclusions from the results. First, macroeconomic variables have only little impact on the agricultural commodity returns (we find a significant influence of industrial production, inflation and to a limited extent of GDP growth almost exclusively for sugar). Second, the activity in the futures market represented by the commitment of traders drives the returns with a significance higher than 10 % only in a single case of wheat (in the overall time-span). We suggest that the negative coefficient at the variable’s second lag implies, that elevated activity pushes the market closer to its equilibrium and thus, reduces the returns. Finally, we find a positive impact of the exchange rate on the soybean returns in the overall time span. This results from the fact that the U.S. is the largest producer of soybeans in the world and thus, exports vast proportion of its production 4 Conclusions We assessed an impact of multiple economic and financial drivers on the agricultural commodities price movements. We considered 4 macroeconomic and 5 financial variables and we employed the framework of mixed-data sampling to include all the drivers in a single linear model. Our main finding is a strong evidence of the agricultural commodity market financialization after the financial crisis of 2008. We do not confirm the link between the agricultural commodities markets and the energy market suggested in the previous research. We also observe a significant (negative) impact of the activity in the futures markets (represented by the commitment of traders) only in the case of wheat in the overall time-span. The further research may be extended on more days in the month, whereas there were presented only the results for the first day in a month in this paper. One can also use the presented results as a ground for the prediction of future values and their comparison between the commodities. Finally, the models can be complemented with additional drivers when there are identified some. 491 Acknowledgments I am grateful for financial support from the Programme of Rector of Masaryk University in Brno. 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Energy Economics, vol. 44, pp. 22–35. 492 Effect of zero lower bound on large firms financing in the Czech Republic Dagmar Linnertová1 , Veronika Kajurová2 1 Masaryk University Faculty of Economics and Administration, Department of Finance Lipová 41a, Brno, Czech Republic E-mail: dagmar.linnertova@mail.muni.cz 2 Mendel University Faculty of Business and Economics, Department of Economics Zemědělská 1, Brno 61300, Czech Republic E-mail: vkajurova@seznam.cz Abstract: The effect of monetary policy might be analyzed using several channels. One of these channels is known as the balance sheet channel and shows how monetary policy affects the credit portfolio of financial intermediaries as well as other economic agents. For instance, a contractionary monetary policy would affect banks’ ability to grant loans, leading to credit rationing. This has implications for credit availability to borrowers, especially small-scale borrowers with less sophistication and collateral to back-up their loan demand. Most of the current studies aiming to analyze the balance sheet channel during the monetary tighten period. The purpose of our study is to investigate the changes in financial structure of Czech blue chips traded in the PSE during the period of zero lower bound. Firm-level financial data for Czech firms are gathered from Bloomberg terminal and Amadeus database and panel regression is employed to determinant how financing indicators react to monetary policy changes. Keywords: monetary policy, interest rate, transmission mechanism, balance sheet channel JEL codes: E32, E44, E51 1 Introduction The financial crisis, which started in August 2007, affected several parts of global financial systems and influences the financing conditions of both the financial and nonfinancial sectors. Since the beginning of the crisis, the ECB followed by other central banks have introduced a wide range of unconventional measures in order to meet its inflation objective, including low or negative deposit facility rates, targeted long term refinancing operations, and asset purchases. The aim of monetary policy is to determine economic activities. The transmission mechanism might affect economic aggregates such as inflation, output, interest rates, exchange rates or employment. Monetary policy can be transmitted in the economy in several channels: the interest rate, the bank credit, the balance sheet, the exchange rate, the asset price and the expectations channels. The transmission mechanism of monetary policy is described in Figure 1. 493 Figure 1 Transmission mechanism of monetary policy Source: Authors´, ECB (2017) The central points of financial conditions of private sector are developments in benchmark interest rates (key ECB or central banks interest rates, money market rates and government bond yields). These rates are the main determinants of the conditions of direct financing in financial markets for both non-financial and financial corporations. In the EU area, bank-based financing is the predominant source of external debt financing for the non-financial private sector (ECB, 2012). According to the credit channel theory, the effects of monetary policy on interest rates are boosted by changes in external finance premium which represent the difference in costs between funds raised externally (issuance of debt or equity) and funds generated internally by retaining earnings. A change in monetary policy that raises or lowers openmarket interest rates tends to change the external finance premium in the same direction (Bernanke and Gerter, 1995). Regarding the balance sheet channel stresses the positional impact of changes in monetary policy on borrowers’ balance sheets and income statements. A contractionary monetary policy such as sale of treasury instruments would affect banks’ ability to grant loans, leading to credit rationing. This has implications for credit availability to borrowers, especially small-scale borrowers with less sophistication and collateral to back-up their loan demand. Also, low credit leads to an increase in interest rates thereby raising the cost of credit to small users with low collateral. Only firms with healthy balance sheets are able to borrow from the capital market, Under-capitalized firms are forced either to borrow from banks and raise funds at higher interest rates that reflect the cost of monitoring, or self-finance their projects. Monetary policy can affect the access of firms to external finance because it alters the costs of funds, Bougheas et al. (2006). The aim of the paper is to investigate the existence of the balance sheet channel for Czech very large and large firms in the very low interest rates environment. We take into account firm characteristics to find the effect of monetary policy on the access of firms to external finance. 494 2 Methodology and Data Using yearly data our data set gathered from Amadeus database contains 9 017 very large and large Czech firms, Bloomberg was used to obtain market and macroeconomic data. The analyzed period was chosen from 2009 to 2015, the development of 3M PRIBOR as monetary policy proxy and the spread between 3M PRIBOR and 10year Czech government bond yield is demonstrated in Figure 2. Figure 2 3M PRIBOR Rate and Spread Source: Authors´in Eviews, data Bloomberg The summary statistics of variables are reported in Table 1. For our companies short term debt represented 58.13 percent of total debt and total debt represents 20.18 percent of total liabilities of Czech firms on average. Table 1 Descriptive statistics Mean Median Max Min Std.Dev Short-term debt/ total debt 0.5813 0.6692 3.4355 -0.5360 0.4044 Total debt/ total liabilities 0.2018 0.1639 3.7781 -0.0934 0.1736 MP in % 0.7983 0.5000 1.6500 0.2700 0.5183 Size 9.3282 9.2336 17.0463 1.8124 1.3744 Collateral 0.3863 0.3666 5.6202 -0.5236 0.2634 Gearing 2.0273 0.3923 3815.251 -0.3670 41.6510 Profit 11.2429 10.0575 936.7920 - 996.1540 54.3106 Age 14.8580 16.0000 70.0000 -0.0000 7.1174 GDP growth in % 0.9755 1.3000 4.0000 -3.6000 2.4722 Sentiment growth in % 0.0347 0.0632 0.1454 -0.1288 0.0840 Source: authors´, data Amadeus, Bloomberg 495 The logarithm of total assets is used to indicate the impact of SIZE and it is the key proxy for capital market access in Gerter and Gilchrist (1994) Bougheas et al. (2006). Monetary policy is represented by 3M PRIBOR development and reflects credit supply (Atanasova and Wilson, 2004). The GDP growth rate controls for cyclical effects, as an increase in the GDP growth rate encourage firms to shift from debt to non-debt liabilities. Following Bougheas et al. (2006) these measures of firm-specific characteristics are chosen. AGE measures the importance of firm history for access to external financing, COLLATERAL measures the support for borrowing, PROFIT is represented by ROE and GEARING is indebtedness of a firm with the respect of its equity. SENTIMENT measures confidence of economic agents about current and future economic situation and it is aimed for the Czech Republic. A characteristic of variables is summarized in Table 2. As dependent variables that represent financial choice are used ratios: short term debt to total debt and total debt to total liabilities reflecting short and long term time horizon. The first dependent variable states for access to market finance versus bank finance, where the majority of short-term debt is bank finance, the second measure refers the overall availability of external debt. Table 2 Variable characteristics Abbreviation Variable Description MP 3M PRIBOR Proxy for monetary policy SIZE Logarithm of total assets Proxy for capital market access AGE History of a firm Importance of track records for the change in the composition of firm external finance COLLATERAL Tangible assets/ Total assets Support for borrowing PROFIT ROE Profitability scaled by capital GEARING Total loans to shareholder funds Indebtedness of firm in relations to their equity GDP growth rate YoY change in GDP Trade of between debt and non-debt liabilities SENTIMENT Economic Sentiment indicator for the Czech republic Proxy for market participant anticipations Source: authors´ Investigate the relationship between the financial choices of firm and their firm-specific characteristics standard panel model is employed. The formal equation is following: 2 o2 Ä2 á2 (1) Where ~ 1,2, … , C refers to cross-section firms, 1,2, … , refers from time period, 2 and Ä2 state the dependent variable and the vector of explanatory variables from firm i and year t, ª2 is the error term and o2 is a vector of firm-specific intercepts. The Hausman test was used to compare estimates from random effects model against a fixed effects alternative. Rejecting the hypothesis of no systematic difference between coefficients obtained from the models, the fixed effects model was chosen. The results are demonstrated in Table 4. The theoretical impact of firm-specific characteristics into dependent measures is summarized in Table 3. The ratio short-term debt to total debt (variable for bank lending) will increase for small, with higher level of indebtedness and lower level of collateral and profitability. Total debt to liability is expected to increase for larger, less risky and more collateralized firms with higher ROE. 496 The increase in the GDP growth rate encourage firms to use mode non-debt capital, thus expected effect is negative for both measures. Table 3 Expected effects Expected effect Short-term debt/ total debt Expected effect Total debt/ total liabilities Size Negative Positive Profit Negative Positive Collateral Negative Positive Gearing Negative Positive GDP growth Negative Negative Source: authors´ The SIZE measured as logarithm of total asset is a significant factor affecting the debt ratios. The firms with higher share of total assets have better access to long term debt (positive sigh of coefficient) and reduce their short term debt (coefficient is negative). This results support finding of Gertler and Gilchrist (1994) or Bougheas et al. (2006) that firm size is important factor influencing the access to debt and bank loans. Further, Oliner and Rudebush (1996) and Bougheas et al. (2006) we find that small firms use mostly short-term bank loans. The size of COLLATERAL support access to long-term debt, firms with higher value of tangible assets to total assets could reduce the portion of short term debt in the favour of long-term debt. The same effect was found for GEARING that support longer form of debt financing. GDP growth rate supports in short run short term form of debt financing, in the long run encourage firms to orient toward equity financing. The ROE lower level of debt financing, AGE support using of long-term debt, older firms have easier access to longterm financing. The effect of SENTIMENT is taken into account only in the short period but this findings correspond with the construction of this indicator. In the long run the role of SENTIMENT is irrelevent. The findings for monetary policy are ambiguous but support the existence of balance-sheets channel. In the period 2009 - 2015 decreasing PRIBOR rate positively influenced the level of total debt to total liabilities for the Czech large and very large firms at the same moment the impact on short-term debt to total debt is negative. Same finding for Germany manufacturing firms were confirmed in Kajurova and Linnertova (2017). This can be explained by the construction of dependent variables, when the effect of low interest rate disappears, as we use ratios. Table 4 Results for Short-term debt/ total debt and Total debt/ total liabilities (2009 - 2015) Short-term debt/ total debt Total debt/ total liablities Constant 1.5683*** (19.3577) -0.1414*** (-9.9886) Monetary policy -0.0548*** (-5.2428) 0.0054** (2.1758) Size -0.0799*** (-12.4571) 0.0205*** (27.0741) Collateral -0.3052*** (-14.1299) 0.1424*** (31.6480) Gearing -0.0002* (-1.8055) 0.0003*** (12.2496) 497 Profit -0.0001*** (-2.6336) -0.0001*** (-6.4829) Age -0.0052* (-1.6602) 0.0008 (1.0302) GDP growth 0.0026*** (2.8350) -0.0011*** (-4.9156) Sentiment -0.1062*** (-5.0558) 0.0023 (0.4508) R-squared 0.7534 0.8188 Adj. R-squared 0.6873 0.7823 Prob(F-statistic) 0.0000 0.0000 D-W stat. 1.6185 1.2662 Number of obs. 23 252 43 230 Source: authors´in Eviews 4 Conclusions The paper investigates the impact of monetary loosing on financial position of large and very large Czech firms. We take into account firm-specific characteristics to determine the access to short term and long term funds. We found out that small, young firm with low collateral are oriented on short term debt financing. The large, well-established firms with higher level of collateral might be founded by long-term debt or open to equity financing. The monetary policy affects the structure of company debt in long and short run. We suggest in zero lower bound the large firms replace short term debt by the long term debt. Acknowledgments The support of the Masaryk University internal grant MUNI/A/1039/2016 is gratefully acknowledged. References Atanasove, C. V., Wilson, N. (2004). Disequilibrium in the UK corporate loan market. Journal of Banking & Finance, vol. 28(3). Bernanke, B. S., Gertler, M. (1995). Inside the Black Box: The Credit Channel of Monetary Policy Transmission. The Journal of Economic Perspectives, vol. 9(4), pp. 27– 48. Bougheas, S., Mizen, P., Yalcin, C. (2006). Access to external finance: Theory and evidence on the impact of monetary policy and firm-specific characteristics. Journal of Banking & Finance, vol. 30 (1), pp. 199-227. ECB (2012). Assessing the financing conditions of the Euro area private sector during the sovereign debt crisis. Retrieved from: https://www.ecb.europa.eu/pub/pdf/other/art2_mb201208en_pp77- 95en.pdf?bc8956f9157d2edad22d604789b17b0a. Gertler, M., Gilchrist, S. (1994). Monetary Policy, Business Cycles and the Behavior of Small Manufacturing Firms. Quarterly Journal of Economics, Vol. 109, pp. 309 – 340. Kajurová, V., Linnertová, D. (2017). How Does the Financial Position of German Firms Change in a Response to the ECB's Monetary Policy?. Working Paper. Oliner, S. D., Rudebusch, G. D. (1996). Monetary Policy and Credit Conditions: Evidence from the Composition of External Finance: Comment. The American Economic Review, Vol. 86(1), pp. 300 – 309. 498 Assessment of Impact of Items Reducing Tax Base and Tax on Total Amount of Corporate Income Tax in the Czech Republic in Selected Periods Karolina Lisztwanová1 , Iveta Ratmanová2 1 VSB-TU Ostrava Faculty of Economics, Department of Finance Sokolská třída 33, 702 00 Ostrava, Czech Republic E-mail: karolina.lisztwanova@vsb.cz 2 VSB-TU Ostrava Faculty of Economics, Department of Finance Sokolská třída 33, 702 00 Ostrava, Czech Republic E-mail: iveta.ratmanova@vsb.cz Abstract: A tax policy as an important part of a fiscal policy influences many financial and operational decisions of enterprises. Regarding a corporate income tax, the aim of this paper is to state a value of tax savings with respect selected items reducing a tax base and with respect tax credits as items decreasing enterprises` a final tax liability. It means that attention is devoted the first of all to impact of tax losses, research and development expenses, expenses connected with donation activities of companies and investment incentives. As a source data are used data provided by Ministry of Finance of the Czech Republic connected with a selected period between years 2005 and 2015. We concentrate our attention not only on total summarized data but moreover on selected sectors. To be able to express and to compare changing of above-mentioned items, we firstly use base and chain indexes. Moreover, a method of a pyramidal decomposition of a top indicator i.e. the final tax liability is used, so that we could calculate and find out an impact of selected items on this value. In case of identified multiplicative relationship among individual items, a functional method is applied. Regarding results of our research, we may confirm that impact of above mentioned items changes in a selected period and especially in case of tax losses, we can identify a significant influence in determining of a final tax liability. Respecting research and development expenses, we may state that their impact grows up in observed period. When it comes investment incentives and donation activities development of these items is stable growing. Keywords: corporate income tax, tax base, tax savings, pyramidal decomposition, functional method JEL codes: H20, G30, K34, H25, G39 1 Introduction A tax policy is considered as an important part of a fiscal policy of countries. A final form of individual tax systems must be convenient with main tax principles, i.e. fairness, effectiveness, flexibility, political transparency and legal perfection or administrative simplicity (Kubátová, 2015). Moreover, a good a tax system eliminates substitution and distortion effects of a taxation and supports desirable behaviour of tax subjects respecting stimulating function of taxes. There are different ways to create the kind of a tax system and there are different possibilities how to adjust individual taxes to these requirements. Four tax objects exist – head, property, consumption and income. The last one, the income concentrates attention on a value of the income of the individual subjects. We can find not only individual persons among these subjects, but non-financial enterprises, financial institutions or non-profit enterprises that can be levied with this kind of the tax as well. The aim of this type of the taxation is to tax a taxable profit but also to support desirable activities of the enterprises. Consequently, these intentions can be observed 499 not only in individual adjustments of accounting profit to the taxable profit. The corporate income taxation may use further aspects influencing a final tax liability of the tax subjects. For example, individual items reducing tax base or tax reliefs may be applied. Regarding these facts, the aim of our paper is to concentrate on development of the main items reducing the tax base and the tax reliefs of the corporates during a selected period and evaluation of the impact of these items on corporates` final tax liability. The individual tax systems can be described as a mix of direct and indirect taxes. The corporate income tax of the Czech Republic as a direct tax is regulated by the Act. 586/1992 Coll., on Income Taxes. The tax quota and the ratio revenues of this tax to total tax revenues (OECD) are presented in figure 1. Figure 1 Changes of tax quota, tax revenues and statutory tax rate Source: OECD and Act no. 586/1992 Coll., on Income Taxes As can be seen the statutory tax rate of the corporate income decreased in selected period. This rate has been decreased from the value 26 % to 19 %. On the other hand, the tax quota has not changed significantly at all and can be stated that its value was higher than 3 % and lower than 5 %. The development trend of the ratio between the corporate income tax revenues and the total tax revenues was variable, but average value was 11.06 % and mean value 10.63 %. Regarding data presented in this figure is clear that the impact of the statutory tax rate reduction on these ratios was not important. The following figure 2 expresses in more detail information describing a relationship first among the development of value of the total tax liability, the total tax revenues and the numbers of tax returns (Finanční správa). It is clear that the total tax liability was affected by a reduction of enterprises` earnings. Nevertheless, the number of tax returns increased in spite of the fact that economy crisis influenced the profitability of companies. It is interesting to compare differences between the value of total tax liability and the value of total tax revenues of the Czech Republic. These items differ, because the tax revenues include actually paid tax amount and tax advances. 0 5 10 15 20 25 30 0,00 5,00 10,00 15,00 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 CIT/GDP (%) CIT/Total Tax Revenues (%) Statutory Tax Rate CIT (%) 500 Figure 2 Changes of tax revenues, tax liability and numbers of tax returns in selected period Source: Finanční správa The Act. 586/1992 Coll., on Income Taxes has been amended more than hundred times since its inception. The changes of individual parts of this act are not only related to a definition of tax base, tax rates, tax exemption or others key aspects. The changes can be determined in the area of the items reducing tax base or in the tax reliefs as well. The tax subject, the tax base, the tax exemption, the tax period, the deduction from the tax base, the tax rate and the tax relief can be considered as the main construction elements of the taxes and the taxation influencing a level of a final tax burden. In the case of the corporate income tax, the tax subjects are entities that are obliged to tolerate and pay the tax. The tax base is a difference between the taxable incomes and the taxable expenses, the tax exemption is part of the tax base that is not taxed, the tax period is a period for which the taxable profit is determined and the tax rate is an algorithm that determines a basic value of the corporate income tax (Kubátová, 2015). Reductions from the tax base are closely linked with the stimulation function of the taxation, concentrate on supporting of specific purposes and decrease the adjusted tax base (Široký, 2008). It can be claimed that the items reducing tax base (IRTB) cuts down the taxable profit whereas the tax reliefs (allowances) influence the calculated tax. Respecting legislation of the Act. 586/1992 Coll., on Income Taxes the relationship among the tax base, the deductions of the tax base, the tax reliefs and the final value of the tax can be expressed by following formula: , (1) where ATB is the adjusted tax base, STR is the statutory tax rate, IRTB is the items reducing the tax base, TR is the tax reliefs and TTL is the total tax liability. The following formula (2) defines a rule of an application of the items reducing tax base and the tax reliefs respecting long-term decisions: (2) The items reducing tax base of the corporate income tax has been changed for several times over the last twenty-five years. Comparing a different period only the one kind of deductions can be used all the time - the tax loss. The carry forward tax loss refers of applying a previous year`s loss to reduce the current year`s profits for tax purposes (Act. 586/1992 Coll., on Income Taxes). The period that may apply this type of the deductions has been decreased from seven years to five years. The deductible item to support research and development expenses of enterprises was used in the year 2005 (Act. 586/1992 Coll., on Income Taxes) for the first time. A specific attribute of this item is possibility firstly as the taxable expense to decrease the taxable earnings and secondly as the item reducing tax base. This reality means that this 501 expenses influence the final tax liability twice. The taxpayers can reduce their tax base by implementing projects, which are in the form of experimental or theoretical works of design or construction work, calculations, designs, technologies, a production of functional samples or product prototypes. This deductible item can be applied for up to three tax years immediately following the taxable period in which the deduction was incurred. Third item reducing the taxable amount is the value of gratuitous transactions (donations) determined to the specific purposes (Act. 586/1992 Coll., on Income Taxes). The aim of this reduction of the tax base is to support financing of education, culture, social, medical, environmental, humanitarian or charitable purposes. The value of the donations decreases the tax base in the taxable period in that the gift was demonstrably provided. Moreover, the tax law specifies the minimum value of the donations. Comparing the items reducing the tax base and the tax reliefs, the tax allowances affect the final tax entirely different way as has been mentioned above. The tax reliefs respecting the Czech tax law of the corporate income tax are connected first with investment incentives. The incentives are one of the form of a public business support and can be applied up to the amount of the tax liability. This kind of the tax reliefs can be used for five consecutive tax period. First taxable period for which the reliefs can be applied is the tax period in which the taxpayer fulfilled the general conditions under the legislation connected with the investment incentives and the special conditions set out in the Act. 586/1992 Coll., on Income Taxes. Finally, this tax act stipulates the possibility of applying the tax reliefs of the taxpayers, who employ just persons with disabilities (Act. 586/1992 Coll., on Income Taxes). According to this, employers may reduce their tax liability for each employee with a disability. 2 Methodology and Input Data Respecting information mentioned above the items reducing tax base and the tax reliefs are an indirect form of business` support. Intensity of usage of these kinds of supporting changed during a period in the Czech Republic. Our attention is concentrated on expression a value of this support in period between years 2005 and 2015. As a source information, we used data provided by Finanční správa that contain total annual information about the earnings before taxation (EBT), the value of the tax base, the items reducing tax base, the tax reliefs and the total tax liability. The following table 1 describes not only the changes of the tax reliefs and the items reducing tax base, but also the changes of the total tax liability (TTL), the statutory tax rate (STR) and the adjusted tax base (ATB I.). The item of the adjusted tax base I. represents corrected accounting profit respecting adjustments mentioned in section mark 23, 24 and 25 ot The Act. 586/1992 Coll., on Income Taxes. Table 1 Changes of selected items within period 2005 – 2015 in v % 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 TTL 100.0 114.1 132.7 117.5 95.7 93.4 92.4 93.6 97.9 106.8 115.9 STR 100.0 92.3 92.3 80.8 76.9 73.1 73.1 73.1 73.1 73.1 73.1 ATB I. 100.0 118.3 134.3 139.5 121.8 124.9 128.5 126.2 143.1 151.2 152.6 Tax Loss 100.0 81.5 69.1 107.7 105.7 97.9 125.0 84.5 105.6 84.3 85.9 R&D 100.0 131.7 160.1 147.9 157.2 213.4 305.2 325.3 385.2 375.9 421.5 Donations 100.0 116.2 116.2 111.9 110.4 114.0 122.5 121.8 171.5 170.4 172.0 Reliefs 100.0 118.6 139.5 88.7 85.4 108.4 127.1 167.0 413.5 431.1 171.0 Source: authors` calculation according data of Finanční správa As can be seen in the table 1 that use the base index and the input values are data of the year 2005 of individual items, the final total tax liability changed during selected period. 502 Table 2 Year-on-year changes of selected items within period 2005 – 2015 in % 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 TTL 100.0 114.1 116.3 88.5 81.5 97.6 99.0 101.3 104.5 109.1 108.5 STR 100.0 92.3 100.0 87.5 95.2 95.0 100.0 100.0 100.0 100.0 100.0 ATB I. 100.0 118.3 113.5 103.8 87.3 102.5 102.9 98.2 113.4 105.6 101.0 Tax Loss 100.0 81.5 84.9 155.9 98.1 92.6 127.7 67.6 124.9 79.9 101.9 R&D 100.0 131.7 121.6 92.3 106.3 135.8 143.0 106.6 118.4 97.6 112.1 Donations 100.0 116.2 100.0 96.3 98.7 103.2 107.5 99.4 140.8 99.4 100.9 Reliefs 100.0 118.6 117.6 63.6 96.3 126.9 117.2 131.5 247.5 104.2 39.7 Source: authors` calculation according data of Finanční správa The data stated in the table 2 differs from the data of the table 1 because expresses the continuous year-on-year changes. The main question is, if it is possible to express, respecting public published information provided by Finanční správa, the power of influence of the individual elements affecting this value. We used methodology of pyramidal decomposition so that we could find out the answer this question. The pyramidal decomposition of individual indicator is derived from an idea that a top indicator can be decomposed on partial indicators (Dluhošová, 2010). Moreover, it is possible mathematically to identify the certain operations among these individual indicators. Finally, can be stated that the change of the top indicator can be explained through the changes of the individual indicators: ∆ ( ∑ ∆(&~~ , (3) where x is the analysed indicator, ∆ * is increment in the influence of the analysed indicator, ai is the indicator by which ∆ * can by partially explained, ∆(?Æ is the influence of the indicator ai on analysed indicator x. In such systems, two types of the operations can be identified - aditive or multiplicative. The additive operation is stated as: ∆(?Æ ∆?Æ ∑ ∆?ÆÆ ∙ ∆ *, (4) where ∆ai = ai,1 – ai,0, ai,0, and ai,1 is the value of the indicator i respecting starting (0) and ending (1) state. The multiplicative operations can be stated by different methods. One of them is the functional method that expresses the combined simultaneous impact of the all indicators explaining of their influence on the top indicator (Zmeškal, Dluhošová, Tichý, 2004). As input information, the relative changes of the items are used. Respecting the multiplicative operation between two indicators, the influences can be formulated as: ∆(?Â Ë ∙ ? ∙ `1 ∙ ? a ∙ ∆ *, (5) ∆(? Ë ∙ ? ∙ `1 ∙ ? a ∙ ∆ *. (6) where &~ and Rx are relative changes of indicators. We created the following pyramidal decomposition (figure 3) so that we could calculate the power of influences of the changes of the individual items on the changes of the total tax liability. The main aim of this decomposition was to state the influences of the changes of the items reducing tax base, the tax reliefs,the tax rate and the adjusted tax base I. on the total tax liability. 503 Figure 3 Pyramidal decomposition of total tax liability Source: Authors` processing according data of Finanční správa 3 Assessment of Influence of Individual Indicators The following tables 3 and 4 show results of our analysis. Data mentioned in these tables describe power of inluence of the individual indicators in selected periods in mil. CZK and as percentages. The changes of the total tax liability between two following periods are main input information. Then the impact of the individual items is expressed as increasing or decreasing of the total tax liability. It means that the total sum of changes of the individual items is equal to the change of total tax liability. Regarding presented data is clear that increasing of the total tax liability was not only influenced by increasing of all items. That is caused by usage of the functional method as a tool of evaluating of the final impact of the partial indicators on the top indicator. Ultimately, it means that the changes of the indicators` influences were positive or negative. Table 3 Power of influences of individual indicators in mil. CZK 05-06 06-07 07-08 08-09 09-10 10-11 11-12 12-13 13-14 14-15 ∆ TTL 17,662 23,335 -19,143 -27,202 -2,928 -1,203 1,512 5,300 11,206 11,326 Reliefs -731 -820 1,994 129 -902 -732 -1,569 -9,672 -689 10,206 STR -11,112 0 -21,481 -6,664 -6,268 0 0 0 0 0 ATB I. 26,392 22,169 6,700 -20,921 3,466 3,942 -2,524 18,514 8,835 1,623 Tax Loss 3,449 2,202 -6,465 308 1,137 -3,829 5,723 -2,979 3,000 -223 R&D -249 -215 87 -60 -345 -549 -120 -359 56 -273 Donations -87 -0.21 21 6 -15 -35 3 -204 4 -7 Source: authors` calculation according data of Finanční správa Table 4 Power of influences of individual indicators in % 05-06 06-07 07-08 08-09 09-10 10-11 11-12 12-13 13-14 14-15 ∆ TTL 100 100 100 100 100 100 100 100 100 100 Reliefs -4.14 -3.52 -10.42 -0.47 30.81 60.83 -103.76 -182.50 -6.15 90.12 STR -62.92 0.00 112.21 24.50 214.06 0.00 0.00 0.00 0.00 0.00 ATB I. 149.43 95.00 -35.00 76.91 -118.34 -327.75 -166.93 349.32 78.84 14.33 Tax Loss 19.53 9.44 33.77 -1.13 -38.83 318.36 378.46 -56.21 26.77 -1.97 R&D -1.41 -0.92 -0.45 0.22 11.79 45.66 -7.97 -6.77 0.50 -2.41 Donations -0.49 0.00 -0.11 -0.02 0.51 2.91 0.20 -3.85 0.04 -0.06 Source: authors` calculation according data of Finanční správa Then we more concentrated our attention on the development of the individual items reducing tax base and the tax reliefs in period 2005 – 2015 as can be seen in the following figure 4 as well. This figure ilustrates the comparison of the tax savings of the total tax liability arising thanks to the application of the tax reliefs and thanks to Tax Loss + +R&D Donations Total Tax Liability Tax ReliefsAdjusted TB II. Tax Rate· Adjusted TB I. IRTB- 504 application of the total items reducing tax base. The value of the tax savings connected with the items reducing tax base was derived from the total value of these items and the actual valid value of the statutory tax rate. Figure 4 Comparison of tax savings of tax reliefs and IRTB Source: Authors` calculation according data of Finanční správa Data mentioned in figure 4 doubtless show that mostly the total sum of the tax saving connected with all items reducing tax base was higher than the tax saving applied through the tax reliefs. The following figure 5 further extends our analysis respecting the ratios of individual items reducing tax base on the total value of the tax savings. With regard data of above mentioned figure 4 is clear that the tax loss were the most important indicator that brought the highest value of the tax savings. The total ratio of donations respecting the value of the total tax savings does not change significantly and can be stated that its development is the approximately same. The item that requires our attention is the tax saving arising thanks to the deductible item to support research and development expenses of enterprises. As can be seen, its ratio on the total value of the tax savings increased especially in the last five years. This increasing can be explained, for example, respecting the changes made by Ministry of Finance the Czech Republic that research is not only subject of interest of research-development organizations, but can also take place in ordinary businesses. Ministry of Finance the Czech Republic in the year 2010 also identified other aid criteria for the assessment of the R & D activity, and thanks to this the taxpayers are not obliged to meet all these criteria at the same time. Moreover, can be claimed (Hamáček, 2011) that the administrative burden of this deduction is much lower compared to direct grant. 4 Conclusions Respecting the value of the impact we determinated the total order of all indicators. Table 5 Total power of influences of individual indicators 05-06 06-07 07-08 08-09 09-10 10-11 11-12 12-13 13-14 14-15 Sum % of sum Order mil. CZK mil. CZK mil. CZK mil. CZK mil. CZK mil. CZK mil. CZK mil. CZK mil. CZK mil. CZK mil. CZK % Reliefs 731 820 1,994 129 902 732 1,569 9,672 689 10,206 27,444 0.1247 4 STR 11,112 0 21,481 6,664 6,268 0 0 0 0 0 45,525 0.2069 2 ATB I. 26,392 22,169 6,700 20,921 3,466 3,942 2,524 18,514 8,835 1,623 115,084 0.5230 1 Tax Loss 3,449 2,202 6,465 308 1,137 3,829 5,723 2,979 3,000 223 29,315 0.1332 3 R&D 249 215 87 60 345 549 120 359 56 273 2,314 0.0105 5 Donation s 87 0.21 21 6 15 35 3 204 4 7 383 0.0017 6 220,066 Source: authors` calculation according data of Finanční správa We evaluated the indicator with the highest absolute value as the indicator with the most power of influence. Regarding data mentioned in table 5 is clear that in the observed 505 period the most important indicator that influenced the changes of the total tax liability was the adjusted tax base I. followed by the changes of the statutory tax rate. The final impact of the tax loss and the tax reliefs were not so important. The changes of R&D and the donations were connected with the least impact on the change of the total tax liability in the observed period. Comparing the impact of the tax reliefs and the individual items reducing tax base, the power of the influence of the tax loss was the highest among all items reducing tax base and was higher then the final impact of the tax reliefs. The changes of donation amount the least affected the changes of the total tax liability. Thanks to results mentioned in our paper was confirmed that is possible to assess the changes of total tax liability of the corporate income tax with usage of the pyramidal decomposition of stated top indicator. It was verified that our scheme of individual relationship among indicators is able to determine the final impact of selected items on the total tax liability within tested period. In case of determined multiplicative relationship between the indicators the functional method was used, because only this one is able correctly without distortion to identify impact of indicators in case when the values of changes of items are negative. Acknowledgments This paper was supported by the SGS Project VŠB – TU Ostrava SP2017/148 "Finanční rozhodování podniků a finančních institucí za rizika”. References Act no. 586/1992 Coll., on Income Taxes. Dluhošová, D., Zmeškal, Z., Richtarová, D., Valecký, J. (2010). Finanční řízení a rozhodování podniku, 3th ed. Praha: Ekopress. Finanční správa (2017). Analýzy a statistiky: Daňová statistika k roku 2005-2015. Retrieved from: http://www.financnisprava.cz/cs/dane/analyzy-a-statistiky/danova- statistika. Finanční správa (2017). Údaje z výběru daní: Vývoj inkasa vybraných daní v ČR v letech 1993-2016. Retrieved from: http://www.financnisprava.cz/cs/dane/analyzy-a- statistiky/udaje-z-vyberu-dani. Hamáček, L. (2011). Proč daňoví poplatníci nevyužívají odčitatelnou položku na výzkum a vývoj? Retrieved from: http://www.danarionline.cz/archive/document/doc- d34321v43814-pr. Kubátová, K. (2015). Daňová teorie a politika, 6th ed. Praha: Wolters Kluwer. OECD (2017). Tax on corporate profits. Retrieved from: https://data.oecd.org/tax/tax- on-corporate-profits.htm#indicator-chart. Široký, J. (2008). Daňová teorie s praktickou aplikací, 2nd ed. Praha: C. H. Beck. Zmeškal, Z., Dluhošová, D., Tichý, T. (2004). Financial Models, 1st English ed. Ostrava: VSB-Technical University of Ostrava, Faculty of Economics. 506 Brexit Implications on the Czech Republic via GNI Based Contributions to the EU Budget Dušan Litva University of Economics in Prague Faculty of Business and Administration, Department of Microeconomics Nám. W. Churchilla 1938/4, 130 67 Praha 3, Czech Republic E-mail: xlit00@vse.cz Abstract: After activation of Article 50 on 29th March 2017 Brexit will become reality within next 2 years with all its consequences and implications. UK was significant contributor to the EU budget with its net contribution of 11,2 billion Euro in 2015. Leaving EU will hence significantly reduced incomes to the EU budget. There are several options how to close this gap- increase contribution per each member state, decrease receipts per each country, define new revenue streams to the EU budget or simply reduce overall costs. Czech Republic is clearly net receiver of EU funds with its net positive position of 5,6 billion Euro in 2015. As Czech Republic is more receiving than contributing, choice of solution how to close Brexit gap will influence its current net position. Paper is studying all theoretical options and their impact on Czech Republic. Losses in net balance of Czech Republic due to Brexit might be in year 2020 from 0,2 to 0,9 bil. Euro according to different scenarios to close UK contribution gap. Relates study of OST EU estimated worsening of cumulative net position of Czech Republic due to Brexit vote till 2020 from 0,8 bil. Euro to 2,1 bil. Euro. Key words: Brexit, Czech Republic, European Union, GNI, contribution, budget, gap JEL code: H68 1 Introduction UK voted to leave from EU in referendum on 23rd June 2016. On 29th March 2017 British prime minister Teresa May activated Article 50 of EU Constitutions which started 2 years negotiations prior to UK exit. UK government announced hard Brexit. The UK makes its contributions to the EU budget in the same way as all member states (Morgan, 2017). However, the UK receives a rebate on its net contribution (Irwin, 2016). The rebate was introduced in the mid-1980s to address the issue of the UK having relatively large net contributions to the EU budget. The UK’s contribution to the EU budget, after the rebate was applied, was an estimated £12,9 billion in 2015. The UK received total public sector receipts from the EU budget of £4,4 billion. Estimated net contribution was £8,5 billion in 2015. From Czech Republic standpoint, Brexit will bring weakening of position of Czech Republic as a net EU funds receiver, as EU budget income will be significantly reduced. 2 Methodolgy and Data The EU’s spending is organised around a seven-year period, known as the multiannual financial framework (MFF). The MFF sets out the EU’s spending priorities and sets spending limits for the seven years (Oliver, 2015). The current MFF covers the period 2014-20 and allows the EU to commit to spending of €960 billion (2011 prices). This is a real terms reduction on the previous MFF 2007-13. There are several possible scenarios and funding strategies after Brexit regarding EU budget and its recovery after UK stop contributing. In analysis bellow is shown possible implication on Czech Republic which is currently clearly positive in balance and hence net receiver with prevailing receipts over contributions. Table 1 is showing receipts, contributions and net balance for Czech Republic (*current prices, exchange rate 0,76 GBP/Euro). Calculated are also ratios of each parameter to 507 the EU budget. It is important to highlight obvious difference in ratio of contribution to the EU budget and receipts to the EU budget which is a key in context of impact on net balance and after Brexit EU budget creation. Table 1 Contribution and receipts of CR to the EU budget in 2015 bil. Euro* CR contribution 1,6 CR receipts 7,2 CR net balance +5,6 Calculated ratios % CR contribution/ EU budget 1,1 CR receipts/ EU budget 4,9 CR net balance/ EU budget 3,8 Source: Keep (2016), own elaboration Table 2 is showing contributions, receipts and net balance of UK (*current prices, exchange rate 27,01 CZK/Euro). It is obvious that UK more contributed than received which was one of key factor for Brexit. Table 2 Contribution and receipts of UK to the EU budget in 2015 bil. Euro* UK contribution 17 UK receipts 5,8 UK net balance -11,2 Calculated ratios % UK contribution/ EU budget 11,6 UK receipts/ EU budget 4,0 UK net balance/ EU budget 7,7 Source: Keep (2016), own elaboration Table 3 is showing UK contributions and receipts to the EU budget in period of 2009 to 2015. Table 3 Contribution and receipts of UK to the EU budget 2009 2010 2011 2012 2013 2014 2015 Total contribution (bil. Euro) 11,4 16,1 16,1 16,6 19,1 18,9 17 Total receipts (bil. Euro) 5,8 6,3 5,4 5,5 5,3 6,1 5,8 Balance (bil. Euro) -5,7 -9,7 -10,7 -11,2 -13,8 -12,9 -11,2 Source: Keep (2016), own elaboration Table 4 is showing prediction of EU budget before Brexit vote. There is obvious 2,7 % average growth between the years. Table 4 EU budget prediction Year 2014 2015 2016 2017 2018 2019 2020 Budget (bil. Euro) 143 146 150 154 158 163 168 Source: Keep (2016) 508 3 Results and Discussion As mentioned before there are several options how to solve gap in the EU budget created by Brexit vote and UK stop contributing. A) Keep EU budget constant as planned a. Decrease receipts per country, do not change contributions First option is to keep EU budget constant as overall costs might not be possible to reduce. This goal can be reached by increase of contributions per each country (scenario A-b) or decrease of receipts (A-a) per each country, possibly combinations of both options to fully offset missing contributions from UK. Table 5 is showing calculations for reduction of receipts of Czech Republic which was calculated as a % proportional decrease of receipts which was equal to % proportional decrease of EU budget given by UK contributions gap. Net balance is indicating possible overall losses for Czech Republic. Indication „na“ means „not applicable“ not realistic scenario as UK has 2 years to leave EU since announcement to leave. Possibly realistic might be prediction since 2018. Assumptions were constant ratios of parameters shown in table 5 (year 2015) for full observed period (2015-2020). Visualisation of change of net balance is shown in figure 1. Table 5 Change of net balance of CR for scenario A-a 2015 2016 2017 2018 2019 2020 EU budget, bil. Euro 146 150 154 158 163 168 UK contribution, bil. Euro 17,0 17,5 17,9 18,4 19,0 19,6 CR receipts if no Brexit, bil. Euro 7,17 7,37 7,56 7,76 8,01 8,25 CR receipts reduction to cover Brexit, bil. Euro na 0,86 0,88 0,90 0,93 0,96 CR receipt total after Brexit, bil. Euro na 6,51 6,68 6,86 7,07 7,29 CR net balance if no Brexit, bil. Euro 5,6 5,8 5,9 6,1 6,3 6,5 CR net balance after Brexit, bil. Euro na 4,92 5,05 5,18 5,34 5,51 Difference, bil. Euro na 0,86 0,88 0,90 0,93 0,96 Source: Keep (2016), own elaboration Figure 1 Net balance for scenario of decrease of receipts per country and not changing contribution Source: Keep (2016), own elaboration 4,92 5,05 5,18 5,34 5,51 5,8 5,9 6,1 6,3 6,5 4,8 5 5,2 5,4 5,6 5,8 6 6,2 6,4 6,6 1 2 3 4 5 mld.Eur Year Net balance: Scenario A-a Brexit No Brexit 509 b. Increase contribution per country, do not change receipts Option A-b is showing calculation of change in net balance in case required contribution per country will increase and receipts will remain constant. Assumptions were again constant ratios of parameters shown in table 6 (year 2015) for full observed period. From the calculations of net balance is obvious that losses are much lower than in scenario Aa. Results might be explained by the fact that ratio CR contribution/ EU budget is 1,1 % vs. ratio of CR receipts/ EU budget is 3,9 % and hence proportional decrease of receipts has much stronger effect than proportional increase of contributions resulting in differences of final net balance for both cases. Figure 2 is showing visualisation of net balance. Table 6 Change of net balance of CR for scenario A-b 2015 2016 2017 2018 2019 2020 EU budget, bil. Euro 146 150 154 158 163 168 UK contribution, bil. Euro 17,0 17,5 17,9 18,4 19,0 19,6 CR contribution if no Brexit, bil. Euro 1,55 1,59 1,64 1,68 1,73 1,79 CR contribution to cover Brexit, bil. Euro na 0,19 0,19 0,20 0,20 0,21 CR contribution total after Brexit, bil. Euro na 1,83 1,87 1,93 1,99 1,78 CR net balance if no Brexit, bil. Euro 5,6 5,8 5,9 6,1 6,3 6,5 CR net balance after Brexit, bil. Euro na 5,59 5,74 5,89 6,07 6,26 Difference, bil. Euro na 0,19 0,19 0,20 0,20 0,21 Source: Keep (2016), own elaboration Figure 2 Net balance for scenario of increase contribution per country and not changing receipts Source: Keep (2016), own elaboration B) Decrease overall budget a. Decrease both- contributions and receipts and keep current ratios of budget split Option B means decrease of overall budget which can be caused by increasing of current contributions and decreasing of current receipts. Ratio of current contributions and receipts to the EU budget can remain the same for each country or might also change. Option B-a is assuming constant ratio as current. Results of net balance losses are in- 5,59 5,74 5,89 6,07 6,26 5,8 5,9 6,1 6,3 6,5 5,4 5,6 5,8 6 6,2 6,4 6,6 1 2 3 4 5 mld.Eur Year Net balance: Scenario A-b Brexit No Brexit 510 between A-a and A-b scenarios which are reasonable results as touched and contributions (with lower significance to final net balance) and receipts (with higher significance to final results). Net balance change for scenario A-b is shown in table 7. Figure 3 is showing differences in net balance for Brexit and No- Brexit scenario. Table 7 Change of net balance of CR for scenario B-a 2015 2016 2017 2018 2019 2020 EU budget if no Brexit, bil. Euro 146 150 154 158 163 168 UK contribution, bil. Euro 17,0 17,5 17,9 18,4 19,0 19,6 EU budget after Brexit, bil. Euro na 132,5 136,1 139,6 144,0 148,4 CR contribution if no Brexit, bil. Euro 1,55 1,59 1,64 1,68 1,73 1,79 CR contribution to reduced EU budget, bil. Euro na 1,41 1,45 1,48 1,53 1,58 CR receipts if no Brexit, bil. Euro 7,17 7,37 7,56 7,76 8,01 8,25 CR receipts from reduced EU budget, bil. Euro Na 6,51 6,68 6,86 7,07 7,29 CR net balance if no Brexit, bil. Euro 5,6 5,8 5,9 6,1 6,3 6,5 CR net balance after Brexit, bil. Euro na 5,10 5,24 5,37 5,54 5,71 Difference, bil. Euro na 0,67 0,69 0,71 0,73 0,75 Source: Keep (2016), own elaboration Figure 3 Net balance for scenario of decrease both- contributions and receipts and keep current ratios of budget split Source: Keep (2016), own elaboration b. Decrease both- contributions and receipts and change current budget split (with higher probability to decrease receipts of member countries with positive net balance) There are possible also changes in ratio of contribution and receipts to the EU budget which might cause mix of obtained results. In this case, highest probability is to decrease more significantly receipts of countries in positive net balance as for example Czech Republic, Poland or Slovakia (EIU, 2016). C) Define new incomes a. Reduce overall costs b. Extend other revenue streams 5,1 5,24 5,37 5,54 5,71 5,8 5,9 6,1 6,3 6,5 5 5,2 5,4 5,6 5,8 6 6,2 6,4 6,6 1 2 3 4 5 mld.Eur Year Net balance: Scenario B-a Brexit No Brexit 511 a. Possible solution might be also decrease of the EU costs or increase of efficiency and productivity which could be helpful to close UK contribution gap (Brutton, 2014). Most probably final decision of the EU budget recovery will be mixture of all mentioned alternatives. Results might change significantly according to ability of Czech Republic to raise and utilize EU funds. Possible is also scenario that UK will partially contribute to EU budget same as Northern Europe countries which can influence final balance for budget creation however likelihood is very low as per announced hard Brexit (Woodford, 2016). b. Euro budget will face a big gap after UK stop contributing. GNI based contribution counts for 69 % of total revenues to EU budget (European Commission, 2015). Rest of the revenues is coming from member states VAT, sugar levies, custom duties, EU staff salary taxes, non- EU countries contribution, surplus or previous budget, interest on late payments, interests earned on bank accounts, fines for companies breaking rules and donations. Some of the revenues are paid from internal EU countries and some of them are not. EU has opportunity to increase revenue streams from external resources mostly in custom duties, fines for company breaking rules, interests on late payments or donations and non- EU countries contribution (Ferrera, 2017). Chart at figure 4 is showing comparison of scenarios A-a, A-b and B-a discussed above. From the picture is obvious that different solutions can have different implications on Czech economics and overall net balance. Scenario A-a is the most positive option because it is having highest net balance from all the options. As it was discussed before ratio of CR contribution/ EU budget is 1,1 % (related to scenario A-a) while ratio of CR receipts/ EU budget is 3,9 % (related to A-b) and hence proportional decrease of receipts has much stronger effect than proportional increase of contributions resulting in differences of final net balance for both cases. Scenario B-a is having position in-between A-a and A-b due to the fact that it is proportionally decreasing. Figure 4 Impact of Brexit on EU budget and implication on Czech Republic for different scenarios Source: Keep (2016), own elaboration 4 Conclusions Decrease of incomes to Czech Republic from Euro funds due to elimination of one of the key European Union budget contributor can switch prevailing position of Czech Republic from EU receiver to contributor- losses in net balance of Czech Republic due to Brexit 0,86 0,88 0,9 0,93 0,96 0,19 0,19 0,2 0,2 0,21 0,67 0,69 0,71 0,73 0,75 0 0,2 0,4 0,6 0,8 1 2016 2017 2018 2019 2020 mld.Eur Year Net balance: Scenarios comparison Scenario A-a Scenario A-b Scenario B-a 512 might be in year 2020 from 0,2 to 0,9 bil. Euro according to scenario of the EU budget creation in order to close UK contribution gap. For comparison, study of OST EU estimated worsening of cumulative net position of Czech Republic due to Brexit vote till 2020 from 0,8 bil. Euro to 2,1 bil. Euro (OSTEU, 2016). References Bruton, J. (2014). 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Out and down Mapping the impact of Brexit, London, UK. Úřad vlády České republiky (2016). Analýza makroekonomických dopadů vystoupení Velké Británie z Evropské unie. OSTEU Podkladový materiál. Woodford (2016). The economic impact of ‘Brexit’. Woodford Investment Management LLP, Oxford. 513 Masaryk University Faculty of Economics and Administration Department of Finance and Institute for Financial Market European Financial Systems 2017 Proceedings of the 14th International Scientific Conference Edited by Josef Nešleha, Karel Urbanovský, Tomáš Plíhal Editorial Board: Petr Pirožek, Petr Suchánek, Milan Viturka, Vladimír Hyanek, Eva Hýblová, Daniel Němec, Markéta Matulová Published by Masaryk University, Brno 2017 1st edition, 2017, number of copies 150 Printed by: Tiskárna KNOPP s.r.o., Nádražní 219, 549 01 Nové Město nad Metují ISBN 978-80-210-8609-8 ISBN 978-80-210-8610-4 (online : pdf)