DOI: 10.5817/FAI2017-2-2 No. 2/2017
19
Capital Structure Modelling and Analysis of its Impact
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 investigate the impact of company's capital
structure on its performance. To achieve the goal, the data of Slovak businesses
were used. An input analysis of the capital structure of the selected sector was
carried out in order to generalize and elaborate conclusions aimed at the capital
structure of the businesses analysed. 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. Based on 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 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
Introduction
Business performance measurement is a highly current issue. When measuring
performance, different indicators are applied. They differentiate based on the area
of financial health that they prefer. In this paper, we focus on measuring the impact
of capital structure on business performance. The definition of performance should
be based on the definition of European Foundation for Quality Management (EFQM,
1999) according to which performance is “the level of results achieved by
individuals, groups, organizations and processes”. The concept of performance is
also defined by other authors, who use this term in defining the essence of the
existence of an enterprise in market environment and rela te it to business success
and ability to survive. Performance is measured by the level of profit in the case
we proceed from business ability to appreciate available resources (Veber, 2004;
20
Fibírová and Šoljaková, 2005; Sedláček, Suchánek and Špalek, 2012). According
to Wagner (2009), the performance of an enterprise is a characteristic that
describes the way in which an enterprise carries out a certain activity similar to the
way in which this activity is performed. Neumaierová and Neumaier (2002); Frost
(2005); Šulák and Vacík (2005) are among the authors who understand the
performance as the enterprise’s ability to capitalize its investments embedded into
business in the best way.
1 Literature Review
Generally, we can state that business performance measurement means the
assessment of its ability to achieve goals in the optimal way. The most common
method of assessing business performance is the method of fundamental and
technical analysis, which evaluates the enterprise in economic terms based on a
detailed study and analysis of financial statements (Fisher, 1992). According to
many Slovak and foreign authors (Ittner et al., 2003; Dixon et al., 1990; Pavelková
and Knápková, 2009; Synek et al., 2007; Petřík, 2009), financial indicators, which
include capital structure indicators, are used as the most common tools to measure
the performance of companies. These conventional indicators map the main
activities of the company in the areas of profitability, ability to pay, and investment
area in terms of value for investors.
According to the argument that the objective is not only to measure, but in
particular to improve performance (Hammer, 2007), it must be noted that these
conventional financial ratios have a low predictive value in analysing and evaluating
the financial performance of the company, in terms of making tactical and strategic
decisions in management. This is caused by the fact that these results are judged
rather isolated. Conventional performance indicators do not answer the question
why the overall results achieve such values or which areas of the company should
be improved in order to meet strategic company objectives. It is therefore
important to supplement conventional financial indicators with other more dynamic
and more prospective indicators, which are adjusted to specific competitive
conditions. It means to focus on monitoring and comparing of implementation
results describing performance with the planned level of performance, monitoring
the strategies direction during their implementation, identifying the accompanying
problems of fundamental importance, and performing the necessary changes and
adjustments (Dudoková, 2004). Development of modern indicators of performance
evaluation is focused on the processing and designing of indicators most closely
connected to the value of shares. These indicators should also allow for usage of
the most of accounting information and data, to include calculation of risk, to take
into account the range of related capital, and finally, they should allow performance
evaluation and also the enterprises’ valuation (Mařík and Maříková, 2005). The
performance assessment should be approached from different perspectives: when
assessing it from the shareholder’s position, the evaluation is based on return of
invested capital into the company while every shareholder is expecting profitability
adequate to risk (Neumaierová and Neumaier, 2002).
21
Therefore, basic financial fields of evaluation and measurement of business
performance according to Kislingerová et al. (2011) can be supplemented by more
recent and modern indicators and methods, namely evaluation using modern
methods with the application of market characteristics such as indicators EVA,
INEVA MVA, RONA, WACC or indicators based on FCF, CVA and others. From these
indicators the best tool for performance measurement is Economic Value Added,
which includes the impact of capital structure through capital structure risk used
for Cost of equity evaluation.
One of the fundamental problems of business performance 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 a 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. 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 commonly 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 and Gallo,
2015), which results in the synergic effect of the indicators’ impact on business
performance. Development of debt ratios for performance evaluation should be
focused on the processing and design of indicators which are most closely
connected to the performance evaluation (Suhányiová and Suhányi, 2011).
The idea of proper capital structure has been dealt with by 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
business activities (Závarská, 2012) have been published. An intensive discussion
on this issue started when the original work by 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, the second of dynamic theories of capital structure.
Static theories deal with the issue of optimal indebtedness and strive to answers
the question 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
the optimal capital structure due to the specific conditions of each enterprise.
Dynamic theories include the theory of hierarchical order and the signalling model.
22
When deciding on business capital structure it is also important to assess from what
amount of economic profit it is advisable to start using debt in addition to equity.
For this purpose, the analysis of the point of indifference of the capital structure is
used. It determines when it is appropriate to finance business needs by debt and
when by equity (Jánošová, 2008). The capital structure is influenced by several
factors, for example business risk, corporate tax position, financial flexibility,
managerial conservatism and aggression (Růčková, 2008). In addition to these
factors, the decision on the composition of capital is also influenced by business
capital costs, namely Cost of equity, Cost of debt, and Cost of capital. For the
calculation of the Cost of equity, a number of methods are presented in the theory.
In this paper we applied valuation of Cost of equity with the use of Capital Assets
Pricing Model – CAPM (with the acceptance of external, market, systematic risks)
and Build-up model (with the acceptance of internal, specific, unsystematic risks).
Cost of capital was calculated as Weighted average cost of capital (WACC). These
models use quantification of the different risks that enter the valuation of capital.
Therefore, when deciding on the business optimal capital structure it is necessary
to quantify the risks and analyse the impact of risks arising from the capital
structure on the valuation of capital and business performance.
Financial theory definitely confirms a certain link between capital structure and
performance. In general, debt is cheaper than equity. A 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 positively
affect the amount of economic profit. On the other hand, with a rise in
indebtedness, the risk of bankruptcy grows (Kiseľákova and 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 a 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
The company under investigation 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
23
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 it does not have long-term liabilities. 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: In this paper we calculated the performance applying Economic Value
Added (EVA), which is the best known and most utilized modern indicator of
performance measurement. This model is known from the 1980s. Authors of the
EVA model are representatives of Stern Stewart & Co., American researchers Joel
M. Stern and G. Bennett Stewart III. The main task of EVA model is the
measurement of business economic profit. Extensive use of EVA model dates back
to 1989. We used EVA Equity and EVA Entity model for performance calculation.
Formula (1) indicates that the Economic Value Added is expressed in two ways.
𝐸𝑉𝐴 = (𝑅𝑂𝐸 − 𝑟 ) × 𝐸 (1)
where 𝐸𝑉𝐴 stands for Economic Value Added, 𝑅𝑂𝐸 is Return on Equity, 𝐸 is Equity,
and 𝑟 represents Rate of Alternative Cost of equity.
One formula shows what is known as the Spread (𝑅𝑂𝐸 − 𝑟 ), which expresses the
relative 𝐸𝑉𝐴 𝐸⁄ (Neumaierová and Neumaier, 2016). The relative EVA is needed as
input to the correlation matrix.
Formula for the calculation of EVA Entity is as follows:
𝐸𝑉𝐴 = 𝑁𝑂𝑃𝐴𝑇 − 𝑊𝐴𝐶𝐶 × 𝐶 (2)
where 𝑁𝑂𝑃𝐴𝑇 stands for Net Operating Profit after Tax, 𝑊𝐴𝐶𝐶 is Weighted Average
Cost of Capital, and 𝐶 represents Paid Capital.
Weighted Average Cost of Capital (WACC) is expressed by the formula:
𝑊𝐴𝐶𝐶 = 𝑟 × (1 − 𝑑) × + 𝑟 × (3)
where 𝑟 stands for Cost of debt, d is income tax rate applicable for evaluated
business, and D represents market value of debt invested in the business (interest-
bearing).
For the calculation of Cost of equity, we used CAPM with the acceptance of market,
external and systematic risks. This model is not used in its original format as it was
processed by J. Treynor (1961 and 1962), W. Sharpe (1964) and J. Lintner (1965).
These authors published articles about the CAPM model, which were processed in
articles and publications of H. Markowitz, who dealt with the portfolio theory and
risk diversification. W. Sharpe, H. Markowitz and M. Miller shared the Nobel Prize
24
for the application of CAPM model (Horváthová and Mokrišová, 2014). In this paper
CAPM inputs were derived from the modified formula of Damodaran (2001):
𝑟 = 𝑟 + 𝛽 × 𝐸𝑅𝑃 + 𝐶𝑅𝑃 (4)
where 𝑟 stands for Risk-free rate of return of US T-bonds, 𝐸𝑅𝑃 is Equity Risk
Premium of US market, 𝛽 represents coefficient of systematic risk, and 𝐶𝑅𝑃 is
Country Risk Premium (Damodaran, 2001). When calculating Cost of equity
applying this formula, academics (Mařík, Maříková 2005; Horváthová, Mokrišová,
Suhányiová 2016) recommend using inputs from the US market.
In order to compare performance results and the influence of risks on performance,
we applied Build-up model for Cost of equity calculation. This model accepts internal
- financial risks and internal and external business risks. It is used for the
calculation of Cost of equity in the case we cannot use CAPM – it is the case when
business shares are not traded on the stock market and β coefficient cannot be
estimated. Build-up model is an empirical method of estimating the expected return
on equity. It is a typical German approach to equity valuation. Its aim is to use as
much factors as possible, therefore, it is often called comprehensive Build-up model
(Vochozka et al., 2012). This method is based on the most complete consideration
of individual risk factors (Osčatka, 2004). Principle of Build-up model is based on
the assumption that independent variables are fundamental factors. Risk of these
factors is evaluated and incorporated into equity valuation (Neumaierová and
Neumaier, 2002). Based on the determination of fundamental factors, there are
several Build-up models. Recent empirical studies of Fama and French showed that
capital market accepts two risks (Neumaierová and Neumaier, 2002): risk of
smaller companies in the form of risk premium for lower stocks liquidity in the
market and risk arising from the fact that market value of business does not exceed
its book value. Interest rate calculated with the use of Build-up model includes:
risk-free rate of return (usually return on government bonds) and premium for
specific risks. Main difference of this method compared to CAPM is that Build-up
model does not include β coefficient, which represents systematic risk. Based on
the above-mentioned, this method can be expressed by the formula:
𝐸(𝑟 ) = 𝑟 + 𝑅𝑃 (5)
where 𝐸(𝑟 ) is Cost of equity, rf is risk-free rate of return, RP is risk premium, which
consists of various factors. In its basic classification it is divided into business risk
factors, for example factors of market risk, factors related to size of the business
and other specific factors (Štefko and Krajňák, 2013) and financial risk factors, for
example the risk of fluctuations in cash flow. Risk Premium according to Mařík et
al. (2011) is calculated using the following formula:
𝑅𝑃 = 𝑟 + 𝑟 (6)
25
where 𝑟 stands for Risk premium for business risk and 𝑟 is Risk premium for
financial risk (Mařík et al., 2011).
Due to the data compatibility, we used indicator Spread (𝐸𝑉𝐴 𝐸⁄ ) as a relative
performance measure. We applied this indicator in the correlation matrix and
Principal Component Analysis.
Performance of the company measured by EVA Equity and also indicator Spread
achieved negative values in most of the analysed years (Table 1). Cost of equity,
which entered their calculation, was quantified applying CAPM.
Table 1 Development of company's performance
2010 2011 2012 2013 2014 2015 2016
EVAEquityCAPM (€) -570,237 111,907 -737,738 -504,351 -507,815 -276,004 -764,703
Spread -0.05 0.01 -0.06 -0,04 -0.04 -0.02 -0.05
Source: Authors' elaboration
We chose Total debt to total assets, Equity to debt ratio, Current liabilities to total
assets, Equity to fixed assets ratio, Interest coverage, and Financial leverage as
the capital structure indicators. These indicators are closely linked to business
capital structure and we also used them as the inputs for correlation matrix. Values
of selected indicators are presented in Table 2.
Table 2 Selected capital structure indicators
TD/TA E/TD
Financial
leverage
Interest
coverage
E/FA CL/TA
2010 0.24 3.16 1.32 81.44 1.77 0.20
2011 0.23 3.43 1.29 109.29 2.01 0.19
2012 0.18 4.67 1.21 137.01 2.13 0.15
2013 0.16 5.12 1.20 803.30 2.27 0.14
2014 0.16 5.07 1.20 63,193.16 2.44 0.15
2015 0.22 3.65 1.27 104,486.73 2.28 0.20
2016 0.24 3.16 1.32 32,170.90 2.40 0.22
Note: TD represents total debt,
TA total assets, E equity, FA fixed assets and CL current liabilities.
Source: Authors' elaboration
As already mentioned, the capital structure indicators show that the company's
average indebtedness is at the level of 22%. This indebtedness is caused by shortterm
liabilities. Values of Interest coverage are high because the company has low
interests. The business is overcapitalized, has high Financial leverage and high
Equity to debt ratio. These values are favourable to ensure business stability. On
the other hand, it should be noted that these values negatively affect business
profitability, which is a fundamental factor of performance.
26
We used correlation matrix processed in Software Statistica (Table 3) to analyse
relationships between capital structure indicators and relative performance
indicator Spread. In the correlation matrix, the correlations with P values lower
than significance level of 0.05 were highlighted. To interpret correlation coefficient,
we used Cohen (1998) scale according to which the absolute value of correlation
coefficient above 0.5 is interpreted as a strong correlation, a value from 0.3 to 0.5
as a moderate correlation, 0.1 to 0.3 as a weak correlation, and a correlation
coefficient value below 0.1 as a trivial correlation.
Table 3 Correlation matrix
TD/TA E/TD
Financial
leverage
Interest
coverage
E/FA CL/TA EVA/E
TD/TA
1.0000 -.9971 .9998 -.0150 -.4541 .9607 .2332
p= --- p=.000 p=.000 p=.975 p=.306 p=.001 p=.615
E/TD
-.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
E/FA
-.4541 .4564 -.4535 .5659 1.0000 -.2105 -.1179
p=.306 p=.303 p=.307 p=.185 p= --- p=.650 p=.801
CL/TA
.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= ---
Note: TD represents total debt,
TA total assets, E equity, FA fixed assets and CL current liabilities.
Source: Processed by authors in STATISTICA
The matrix (Table 3) shows 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 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 the other indicators
analysed. Interest coverage can be considered a key performance indicator that
does not correlate with any indicator within the given group. None of capital
structure indicators correlate with the indicator Spread. Based on the above
mentioned, it can be stated that there is no significant relationship between the
selected debt ratios and performance.
As the multicollinearity among capital structure indicators was confirmed, we
applied Principal Component Analysis (PCA) processed in Software Statistica. With
27
the use of this method, we verified the results obtained applying the correlation
matrix. We applied the same capital structure indicators as in the correlation matrix
and we identified 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
For the comparison of business performance in analysed years, we also constructed
the plot of scores (Figure 2). It represents the coordinates of the analysed years in
the new area defined by main components. Using the plot of scores, we can find
out whether the performance in the analysed years is similar or whether they differ
from each other.
Based on the plot of scores we can say that individual years differ in terms of
performance. Quadrant A of the plot of scores (top left quadrant in Figure 2) houses
years 2012 and 2013 in which the lower value of Total debt to total assets and
Financial leverage negatively influenced business performance.
28
Figure 2 Plot of scores
Projection of the cases on the factor-plane ( 1 x 2)
Cases with sum of cosine square >= 0,00
2010
2011
2012
2013
2014
2015
2016
-4 -3 -2 -1 0 1 2 3 4
Factor 1: 68,98%
-3,0
-2,5
-2,0
-1,5
-1,0
-0,5
0,0
0,5
1,0
1,5
2,0
2,5
Factor2:25,26%
Legend: Quadrant A – 2012, 2013; Quadrant B – 2010, 2011; Quadrant C – 2014;
Quadrant D – 2015, 2016
Source: Processed by authors in STATISTICA
Years 2010 and 2011 in which the higher value of Total debt to total assets and
Financial leverage positively influenced business performance are located in
quadrant B of the plot of scores (top right quadrant in Figure 2). In the year 2011
the analysed company also achieved a positive value of EVA indicator.
The lower value of Total debt to total assets and Financial leverage and the higher
value of Equity to fixed assets ratio negatively influenced business performance in
the year 2014 located in quadrant C of the plot of scores (bottom left quadrant in
Figure 2). These results were distorted by the extreme value of Interest coverage.
In the years 2015 and 2016 located in quadrant D of the plot of scores (bottom
right quadrant in Figure 2) the higher value of Total debt to total assets and
Financial leverage positively influenced business performance but these results
were distorted by the extreme values of Interest coverage too.
To confirm the impact of capital structure and cost of capital on performance, we
used multiple linear regression model processed in software Gretl. Since the
correlation matrix did not confirm significant relationships between capital structure
indicators and indicator 𝐸𝑉𝐴 𝐸⁄ , we did not apply capital structure indicators as
independent variables in regression models. Instead of them, we used equity, debt,
Cost of equity, Cost of debt and Cost of capital. We applied 𝐸𝑉𝐴 ,
𝐸𝑉𝐴 , 𝐸𝑉𝐴 and 𝐸𝑉𝐴 as the dependent variables. Based on it we
constructed four regression models.
29
The results of F-test showed that two regression models were statistically
significant, P-Value (F) < 0.05. First of them was EVA Equity model applying Buildup
model for Cost of equity calculation. This model demonstrated a significant
impact of Cost of equity and equity on EVA Equity. In the case of these indicators,
P–Value was lower than 0.05. Estimated multiple regression model for 𝐸𝑉𝐴 :
𝐸𝑉𝐴 = 𝛼 + 𝛽 𝑅𝑂𝐸 + 𝛽 𝑟 + 𝛽 𝐸 (7)
𝐸𝑉𝐴 = 2.941𝐸 + 06 − 389.218𝑅𝑂𝐸 − 1.557𝐸 + 07𝑟 − 0.124𝐸 (8)
The second statistically significant model was EVA Entity model applying Build-up
model for Cost of equity calculation In this model all indicators showed a statistically
significant impact on 𝐸𝑉𝐴 , P-Value < 0.05. Estimated multiple regression
model for 𝐸𝑉𝐴 :
𝐸𝑉𝐴 = 𝛼 + 𝛽 𝐷 + 𝛽 𝑟 + 𝛽 𝑟 + 𝛽 𝑊𝐴𝐶𝐶 (9)
𝐸𝑉𝐴 = 291,581 + 0.028𝐷 + 7.575𝐸 + 06𝑟 − 3,868𝐸 + 06𝑟 −
48,289.6𝑊𝐴𝐶𝐶 (10)
We also constructed the third regression model - EVA Equity model with the use of
CAPM for Cost of equity calculation. According to results of F-test, this model was
not statistically significant. Estimated multiple regression model for 𝐸𝑉𝐴 :
𝐸𝑉𝐴 = 𝛼 + 𝛽 𝑅𝑂𝐸 + 𝛽 𝑟 (10)
𝐸𝑉𝐴 = 1.149𝐸 + 06 + 0.004𝑅𝑂𝐸 − 0,096𝑟 (11)
EVA Entity model with the use of CAPM for Cost of equity calculation was not
statistically significant either. Estimated multiple regression model for 𝐸𝑉𝐴 :
𝐸𝑉𝐴 = 𝛼 + 𝛽 𝐸 + 𝛽 𝑊𝐴𝐶𝐶 + 𝛽 𝑟 (12)
𝐸𝑉𝐴 = 1.149𝐸 + 06 − 0. 𝐸 + 1.009𝑊𝐴𝐶𝐶 − 104.995𝑟 (13)
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 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 thus in equity on the performance was confirmed when we calculated
the Cost of equity applying Build-up model. Based on the above mentioned, we
30
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.
To analyse the impact of the change in capital structure on selected performance
indicators, we used modelling based on percentage change of equity and debt and
we studied the impact of this change on the performance of analysed business. In
modelling we used the principles of static models for business capital structure
optimization. The first was a conventional model which assumes that there is an
optimal capital structure for each business with debt ratio lower than the maximum
possible limit. The opposite of this model is model of Modigliani and Miller (MM
model), who argue that value of the company is independent of business capital
structure (Sivák, Mikócziová 2009). The mentioned models of capital structure
optimization are not generally applicable. However, based on them we can form a
base necessary for successful business capital structure management. Results of
the modelling are presented in the next part of the paper.
Figure 3 provides the comparison of Cost of equity calculated by CAPM and Buildup
model. The change in Cost of equity calculated by CAPM and Build-up model
(Figure 3) 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.
Figure 3 Cost of equity for different capital structure
Note: CS – Capital structure, E – Equity, D – Debt
Source: Authors' elaboration
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
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
45.00%
Costofequity
Capital structure modelling
reCAPM reBU
31
begins to grow. The impact of increased indebtedness begins to appear in this
capital structure when calculating systematic risk.
The systematic risk grows due to the indebtedness. We also noticed an increase in
Cost of equity calculated by Build-up model, but this growth was more moderate.
Based on the above presented calculation, it can be stated that on a certain debt
line, Cost of equity increases because the risks which the owners of capital have to
bear, increase too.
Figure 4 Cost of capital for different capital structure
Source: Authors' elaboration
Figure 4 shows the development of Weighted average cost of capital. These costs
increased starting from the capital structure of 60% equity and 40% debt,
approximately. This increase was associated with rising risks for both owners and
creditors as a result of growing debt. Similarly to Figure 3, we can see that Cost of
equity is higher in the case of CAPM application.
Figure 5 shows a change in the ROE, SpreadCAPM and SpreadBU depending on
Financial leverage. The most significant is the change in ROE. Development of
SpreadCAPM and SpreadBU is approximately the same, the change occurs in the
capital structure of 40% equity and 60% debt. SpreadBU grows faster in the case
of this capital structure.
Figure 6 proves that by increasing the share of debt in capital, the economic profit
increases as well as the value of the performance. 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.
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
14.00%
16.00%
18.00%
20.00%
Actual
CS
90%E
10%D
80%E
20%D
70%E
30%D
60%E
40%D
50%E
50%D
40%E
60%D
30%E
70%D
20%E
80%D
10%E
90%D
WACC
Capital structure modelling
WACCreCAPM WACCreBU
Polynomial WACC re CAPM Polynomial (WACC re BU)
32
Figure 5 Development of ROE and Spread for different capital structure
Source: Authors' elaboration
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.
Figure 6 Development of economic profit for different capital structure
Source: Authors' elaboration
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
ROE,Spread
Capital structure modelling
ROE SpreadCAPM SpreadBU
-400,000
-200,000
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
EVA
Capital structure modelling
EVAequityCAPM (€) EVAequityBU (€)
EVAentity CAPM (€) EVAentity BU(€)
33
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 increases by a
gradual increase in debt, despite increasing risks. In this case, Financial leverage,
Return on equity, and Cost of equity increase as a result of the growing systematic
risk. However, the increase in profitability is faster than the increase in the Cost of
equity.
Conclusions
The calculation of the selected indicators proved that the indicator Interest
coverage does not correlate with any capital structure indicator. However, in the
case of the analysed business, the results may be distorted due to a real capital
structure. We can describe the capital structure indicators using two main
components. The calculations of the indicators that enter the quantification of the
EVA indicator and the calculation of the EVA indicator itself showed that the change
in the capital structure changes ROE, Spread, Cost of equity, Cost of capital, and
also the value of EVA indicator. Due to decline in Equity, Cost of capital grows faster
in the case of CAPM – when accepting external risk. This is due to the fact that the
value of β coefficient increases as a result of rising indebtedness. The margin value
of the capital structure, from which Cost of equity calculated by CAPM grows faster,
is 40% equity and 60% debt. As a result, performance calculated by EVA indicator
grows more slowly.
When we calculated the Cost of equity applying Build up model, an increase in this
cost was not so fast when changing the capital structure in favour of debt. As a
result, the value of the EVA indicator increases faster. This is also the expected
reason for the results of regression analysis. It should also be noted that even in
the case of this model, the margin capital structure is 40% equity and 60% debt.
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. Based on the above
mentioned results we can assume that the change in the capital structure affects
business performance, but some simplifications and assumptions need to be taken
into account. It is not possible to recommend one of the models (CAPM, Build-up)
as more reliable. Each of them accepts risks only from one point of view (CAPM –
external risks, Build-up – internal risks). Therefore, we suggest creating a new one
with the acceptance of both risks. This analysis will be a subject of future research,
where we will focus on more extensive data collection and provide more detailed
analyses.
34
Acknowledgments
This paper was prepared within grant scheme VEGA no. 1/0887/17 – Increasing
the competitiveness of Slovakia within the EU by improving efficiency and
performance of production systems.
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