DOI: 10.5817/FAI2016-2-1 No. 2/2016
5
Determining Accuracy of IN05 Index and its Improvement
University of Pardubice
Faculty of Economics and Administration, Institute of Business Economics and Management
E-mail: michal.kubenka@upce.cz
Abstract: The IN05 index is probably the only one model in the world that predicts
economic value added (EVA). Determining a positive or negative EVA is conducted
based the level of return on equity (ROE). The threshold of the level depends on the
changing value of implicit cost of equity (re). The Czech economy has undergone
many changes including the economic crisis during the ten years following the
creation of the IN05 index. This research uses financial data of 1,224 companies in
the manufacturing sector. A very low current prediction power of IN05 was detected
in prediction of prosperity. The main benefit of the research is finding a way to
increase accuracy of economic value added prediction.
Keywords: IN05 index, prosperity, economic value added, ROE
JEL codes: G24, G33
Introduction
Bankruptcy models are becoming more popular especially since 2008 when the whole
world was hit by the global financial crisis. These models are designed to help analyze
not only one's own company but they also allow business partners to recognize the
financial situation of companies of interest.
However, the most important attribute of these models is the accuracy of their ability
to predict bankruptcy or financial stability of a company in the future, i.e. ex ante in a
few years, as stated by Castagnolo and Ferro (2014), Seppa (2014). A typical
company, that is most keen to know the financial situation of its partner companies,
is a bank. But for the company it is also very useful and instructive to compare with
the companies operating in the same sector.
The models are very popular especially because they can be applied easily and
provide unambiguously worded outcomes. The difference between the user attitudes
to specific methods of determining the financial health of the company is very clearly
handled by Mr. and Mrs. Neumaier (2008). They maintain that prosperity and
bankruptcy models are more popular compared e. g. to pyramidal systems of
indicators (alternative aggregate indicator). That's because they are considerably
simpler and basic knowledge is sufficient for processing e. g. in Excel software. To
interpret the results is also easier for users.
first
bankruptcy model was made in 1968, described in Altman (1968). This model
was designed for manufacturing companies in the USA and predicted bankruptcy for a
period of up to three years in advance. One year ahead was the highest accuracy of
prediction of bankruptcy amounting to 95%, two years by 72%, three-year forecasts
with an accuracy of only 48%. For creation of Z score, Altman used discriminant
6
analysis. Training set included 33 bankruptcy companies and 33 financial healthy
also for companies not traded at stock exchange, as stated by Altman (2002). It was
in 1983.
In the eighties indexes emerged for companies operating in Germany, Austria and
Switzerland. One of these indexes was the Creditworthiness index. The
Creditworthiness index classifies companies for creditworthy and bankruptcy. In these
countries and also in the Czech Republic, the Creditworthiness index is well known
and used. As Choi (2003) notes, even despite that it was validated only on test set of
21 companies.
Another worldwide famous model called the Taffler index was created in the UK by
Taffler (1982). It was published in 1977 and constructed through the use of linear
discriminant analysis. To create the model, 46 bankrupting and 46 prosperous
companies were investigated, specifically through financial statements in years 1969
to 1975. The author of the model determines the accuracy of the model as follows:
correct identification of the bankruptcy with probability of 97% one year
before bankruptcy
prediction of a bankruptcy with 70% probability two years ago
with probability of 61% three years ago
The Neumaiers' created IN indexes. All of them constructed with data set of
companies operating manufacturing industry in the Czech Republic. Overall they
created four indexes. They called them IN95 index (in order to prevent bankruptcy),
IN99 index (for prosperity prediction), IN01 and IN05 index are both used for
bankruptcy and also prosperity prediction. The IN05 (2005) index is a modification of
the IN01 index (2001). IN05 index is based on the analysis of 1526. Authors declare
a success rate at 88% for economic value added prediction and probability of 77% for
models were usually created differently, the structure of most of them is similar, as
the combination of ratios and assigned weights of importance. However, IN indices
for prosperity prediction (IN99 & IN05) were made on a sample of real companies,
therefore they are exceptional.
called the P 'model. This model does not use classical statistical methods, but the
methods for selecting the data used in data mining. The model was based on analysis
of sample including 1560 Slovak companies of which 103 went bankrupt. Authors
declare accuracy 78.24%.
based also on linear discriminant analysis with a data set of 1,508 companies
operating in the manufacturing industry in the Czech Republic. There were 880
financially stable companies and 628 companies in bankrupt. The authors declare
overall accuracy from 87.81% to 91.7% (on the base of weighted accuracy). The
ability to determine creditworthiness reaches from 97.89% to 91.47% for the model.
7
However the ability to predict bankruptcy is significantly lower from 69.91% to
61.18%.
The newest knowledge confirms that the time as well as branches are key for some
with the sample of 34533 companies from manufacturing and construction branch
we found that only 8 of 17 analysed predictors were significant in all of the
analysed five year prior bankruptcy, 8 of them in the branch of manufacturing (i.e.
EBITDA/TA, S/TA, S/St., EBIT/Int., EBITDA/Int., FA/TA, S/OR and S/CL.), the same
set (with exception of S/TA) is also significant in the construction branch. As same
predictors are time specific, or even not significant at all, this lead us to conclusion,
that only some predictors could be used as early warning indicators or used to predict
bankruptcy more years ahead.
Where:
EBITDA/TA - EBITDA/total assets,
S/TA Sales/total assets,
S/St. Sales/stock,
EBIT/Int. - EBIT/interest,
EBITDA/Int. - EBITDA/Interest,
FA/TA - EBITDA/total assets,
S/OR - Sales/operating revenue,
S/CL - Sales/current liabilities.
The presented accuracy is often debatable, mainly due to the fact, that:
authors created the model for a small sample and thus the presented accuracy
is not statistically based,
authors presented the total accuracy based on the average of achieved
accuracy in the sphere of bankruptcy prediction as well as prosperity
prediction,
authors confuse the accuracy and the rate of return,
when comparing the accuracy of models the researchers do not regard the fact
that the prosperity IN99 and IN05 (or IN01) is derived from EVA.
authors take into account or make average of achieved accuracy in the sphere
of bankruptcy or prosperity prediction,
results of authors are not comparable, as they work with various ratios of
bankrupting and financially sound companies.
models and states that ratios of ensemble work with researches of specific economic
di
the bankruptcy moment that is related to the legislation of specific country.
The evaluation of predictive capability of bankruptcy models is usualy performed
based on the comparison of current situation with the situation one year before. Thus
the prosperity or bankruptcy predicted by the model (in the year X) versus the
current situation of the following year (X + 1).
8
Then the accuracy of bankruptcy model prediction is (in relation to the table 1)
defined in various ways:
1) as the conditioned probability j = TPj / (TPj + FPj
and Neumaier (2005), Altman (2006),
2) as the probability (rate of return) pj = TPj / (TPj + FNj) applied by Delina and
3) as the weighted average pjnj = (TPj + TNj) / (TPj + FPj + TNj + FNj) applied by
4) as the weighted average with subjectively defined weights for the purpose of
5) as the arithmetic average pjnj = ((TPj / (TPj + FNj)) + (TNj / (TNj + FNj)))/2
Table 1 Contingency Table of Possible Results
i, cj) = true i, cj) = false
(di, cj) = true TPj FPj
(di, cj) = false TNj FNj
Note: t
Source: Lee (2009)
Where:
TPj number of companies that went bankrupt and their bankruptcy was
predicted by the model
FPj number of companies that went bankrupt, but their financial health was
predicted
TNj number of companies that went not bankrupt and their financial health was
also predicted
FNj number of financially sound companies for which the bankruptcy was
predicted
Table 2 Verification of Accuracy of Prosperity and Prosperity-bankruptcy Models
Model
Incorrect
bankruptcy
prediction
Accuracy of
bankruptcy
prediction pj
Rate of return of the
bankruptcy prediction
j
Year
a)
Index of
prosperity
78.02% 21.98% 58.25% 97-07 SK
b) IN05 85.41% 14.59% 73.79% 97-07 SK
c) IN01 x 60% x 04-08 CZ
d) IN05 x 70% x 04-08 CZ
e) IN05 62% 04-08 CZ
Model
Incorrect
prosperity
prediction
EVA prediction
accuracy pj
Rate of return of EVA
prediction
j
Year
f) IN99 x 85%* / 47%** x 98/04 CZ
g) IN01 x 77% x 2000 CZ
h) IN05 x 83% x 2004 CZ
* at the index compilation; ** at the verification in the year 2005
9
The prosperity model accuracy is not shown in measurement of prosperity, except for
one exception. Results of several researches are shown in the table 2. The following
validation was focused on measurement of the capability to predict the bankruptcy
using the prosperity model (Index of prosperity) and to predict the prosperity using
the model (IN99) and the prosperity bankruptcy model (IN01 and IN05)
(2015) emphasizes that IN99, IN01 and IN05 are specified by the fact that only those
for which there is a presumption of economic value added creation are classified into
the category of creditworthy companies, and thus the only presumption of positive
and large industrial firms, so for these firms it will have the best expla
That is why the current prediction capability of this model will be tested on middlefollowing
form:
IN05 = 0.13 X1 + 0.04 X2 + 3.97 X3 + 0.21 X4 + 0.09 X5 (1)
Where:
X1 = total assets / liabilities
X2 = earnings before interest and taxes / interest paid
X3 = earnings before interest and taxes / total assets
X4 = revenues / total assets
X5 = current assets / current liabilities
When IN05 is greater than 1.60 the firm produces a positive EVA value with a
probability of 83%. When IN05 is smaller than 0.90, the firm gets in financial distress
("bankruptcy zone") within a year with a probability of 77% (for middle-sized
The goal of this research is determining accuracy of IN05 Index in prediction
of prosperity and Its Improvement.
1 Data and Methodology
1.1 Data and Calculation of IN05 Index
The sample for testing the prosperity prediction using the model IN05 was
compound of 1224 companies from the Czech Republic, from the manufacturing
industry, with turnover above 300 million that showed no negative symptoms
2012. This sample was compared with the prosperity level of analyzed companies in
2013. It means that the investigated period was one year. Data were drawn from the
database of economic entities MagnusWeb of the company Bisnode.
The financial data from the balance sheet and profit and loss statement of chosen
companies were applied in the formula (1).
10
Input values were defined as follows:
earnings before interest and taxes
revenues are the sum of all revenue categories I. up to XIII., i.e. no. 1 + no.
4 + no. 19 + no. 26 + no. 28 + no. 31 + no. 33 + no. 37 + no. 39 + no. 42
+ no. 44 + no. 46 + no. 53,
current liabilities including short-term liabilities and short-term loans, i.e. line
Resulting values of intermediate calculations were adapted as follows:
within the calculation of interest coverage, i.e. components X2 the problems
of zero division were found in about one quarter of cases. Resulting values X2
were limited at -9 in case of the negative EBIT value and +9 in case of
positive EBIT values.
there were also extremes with resulting values X2 directed towards +/due
to low values of cost interests approaching the zero. These extremes
were reduced up to the interval <-9; +9>. Such reduction was performed in
about one third of cases.
1.2 Procedure of Calculation of Economic Value Added EVA
If the model IN05 in the year X (in this research the year 2012) achieves for a
specific company a value higher than 1.6, the company should reach the positive EVA
value in the following year X+1 (in this research the year 2013).
The abbreviation EVA refers to the so-called economic value added. Officially, the
historical beginning of this indicator is considered the year of 1982. At that time Joel
Stern founded the Stern Stewart & Co. and began to use it for the performance
measurement of companies. As the unofficial beginning of this indicator can be
considered earlier in period of 90s of the 19th century when Alfred Marshall
mentioned economic and accounting profit as the first as stated by Kyriazis and
Anastassis (2007).
We rank the EVA indicator among the so called value indicators. Another well-known
example is the indicator of marked value added (MVA). The task of the EVA is to
express the value of economic profit. Economic profit includes cost of equity, it
means we must take opportunity costs into account.
Economic value added represents part of company profit generated over the
expectations of investors. This money can be employed to invest in company
development as stressed by Tortella and Brusco (2003), Kangarloei et al. (2012).
There are several options how to calculate the value of EVA. Let's introduce the main
ones. E.g. Vochozka and Stehel (2013) express it in this way:
EVA = EBIT(1-t)- (2)
Where:
EVA Economic Value Added
11
EBIT Earnings before Interests and Taxes
t Income Tax Rate
C Capital provided for Remuneration
WACC Weighted Average Cost of Capital.
Another way of EVA calculation is using the indicator NOPAT (Net Operating Profit
After Taxes). Then the formula looks as follows according to Tortella and Brusco
(2003):
EVA = NOPAT - (3)
Where:
EVA Economic Value Added
NOPAT Net Operating Profit After Taxes
D Debt Book Value
Ebv Equity Book Value
WACC Weighted Average Cost of Capital.
There are numerous types of EVA calculations. In order to verify the prediction
capability of IN05 the EVA calculation, as applied by authors of the model, IN05 will
be used.
(4)
Where:
re
UZ WACC
- weighted average of costs on capital,
A - are assets,
D - tax rate,
U - cost interests,
BU + O - bank loans and obligations,
VK For
the calculation by rating methodology, it is necessary first to define the value of
(2002) this method is applied under the following simplifying presumptions:
The current or estimated interest rate is the value of loan capital.
The market value of loan capital equals to the account value.
or load capital, in case of the indebtedness change, the total capital costs are
only re-distributed among owners and creditors.
Operational economical results equals to the EBIT value, i.e. to the sum of
gross profit and cost interests.
WACC = rate of the risk-free assets + rcompany + rfinstab + rLA (5)
12
Where:
rcompany - extra charge for the volume of business risk,
rfinstr - extra charge for the risk resulting from capital structure,
rfinstab - extra charge for the risk that the company is not able to pay back its
liabilities,
rLA- is the risk extra charge for the size of company.
EVA shall be defined based on the positive spread value (ROE re), which is
the relation used also by the Ministry of industry and trade for creation of statistics
for particular branches:
EVA = (ROE re) * E (6)
Where:
ROE - return of equity
E equity
(ROE re) - spread
2 Results and Discussion
Results of the application stated in table 4 show different accuracy with comparison
of values given by the authors in the year 2005. The accuracy of IN05, based on data
from the year 2004, was 83% for the sphere of prosperity prediction for middle-sized
companies. The updated value of accuracy based on data from the year 2012/2013
corresponds to the column f.) tab. 4, i.e. to the accuracy of 89.37%. The reliability
interval
P (7)
Where:
p - the original accuracy of IN05 amounting to 83%,
n - the size of the base , i.e. TPj + FPj, i.e. 254,
- determined at the level of 5%,
The resulting reliability interval for the original accuracy of 83% is within <78.38;
87.62> percent. Thus it can be stated that the resulting accuracy of IN05 within the
prosperity measurement, based on the methodology f.) tab. 4, has increased by
6.37% after the period of 9 years (important notice: on sample of companies with no
negative symptoms it means failure, execution, insolvency, extinction, negative
In the opinion of the author the most objective accuracy measurement, for the
sphere of prosperity, is the calculation based on the approach stated in column h.)
tab. 4. This one takes into account the whole correct predictions and compares it with
the whole tested ensemble of companies, including those that are within the grey
zone. Based on this index the capability of model IN05 to predict the positive EVA
13
achieves 49,26%. If we do not take into account the uncertainty area (grey zone),
the prediction capability grows up to 75.38%.
Table 4 Result of Application IN05 at the Original Grey Zone <0.90;1.60>
Column: a. b. c. d. e. f. g. h.
IN052012 vs.
EVA2013
Rate of
return pj =
TPj / (TPj +
FNj)
Conditioned
probability j
= TPj / (TPj
+ FPj)
Weighted
average pjnj =
(TPj + TNj) /
(TPj + FPj +
TNj + FNj)
Weighted
average pjnj =
(TPj + TNj) /
(TPj + FPj + TNj
+ FNj + GZ)
Good prediction
+ EVA
TPj 227 18.55
57.18% 89.37% 75.38% 49.26%
Defect prediction
+ EVA
FPj 27 2.21
Good prediction
- EVA
TNj 376 30.72
Defect prediction
- EVA
FNj 170 13.89
Grey zone GZ 424 34.64
Suma 1224 100%
Source: Author
In order to increase the accuracy, the tests were performed when original evaluation
scale limits <0.90;1.60> were shifted. According to the methodology g.) tab. 5 the
accuracy was growing with lowered upper limit. The highest accuracy of 75.50% was
achieved when the upper limit was lowered at the value 1.4. At the same time the
model was not able to include 26.63% of companies into the category +EVA or EVA
and incorrectly included 44.61% of companies from the tested sample of 1224
companies.
Table 5 Results of IN05 Application with Moved Limits, without Grey Zone
Upper limit 1.6 1.5 1.4 1.3 1.2 1.1 1.0 0.9 0.8 0.7 0.6 0.5
Lower limit 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.8 0.7 0.6 0.5
Good prediction + EVA 227 252 302 341 396 453 492 524 565 592 608 632
Defect prediction + EVA 27 36 50 65 90 105 136 154 186 212 247 290
Good prediction - EVA 376 376 376 376 376 376 376 376 344 318 283 240
Defect prediction - EVA 170 170 170 170 170 170 170 170 129 102 86 62
e. pj = TPj / (TPj + FNj) 57.18 59.72 63.98 66.73 69.96 72.7174.3275.50 81.41 85.30 87.61 91.07
f. j = TPj / (TPj + FPj) 89.37 87.50 85.80 83.99 81.48 81.1878.3477.29 75.23 73.63 71.11 68.55
g. GOOD 1: pjnj= (TPj + TNj) /
(TPj + FPj + TNj + FNj)
75.38 75.30 75.5075.32 74.81 75.0973.9473.53 74.26 74.35 72.79 71.24
h. GOOD 2: pjnj = (TPj + TNj) /
(TPj + FPj + TNj + FNj + GZ)
49.26 51.31 55.39 58.58 63.07 67.7370.9273.53 74.26 74.3572.79 71.24
i. WRONG: (FPj + FNj) / (TPj +
FPj + TNj + FNj + GZ)
50.74 48.69 44.61 41.42 36.93 32.2729.0826.47 25.74 25.65 27.21 28.76
j. Grey zone 34.64 31.86 26.63 22.22 15.69 9.80 4.08 0.00 0.00 0.00 0.00 0.00
Source: Author
14
Relation between threshold (limit) and model accuracy is seen also in graphical form
below. There is evident that to increase the accuracy we need to raise the grey zone.
E.g. accuracy 89.37% (type f. with limits 0.9/1.6)) is not the maximum achievable
value. This accuracy can be even more increased. But with limits 0.9/1.6 there is
connected also higher grey zone as seen on the graph. Author does not recognize the
maximization of accuracy through the existence of the grey zone.
Figure 1 Relation between Threshold(s) and Accuracy
Note: j = TPj / (TPj + FPj), g. pjnj 1= (TPj + TNj) / (TPj + FPj + TNj + FNj), h. pjnj 2= (TPj +
TNj) / (TPj + FPj + TNj + FNj + GZ), i. WRONG: (FPj + FNj) / (TPj + FPj + TNj + FNj + GZ), j.
Grey zone
Source: Author
The methodology can not be used to find the maximum accuracy. This is because it
ignores good prediction of negative EVA (failed prediction of negative EVA). The
methodology g seems to be appropriate because it reflects the ability to predict both
positive and negative economic value added. However, the result can be misleading
due to the fact that a large portion of the analyzed sample is not classified
(companies included in grey zone). As the most suitable methodology seems
methodology h. The methodology works with the whole sample and the total
accuracy includes the whole sample, both classified and unclassified companies.
is the most suitable to move limits at the value 0.7
and to eliminate the grey zone at the same time. The model accuracy, in predicting
the prosperity according to the methodology h.), achieves the maximum accuracy
amounting to 74.35% with zero grey zone and 25.65% of error rate from the
complete tested sample.
0
10
20
30
40
50
60
70
80
90
100
f.
g.
h.
i.
j.
15
Conclusions
It is not easy to assess the financial stability of business partners or debtors and/or
to conduct it on the basis of publicly available data in a speedy, simple and precise
manner. It is obvious that these diagnostic/predictive models always operate with
some degree of inaccuracy. The question is, what can the maximum achievable value
of accuracy be?
Finite precision is affected by many factors. Especially in the phase of model creation
these factors are size and homogeneity of the training set, data quality, methodology
for data processing and technique of model creation. Following this phase of accuracy
testing the key factors are size and homogeneity of the validation set, data quality
(accounting provides a fair view), process for treating of data, as well as the
methodology chosen for the measurement of total model accuracy.
The aim of this research was to define the accuracy of the IN05 model after the
period of 9 years from its creation. This prosperity-bankruptcy model was, as one of
the few, created based on real data using the multiple discrimination analysis. The
accuracy was derived from the capability of the model to predict the economic value
added in the following year. This enables to verify its up-to-date condition. The
results of calculations of the tested sample showed, with statistical significance, that
the accuracy of prosperity prediction has increased from 83 to 89.37%, when the
accuracy is calculated based on TPj / (TPj + FPj). At the same time, 50.74% of
companies (from the whole sample 1224) were classified incorrectly.
The testing of the threshold of <0.90;1.6> shift up to the unified critical limit at 0.7
resulted in the best result of the prediction capability of this model, based on (TPj +
TNj) / (TPj + FPj + TNj + FNj + GZ). The achieved accuracy of 74.35% is the ratio of
all correctly classified companies related to the size of the whole sample (1224). The
conditioned probability of the correct classification +EVA (based on TPj / (TPj + FPj) is
73.63%. In order to reach a quick classification of the company, using the IN05 idex,
aiming to define its prosperity, the author recommends using the newly defined limit
of 0.7.
In addition, the research conducted confirmed that the declared accuracy of models
can be considerably different depending on the methodology of accuracy
quantification (tab. 4 columns from e.) to h.)). In conclusion, it shall be pointed out
that the research conducted confirmed that the accuracy of models can change with
time, especially if their accuracy evaluation is based on spread value connected with
variable re value. For example, the mean of re and ROE changed as follows in these
years: re 10.58% in 2007, 11.14% in 2008, 15.36% in 2009, 13.52% in 2010,
13.66% in 2011, 8.40% in 2012, 7.42% in 2013, ROE 15.11% in 2007, 10.85% in
2008, 6.45% in 2009, 11.61% in 2010, 11.52% in 2011, 21.88% in 2012, 20.33% in
2013.
16
References
Altman, E. (1968). Financial Ratios, Discriminant Analysis and the Prediction of
Corporate Bankruptcy. The Journal of Finance, 23(4), pp. 589 609.
Altman, E. I. (2002). Revisiting Credit Scoring Models in a Basel 2 Environment. NYU
Working Paper. Available at: <http://citeseerx.ist.psu.edu/viewdoc/download?
doi=10.1.1.196.55&rep=rep1&type=pdf>.
Castagnolo, F. and Ferro, G. (2014). Models for predicting default: towards efficient
forecast. The Journal of Risk Finance, 15(1), pp. 52 70.
Choi, F. D. S. (2003). International Finance and Accounting Handbook. New Jersey:
John Wiley and Sons.
Delina,
podmienkach SR. Ekonomika a management, 3(16), pp. 101 112.
Kangarloei,
S. J., Motavassel, M., Arzanlu, E. and Soleimani, B. (2012). The
Investigation of the Relationship between Economic Value Added (EVA) and Return
on Assets (ROA) in Tehran Stock Exchange (TSE). Business Management Dynamics,
1(11), pp. 01-05.
Czech
Republic Case. In Advances in Finance & Accounting: Proceeding of the 1st
WSEAS International Conference on Finance, Accounting and Auditing (FAA 12), pp.
86-91.
-
17
Kyriazis,
D. and Anastassis, C. (2007). The Validity of the Economic Value Added
Approach: an Empirical Application. European Financial Management, 13(1), pp. 71
100.
-
-
-
- Neumaier,
,
Ekonomika a management, 4, pp. 1-10.
Seppa,
R. (2014). Implication of imide-debt: signalling for bankruptcy probabilities
within small firms. Baltic Journal of Management, 9(2), pp. 168 188.
Taffler, R. J. (1982). Forecasting Company Failure in the UK Using Discriminant
Analysis and Financial Ratio Data. The Journal of the Royal Statistical Society. 145(3),
pp. 342 358.
18
Tortella, B. D. and Brusco, D. S. (2003). The Economic Value Added (EVA): An
Analysis of Market Reaction. Advances in Accounting, 20, pp. 265 290.
Vestnik of Astrakhan State Technical University Series: Economics. 1, pp. 9-16.