=Paper=
{{Paper
|id=Vol-2422/paper18
|storemode=property
|title=Model of Bankruptcy Probability Based on the Analysis of Industrial Enterprises of Ukraine
|pdfUrl=https://ceur-ws.org/Vol-2422/paper18.pdf
|volume=Vol-2422
|authors=Tetiana Melikhova,Andriy Makarenko,Olena Mikhailytsa,Andriy Pozhuyev
|dblpUrl=https://dblp.org/rec/conf/m3e2/MelikhovaMMP19
}}
==Model of Bankruptcy Probability Based on the Analysis of Industrial Enterprises of Ukraine==
https://ceur-ws.org/Vol-2422/paper18.pdf
226
Model of Bankruptcy Probability Based on the Analysis
of Industrial Enterprises of Ukraine
Tetiana Melikhova[0000-0002-9934-8722], Andriy Makarenko,
Olena Mikhailytsa and Andriy Pozhuyev
Zaporizhzhya National University, 10, Zhukovskogo Str., Zaporizhzhya, 69600, Ukraine
{tanya_zp_zgia, apm2008}@ukr.net,
{elenamikhaylutsa7, scorpio6828}@gmail.com
Abstract. In present work, the peculiarities of simulation model of enterprises
bankruptcy probability that exist in European, world and domestic practices were
considered. The scientific econometric approach was applied to determine the
overall presence and strength of the relation between the economic indicators of
industrial enterprises. A financial analysis of large industrial manufactures in the
region of Ukraine was conducted. To form the information base of the study, the
authors estimated liquidity, solvency, business activity and profitability ratios
that affect the financial condition of enterprises. They revealed the most
significant ratios of financial condition analysis. According to the analysis of
existing models of bankruptcy probability in the context of these industrial
enterprises, an improved model for assessing the risk of bankruptcy was proposed
and evaluated. The proposed model for estimating the probability of bankruptcy,
taking into account the influence of the most significant ratios of financial
analysis, confirmed that the percentage of provided bankruptcies and stable
activities are acceptable and indicate high quality of the resulting equation. The
IBM SPSS Statistics system was used to process the data, check the assumptions
and prepare valid conclusions. The improved model will allow it to be used in
the practice of diagnosing the probability of bankruptcy of industrial enterprises,
which will help identify the threat of bankruptcy in time and ensure stable
operation of the industrial enterprise.
Keywords: the financial analysis, correlation ratio, regression, model of
bankruptcy probability.
1 Introduction
One of the priorities of Ukrainian economy is the rapid development of industrial
manufactures in each region. Sales volume is one of the main indicators by which the
results of economic and production process of manufactures as well as the area and
Ukraine as a whole are estimated. Existing threats to the internal and external
environment of industrial enterprises affect the probability of their bankruptcy.
The development of economic innovative path implies, first of all, the possibility
and necessity of making sound economic decisions on a strict and logically verified
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basis. Mathematical and, in particular, statistical research methods make it possible to
substantiate and verify the adequacy of the measures applied to a particular economic
object in particular circumstances.
2 Literature Review
The main issues and description of the world problem under consideration in the present
work are based on the review and analysis of foreign and domestic publications.
According to authors such as I. Andryushchenko [1] a peculiar place at the macrolevel
is taken by the analysis of economic performance of the Ukrainian industrial
development. I. Sitak, D. Korobkov and V. Mishchenko [2] insist on the importance of
analyzing the financial condition of industrial enterprises for the industry development
as a whole.
The authors of [3] tried to consider the main existing trends in the area of digitization
of the socio-economic sphere. The consideration is focused on the development of the
country’s economy that directly depends on a society digital development level.
The study of particular use of neurocomputing in financial sphere can be found in
researches of A. Galushkin, O. Khlystova, A. Mints, V. Mosvenok, etc. At present,
there is a widespread appearing in the domestic market of a vast number of both
universal neuropackages for solving technical analysis problems and specialized expert
systems and neuropackages designed for solving more complex and difficult to
formalize problems from the financial field. The authors of [4-6] give a brief list of the
main tasks where neurocomputers have effectiveness that is much higher than the
effectiveness of both common regression analysis methods and expert systems based
on the construction of a formal model of an object or phenomenon. The principles of
neural networks construction and their main functioning characteristics are also
described.
The characteristics of Ukrainian enterprises crisis conditions considered in [7]. To
analyze the probability of bankruptcy, four economic and mathematical models were
proposed and calculated using various modeling tools and different number of factors.
The authors analyzed two models of linear regression and two models based on neural
networks, proposed and tested several methods for predicting the bankruptcy
probability at a macroeconomic level, which made it possible to obtain adequate results.
It should be mentioned, that works of several foreign scientists are devoted to the
study of issues under consideration. Thus, in [8] the main principles on which the
models of neural networks are based and which must be followed to be effective are
presented. A comparison of the regression analysis and neural networks with the hybrid
method suggested in [9] showed the superiority of the neuroregression method. A
comparative analysis of the two main models for forecasting in [10] is based on the
minimum predicted error. The results of the multidimensional regression approach of
OLS and the non-parametric approach of the neural network were processed, and the
method with the lowest average overall absolute percentage error has been defined.
Later in [11], a completely new theory of asymptotic distribution was suggested for
standard methods, such as regression, correlation analysis, and covariance. The present
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technique is based on a fixed time interval, which permits the number of high-frequency
returns for this period to go to infinity. The authors of [12] present an algorithm for
conducting statistical forecasting of economic indicators, which is based on the
consistent application of individual methods of mathematical statistics to build the most
reliable and adequate econometric models of indicators relationship affecting the
investment and innovation potential of the region.
Researchers E. Raevneva and O. Gorokhovaya [13] believe that when conducting
financial and economic activities, industrial enterprises are affected by various risks,
threats to their stability, which enables bankruptcy. To ensure the economic security of
an enterprise, E. Ponomarenko [14] recommend analyzing and predicting the future
operation of an enterprise.
There are many foreign and domestic models for defining the probability of
bankruptcy of an enterprise, namely the Altman [15], Springgate [16], Taffler and
Tishou [17], Saifulin-Kadykov [18] and others models. Some of them are really based
on a multivariate regression equation, others use a mixture of financial ratio analysis.
They are recommended for analysis, if you need to take into account current
business trends and the impact of promising technologies on the structure of financial
indicators.
During of the study, a significant number of publications on this topic were found,
confirming the relevance of the chosen direction. Despite the existence of different
authoring methods for assessing the probability of bankruptcy, their calculation results
are not always able to show the real financial situation of enterprises, as it should be
borne in mind that most of the methods used are developed by foreign scientists, so the
issue of their adaptation to the activities of Ukrainian enterprises remains unresolved.
3 Materials and Methods
To analyze the economic situation, various indicators are used, which are
interconnected stochastically (not strictly). Using the available statistical observations,
namely the sales volume on the main activities types for the period 2012-2017 for
industrial enterprises of Ukraine, Zaporizhzhya region and its two profiled enterprises,
the behavior of the object under study is simulated. For simulation being correct, it is
advisable to use an econometric, in particular, correlative, approach, which allows to
test statistical hypotheses about the presence and strength of the correlation.
The obtained correlation ratio will make it possible to establish the closeness of
linear correlation between the economic indicators under consideration, to correctly
determine the type of relationship – direct or indirect, and also to make the right
decisions concerned the choice of various indicators analyses.
Considering the sales volume data in the metallurgical branch and mechanical
engineering, the main hypothesis is put forward about the absence of a correlation link
between the analyzed indicators; the hypothesis of a correlation link presence is
considered as an option. The linear correlation ratio is used to assess the degree of
relation closeness [19].
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n
___ 1 / n xi yi x y
cov( x. y ) xy x y i 1
r
x y x y n n
1 / n ( xi x ) 2 1 / n ( yi y ) 2
i 1 i 1
where xi, yi are the values of the first and second measured parameters in each
observation respectively; xi , yi are the average values of necessary measured
parameters; n is the number of paired observations of variables X and Y; σx, σy are the
normal deviations calculated for all particular values of the first and second parameters,
respectively.
For the period from 2010 to 2017, according to the statistics service (Table 1), the
volume of products sold in Ukrainian industry increased by 2.06 times in metallurgy,
and 1.73 times in machine-building. So, the analysis showed that for 2010-2017, the
share of metallurgy in the overall volume of industrial manufactured products in
Ukraine decreased by 3.4 points, and the share of machine-building decreased by 2.9
points. The factors that restrain industrial production are insufficient demand for
products, lack of professional workforce, high-quality raw materials and modern
equipment. Considering the sampling of data for Ukraine in metallurgical production
(X) and mechanical engineering (Y) using software tools, we define the empirical value
of the correlation ratio, equal to 0.775.
Table 1. The volume of industrial products sold (goods, services) by type of economic activity
in Ukraine in 2010-2017.
Kind of Metallurgical
Engineering
activity production
UAH million % of total UAH million % of total
Year
2010 200001.9 19.1 97056.9 9.3
2011 241884.7 18.5 130847.9 10.1
2012 223294.1 16.3 140539.3 10.3
2013 207305.3 15.7 113926.6 8.6
2014 237393.0 16.6 101924.7 7.1
2015 278502.8 15.7 115261.7 6.5
2016 318195.9 14.8 131351.8 6.1
2017 411372.3 15.7 168281.9 6.4
To analyze the strength of the relationship between variables, the Cheddock scale
was used, according to which, the correlation ratio in range from 0.7 to 0.9, the relations
between the parameters studied are high. For the value level, the critical value of the
correlation ratio is 0.71. Thus, the relation between the volume of manufactured
industrial products sold in engineering and metallurgy is statistically significant at 5%
level and is positive.
In view of the fact that not all regions of Ukraine are industrial, it is interesting to
analyze the Zaporizhzhya region, which is one of the leaders in this area. For the period
from 2012 to 2018, according to the statistics service (Table 2), the volume of products
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sold in the Zaporizhzhya region in the metallurgical industry increased by 3.58 times,
and in mechanical engineering it increased by 1.73 times. The analysis for 2012-2018
showed that the share of metallurgy in the total volume of products manufactured in the
Zaporizhzhya region increased by 9.7 points, the share of engineering decreased by
10.5 points.
Table 2. The volume of industrial products sold (goods, services) by economic activity in the
Zaporizhzhya region in 2012-2018.
Kind of Metallurgical
Engineering
activity production
% of UAH % of
UAH million
Year total million total
2012 23878088.3 30.7 18061561.8 23.2
2013 22375716.1 29.5 14841908.8 19.6
2014 34250059.3 36.2 14766398.2 15.6
2015 47991811.4 36.7 19042831.6 14.6
2016 50462344.4 35.2 17448742.0 12.2
2017 71074449.0 37.8 25242565.3 13.5
2018 85428031.9 40.4 26923298.7 12.7
The decline in the machine building share occurred due to the decrease in car
production and in general its complete stop by one of the leading machine-building
enterprises in the Zaporizhzhya region PJSC ZAZ, as well as due to the interruption of
business ties with Russian enterprises due to the antiterrorist operation in Ukraine. The
growth in the share of metallurgy occurred despite the fact that many enterprises use
outdated equipment and technologies, but they have qualified personnel who provide
high labor productivity to enterprises.
Let’s consider a sample of data on the Zaporizhzhya region in the metallurgical
industry (X) and mechanical engineering (Y). Figure 1 shows the scatterplots with a
regression straight line and a confidence interval, which permits visualizing the
correlation between two factors, namely the sales volume in the metallurgical industry
and mechanical engineering within the Zaporizhzhya region. There is a strong positive
correlation. This assumption is approved by the software empirical value of the
correlation ratio, that is 0.914. To analyze the strength of the relationship between the
variables, the Cheddock scale was used, according to which, when the correlation ratio
is above 0.9, there is a strong relationship between the parameters under consideration.
Fig. 1. Scatterplot in the Zaporizhzhya region
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To define statistical reliability of the obtained value, we work with the data of
corresponding Pearson’s table of critical values for linear correlation ratio. For the
corresponding value level α=0.01, we find the critical value r, equal to 0.87 for this
correlation analysis. Since the empirical value (0.914) is more critical (0.87), it can be
concluded that the correlation ratio value is considered statistically significant. The
main hypothesis about the insignificance of the correlation between qualitative signs is
rejected and an alternative one is accepted. In other words, the relationship between the
volume of sold manufactured products of mechanical engineering and metallurgy is
statistically significant at the 1% level and is positive.
The obtained directly proportional dependence indicates that the higher the obtained
correlation ratio, the higher the dependence between qualitative characteristics, and
vice versa.
However, in Ukraine most of the big industrial manufactures were built in times of
the Soviet Union. Based on the Report on financial results (Table 3), the volume of sold
industrial products (goods, services) at PJSC “Zaporizhstal” in 2013-2017 increased by
3.4 times, and at PJSC “Motor Sich” – 1.77 times. The increase insales volume of the
analyzed industrial enterprises is more associated with the rise in prices, which are
caused by inflation and the hryvnia exchange rate decline, rather than with the real
growth in output volumes.
Table 3. The volume of industrial products sold (goods, services) of large enterprises of the
Zaporizhzhya region in 2013-2017.
Industrial enterprise
Year
PJSC “Zaporizhstal” PJSC “Motor Sich”
2013 13579218 8583924
2014 22110517 10730122
2015 31395478 13830655
2016 33158709 10546207
2017 46746886 15150429
Within the framework of two enterprises, the empirical value of the calculated ratio
is 0.873, which also indicates the statistical significance of the correlation between the
volumes of goods sold in the metallurgical industry and mechanical engineering. As
the relation exists, we can fore cast the values of some data based on certain values of
other data (metallurgy). To put it simply, the stronger the connection, the closer our
prediction will be.
To build the model, we analyzed the financial status and profitability of PJSC
“Zaporizhstal” and PJSC “Motor Sich” using eight ratios selected in the model.
Financial analysis of PJSC “Zaporizhstal” showed that: the absolute liquidity ratio in
2013-2017 was less than the standard value and was 0.032; 0.021; 0.034; 0.06; 0.01,
correspondingly, which indicates the irrational use of finances; solvency ratio
(autonomy) of funds in 2013 increased from 0.432 to 0.499 in 2016, and then dropped
to 0.423 in 2017, this indicator was close to the normal value only in 2016; the ratio of
own working capital in 2013-2015 is below the standard, but in 2016-2017 the ratio
was 0.209 and 0.18, which is higher than the standard value, which indicates the
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company’s financial instability in 2016-2017 and the inability to carry out active
operation; the asset negotiability ratio in 2013-2017 was approximately the same at
0.97; 1.19; 1.188; 0.94; 0.91, respectively, which indicates the adequacy of current
assets, since during the analyzed period there was a full cycle of manufacturing and
circulation, as well as an equal ratio between revenues and the average annual amount
of assets; the negotiability ratio of accounts payable in 2013-2017 amounted to 3.715;
3.24; 4.922; 3.703; 2.443 respectively, which indicates the use of creditors’ funds as a
source of financing for their debtors, and another part of the finances is used by the
enterprise to finance business operations; the negotiability ratio of accounts receivable
in 2013-2017 amounted to 9.656; 8.488; 5.696; 3.096; 2.023 respectively. That is, in
2017 compared to 2013, this indicator decreased by 4.8 times, which indicates an
inefficient management of receivables in the enterprise; return on assets increased from
0.001 in 2013, 0.06 in 2014, 0.068 in 2015 to 0.133 in 2016, and in 2017 dropped to
0.07, which indicates a decrease in assets utilization efficiency; the return on equity
ratio indicates that in 2014-2017, 0.13 UAH, 0.14 UAH, 0.26 UAH and 0.14 UAH of
net profit were received for each attracted hryvnia of own funds.
The financial analysis of PJSC “Motor Sich” showed that: the absolute liquidity ratio
in 2013-2017 was higher than the standard value and was 0.54; 0.377; 0.362; 0.616;
0.528 respectively, which indicates a rational use of funds; the solvency ratio
(autonomy) in 2013-2017 was 0.7; 0.649; 0.686; 0.647; 0.663, respectively, which is
higher than the standard value and shows a high level of solvency of the enterprise; the
ratio of own working capital in 2013-2017 was 2.263; 1.816; 1.787; 2.871; 3.127
respectively, which is higher than the standard, and indicates the financial stability of
the company and the ability to carry out vigorous activity; the assets negotiability ratio
in 2013-2017 was 0.689; 0.721; 0.741; 0.461; 0.556, respectively, this indicates the
insufficiency of current assets, since for the analyzed period there is an incomplete
cycle of production and circulation; the negotiability ratio of accounts payable during
2013-2017 amounted to 24.062; 18.208; 9.802; 6.906; 15.155 respectively, which
indicates the use of creditors’ funds to finance business operations; the negotiability
ratio of accounts receivable in 2013-2017 amounted to 5.829; 5.699; 6.128; 4.903; 4.69
correspondingly, which shows a slight decrease in the amount of receivables; the return
on assets in 2013-2017 was 0.106; 0.105; 0.182; 0.086; 0.114, respectively, during the
researched period, the above data are at approximately the same level, and indicate how
much net profit was received for each hryvnia of assets invested; return on equity ratio
in 2013-2017 was 0.154; 0.156; 0.272; 0.129; 0.174 respectively, and it shows how
much net profit was received for each attracted hryvnia of own funds.
In our opinion, the assessment of economic security level should be based not only
on indicators of financial condition, but also on an assessment of bankruptcy possibility
of an enterprise, i.e. there is a correlation between these categories.
Modern scholars when conducting financial analysis widely use foreign approaches
to predict the probability of bankruptcy of an industrial enterprise, namely the Altman
model. From 2013 to 2017, the calculation results for the Altman model showed a low
probability of bankruptcy at PJSC “Zaporizhstal”, namely: 3.756; 4.071; 4.302; 4.712;
4.874, respectively, and at PJSC “Motor Sich”, namely: 7.042; 6.829; 10.508; 5.321;
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5.74 respectively, which is a consequence of the stable financial condition of enterprises
(Table 4).
Table 4. Analysis of the likelihood of bankruptcy of large enterprises of the Zaporizhzhya
region in 2013-2017 using the Altman model.
Industrial enterprise
Year
PJSC “Zaporizhstal” PJSC “Motor Sich”
2013 3.756 7.042
2014 4.071 6.829
2015 4.302 10.508
2016 4.712 5.321
2017 4.874 5.740
The use of foreign models to define the probability of bankruptcy in Ukraine is not
quite correct, since they are built on the experience of foreign companies. It is very
different from the working conditions of domestic enterprises and the threshold values
of the standard are defined on the basis of the past of foreign companies’ activity.
Analysis of bankruptcy probability of twenty-nine Zaporizhzhya industrial
enterprises for over 5 years using seven selected models of Altman, Springate, Taffler
and Tishou, Saifulin-Kadykov, Lis, Conan, Golder and Beaver showed that using these
models in practice gives the opposite results, namely, according to one model – a low
probability, and to the other – a high probability of bankruptcy. Therefore, after
analyzing the existing foreign models, we built our own improved model for estimating
the probability of bankruptcy for Ukrainian companies, using data from their financial
statements.
The problem of applying a larger period sample is complicated by the fact that in
2013 the National Regulation (Standard) of Accounting 1 “General Requirements for
Financial Reporting” was approved with new forms of financial reporting. Financial
statements of enterprises must be submitted to the state fiscal service before March 1
of a current year, and to statistics bodies – by February 28 of a present year, and put it
in the official website until April 30, therefore the data range for analysis is selected
from 2013 to 2017.
Most often, scientists suggest discriminatory models for evaluating the probability
of bankruptcy for use in practice, but these models are not perfect for evaluating
Ukrainian enterprises lately, since the correctness of their results depends on the period
and year of the selected statements for financial analysis. Most of the models were
derived earlier and they are not adapted to the financial reporting form, which was
changed in 2013. We took the Safulin-Kadykov approach as a basis, where the author
chose 5 financial statements indicators, namely, the coefficients: providing with own
funds, current liquidity, asset turnover, profitability of sales and equity.
According to the results of the analysis of the reporting of the studied industrial
enterprises, we selected 8 significant indicators, in our opinion, that most characterize
their financial condition. We selected several key indicators from each group that best
reflect the real state of the company, namely the groups: liquidity, solvency, business
activity and profitability. The main criterion for the selection of indicators was the
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availability of different source data for their calculation in the financial statements of
the company.
According to the results of the analysis of existing domestic and foreign models of
bankruptcy probability in the context of these Zaporizhzhya regional industrial
enterprises, the improved model is proposed, which is founded on the impact of the
most valuable ratios of financial analysis and profitability. The resulting model for
predicting the bankruptcy probability includes eight ratios of financial analysis and
profitability, and has the following form:
Z=0.5x1+0.3x2+x3+0.3x4+0.01x5+0.05x6+0.3x7+0.3x8
where x1 – the absolute liquidity ratio; x2 – solvency ratio (autonomy); x3 – the ratio of
own working capital; x4 – assets negotiability ratio; x5 – negotiability ratio of accounts
payable; x6 – negotiability ratio of receivables; x7 – asset profitability ratio; x8 – ratio of
return on equity.
Using discriminant analysis based on the results of practical data processing at
twenty-nine industrial enterprises over the past five years, the coefficients to the
selected eight indicators were determined using the SPSS Statistics system. On the basis
of the obtained correlation coefficient between these indicators, it was established that
there is a low relationship between them.
Approbation of the improved model made it possible to group the results of
predicting the probability of bankruptcy in four ranges. The results of the model
obtained and the boundaries of the range were verified with real data on the financial
condition of enterprises.
In contrast to the existing models, in the bankruptcy probabilities evaluating scales
with either two values (high and low) or three (high, medium or uncertain, and low),
there defined four groups of values, namely: very low, low, possible and high
bankruptcy probability. The resulting range of values is: if x>1.3 – very low enterprise
bankruptcy probability, if 0.6