Download Industry characteristics and operations efficiency of joint

Poznan University of Economics
Faculty of Informatics and Electronic Economy
Sergiusz Herman
Industry characteristics and operations efficiency of joint-stock
companies in Poland and their bankruptcy prediction
PHD DISSERTATION
ABSTRACT
Advisor:
dr hab. Dorota Appenzeller, prof. nadzw. UEP
Poznań 2016
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The bankruptcy of companies is a characteristic of every developed market economy.
The risk of bankruptcy is an object of interest for a wide group of stakeholders, including
owners, employees, managers, creditors and suppliers. Negative consequences of brakruptcy
led to many attempts to predict it. A dynamic growth of interest in this subject was
encouraged in 1920s and 1930s which was the result of the economic crisis at the time. Since
then, researchers as well as businessmen have tried to find more and more effective tools for
the bankruptcy prediction.
A review of the scientific literature indicated that the growth of interest in this area can
be noticed in the use of more and more advanced statistical methods. It is a consequence of
the development of those methods and information technology, which can be used for
predicting the bankruptcy of companies. Therefore, statistical methods that used to be highly
popular are often replaced by methods developed in the fields of artificial intelligence and
machine learning. There are many descriptions of empirical studies in which authors have
compared effectivenes of various methods used for the development of bankruptcy prediction
models. P. Ravi Kumar and V. Ravi [2007] reviewed and characterised as many as 128 of
such publications.
The second area of interest for researchers of the bankruptcy prediction is expanding
the set of prediction variables used for this purpose. Popular bankruptcy prediction models are
based on financial ratios calculated from companies’ financial statements (balance sheet as
well as profit and loss account). Despite a wide selection of those ratios, other explanatory
variables are also used, for example indicators based on a cash flow statement. D. Wędzki
[2008] reviewed Polish and foreign empirical studies connected with the use of such ratios in
the bankruptcy prediction.
Another example of expanding the set of explanatory variables is the use of the
efficiency ratio of a company performance. Empirical studies conducted in Poland and all
over the world unequivocally indicated that one of the main reasons behind the bankruptcy is
a poor management of the business [Baldwin and others 1997, Sudoł and Matuszak 2002,
Szczerbak 2005, etc]. According to the literature, it results in a poor efficiency of such
entities. Therefore, foreign empirical research is beginning to take into account the efficiency
ratio as an additional explanatory variable in bankruptcy prediction models [Xu and Wang
2009, Yeh and Hsu 2010]. There is a lack of such studies in Poland.
Apart from using more advanced statistical methods and looking for new prediction
variables, there is a third area of study of bankruptcy prediction. It consists of building models
depending on characteristics of the industry of researched companies. This approach is
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approved by, among others, E. I. Altman [1983, p. 125], a world-renowned authority on the
bankruptcy prediction. He emphasized that the estimation of bankruptcy prediction models
should be based on financial data of companies which business activity is as much
homogenous as possible. Due to a difficulty of gathering a research sample that is big enough,
Polish researchers rarely try to build models depending on industries. There are only two
examples of authors who have compared the choice of classification for industry models and a
“general” model (which does not include the characteristics of industry) [Hołda
2006;Juszczyk and Balina 2014]. The dissertation author believes that due to the used method
of estimating the prediction error of the models, none of those results can unequivocally and
certainly answer the question whether industry models improve predictability compared to
“general” models.
Independently on the chosen bankruptcy prediction method, the set of variables and
the industry of the study, the evaluation of the tool predictability – classifier – is an essential
part. According to the literature, the evaluation should be based on a large, independent
sample of objects that were not used while developing the model. However, it is often
difficult to gather such research sample, in particular in the Polish capital market. In this
situation, one of the estimation method of classifier prediction error should be used. There are
some foreign publications which aim to carry out an empirical comparative analysis [BragaNetto and Dougherty 2004, Milonaro and others 2005, Kim 2009, etc.]. Those studies concern
the most popular classifiers in medicine. There is a lack of research on estimating prediction
error methods in the Polish literature.
Taking into account all the above-mentioned information, the main purpose of this
dissertation is to examine the impact of industry characteristics and the performance
efficiency ratio of joint-stock companies on the predictability of models used for the
bankruptcy prediction.
The achievement of the main purpose will allow for the verification of two main
hypotheses of the dissertation, that is:
H1. Industry models of joint-stock companies bankruptcy prediction in
Poland provide a higher level of predictability compared to “general”
models, which do not include industry characteristics.
H2. Using performance efficiency ratio of joint-stock companies in
Poland obtained from DEA improves the predictability of models for
the bankruptcy prediction.
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Apart from two main hypotheses, also the following auxiliary hypothesis was verified in
the dissertation:
H3.The performance efficiency ratio of joint-stock companies in Poland obtained from
DEA is an important determinant of their bankruptcy.
The dissertation is divided into six chapters. First three chapters consist of a theoretical
part, whereas next chapters present results of the empirical analysis. In the theoretical part, a
definition of bankruptcy, reasons for it and statistical data of bankruptcy in Poland and other
European countries were presented. Furthermore, this part consists of a short history of the
development of methods used for the bankruptcy prediction, descriptions of some of those
methods and a review of bankruptcy prediction models prepared for the Polish capital market.
These models were divided according to the inclusion of industry characteristics of the
entities or lack of it. The third chapter covers the topic of efficiency, its definition, basic
parametric and nonparametric methods of its measurement. Also, a review of empirical
studies by authors who measure efficiency to predict bankruptcy of companies was presented
in this chapter.
The empirical part is significant for the dissertation. Empirical studies were conducted
on 180 joint-stock companies in the Polish capital market. They represent three industries of
the economy that is construction, manufacturing and trade. A linear discriminant analysis was
used.
In the first phase of the study, 12 estimators were compared in terms of statistical
features of classifier true error rate. Three measures were used for this purpose, including
bias, standard deviation and mean squared error. On that basis, it was determined that the
0.632+ estimator, which was calculated with the use of bootstrapping method, has the most
valuable features for the bankruptcy prediction of joint-stock companies in Poland. This part
of the dissertation also includes the comparable analysis of six chosen statistical methods of
selecting variables for a model. They were compared on the basis of their selectivity
(understood as an average number of variables implemented into the model for a certain
method) as well as an average prediction error of models predicting bankruptcy developed
with the use of those methods. Results showed that compared methods considerably differ in
terms of their selectivity. Furthermore, it turned out that the choice of variables selection
methods to the model affects its predictability. Methods which are based on the absolute value
of t statistic guarantee lower values of prediction error than other methods that were verified.
Those differences are statistically significant.
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The choice of the best estimation method of prediction error and the selection of
variables to the model allowed the author to construct and estimate prediction error of
industry and general models (which does not include industry characteristics of the researched
companies). The results indicated that industry models do not have a lower prediction error,
so at the same time higher predictability, when compared to general models. It means that the
hypothesis H1 of the dissertation, which says that industry models of predicting bankruptcy
of joint-stock companies in Poland have a higher predictability compared to general models
which do not include industry characteristics, was not confirmed.
This part of the dissertation also includes determinants of joint-stock companies
bankruptcy in chosen industries. A type of developed industry models as well as variables
used for developing models estimating the value of their real prediction errors were taken into
account. It proved that determinants of bankruptcy of joint-stock companies in Poland differ
depending on the industry. It confirms that while developing bankruptcy prediction models,
industry should be considered despite the fact that it does not improve their predictability.
In the third part of the study the efficiency of joint-stock companies were measured.
The BCC model was used for this purpose. By comparing the level of efficiency rates for
going concerns and companies that declared bankruptcy, it turned out that an average
efficiency ratio is always higher in the case of healthy companies independent on the industry.
According to the results, a difference is larger when a company is closer to the bankruptcy.
The conclusion is (intuitively) that a lower efficiency of joint-stock companies in Poland leads
to the deterioration of their financial situation which may lead to bankruptcy. As a result the
H3 hypothesis was confirmed. It assumed that the performance efficiency ratio of joint-stock
companies in Poland obtained from DEA is an important determinant of their bankruptcy.
This information was used to verify the influence of including efficiency ratio on
bankruptcy prediction models for joint-stock companies. For this purpose, the change of the
prediction error for general and industry models that include efficiency ratio was estimated. It
turned out that the inclusion of this additional variable only slightly decrease the prediction
error of analysed models. However, the efficiency ratio in models always improves
predictability of companies which declared bankruptcy. It means that the H2 hypothesis was
confirmed – using performance efficiency ratio of joint-stock companies in Poland obtained
from DEA improves the predictability of models for bankruptcy prediction.
What is more, efficiency determinants of joint-stock companies in the analysed
industries were established in this part of the dissertation. In order to do this, the generalised
DEA (GDEA) was used. The results of the analysis confirmed that the efficiency measure of
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companies from different industries requires different financial variables showing inputs as
well as outputs of their activity. The use of efficiency ratios estimated with this method results
in a better bankruptcy prediction of joint-stock companies.
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Literature (selected)
Altman, E.I., 1983, Corporate financial distress: A complete guide to predicting, avoiding,
and dealing with bankruptcy (1st ed.), John Wiley & Sons, New York.
Baldwin J., Gray, T., Johnson, J., Proctor, J., Rafiguzzaman, M., Sabourin, D., 1997, Failing
Concerns: Business Bankruptcy in Canada, Ministry of Industry, Ottawa.
Braga-Neto, U.M., Dougherty, E.R., 2004, Is Cross-validation for Small-sample Microarray
Classification?, Bioinformatics, 20(3), s. 374–380.
Hołda, A., 2006, Zasada kontynuacji działalności i prognozowanie upadłości w polskich
realiach gospodarczych, Wydawnictwo Akademii Ekonomicznej w Krakowie, Kraków
Juszczyk, S., Balina, R., 2014, Prognozowanie zagrożenia bankructwem przedsiębiorstw
w wybranych branżach, Ekonomista nr 1,. s. 67-95.
Kim J.H., 2009, Estimating classification error rate: Repeated cross-validation, repeated holdout and bootstrap, Computational Statistics and Data Analysis 53, s. 3735-3745.
Molinaro, A.M., Simon, R., Pfeiffer, R.M., 2005. Prediction error estimation: A comparison
of resampling methods, Bioinformatics 21, s. 3301-3307.
Ravi Kumar, P., Ravi, V., 2007, Bankruptcy prediction in banks and firms via statistical and
intelligent techniques – A review, European Journal of Operational Research 180, s. 1–28.
Sudoł, S., Matuszak, M., 2002, Przyczyny rozwoju i upadku polskich przedsiębiorstw
przemysłowych w okresie transformacji ustrojowej 1990-1998, Wydawnictwo Naukowe
Uniwersytetu Mikołaja Kopernika, Toruń.
Szczerbak M., 2005, Przyczyny upadłości przedsiębiorstw w świetle opinii syndyków
i nadzorców sądowych, w: Kuciński, K., Mączyńska, E.(red.), Zagrożenie upadłością,
Szkoła Głowna Handlowa – Oficyna Wydawnicza, Warszawa, s. 36-45.
Wędzki, D., 2008, Przepływy pieniężne w prognozowaniu upadłości przedsiębiorstwa.
Przegląd literatury, Badania Operacyjne i Decyzje, nr 2, s. 87-104.
Xu, X., Wang, Y., 2009, Financial failure prediction using efficiency as a predictor, Expert
Systems with Applications, Volume 36, Issue 1, s. 366–373.
Yeh, C.C., Chi, D.J., Hsu, M.F., 2010, A hybrid approach of DEA, rough set and support
vector machines for business failure prediction, Expert Systems with Applications, Volume
37, Issue 2, s. 1535–1541.
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