Bankruptcy prediction in Visegrad group countries using multiple discriminant analysis

• Authors: Tomas Kliestik , Jaromir Vrbka , Zuzana Rowland

Abstracts

EN

Research background: The problem of bankruptcy prediction models has been a current issue for decades, especially in the era of strong competition in markets and a constantly growing number of crises. If a company wants to prosper and compete successfully in a market environment, it should carry out a regular financial analysis of its activities, evaluate successes and failures, and use the results to make strategic decisions about the future development of the business. Purpose of the article: The main aim of the paper is to develop a model to reveal the un-healthy development of the enterprises in V4 countries, which is done by the multiple discriminant analysis. Methods: To conduct the research, we use the Amadeus database providing necessary financial and statistical data of almost 450,000 enterprises, covering the year 2015 and 2016, operating in the countries of the Visegrad group. Realizing the multiple discriminant analysis, the most significant predictor and the best discriminants of the corporate prosperity are identified, as well as the prediction models for both individual V4 countries and complex Visegrad model. Findings & Value added: The results of the research reveal that the prediction models use the combination of same financial ratios to predict the future financial development of a company. However, the most significant predictors are current assets to current liabilities ratio, net income to total assets ratio, ratio of non-current liabilities and current liabilities to total assets, cash and cash equivalents to total assets ratio and return of equity. All developed models have more than 80 % classification ability, which indicates that models are formed in accordance with the economic and financial situation of the V4 countries. The research results are important for companies themselves, but also for their business partners, suppliers and creditors to eliminate financial and other corporate risks related to the un-healthy or unfavorable financial situation of the company.

Keywords

EN

bankruptcy; prediction model; discriminant analysis; Visegrad group; financial analysis

• Publisher: Instytut Badań Gospodarczych
• Journal: Equilibrium. Quarterly Journal of Economics and Economic Policy
• Year: 2018
• Volume: 13
• Issue: 3
• Pages: 569-593

Dates

published

2018

Contributors

author

Tomas Kliestik

• Institute of Technology and Business in Ceske Budejovice

author

Jaromir Vrbka

• Institute of Technology and Business in Ceske Budejovice

author

Zuzana Rowland

• Institute of Technology and Business in Ceske Budejovice

References

• Adamko, P., & Svabova, L. (2016). Prediction of the risk of bankruptcy of Slovak companies. In Proceedings of the 8th international scientific conference on managing and modelling of financial risks. Ostrava: VSB-Technical University.
• Al Khatib, K., & Al Bzour, A. E. (2011). Predicting corporate bankruptcy of Jordanian listed companies: using Altman and Kida models. International Journal of Business and Management, 6(3).
• Altman, E. I. (1968). Financial ratios. Discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4). doi: 10.2307/2978933.
• Altman, E. I., Haldeman, R. G., & Narayanan, P. (1977). ZETA analysis. A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1(1). doi: 10.1016/0378-4266(77)90017-6.
• Altman, E. I. (2000). Predicting financial distress of companies: revisiting the Z-score and ZETA®Models. Retrieved from: http://pages.stern.nyu.edu/~ ealtman/Zscores.pdf (20.4.2018).
• Altman, E. I. (2002). Bankruptcy, credit risk and high yield junk bonds. New York: Blackwell Publishers.
• Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2014). Dis-tresses firm and bankruptcy prediction in an international context: a review and empirical analysis of Altman’s Z-score model. SSRN. doi:10.2139/ssrn. 2536340.
• Andrea, R., & Dorisz, T. (2015). Financial competitiveness analysis in the Hungarian diary industry. Journal of Global Competitiveness, 24(4). doi: 10.1108/CR-03-2015-0016.
• Antonowicz, P. (2014). The multi-dimensional structural analysis of bankruptcy of enterprises in Poland in 2013 – results of empirical studies. Journal of International Studies, 7(1). doi: 10.14254/2071-8330.2014/7-1/3.
• Bauer, P., & Edrész, M. (2016). Modelling bankruptcy using Hungarian firm-level data. Magyar Nemzeti Bank Occasional Papers, 122.
• Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of accounting research, 4(1). doi: 10.2307/2490171.
• Bellovary, J., Gacosmimo, D., & Akers, M. (2007). A review of bankruptcy pre-diction Studies: 1930 to Present. Journal of Financial Education, 33.
• Binkert, C. H (1999). Early recognition of corporate crises with the help of suitable methods in the German and Slovak economic area. Bratislava: University of Economics in Bratislava.
• Boratyńska, K. (2016). Corporate bankruptcy and survival on the market: lessons from evolutionary economics. Oeconomia Copernicana, 7(1). doi: 10.12775/ OeC.2016.008
• Chrastinova, Z. (1998). Methods of assessment of economic credibility and prediction of financial situation of agricultural companies. Bratislava: Research institute of agricultural and food economics.
• Delina, R, & Packová M. (2013). Validity of bankruptcy prediction models in conditions of SR. E&M Economics and Management, 16.
• Dimitras, A. I., Zanakis, S. H., & Zopoundis, C. (1996). A survey of business failure with an emphasis on prediction method and industrial applications. European Journal of Operational Research, 90. doi: 10.1016/0377-2217(95)00070-4.
• Dorgai, K., Fenyves, V., & Suto, D. (2016). Analysis of commercial enterprises’ solvency by means of different bankruptcy models. Gradus, 3(1).
• Doucha, R. (1996). Corporate financial analysis - practical application. Praha: VOX Consult.
• El Khoury, R., & Al Beaino, R. (2014). Classifying manufacturing firms in Lebanon: an applications of Altman´s model. Procedia: Social and behavioural sciences, 109(1). doi: 10.1016/j.sbspro.2013.12.413.
• Gajdka, J., & Stos, D. (2003). Ocena kondycji finansowej polskich spólek publiz-cnych w okresie 1998-2001. Szczecin: Uniwersytet Szczecinski.
• Grice, J. S., & Dugan, M. T. (2001). The limitations of bankruptcy prediction models: Some cautions for researchers. Review of quantitative finance and ac-counting, 17(2).
• Grunwald, R., & Holečková, J. (2007). Financial analysis and planning in enter-prises. Praha: Ekopress.
• Gundova, P. (2015). Verification of the selected prediction methods in Slovak companies. Acta academica karviniensia, 4.
• Gurcik, Ľ. (2002). Business economy. Nitra: Slovak University of Agriculture.
• Hampel, D., Vavrina, J., & Janova, J. (2012). Predicting bankruptcy of companies based on the production function parameters. In J. Ramík & D. Stavarek (Eds). 30th international conference mathematical methods in economics. Karviná: Silesian University in Opava, School of Business Administration.
• Hamrol, M., Czajka, B., & Piechocki, M. (2004). Upadlosc przedsiębiorstwa - model analizy dyskryminacyjnej. Przeglad Organizacji, 4.
• Hebak, P. (2005). Multivariate statistical methods. Prague, CR: Informatorium.
• Holda, A. (2001). Forecasting of the enterprise in conditions of the Polish economy with the use of a discrepancy function. Rachunkovosc.
• Hurtosova, J. (2009). Formation of a rating model, a tool to assess the corporate credit capability. Bratislava: University of Economics in Bratislava.
• Jakubík, P., & Teplý, P. (2011). The JT index as indicator of financial stability of corporate sector. Praque Economic Papers, 2.
• Juszczyk, S., & Balina, R. (2014). Forecasting the bankruptcy risk of enterprises in selected industries. Ekonomista, 1.
• Kamenikova, K. (2005). Determination of the use of the financial development prediction models in conditions of Slovakia. Acta Montanistica Slovaca, 10(3).
• Karas, M., & Reznakova, M. (2014). A parametric or nonparametric approach for creating a new bankruptcy prediction model: the evidence from the Czech Republic. International Journal of Mathematical Models and Methods in Applied Sciences, 8.
• Karas, M., & Reznaková, M. (2018). Building a bankruptcy prediction model: could information about past development increase model accuracy? Polish Journal of Management Studies, 17(1). doi: 10.17512/pjms.2018.17.1.10.
• Karas, M., & Reznakova, M. (2015). Predicting bankruptcy under alternative conditions: the effect of a change in industry and time period on the accuracy of the model. Procedia-Social and Behavioral Sciences, 213. doi: 10.1016/j.sbspro.2015.11.557.
• Karas, M., Reznakova, M., Bartos, V., & Zinecker, M. (2013). Possibilities for the application of the Altman model within the Czech Republic. In K. Kalampouka & C. Nastase (Eds.). Proceedings of the 4th international conference on finance, accounting and law. Chania: WSEAS Press, Business and Economics Series.
• Karbownik, L. (2017). Methods for assessing the financial risk of enterprises in the TSI sector in Poland. Łódz: Wydawnictwo Uniwersytetu Łódzkiego.
• Kliestik, T., Misankova, M., Valaskova, K., & Svabova, L. (2018). Bankruptcy prevention: new effort to reflect on legal and social changes. Science and Engineering Ethics, 24(2). doi: 10.1007/s11948-017-9912-4.
• Kliestik, T, & Svabova, L. (2016). Some remarks on the regional disparities of prediction models constructed in the Visegrad countries. In Proceedings of the 8th international conference the economies of Balkan and Eastern Europe countries in the changing world. Split.
• Kliestikova, J., Misankova, M., & Kliestik, T. (2017). Bankruptcy in Slovakia: international comparison of the creditor´s position. Oeconomia Copernicana, 8(2). doi: 10.24136/oc.v8i2.14.
• Korol, T. (2010). Forecasting bankruptcies of companies using soft computing techniques. Finansowy Kwartalnik Internetowy e-Finanse, 6.
• Kovacova, M., & Kliestik, T. (2017). Logit and probit application for the prediction of bankruptcy in Slovak companies. Equilibrium. Quarterly Journal of Economics and Economic Policy, 12(4). doi: 10.24136/eq.v12i4.40
• Kral, P., & Kanderova, M. (2009). Multivariate statistical methods aimed at solving the tasks of the economic practice. Banská Bystrica, SR: Matej Bel Univer-sity.
• Kubickova, D., & Nulicek, V. (2017). Bankruptcy model construction and its limitation in input data quality. In P. Jedlicka, P. Maresova & I. Soukal (Eds.). Proceedings of the 15th international scientific conference on Hradec economic days. Hradec Kralove: University of Hradec Kralove.
• Li, J., & Ragozar, R. (2012). Application of the Z- score model with consideration of total assets volatility in predicting corporate financial failures from 2000 – 2010. Journal of Accounting and Finance, 12(1).
• Maczynska, E. (1994). Assessment of the condition of the company (simplified methods). Zycie Gospodarcze, 38.
• Maczynska, E. (2004). Early warning systems. Nowe Zycie Gospodarcze, 12.
• Mandru, L. (2010). The diagnosis of bankruptcy risk using score function. In Proceedings of the 9th WSEAS international conference on artificial intelligence, knowledge engineering and database. UK: World scientific and Engineering academy and society press, University of Cambridge.
• Meluzin, T., Balcerzak, A. P., Pietrzak, M. B., Zinecker, M., & Doubravský, K. (2018a). The impact of rumours related to political and macroeconomic uncertainty on IPO success: evidence from a qualitative model. Transformations in Business & Economics, 17 2(44).
• Meluzin, T., Zinecker, M., Balcerzak, A. P., Doubravský, K., Pietrzak, M. B., & Dohnal, M. (2018b), The timing of initial public offerings – non-numerical model based on qualitative trends. Journal of Business Economics and Management, 19(1). doi: https://doi.org/10.3846/jbem.2018.1539.
• Meluzín, T., Pietrzak, M. B., Balcerzak, A. P., Zinecker, M., Doubravský, K., & Dohnal, M. (2017). Rumours related to political instability and their impact on IPOs: the use of qualitative modeling with incomplete knowledge. Polish Journal of Management Studies, 16(2). doi: 10.17512/pjms.2017.16.2.15.
• Merwin, C. (1942). Financing small corporations in five manufacturing industries. 1926-1936. New York: National bureau of economic research.
• Michaluk, K. (2003). Effectiveness of corporate bankruptcy models in Polish economic conditions. In L. Pawłowicz & R. Wierzba (Eds.). Corporate finance in the face of globalization processes. Warszawa: Wydawnictwo Gdanskiej Akademii Bankowej.
• Mihalovic, M. (2016). Performance comparison of multiple discriminant analysis and logit models in bankruptcy prediction. Economics & Sociology, 9(4). doi: 10.14254/2017-789X.2016/9-4/6.
• Neumaier, I., & Neumaierova, I. (1995). Try to calculate INDEX IN 95. Terno, 5.
• Neumaier, I., & Neumaierova, I. (1999). Financial analysis INFA – application in energy sector. Sektorové a odvětvové analýzy Aspekt Energetika, 4(1).
• Neumaier, I., & Neumaierova, I. (2001). Analysis of the value formation – application of financial analysis INFA. Sektorové a odvětvové analýzy Aspekt, 8(5).
• Neumaier, I., & Neumaierova, I. (2005). Index IN 05. Brno: Masaryk University in Brno.
• Pisula, T., Mentel, G., & Brozyna, J. (2013). Predicting bankruptcy of companies from the logistics sector operating in the Podkarpacie region. Modern Management Review, 18.
• Pitrova, K. (2011). Possibilities of the Altman Zeta model application to Czech Firms. E&M Economics and Management, 3.
• Prusak, B. (2005). Modern methods of forecasting the financial risk of enterprises. Warszawa: Difin.
• Prusak, B. (2018). Review of research into enterprise bankruptcy prediction in selected central and eastern European countries. International Journal of Financial Studies, 6(3). doi:10.3390/ijfs6030060.
• 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(1). doi: 10.1016/j.ejor.2006.08.043.
• Rybarova, D., Braunova, M., & Jantosova L. (2016). Analysis of the construction industry in the Slovak Republic by bankruptcy model. Procedia –Social and Behavioral Science, 230. doi: 10.1016/j.sbspro.2016.09.038.
• Satish, Y. M., & Janakiram, B. (2011). Turnaround strategy using Altman model as a tool in solar water heater industry in Karnataka. International Journal of Business and Management, 6.
• Sedlacek, J. (2011). Corporate financial analysis. Brno: Computer Press.
• Sharifabadi, M. R., Mirhaj, M., & Izadinia, N. (2017). The impact of financial ratios on the prediction of bankruptcy of small and medium companies. Quid-Investigation Cienca y Tecnologia, 1(SI).
• Szetela, B., Mentel, G., & Brozyna, J. (2016). In search of insolvency among European countries. Economic Research – Ekonomska Istrazivanja, 29(1). doi: 10.1080/1331677X.2016.1237301.
• Tamari M. (1966). Financial ratios as a means of forecasting bankruptcy. Management International Review, 6(4).
• Tian, S., Yu, Y., & Guo, H. (2015). Variable selection and corporate bankruptcy forecasts. Journal of Banking & Finance, 52(C). doi: 10.1016/j.jbankfin.2014.12.003.
• Virag, M., & Hajdu, O. (1996). Bankruptcy model calculations based on financial indicators. Bankszemle, 15(5).
• Vochozka, M., Strakova, J., & Vachal, J. (2015). Model to predict survival of transportation and shipping companies. Naše More, 62(SI). doi: 10.17818/NM/2015/SI4.
• Vochozka, M., Rowland, Z., & Vrbka, J. (2016). Evaluation of solvency of potential customers of a company. Matematyčne modeljuvannja v ekonomici, 5(1).
• Wu, Y., Gaunt, C., & Gray, S. (2010). A comparison of alternative bankruptcy prediction models. Journal of Contemporary Accounting and Economics, 6(1). doi: 10.1016/j.jcae.2010.04.002.
• Zalai, K. (2008). Special aspects of forecasting the financial development of Slovak companies. Biatec, 8.
• Zvarikova, K., Spuchlakova, E., & Sopkova, G. (2017). International comparison of the relevant variables in the chosen bankruptcy models used in the risk management. Oeconomia Copernicana, 8(1), doi: 10.24136/oc.v8i1.10.

Identifiers

DOI

10.24136/eq.2018.028

Biblioteka Nauki

22446534