Decision tree based model of business failure prediction for Polish companies
Keywords:decision trees, prediction model, financial ratios, business failure, Polish companies
Research background: The issue of predicting the financial situation of companies is a relatively young field of economic research. Its origin dates back to the 30's of the 20th century, but constant research in this area proves the currentness of this topic even today. The issue of predicting the financial situation of a company is up to date not only for the company itself, but also for all stakeholders.
Purpose of the article: The main purpose of this study is to create new prediction models by using the method of decision trees, in achieving sufficient prediction power of the generated model with a large database of real data on Polish companies obtained from the Amadeus database.
Methods: As a result of the development of artificial intelligence, new methods for predicting financial failure of the company have been introduced into financial prediction analysis. One of the most widely used data mining techniques in this field is the method of decision trees. In the paper, we applied the CART and CHAID approach to create a model of predicting the financial difficulties of Polish companies.
Findings & Value added: For the creation of the prediction model, a total of 37 financial and economic indicators of Polish companies were used. The resulting decision trees based prediction models for Polish companies reach a prediction power of more than 98%. The success of the classification for non-prosperous companies is more than 83%. The created decision tree-based prediction models are useful mainly for predicting the financial difficulties of Polish companies, but can also be used for companies in another country.
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4). doi: 10.1111/j.1540-6261.1968.tb00843.x.
Aoki S., Hosonuma, Y. (2004). Bankruptcy prediction using decision tree. In H. Takayasu (Ed.). The Application of Econophysics. Tokyo: Springer. doi: 10.1007/978-4-431-53947-6_43.
Berent, T., Blawat, B., Dietl, M., Krzyk, P., & Rejman, R. (2017). Firm's default - new methodological approach and preliminary evidence from Poland. Equilibrium. Quarterly Journal of Economics and Economic Policy, 12(4). doi: 10.24136/eq.v12i4.39.
Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. New York: Routledge. doi: 10.1201/9781315139470.
Brezigar-Masten, A., & Masten, I. (2012). CART-based selection of bankruptcy predictors for the logit model. Expert Systems with Applications, 39(11). doi: 10.1016/j.eswa.2012.02.125.
Brozyna, J., Mentel, G., & Pisula, T. (2016). Statistical methods of the bankruptcy prediction in the logistics sector in Poland and Slovakia. Transformations in Business & Economics, 15(1).
Chen, M. (2011). Predicting corporate financial distress based on integration of decision tree classification and logistic regression. Expert Systems with Applications, 38(9). doi: 10.1016/j.eswa.2011.02.173.
Chen, M. (2012). Comparing traditional statistics, decision tree classification and support vector machine techniques for financial bankruptcy prediction. Intelligent Automation & Soft Computing, 18(1). doi: 10.1080/10798587.2012. 10643227.
Fitzpatrick, F. (1932). A comparison of ratios of successful industrial enterprises with those of failed firm. Certified Public Accountant, 6.
Frydman, H., Altman, E. I., & Kao, D. (1983). Introducing recursive partitioning for financial classification: the case of financial distress. Journal of Finance, 40(1). doi: 10.1111/j.1540-6261.1985.tb04949.x
Irimia-Dieguez, A. I., Blanco-Oliver, A., & Vazquez-Cueto, M. J. (2015). A comparison of classification/regression trees and logistic regression in failure models. Procedia Economics and Finance, 26. doi: 10.1016/s2212-5671(15)00797-2.
Karas, M., & Reznakova, M. (2017). Predicting the bankruptcy of construction companies: a CART-based model. Engineering Economics, 28(2). doi: 10.5755/j01.ee.28.2.16353.
Karas, M., Reznakova, M., & Pokorny, p. (2017). Predicting bankrutpcy of agriculture companies: validating selected models. Polish Journal of Management Studies, 15(1). doi: 10.17512/pjms.2017.15.1.11.
Kass, G. V. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29(2). doi: 10.2307/2986296.
Kliestik, T., Kliestikova, J., Kovacova, M., Svabova, L., Valaskova, K., Vochozka, M., & Olah, J. (2018). Prediction of financial health of business entities in transition economies. New York: Addleton Academic Publishers.
Kliestik, T., Vrbka, J., & Rowland, Z. (2018). Bankruptcy prediction in Visegrad group countries using multiple discriminant analysis. Equilibrium. Quarterly Journal of Economics and Economic Policy, 13(3). doi: 10.24136/eq.2018.028.
Korol, T. (2013). Early warning models against bankruptcy risk for Central European and Latin American enterprises. Economic Modelling, 31. doi: 10.1016/j. econmod.2012.11.017.
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.
Kumar, P. R., & 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.
Li, H., Sun, J., & Wu, J. (2010). Predicting business failure using classification and regression tree: an empirical comparison with popular classical statistical methods and top classification mining methods. Expert Systems with Applications, 37(8). doi: 10.1016/j.eswa.2010.02.016.
Lin, W., Ke, S., & Tsai, C. (2017). Top 10 data mining techniques in business applications: a brief survey. Kybernetes, 46(7). doi: 10.1108/k-10-2016-0302.
Misankova, M., & Bartosova, V. (2016). Comparison of selected statistical methods for the prediction of bankruptcy. In T. Loster, T. Pavelka (Eds.). Conference proceedings of 10th International Days of Statistics and Economics. Prague: Melandrium.
Niewczas, B., & Mientkiewicz, D. (2017). Poland. In D. S. Bernstein (Ed.). The insolvency review. Retrieved from https://thelawreviews.co.uk/edition/the-insolvency-review-edition-5/1149945/poland (29.11.2018).
Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1). doi: 10.2307/2490395.
Pawelek, B., Galuszka, K., Kostrzewska, J., & Kostrzewski, M. (2017). Classification methods in the research on the financial standing of construction enterprises after bankruptcy in Poland. In F. Palumbo, A. Montanari & M. Vichi (Eds.). Data science studies in classification, data analysis, and knowledge organization. Springer. doi: 10.1007/978-3-319-55723-6_3.
Pisula, T., Mentel, G., & Brozyna, J. (2013). Predicting bankruptcy of companies from the logistics sector operating in the Podkarpacie region. Modern Management Review, 18. doi: 10.7862/rz.2013.mmr.33.
Pociecha, J., Pawelek, B., Baryla, M., & Augustyn, S. (2018). Classification models as tools of bankruptcy prediction – Polish experience. In W. Gaul, M. Vichi & C. Weihs (Eds.). Studies in classification, data analysis, and knowledge organization. Springer. doi: 10.1007/978-3-319-55708-3_18.
Popescu, M. E. (2012). Financial distress prediction of the Romanian companies using CHAID models. Metalurgia International, 17(12).
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.
Szymanska-Rutkowska, K., & Galkowski, S. (2017). The new Polish restructuring law: a “second chance” for businesses. Emerging Markets Restructuring Journal, 4.
Tokarski, A. (2018). The phenomenon of bankruptcy of enterprises in the Polish economy in the years 2008-2015. In E. Lotko, U. K. Zawadzka-Pak, & M. Radvan (Eds.). Optimization of organization and legal solutions concerning public revenues and expenditures in public interest (Conference proceedings). doi: 10.15290/oolscprepi.2018.30.
Valaskova, K., Kliestik, T., Svabova, L., & Adamko, P. (2018). Financial risk measurement and prediction modelling for sustainable development of business entities using regression analysis. Sustainability, 10(7). doi: 10.3390/su10072 144.
Weissova, I., Siekelova, A., & Kramarova, K. (2016). Modeling of company´s default probability in relation to its credit risk. Global Journal of Business, Economics and Management: Current Issues, 6(2). doi: 10.18844/gjbem. v6i2.1378.
Wyrobek, J., & Kluza, K. (2018). Efficiency of gradient boosting decision trees technique in Polish companies' bankruptcy prediction. In L. Borzemski, J. Swiątek & Z. Wilimowska (Eds.). Advances in intelligent systems and computing information systems architecture and technology: proceedings of 39th international conference on information systems architecture and technology – ISAT 2018. doi: 10.1007/978-3-319-99993-7_3.
Zieba, M., Tomczak, S. K., & Tomczak, J. M. (2016). Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction. Expert Systems with Applications, 58. doi: 10.1016/j.eswa.2016.04.001.
Zmijewski, M. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22. doi: 10.2307/2490859.