Logit and Probit application for the prediction of bankruptcy in Slovak companies
DOI:
https://doi.org/10.24136/eq.v12i4.40Keywords:
bankruptcy, logit, probit, Slovak companies, financial healthAbstract
Research background: Prediction of bankruptcy is an issue of interest of various researchers and practitioners since the first study dedicated to this topic was published in 1932. Finding the suitable bankruptcy prediction model is the task for economists and analysts from all over the world. forecasting model using. Despite a large number of various models, which have been created by using different methods with the aim to achieve the best results, it is still challenging to predict bankruptcy risk, as corporations have become more global and more complex.
Purpose of the article: The aim of the presented study is to construct, via an empirical study of relevant literature and application of suitable chosen mathematical statistical methods, models for bankruptcy prediction of Slovak companies and provide the comparison of overall prediction ability of the two developed models.
Methods: The research was conducted on the data set of Slovak corporations covering the period of the year 2015, and two mathematical statistical methods were applied. The methods are logit and probit, which are both symmetric binary choice models, also known as conditional probability models. On the other hand, these methods show some significant differences in process of model formation, as well as in achieved results.
Findings & Value added: Given the fact that mostly discriminant analysis and logistic regression are used for the construction of bankruptcy prediction models, we have focused our attention on the development bankruptcy prediction model in the Slovak Republic via logistic regression and probit. The results of the study suggest that the model based on a logit functions slightly outperforms the classification accuracy of probit model. Differences were obtained also in the detection of the most significant predictors of bankruptcy prediction in these types of models constructed in Slovak companies.
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References
Alexy, M. (2015). Financial health of cities in Slovakia. In Proceedings of 17th International scientific conference Finance and Risk, Bratislava.
Agrawal, K., & Maheshwari, Y. (2016). Predicting financial distress: revisiting the option-based model. South Asian Journal of Global Business Research, 5(2), doi: 10.1108/SAJGBR-04-2015-0030
Antonowicz, P. (2014). The multi-dimensional structural analysis of bankruptcy of enterprises in Poland in 2013 ? results of empirical studies. Journal of Interna-tional Studies, 7(1) doi: 10.14254/2071-8330.2014/7-1/3.
Archer, K. J., & Lemeshow, S. (2006). Goodness-of-fit test for a logistic regression model fitted using survey sample data. Stata Journal, 6(1).
Bewick, V., Cheek, L., & Ball, J. (2005). Statistics review 14: logistic regression. Critical Care, 9(1).
Boratyńska, K. (2014). The theoretical aspects of measuring the costs of corporate bankruptcy. Equilibrium. Quarterly Journal of Economics and Economic Poli-cy, 9(3). doi: 10.12775/EQUIL.2014.017.
Boratyńska, K. (2016). Corporate bankruptcy and survival on the market: lessons from evolutionary economics. Oeconomia Copernicana, 7(1). doi: 12775/OeC. 2016.008.
Brada, J. C. (1993). The comparative economics of bankruptcy ? dealing with loss-making firms in capitalist, socialist, and transitional economies. Eastern European Economics, 31(4).
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).
Delina, R., & Packova, M. (2013). Prediction bankruptcy models validation in Slovak business environment. E & M Ekonomie a management, 16(3).
Dixon, Ch. (2016). Why the global financial crisis had so little impact on the bank-ing systems of emergent East Asia. Journal of Self-Governance and Manage-ment Economics, 4(2).
Chrastinova, Z. (1998). Metódy hodnotenia ekonomickej bonity a predikcie fi-nančnej situácie poľnohospodárskych podnikov. Bratislava: VÚEPP. 34.
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8). doi: 10.1016/j.patrec.2005.10.010.
Fitzpatrick, P. (1932). A comparison of ratios of successful industrial enterprises with those of failed firms. Certified Public Accountant, 2.
Gurcik, L. (2002). G-index ? the financial situation prognosis method of agricul-tural enterprises. Agricultural Economics, 48(8).
Hebak, J. (2015). Statisticke mysleni a nastroje analyzy dat. Prague: Informatori-um.
Hosmer, D. W., Hosmer, T., LeCessie, S., & Lemeshow, S. (1997). A comparison of goodness-of-fit tests for the logistic regression model. Statistics in Medicine, 16(9). doi: 10.1002/(SICI)1097-0258(19970515)16:9%3C965::AID-SIM509% 3E3.0.CO;2-O.
Hu, B., Palta, M., & Shao, J. (2006). Properties of R-2 statistics for logistic regres-sion. Statistics in Medicine, 25(8).
Hwang, R. C., Chung, H. M., & Chu, C. K. (2010). Predicting issuer credit ratings using a semiparametric method. Journal of Empirical Finance, 17(1). doi: 10.1016/j.jempfin.2009.07.007.
Jones, S., Johnstone, D:, & Wilson, R. (2015). An empirical evaluation of the per-formance of binary classifiers in the prediction of credit ratings changes. Jour-nal of Banking & Finance, 56.
Kacer, M., & Alexy, M. (2015). Modelling of the default of cities in Slovakia. In Proceedings of 10th international scientific conference on financial manage-ment of firms and financial institutions. Ostrava.
Karan, M. B., Arslan, O., & Alatli, M. (2009). Detection of factors leading to busi-ness failure for petroleum filling stations in Turkey. Iktisat Isletme ve Finans, 24.
Kliestik, T., & Majerova, J. (2015). Selected issues of selection of significant vari-ables in the prediction models. In Proceedings of 10th international scientific conference financial management of firms and financial institutions. Ostrava.
Meloun, M., & Militky, J. (2012). Interaktivni statisticka analyza dat. Prague: Karolinum.
Menard, S. (2000). Coefficients of determination for multiple logistic regression analysis. American Statistician, 54(1). doi: 10.2307/ 2685605.
Mendelova, V., & Bielikova, T. (2017). Diagnosing of the corporate financial health using DEA: an application to companies in the Slovak Republic. Poli-ticka Ekonomie, 65(1).
Mihalovic, M. (2016). Performance comparison of multiple discriminant analysis and Logit models in bankruptcy prediction. Economics and Sociology, 9(4). doi: 10.14254/2071-789X.2016/9-4/6.
Ravi Kumar, P., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques ? A review European. Journal of Opera-tional Research, 180(1).
Rowland Z., Dvorakova L., & Rousek P. (2016). Determination of bankruptcy model of transport companies in the Czech Republic by using multiple discri-minant analysis. Ekonomicko-manazerske spektrum, 10(1).
Rybarova, D., Braunova, M., & Jantosova, L. (2016). Analysis of the construction industry in the Slovak Republic by bankruptcy model. Procedia Social and Behavioral Sciences, 230.
Schonfelder, B. (2003). Debt collection and bankruptcies in Slovakia: a study of institutional development. Post-Communist Economies, 15(2).
Spuchlakova, E., & Michalikova-Frajtova, K. (2016). The selected global bank-ruptcy model. In Proceedings of international scientific conference globaliza-tion and its socio-economic consequences 2016. Rajecke Teplice.
Svabova, L., & Durica, M. (2016). Korelačná analýza prediktorov použitých v bankrotných predikčných modeloch na Slovensku. Ekonomicko-manazerske spektrum, 10(1).
Svabova, L., & Kral, P. (2016). Selection of predictors in bankruptcy prediction models for Slovak companies. In Proceedings of 10th international days of statistics and economics. Prague.
Szetela, B., Mentel, G., & Brozyna, J. (2016). In search of insolvency among Eu-ropean countries. Economic Research ? Ekonomska Istrazivanja, 29(1). doi: 10.1080/1331677X.2016.1237301.
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.
