Obvious and hidden features of corporate default in bankruptcy models
Abstract
The aim of this article is to prove the key role of the structure of the research sample used for accuracy determining on the accuracy of bankruptcy models. The creators of these models report the accuracy usually in the range of 60 to 90%. The authors of this article claim that these values are inaccurate and misleading. The real I. type error should be detected on a sample where obvious features of financial default were eliminated. The research tested more than 1200 of thriving businesses and also 270 businesses in future bankruptcy. The research has determined real current accuracy of selected three bankruptcy models on the standard sample of Czech businesses amounting 67.77%, 62.27% and 74.36%. This confirmed hypothesis no. 1, which says that actual accuracy of bankruptcy model is lower than original accuracy indicated by model makers. An accuracy of 58.70%, 61.59% and 65.94% was measured on a sample where businesses with obvious features of financial distress were eliminated. Due to the modification of the test sample, the order of accuracy has changed. This confirmed hypothesis no. 2. The Index of Karas and Reznakova reached the highest overall accuracy of 80.31% including incorrect prediction of bankruptcy also.
Keyword : corporate default, bankrupt models, financial distress, prediction accuracy
How to Cite
Kuběnka, M., & Myšková, R. (2019). Obvious and hidden features of corporate default in bankruptcy models. Journal of Business Economics and Management, 20(2), 368-383. https://doi.org/10.3846/jbem.2019.9612
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Act No. 182/2006. Coll., on bankruptcy and methods of its settlement. Czech Republic.
Act No. 328/1991. Coll., on bankruptcy and composition. Czech Republic.
Alaka, H. A., Oyedele, L. O., Owolabi, H. O., Oyedele, A. O., Akinade, O. O., Bilal, M., & Ajayi, S. O. (2017). Critical factors for insolvency prediction: towards a theoretical model for the construction industry. International Journal of Construction Management, 17(1), 25-49. https://doi.org/10.1080/15623599.2016.1166546
Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
Altman, E. I. (1993). Corporate financial distress and bankruptcy: A complete guide to predicting and avoiding distress and profiting from bankruptcy. New York: John Wiley and Sons Inc.
Arroyave, J. (2018). A comparative analysis of the effectiveness of corporate bankruptcy prediction models based on financial ratios: Evidence from Colombia. Journal of International Studies, 11(1), 273-287. https://doi.org/10.14254/2071-8330.2018/11-1/21
Beerman, K. (1976). Possible ways to predict capital losses with annual financial statements (Working Paper). University of Düsseldorf.
Berzkalne, I., & Zelgalve, E. (2013). Bankruptcy Prediction Models: A Comparative Study of the Baltic Listed Companies. Journal of Business Management, 7, 72-82.
Bisnode MagnusWeb. (2018). Comprehensive information on companies in the Czech Republic and the Slovak Republic. Bisnode. Retrieved from https://magnusweb.bisnode.cz/udss/htm/?utm_referrer=http%3A%2F%2Fwww.magnusweb.cz%2F
Bowen, R. M., Davis, A. K., & Matsumoto, D. A. (2005). Emphasis on pro forma versus GAAP earnings in quarterly press releases: Determinants, SEC intervention, and market reactions. The Accounting Review, 80(4), 1011-1038. https://doi.org/10.2308/accr.2005.80.4.1011
CreditReform. (2014). Vývoj insolvencí v České republice v roce 2013. Federation of Creditreform Associations. Retrieved from https://www.creditreform.cz/novinky-downloads/vyvoj-insolvenci-v-cr.html
CreditReform. (2016). Vývoj insolvencí v České republice v roce 2015. Federation of Creditreform Associ-ations. Retrieved from https://www.creditreform.cz/novinky-downloads/vyvoj-insolvenci-v-cr.html
CreditReform. (2018a). Corporate insolvencies in Europe. Federation of Creditreform Associations. Retrieved from https://www.creditreform.cz/fileadmin/user_upload/CR-International/local_documents/cz/Presseartikel/analyse_EU-2017-18_english.pdf
CreditReform. (2018b). Vývoj insolvencí v České republice v roce 2017. Federation of Creditreform Associations. Retrieved from https://www.creditreform.cz/ novinky-downloads/vyvoj-insolvenci-v-cr.html
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Delina, R., & Packová, M. (2013). Prediction bankruptcy models validation in Slovak business environment. E&M Economics and Management, 16(3), 101-112.
Dobrovic, J., Lambovska, M., Gallo, P., & Timkova, V. (2018). Non-financial indicators and their importance in small and medium-sized enterprises. Journal of Competitiveness, 10(2), 41-55. https://doi.org/10.7441/joc.2018.02.03
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FitzPatrick, P. (1932). A comparison of ratios of successful industrial enterprises with those of failed companies. The Certified Public Accountant, 6, 727-731.
Gavurova, B., Janke, F., Packova, M., & Pridavok, M. (2017a). Analysis of impact of using the trend variables on bankruptcy prediction models performance. Ekonomicky casopis, 65(4), 370-383.
Gavurova, B., Packova, M., Misankova, M., & Smrcka, L. (2017b). Predictive potential and risks of selected bankruptcy prediction models in the Slovak business environment. Journal of Business Economics and Management, 18(6), 1156-1173. https://doi.org/10.3846/16111699.2017.1400461
Hájek, P., Olej, V., & Myskova, R. (2014). Forecasting corporate financial performance using sentiment in annual reports for stakeholders’ decision-making. Technological and Economic Development of Economy, 20(4), 721-738. https://doi.org/10.3846/20294913.2014.979456
Hornungová, J. (2017). Nonfinancial performance evaluation as significant area of strategic business management. Business: Theory and Practice, 18, 71-78.
Huijuan, L. (2015). Default Prediction Model for SME’s: Evidence from UK Market Using Financial Ratios. International Journal of Business and Management, 10(2), 81-106. https://doi.org/10.5539/ijbm.v10n2p81
Karas, M., & Režňáková, M. (2014). Possibilities for the application of a bankruptcy prediction model for measuring credit risk of a company. In 12th International Conference Proceedings of Hradec Economic Days 2014. Hradec Králové, Czech Republic.
Korol, T. (2013). Early warning models against bankruptcy risk for Central European and Latin American enterprises. Economic Modelling, 31, 22-30. https://doi.org/10.1016/j.econmod.2012.11.017
Kuběnka, M. (2015). Predictive ability of the index of creditworthiness. In 7th International Scientific Conference Finance and Performance of Firms in Science, Education and Practice. Zlín, Czech Republic.
Liang, L., & Wu, D. (2005). An application of pattern recognition on scoring Chinese corporations financial conditions based on backpropagation neural network. Computers and Operation Research, 32(5), 1115-1129. https://doi.org/10.1016/j.cor.2003.09.015
Merkl-Davies, D. M., Brennan, N. M., & McLeay, S. J. (2011). Impression management and retrospective sense-making in corporate narratives: A social psychology perspective. Accounting, Auditing & Accountability Journal, 24(3), 315-344. https://doi.org/10.1108/09513571111124036
Mohelska, H., & Sokolova, M. (2016). Smart, connected products change a company’s business strategy orientation. Applied Economics, 48(47), 4502-4509. https://doi.org/10.1080/00036846.2016.1158924
Neumaierová, I. (2005). Index IN05. In 2nd International Conference Proceedings of European Financial Systems. Brno, Czech Republic.
Neumaierová, I., & Neumaier, I. (2002). Výkonnost a tržní hodnota firmy: Metody, ukazatele, využití v praxi (1st ed.). Praha: Grada Publishing.
Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131. https://doi.org/10.2307/2490395
Pacakova, V., Pardelova, R., Sodomova, E., & Soltes, E. (2003). Statistics for Economists. Bratislava: Iura Edition.
Pendharkar, P. C. (2005). A threshold varying artificial neural network approach for classification and its application to bankruptcy prediction problem. Computers and Operations Research, 32(10), 2561-2582. https://doi.org/10.1016/j.cor.2004.06.023
Rafiei, F. M., Manzari, S. M., & Bostanian, S. (2011). Financial health prediction models using artificial neural networks, genetic algorithm and multivariate discriminant analysis: Iranian evidence. Expert Systems with Applications, 38(8), 10210-10217. https://doi.org/10.1016/j.eswa.2011.02.082
Slavíček, O., & Kuběnka, M. (2016). Bankruptcy prediction models based on the logistic regression for companies in the Czech Republic. In 8th International Scientific Conference Proceedings Managing and Modelling of Financial Risks. Ostrava, Czech Republic.
Smith, R., & Winakor, A. (1935). Changes in financial structure of unsuccessful industrial corporations. Bureau of Business Research (Bulletin 51). Urbana: University of Illinois Press.
Tam, K. (1991). Neural network models and the prediction of bank bankruptcy. Omega, 19(5), 429-445. https://doi.org/10.1016/0305-0483(91)90060-7
Tam, K., & Kiang, M. (1992). Managerial applications of neural networks: The case of bank failure prediction. Management Science, 38(7), 926-947. https://doi.org/10.1287/mnsc.38.7.926
Zavgren, C. V. (1985). Assessing the vulnerability to failure of American industrial firm: A logistic analysis. Journal of Business Finance and Accounting, 12(1), 19-45. https://doi.org/10.1111/j.1468-5957.1985.tb00077.x
Zizlavsky, O. (2016). The Use of financial and nonfinancial measures within innovation management control: Experience and research. Economics & Sociology, 9(4), 41-63. https://doi.org/10.14254/2071-789X.2016/9-4/3
Act No. 328/1991. Coll., on bankruptcy and composition. Czech Republic.
Alaka, H. A., Oyedele, L. O., Owolabi, H. O., Oyedele, A. O., Akinade, O. O., Bilal, M., & Ajayi, S. O. (2017). Critical factors for insolvency prediction: towards a theoretical model for the construction industry. International Journal of Construction Management, 17(1), 25-49. https://doi.org/10.1080/15623599.2016.1166546
Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
Altman, E. I. (1993). Corporate financial distress and bankruptcy: A complete guide to predicting and avoiding distress and profiting from bankruptcy. New York: John Wiley and Sons Inc.
Arroyave, J. (2018). A comparative analysis of the effectiveness of corporate bankruptcy prediction models based on financial ratios: Evidence from Colombia. Journal of International Studies, 11(1), 273-287. https://doi.org/10.14254/2071-8330.2018/11-1/21
Beerman, K. (1976). Possible ways to predict capital losses with annual financial statements (Working Paper). University of Düsseldorf.
Berzkalne, I., & Zelgalve, E. (2013). Bankruptcy Prediction Models: A Comparative Study of the Baltic Listed Companies. Journal of Business Management, 7, 72-82.
Bisnode MagnusWeb. (2018). Comprehensive information on companies in the Czech Republic and the Slovak Republic. Bisnode. Retrieved from https://magnusweb.bisnode.cz/udss/htm/?utm_referrer=http%3A%2F%2Fwww.magnusweb.cz%2F
Bowen, R. M., Davis, A. K., & Matsumoto, D. A. (2005). Emphasis on pro forma versus GAAP earnings in quarterly press releases: Determinants, SEC intervention, and market reactions. The Accounting Review, 80(4), 1011-1038. https://doi.org/10.2308/accr.2005.80.4.1011
CreditReform. (2014). Vývoj insolvencí v České republice v roce 2013. Federation of Creditreform Associations. Retrieved from https://www.creditreform.cz/novinky-downloads/vyvoj-insolvenci-v-cr.html
CreditReform. (2016). Vývoj insolvencí v České republice v roce 2015. Federation of Creditreform Associ-ations. Retrieved from https://www.creditreform.cz/novinky-downloads/vyvoj-insolvenci-v-cr.html
CreditReform. (2018a). Corporate insolvencies in Europe. Federation of Creditreform Associations. Retrieved from https://www.creditreform.cz/fileadmin/user_upload/CR-International/local_documents/cz/Presseartikel/analyse_EU-2017-18_english.pdf
CreditReform. (2018b). Vývoj insolvencí v České republice v roce 2017. Federation of Creditreform Associations. Retrieved from https://www.creditreform.cz/ novinky-downloads/vyvoj-insolvenci-v-cr.html
CZSO. (2018). Podíl aktivních a neaktivních ekonomických subjektů v ČR k 31. 12. 2017. Czech Statistical Office. Retrieved from https://www.czso.cz/csu/czso/cr_od_roku_1989_res
Delina, R., & Packová, M. (2013). Prediction bankruptcy models validation in Slovak business environment. E&M Economics and Management, 16(3), 101-112.
Dobrovic, J., Lambovska, M., Gallo, P., & Timkova, V. (2018). Non-financial indicators and their importance in small and medium-sized enterprises. Journal of Competitiveness, 10(2), 41-55. https://doi.org/10.7441/joc.2018.02.03
EUROSTAT. (2018). Statistical office of the European Union. Retrieved from http://ec.europa.eu/euro-stat/data/statistics-a-z/abc
FitzPatrick, P. (1932). A comparison of ratios of successful industrial enterprises with those of failed companies. The Certified Public Accountant, 6, 727-731.
Gavurova, B., Janke, F., Packova, M., & Pridavok, M. (2017a). Analysis of impact of using the trend variables on bankruptcy prediction models performance. Ekonomicky casopis, 65(4), 370-383.
Gavurova, B., Packova, M., Misankova, M., & Smrcka, L. (2017b). Predictive potential and risks of selected bankruptcy prediction models in the Slovak business environment. Journal of Business Economics and Management, 18(6), 1156-1173. https://doi.org/10.3846/16111699.2017.1400461
Hájek, P., Olej, V., & Myskova, R. (2014). Forecasting corporate financial performance using sentiment in annual reports for stakeholders’ decision-making. Technological and Economic Development of Economy, 20(4), 721-738. https://doi.org/10.3846/20294913.2014.979456
Hornungová, J. (2017). Nonfinancial performance evaluation as significant area of strategic business management. Business: Theory and Practice, 18, 71-78.
Huijuan, L. (2015). Default Prediction Model for SME’s: Evidence from UK Market Using Financial Ratios. International Journal of Business and Management, 10(2), 81-106. https://doi.org/10.5539/ijbm.v10n2p81
Karas, M., & Režňáková, M. (2014). Possibilities for the application of a bankruptcy prediction model for measuring credit risk of a company. In 12th International Conference Proceedings of Hradec Economic Days 2014. Hradec Králové, Czech Republic.
Korol, T. (2013). Early warning models against bankruptcy risk for Central European and Latin American enterprises. Economic Modelling, 31, 22-30. https://doi.org/10.1016/j.econmod.2012.11.017
Kuběnka, M. (2015). Predictive ability of the index of creditworthiness. In 7th International Scientific Conference Finance and Performance of Firms in Science, Education and Practice. Zlín, Czech Republic.
Liang, L., & Wu, D. (2005). An application of pattern recognition on scoring Chinese corporations financial conditions based on backpropagation neural network. Computers and Operation Research, 32(5), 1115-1129. https://doi.org/10.1016/j.cor.2003.09.015
Merkl-Davies, D. M., Brennan, N. M., & McLeay, S. J. (2011). Impression management and retrospective sense-making in corporate narratives: A social psychology perspective. Accounting, Auditing & Accountability Journal, 24(3), 315-344. https://doi.org/10.1108/09513571111124036
Mohelska, H., & Sokolova, M. (2016). Smart, connected products change a company’s business strategy orientation. Applied Economics, 48(47), 4502-4509. https://doi.org/10.1080/00036846.2016.1158924
Neumaierová, I. (2005). Index IN05. In 2nd International Conference Proceedings of European Financial Systems. Brno, Czech Republic.
Neumaierová, I., & Neumaier, I. (2002). Výkonnost a tržní hodnota firmy: Metody, ukazatele, využití v praxi (1st ed.). Praha: Grada Publishing.
Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131. https://doi.org/10.2307/2490395
Pacakova, V., Pardelova, R., Sodomova, E., & Soltes, E. (2003). Statistics for Economists. Bratislava: Iura Edition.
Pendharkar, P. C. (2005). A threshold varying artificial neural network approach for classification and its application to bankruptcy prediction problem. Computers and Operations Research, 32(10), 2561-2582. https://doi.org/10.1016/j.cor.2004.06.023
Rafiei, F. M., Manzari, S. M., & Bostanian, S. (2011). Financial health prediction models using artificial neural networks, genetic algorithm and multivariate discriminant analysis: Iranian evidence. Expert Systems with Applications, 38(8), 10210-10217. https://doi.org/10.1016/j.eswa.2011.02.082
Slavíček, O., & Kuběnka, M. (2016). Bankruptcy prediction models based on the logistic regression for companies in the Czech Republic. In 8th International Scientific Conference Proceedings Managing and Modelling of Financial Risks. Ostrava, Czech Republic.
Smith, R., & Winakor, A. (1935). Changes in financial structure of unsuccessful industrial corporations. Bureau of Business Research (Bulletin 51). Urbana: University of Illinois Press.
Tam, K. (1991). Neural network models and the prediction of bank bankruptcy. Omega, 19(5), 429-445. https://doi.org/10.1016/0305-0483(91)90060-7
Tam, K., & Kiang, M. (1992). Managerial applications of neural networks: The case of bank failure prediction. Management Science, 38(7), 926-947. https://doi.org/10.1287/mnsc.38.7.926
Zavgren, C. V. (1985). Assessing the vulnerability to failure of American industrial firm: A logistic analysis. Journal of Business Finance and Accounting, 12(1), 19-45. https://doi.org/10.1111/j.1468-5957.1985.tb00077.x
Zizlavsky, O. (2016). The Use of financial and nonfinancial measures within innovation management control: Experience and research. Economics & Sociology, 9(4), 41-63. https://doi.org/10.14254/2071-789X.2016/9-4/3