Dynamic Bankruptcy Prediction Models for European Enterprises - Publication - MOST Wiedzy


Dynamic Bankruptcy Prediction Models for European Enterprises


This manuscript is devoted to the issue of forecasting corporate bankruptcy. Determining a firm’s bankruptcy risk is one of the most interesting topics for investors and decision-makers. The aim of the paper is to develop and to evaluate dynamic bankruptcy prediction models for European enterprises. To conduct this objective, four forecasting models are developed with the use of four different methods—fuzzy sets, recurrent and multilayer artificial neural network, and decision trees. Such a research approach will answer the question of whether changes in indicators are relevant predictors of a company’s coming financial crisis because declines or increases in values do not immediately indicate that the company’s economic situation is deteriorating. The research relies on two samples of firms—the learning sample of 50 bankrupt and 50 non-bankrupt enterprises and the testing sample of 250 bankrupt and 250 non-bankrupt firms.


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Journal of Risk and Financial Management no. 12, pages 1 - 15,
ISSN: 1911-8074
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Bibliographic description:
Korol T.: Dynamic Bankruptcy Prediction Models for European Enterprises// Journal of Risk and Financial Management -Vol. 12,iss. 4 (2019), s.1-15
Digital Object Identifier (open in new tab) 10.3390/jrfm12040185
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  7. If X 1 <= 0.015 and X 6 > 0.9 and X 7 <= 0.82 and X 8 <= 1.05 and DX 1 > 70 and DX 8 <= 85 then 0 open in new tab
  8. If X 1 > 0.015 and X 6 <= 0.9 and X 7 <= 0.82 and X 8 <= 1.05 and DX 1 > 70 and DX 8 <= 85 then 0 open in new tab
  9. J. Risk Financial Manag. 2019, 12, 185 open in new tab
  10. In the last stage of the research, the author estimated the decision trees model. The structure of the model is presented in Figure 8. In this model, the following financial ratios were selected: X2 (liquidity ratio), X1 (profitability ratio), X6 (structural ratio), X9 (structural ratio). As can be seen in this model, none of variables representing the change of value of ratios (dynamics) were selected during estimation process of the model. This means it is the only static model in the proposed research. J. Risk Financial Manag. 2019, 12, x FOR PEER REVIEW 11 of 15 open in new tab
  11. References Acosta-González, Eduardo, and Fernando Fernández-Rodríguez. 2014. Forecasting financial failure of firms via genetic algorithms. Computational Economics 43: 133-57. [CrossRef] open in new tab
  12. Agarwal, Vineet, and Richard Taffler. 2007. Twenty-five years of the Taffler z-score model-Does it really have predictive ability? Accounting and Business Research 37: 285-300. [CrossRef] open in new tab
  13. Ahn, Byeong, Sung Cho, and Chang Kim. 2000. The integrated methodology of rough set theory and artificial neural networks for business failure prediction. Expert Systems with Applications 18: 65-74. [CrossRef] open in new tab
  14. Alaka, Hafiz A., Lukumon O. Oyedele, Hakeem A. Owolabi, Vikas Kumar, Saheed O. Ajayi, Olugbenga O. Akinade, and Muhammad Bilal. 2018. Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Systems with Applications 94: 164-84. [CrossRef] open in new tab
  15. Altman, Edward. 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23: 589-609. [CrossRef] open in new tab
  16. Altman, Edward. 2018. Applications of distress prediction models: What have we learned after 50 years from the Z-score models? International Journal of Financial Studies 6: 70. [CrossRef] open in new tab
  17. Altman, Edward, and Herbert Rijken. 2006. A point-in-time perspective on through-the-cycle ratings. Financial Analysts Journal 62: 54-70. [CrossRef] open in new tab
  18. Andres, Javier, Manuel Landajo, and Pedro Lorca. 2005. Forecasting business profitability by using classification techniques: A comparative analysis based on a Spanish case. European Journal of Operational Research 167: 518-42. [CrossRef] open in new tab
  19. Atiya, Amir. 2001. Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Transactions on Neural Networks 12: 929-35. [CrossRef] open in new tab
  20. Balcaen, Sofie, and Hubert Ooghe. 2006. 35 years of studies on business failure: An overview of the classic statistical methodologies and their related problems. The British Accounting Review 38: 63-93. [CrossRef] open in new tab
  21. Bandyopadhyay, Arindam. 2006. Predicting probability of default of Indian corporate bonds: Logistic and Z-score model approaches. The Journal of Risk Finance 7: 255-72. [CrossRef] open in new tab
  22. Barboza, Flavio, Herbert Kimura, and Edward Altman. 2017. Machine learning models and bankruptcy prediction. Expert Systems with Applications 83: 405-17. [CrossRef] open in new tab
  23. Beaver, William H. 1966. Financial ratios as predictors of failure. Journal of Accounting Research 4: 71-111. [CrossRef] open in new tab
  24. Berryman, John. 1992. Small Business Bankruptcy and Failure-A Survey of the Literature. Los Angeles: Small Business Research Institute of Industrial Economics, pp. 1-18. open in new tab
  25. Brabazon, Anthony, and Michael O'Neil. 2004. Diagnosing corporate stability using grammatical evolution. Journal of Applied Mathematics and Computer Science 1: 293-310.
  26. Bradley, Don, and Michael Rubach. 2002. Trade Credit and Small Business-A Cause of Business Failures? Conway: University of Central Arkansas, pp. 1-7.
  27. Callejon, A.M., A.M. Casado, Martina Fernandez, and J.I. Pelaez. 2013. A system of insolvency prediction for industrial companies using a financial alternative model with neural networks. International Journal of Computational Intelligence Systems 6: 29-37. [CrossRef] open in new tab
  28. Chava, Sudheer, and Robert Jarrow. 2004. Bankruptcy prediction with industry effects. Review of Finance 8: 537-69. open in new tab
  29. Cressy, Robert. 2006. Why do most firms die young? Small Business Economics 26: 103-16. [CrossRef] open in new tab
  30. Crone, Sven, and Steven Finlay. 2012. Instance sampling in credit scoring: An empirical study of sample size and balancing. International Journal of Forecasting 28: 224-38. [CrossRef] open in new tab
  31. Davies, David. 1997. The Art of Managing Finance. Lincoln: McGraw-Hill Book Co. open in new tab
  32. Deakin, Edward B. 1972. A discriminant analysis of prediction of business failure. Journal of Accounting Research 3: 167-69. [CrossRef] open in new tab
  33. Delen, Dursun, Cemil Kuzey, and Ali Uyar. 2013. Measuring firm performance using financial ratios: A decision tree approach. Expert Systems with Applications 40: 3970-83. [CrossRef] open in new tab
  34. Dong, Manh Cuong, Shaonan Tian, and Cathy W.S. Chen. 2018. Predicting failure risk using financial ratios: Quantile hazard model approach. North American Journal of Economics and Finance 44: 204-20. [CrossRef] open in new tab
  35. Doumpos, Michalis, and Constantin Zopounidis. 1999. A multinational discrimination method for the prediction of financial distress: the case of Greece. Multinational Finance Journal 3: 71-101. [CrossRef] open in new tab
  36. Doyle, Jeffrey, Weili Geb, and Sarah McVay. 2007. Determinants of weaknesses in internal control over financial reporting. Journal of Accounting and Economics 44: 193-223. [CrossRef] open in new tab
  37. Foster, George. 1986. Financial Statement Analysis, 2nd ed. New York: Prentice Hall.
  38. Ganguin, Blaise, and John Bilardello. 2005. Fundamentals of Corporate Credit Analysis, Standard & Poor's. New York: McGraw-Hill.
  39. Garcia, Vincente, Ana I. Marques, J. Salvador Sanchez, and Humberto Ochoa-Dominguez. 2019. Dissimilarity-Based Linear Models for Corporate Bankruptcy Prediction. Computional Economics 53: 1019-31. [CrossRef] open in new tab
  40. Giannopoulos, George, and Sindre Sigbjornsen. 2019. Prediction of bankruptcy using financial ratios in the Greek market. Theoretical Economics Letters 9: 1114-28. [CrossRef] open in new tab
  41. Grice, John, and Michael Dugan. 2001. The limitations of bankruptcy prediction models-Some cautions for the researcher. Review of Quantitative Finance and Accounting 17: 151-66. [CrossRef] open in new tab
  42. Ho, Chun-Yu, Patrick McCarthy, Yi Yang, and Xuan Ye. 2013. Bankruptcy in the pulp and paper industry: Market's reaction and prediction. Empirical Economics 45: 1205-32. [CrossRef] open in new tab
  43. Hosaka, Tadaaki. 2019. Bankruptcy prediction using imaged financial ratios and convolutional neural networks. Expert Systems with Applications 117: 287-99. [CrossRef] open in new tab
  44. Hosmer, David, Stanley Lemeshow, and Rod X. Sturdivant. 2013. Applied Logistic Regression. Hoboken: John Wiley & Sons. open in new tab
  45. Jackson, Richard H.G., and Anthony Wood. 2013. The performance of insolvency prediction and credit risk models in the UK: A comparative study. The British Accounting Review 45: 183-202. [CrossRef] open in new tab
  46. Jardin, Philippe. 2015. Bankruptcy prediction using terminal failure processes. European Journal of Operational Research 242: 286-303. [CrossRef] open in new tab
  47. Jardin, Philippe. 2016. A two-stage classification technique for bankruptcy prediction. European Journal of Operational Research 254: 236-52. [CrossRef] open in new tab
  48. Jardin, Philippe. 2017. Dynamics of firm financial evolution and bankruptcy prediction. Expert Systems with Applications 75: 25-43. [CrossRef] open in new tab
  49. Jardin, Philippe. 2018. Failure pattern-based ensembles applied to bankruptcy forecasting. Decision Support Systems 107: 64-77. [CrossRef] open in new tab
  50. Jardin, Philippe, and Eric Severin. 2011. Predicting corporate bankruptcy using a self-organizing map-An empirical study to improve the forecasting horizon of a financial failure model. Decision Support Systems 51: 701-11. [CrossRef] open in new tab
  51. Jayasekera, Ranadeva. 2018. Prediction of company failure: Past, present and promising directions for the future. International Review of Financial Analysis 55: 196-208. [CrossRef] open in new tab
  52. Jovanovic, Boyan. 1982. Selection and the evolution of industry. Econometrica 50: 649-70. [CrossRef] open in new tab
  53. Kieschnick, Robert, Mark La Plante, and Rabih Moussawi. 2013. Working capital management and shareholders' wealth. Review of Finance 17: 1827-52. [CrossRef] open in new tab
  54. Kim, Myoung-Jong, and Dae-Ki Kang. 2010. Ensemble with neural networks for bankruptcy prediction. Expert Systems with Applications 37: 3373-79. [CrossRef] open in new tab
  55. Kumar, Pramod, and Vadlamani Ravi. 2007. Bankruptcy prediction in banks and firms via statistical and intelligent techniques-A review. European Journal of Operational Research 180: 1-28. [CrossRef] open in new tab
  56. Laitinen, Erkki K. 2007. Classification accuracy and correlation-LDA in failure prediction. European Journal of Operational Research 183: 210-25. [CrossRef] open in new tab
  57. Lensberg, Terje, Aasmund Eilifsen, and Thomas E. McKee. 2006. Bankruptcy theory development and classification via genetic programming. European Journal of Operational Research 169: 677-97. [CrossRef] open in new tab
  58. Li, Leon, and Robert Faff. 2019. Predicting corporate bankruptcy: What matters? International Review of Economics & Finance 62: 1-19. open in new tab
  59. Liang, Deron, Chia-Chi Lu, Chih-Fong Tsai, and Guan-An Shih. 2016. Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study. European Journal of Operational Research 252: 561-72. [CrossRef] open in new tab
  60. Lin, Fengyi, Deron Liang, Ching-Chiang Yeh, and Jui-Chieh Huang. 2014. Novel feature selection methods to financial distress prediction. Expert Systems with Applications 41: 2472-83. [CrossRef] open in new tab
  61. Lukason, Oliver, and Richard C. Hoffman. 2014. Firm Bankruptcy Probability and Causes: An Integrated Study. International Journal of Business and Management 9: 80-91. [CrossRef] open in new tab
  62. Lyandres, Evgeny, and Alexei Zhdanov. 2013. Investment opportunities and bankruptcy prediction. Journal of Financial Markets 16: 439-76. [CrossRef] open in new tab
  63. Mcleay, Stuart, and Azmi Omar. 2000. The sensitivity of prediction models to the non-normality of bounded and unbounded financial ratios. British Accounting Review 32: 213-30. [CrossRef] open in new tab
  64. Mensah, Yaw. 1984. An examination of the stationarity of multivariate bankruptcy prediction models-A methodological study. Journal of Accounting Research 22: 380-95. [CrossRef] open in new tab
  65. Mihalovic, Matus. 2016. Performance Comparison of Multiple Discriminant Analysis and Logit Models in Bankruptcy Prediction. Economics and Sociology 9: 101-18. [CrossRef] open in new tab
  66. Min, Jae, and Young-Chan Lee. 2005. Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications 28: 603-14. [CrossRef] open in new tab
  67. Nwogugu, Michael. 2007. Decision-making, risk and corporate governance-A critque of methodological issues in bankruptcy/recovery prediction models. Applied Mathematics and Computation 185: 178-96. [CrossRef] open in new tab
  68. Orsenigo, Carlotta, and Carlo Vercellis. 2013. Linear versus nonlinear dimensionality reduction for banks credit rating prediction. Knowledge-Based Systems 47: 14-22. [CrossRef] open in new tab
  69. Pakes, Ariel, and Richard Ericsson. 1998. Empirical implications of alternative models of firm dynamics. Journal of Economic Theory 79: 1-45. [CrossRef] open in new tab
  70. Psillaki, Maria, Ioannis E. Tsolas, and Dimmitris Margaritis. 2010. Evaluation of credit risk based on firm performance. European Journal of Operational Research 201: 873-81. [CrossRef] open in new tab
  71. Ptak-Chmielewska, Aneta. 2019. Predicting Micro-Enterprise Failures Using Data Mining Techniques. Journal of Risk and Financial Managament 12: 30. [CrossRef] open in new tab
  72. Ravisankar, Pediredla, and Vadlamani Ravi. 2010. Financial distress prediction in banks using group method of data handling neural network, counter propagation neural network and fuzzy ARTMAP. Knowledge-Based Systems 23: 823-31. [CrossRef] open in new tab
  73. Succurro, Marianna, Giuseppe Arcuri, and Giuseppina D. Constanzo. 2019. A combined approach based on robust PCA to improve bankruptcy forecasting. Review of Accounting and Finance 18: 296-320. [CrossRef] open in new tab
  74. Sun, Jie, Hui Li, Qing-Hua Huang, and Kai-Yu He. 2014. Predicting financial distress and corporate failure-A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems 57: 41-56. [CrossRef] open in new tab
  75. Tam, Kar Yan. 1991. Neural network models and the prediction of bank bankruptcy. Omega 19: 429-45. [CrossRef] open in new tab
  76. Tian, Shaonan, and Yan Yu. 2017. Financial ratios and bankruptcy predictions: An international evidence. International Review of Economics and Finance 51: 510-26. [CrossRef] open in new tab
  77. Tian, Shaonan, Yan Yu, and Hui Guo. 2015. Variable selection and corporate bankruptcy forecasts. Journal of Banking & Finance 52: 89-100. open in new tab
  78. Tsai, Chih-Fong. 2014. Combining cluster analysis with classifier ensembles to predict financial distress. Information Fusion 16: 46-58. [CrossRef] open in new tab
  79. Watson, John, and Jim Everett. 1999. Small business failure rates-choice of definition and industry effects. International Small Business Journal 17: 33-49. [CrossRef] open in new tab
  80. Wu, Desheng Dash, Yidong Zhang, Dexiang Wu, and David L. Olson. 2010. Fuzzy multi-objective programming for supplier selection and risk modeling: A possibility approach. European Journal of Operational Research 200: 774-87. [CrossRef] open in new tab
  81. Xiao, Zhi, Xianglei Yang, Ying Pang, and Xin Dang. 2012. The prediction for listed companies' financial distress by using multiple prediction methods with rough set and Dempster-Shafer evidence theory. Knowledge-Based Systems 26: 196-206. [CrossRef] open in new tab
  82. Zapranis, Achilleas, and Demetrios Ginoglou. 2000. Forecasting corporate failure with neural network approach: The Greek case. Journal of Financial Management & Analysis 13: 11-21.
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