Bankruptcy prediction using terminal failure processes

European Journal of Operational Research

Volumn 242, Issue 1, 2015, Pages 286-303

  Du Jardin, Philippe ^a  

^a EDHEC BUSINESS SCHOOL   (France)

Author keywords

Bankruptcy prediction; Failure processes; Finance; Forecasting

Indexed keywords

FINANCE; OPERATIONS RESEARCH;

BANKRUPTCY PREDICTION; COMMON MODELS; DESIGN MODELS; FAILURE PROCESS; FINANCIAL HEALTH; PREDICTION ACCURACY; PREDICTION HORIZON; REGRESSION TECHNIQUES;

FORECASTING;

EID: 84920736250     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2014.09.059     Document Type: Article

References (65)

1. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23, 589-609.
2. Altman, E. I. (1984). A further empirical investigation of the bankruptcy cost question. Journal of Finance, 39, 1067-1089.
3. Altman, E. I., Haldeman, R., & Narayanan, P. (1977). Zeta analysis: A new model to identify bankruptcy risk of corporations. Journal of Banking Finance, 1, 29-51.
4. Argenti, J. (1976). Corporate collapse: The causes and symptoms, Halsted Press, New York.
5. Atiya, A. (2001). Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Transactions on Neural Networks, 7, 929-935.
6. Aziz, A., Emanuel, D. C., & Lawson, G. C. (1988). Bankruptcy prediction: An investigation of cash flow based models. Journal of Management Studies, 25, 419-437.
7. Balcaen, S., & Ooghe, H. (2006). 35 years of studies on business failure: An overview of the classical statistical methodologies and their related problems. British Accounting Review, 38, 63-93.
8. Beaver, W. H. (1966). Financial ratios as predictors of failure. Empirical research in accounting, selected studies. Journal of Accounting Research, 4, 71-111.
9. Berg, D. (2007). Bankruptcy prediction by generalized additivemodels. Applied Stochastic Models in Business and Industry, 23, 129-143.
10. Betts, J., & Belhoul, D. (1987). The effectiveness of incorporating stability measures in company failure models. Journal of Business Finance and Accounting, 14, 323-334.
11. Bodt, E. D., Cottrell, M., & Verleysen, M. (2002). Statistical tools to assess the reliability of self-organizing maps. Neural Networks, 15, 967-978.
12. Brabazon, A., & Keenan, P. B. (2004). A hybrid genetic model for the prediction of corporate failure. Computational Management Science, 1, 293-310.
13. Brabazon, A., & O'Neill, M. (2004). Diagnosing corporate stability using grammatical evolution. Journal of Applied Mathematics and Computer Science, 14, 363-374.
14. Charalambous, C., Charitou, A., & Kaourou, F. (2000). Comparative analysis of artificial neural network models: Application in bankruptcy prediction. Annals of Operations Research, 99, 403-425.
15. Charitou, A., Neophytou, E., & Charalambous, C. (2004). Predicting corporate failure: Empirical evidence for the UK. European Accounting Review, 13, 465-497.
16. Cielen, A., Peeters, L., & Vanhoof, K. (2004). Bankruptcy prediction using a data envelopment analysis. European Journal of Operational Research 154, 526-532.
17. D'Aveni, R. A. (1989). The aftermath of organizational decline: A longitudinal study of the strategic andmanagerial characteristics of declining firms. Academy of Management Journal, 32, 577-605.
18. Dakovic, R., & Czado, C., Berg, D. (2010). Bankruptcy prediction in Norway: A comparison study. Applied Economics Letters, 17, 1739-1746.
19. Dambolena, I., & Khoury, S. (1980). Ratio stability and corporate failure. Journal of Finance, 33, 1017-1026.
20. Dash, M., & Liu, H. (1997). Feature selection for classification. Intelligent Data Analysis, 1, 131-156.
21. Deakin, E. B. (1972). A discriminant analysis of predictors of business failures. Journal of Accounting Research, 10, 167-179.
22. Dewaelheyns, N., & Hullem, C. V. (2006). Corporate failure prediction modeling: Distorded by business groups' internal capital markets? Journal of Business Finance and Accounting, 33, 909-931.
23. Dimitras, A. I., Zanakis, S., & Zopounidis, C. (1996). A survey of business failures with an emphasis on prediction methods and industrial applications. European Journal of Operational Research, 90, 487-513.
24. du Jardin, P., & Séverin, E. (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, 701-711.
25. du Jardin, P., & Séverin, E. (2012). Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time. European Journal of Operational Research, 221, 378-396.
26. El Hennawy, R. H., Morris, R. C. (1983). The significance of base year in developing failure prediction models. Journal of Business Finance and Accounting, 10, 209-223.
27. Frydman, H., Altman, E. I., & Kao, D. (1985). Introducing recursive partitioning for financial classification: The case of financial distress. Journal of Finance, 40, 269-291.
28. Gentry, J. A., Newbold, P., & Whitford, D. T. (1985). Classifying bankrupt firms with funds flow components. Journal of Accounting Research, 23, 146-160.
29. Gepp, A., & Kumar, K. (2008). The role of survival analysis in financial distress prediction. International Research Journal of Finance and Economics, 16, 13-34.
30. Grice, J. S., & Dugan, M. T. (2003). Reestimations of the Zmijewski and Ohlson bankruptcy prediction models. Advances in Accounting, 20, 77-93.
31. Hu, Y. C., & Ansell, J. (2007). Measuring retail company performance using credit scoring techniques. European Journal of Operational Research, 183, 1595-1606.
32. Hu, Y. C., & Chen, C. J. (2011). A Promethee-based classification method using concordance and discordance relations and its application to bankruptcy prediction. Information Sciences, 181, 4959-4968.
33. Kohonen, T. (2001). Self-organizing maps. Springer series in information sciences: Vol. 30 (3rd ed.). Springer.
34. Korol, T. (2013). Early warning models against bankruptcy risk for Central European and Latin American enterprises. Economic Modelling, 31, 22-30.
35. 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, 1-28.
36. Laitinen, E. K. (1993). Financial predictors for different phases of the failure process. Omega International Journal of Management Science, 21, 215-228.
37. Laitinen, E. K. (2007). Classification accuracy and correlation: LDA in failure prediction. European Journal of Operational Research, 183, 210-225.
38. Laitinen, E. K., & Laitinen, T. (1998). Cash management behaviour and failure prediction. Journal of Business Finance and Accounting, 25, 893-919.
39. Laitinen, E. K., & Laitinen, T. (2000). Bankruptcy prediction: Application of the Taylor's expansion in logistic regression. International Review of Financial Analysis, 9, 327-349.
40. Laitinen, T. (1991). Financial ratios and different failure processes. Journal of Business Finance and Accounting, 18, 649-673.
41. Lensberg, T., Eilifsen, A., & McKee, T. E. (2006). Bankruptcy theory development and classification via genetic programming. European Journal of Operational Research, 169, 677-697.
42. Leray, P., &Gallinari, P. (1998). Feature selection with neural networks. Behaviormetrika, 26, 145-166.
43. Lin, F., Liang, D., Yeh, C. C., & Huang, J. C. (2014). Novel feature selection methods to financial distress prediction. Expert Systems with Applications, 41, 2472-2483.
44. Mensah, Y. M. (1984). An examination of the stationarity of multivariate bankruptcy prediction models: A methodological study. Journal of Accounting Research, 22, 380-395.
45. Miller, D., & Friesen, P. H. (1977). Strategy-making in context: Ten empirical archetypes. Journal of Management Studies, 14, 253-280.
46. Milligan, G. W. (1981). A Monte-Carlo study of thirty internal criterion measures for cluster analysis. Psychometrika, 46, 187-199.
47. Nam, J. H., & Jinn, T. (2000). Bankruptcy prediction: Evidence from Korean listed companies during the IMF crisis. Journal of International Financial Management and Accounting, 11, 178-197.
48. Neves, J. C., & Vieira, A. (2000). Improving bankruptcy prediction with hidden layer learning vector quantization. European Accounting Review, 15, 253-271.
49. Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18, 109-131.
50. Pinches, G. E., Mingo, K. A., & Caruthers, J. K. (1973). The stability of financial patterns in industrial organizations. Journal of Finance, 28, 389-396.
51. Platt, H. D., & Platt, M. B. (2002). Predicting corporate financial distress: Reflections on choice-based sample bias. Journal of Economics and Finance, 26, 184-199.
52. Platt, H. D., Platt, M. B., & Pedersen, J. G. (1994). Bankruptcy discrimination with real variables. Journal of Business Finance and Accounting, 21, 491-510.
53. Pompe, P. P. M., & Bilderbeek, J. (2005). Bankruptcy prediction: The influence of the year prior to failure selected for model building and the effects in a period of economic decline. Intelligent Systems in Accounting, Finance and Management, 13, 95-112.
54. Psillaki, M., Tsolas, I. E., & Margaritis, D. (2010). Evaluation of credit risk based on firm performance. European Journal of Operational Research, 201, 873-881.
55. Quek, C., Zhou, R. W., & Lee, C. H. (2009). A novel fuzzy neural approach to data reconstruction and failure prediction. Intelligent Systems in Accounting, Finance and Management, 16, 165-187.
56. Scott, J. (1981). The probability of bankruptcy: A comparison of empirical predictions and theoretical models. Journal of Banking and Finance, 5, 317-344.
57. Sueyoshi, T., & Goto, M. (2009). Methodological comparison between DEA (data envelopment analysis) and DEA-DA (discriminant analysis) from the perspective of bankruptcy assessment. European Journal of Operational Research, 199, 561-575.
58. Sun, J., Jia, M. Y., & Li, H. (2011). Adaboost ensemble for financial distress prediction: An empirical comparison with data from Chinese listed companies. Expert Systems with Applications, 38, 9305-9312.
59. Taffler, R. J. (1983). The assessment of company solvency and performance using a statistical model. Accounting and Business Research, 13, 295-307.
60. Wald, A. (1944). On a statistical problem arising in the classification of an individual into one of two groups. Annals of Mathematical Statistics, 15, 145-162.
61. Xiao, Z., Yang, X., Pang, Y., & Dang, X. (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.
62. Zavgren, C. V. (1983). The prediction of corporate failure: The state of the art. Journal of Accounting Literature, 2, 1-38.
63. Zhu, Z., He, H., Starzyk, J. A., & Tseng, C. (2007). Self-organizing learning array and its application to economic and financial problems. Information Sciences, 177, 1180-1192.
64. Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59-82.
65. Zopounidis, C., & Doumpos, M. (2002). Multi-group discrimination using multi-criteria analysis: Illustrations from the field of finance. European Journal of Operational Research, 139, 371-389.