Abstract
Business failure prediction is one of the most essential problems in the field of finance. The research on developing business failure prediction models has been focused on building classification models to distinguish among failed and non—failed firms. Such models are of major importance to financial decision makers (credit managers, managers of firms, investors, etc.); they serve as early warning systems of the failure probability of a corporate entity. The significance of business failure prediction models has been a major motivation for researchers to develop efficient approaches for the development of such models. This paper considers several such approaches, including multicriteria decision aid (MCDA) techniques, linear programming and performs a thorough comparison to traditional statistical techniques such as linear discriminant analysis and logit analysis. The comparison is performed using a sample of 144 US firms for a period of up to five years prior to failure.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Altman, E.I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance. Vol. 23, 589–609.
Altman, E.I. (1993). Corporate Financial Distress and Bankruptcy. John Wiley and Sons, New York.
Altman E.I., Hadelman, R.G. and Narayanan, P. (1977). Zeta Analysis: A New Model to Identify Bankruptcy Risk of Corporations. Journal of Banking and Finance. Vol. 1, 29–51.
Beaver, W.H. (1966). Financial Ratios as Predictors of Failure. Empirical Research in Accounting: Selected Studies. Supplement to Journal of Accounting Research. Vol. 5, 179–199.
Boritz, J.E. and Kennedy, D.B. (1995). Effectiveness of Neural Network Types for Prediction of Business Failure. Expert Systems with Applications. Vol. 9(4), 503–512.
Casey, M., McGee, V. and Stinkey, C. (1986). Discriminating Between Reorganized and Liquidated Firms in Bankruptcy. The Accounting Review. April, 249–262.
Dimitras, A.I., Zopounidis, C. and Hurson, C. (1995). A Multicriteria Decision Aid Method for the Assessment of Business Failure Risk. Foundations of Computing and Decision Sciences. Vol. 20(2), 99–112.
Dimitras, A.I., Zanakis, S.H. and Zopounidis, C. (1996). A Survey of Business Failures with an Emphasis on Prediction Methods and Industrial Applications. European Journal of Operational Research. Vol. 90(3), 487–513.
Dimitras, A.I., Slowinski, R., Susmaga, R. and Zopounidis, C. (1999). Business Failure Prediction Using Rough Sets. European Journal of Operational Research. Vol. 114(2), 263–280.
Duda, R.O., Hart, P.E. and Stork, D.G. (2001). Pattern Classification. John Wiley, New York.
Efron, B. (1983). Estimating the Error Rate of a Prediction Rule: Improvement of Cross-validation. Journal of the American Statistical Association. Vol. 78. 316- 330.
Efron, B. and Tibshirani, R. (1993). An introduction to the bootstrap. Chapman & Hall, New York.
Frydman, H., Altman, E.I. and Kao, D.L. (1985). Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress. Journal of Finance. Vol. XL(1), 269–291.
Gehrlein, W.V. and Wagner, B.J. (1997). Nontraditional Approaches to the Statistical Classification and Regression Problems. Special Issue in Annals of Operations Research. Vol. 74.
Gochet, W., Stam, A., Srinivasan, V. and Chen, S. (1997). Multigroup Discriminant Analysis Using Linear Programming. Operations Research. Vol. 45(2), 213–225.
Gupta, M.C. and Huefner, R.J. (1972). A Cluster Analysis Study of Financial Ratios and Industry Characteristics. Journal of Accounting Research. Spring, 77–95.
Gupta, Y.P., Rao, R.P. and Bagghi, P.K. (1990). Linear Goal Programming as an Alternative to Multivariate Discriminant Analysis: A Note. Journal of Business Finance and Accounting. Vol. 17(4), 593–598.
Jacquet-Lagrèze, E. and Siskos, Y. (1982). Assessing a Set of Additive Utility Functions for Multicriteria Decision Making: The UTA Method. European Journal of Operational Research. Vol. 10, 151–164.
Keeney, R.L. and Raiffa, H. (1993). Decisions with Multiple Objectives: Preferences and Value Trade-offs. Cambridge University Press, Cambridge.
Keasey, K. and Watson, R. (1991). Financial Distress Prediction Models: A Review of their Usefulness. British Journal of Management. Vol. 2, 89–102.
Keasey, K., McGuinness, P. and Short, H. (1990). Multilogit Approach to Predicting Corporate Failure: Further Analysis and the Issue of Signal Consistency. Omega. Vol. 18(1), 85–94.
Laitinen, E.K. (1992). Prediction of Failure of a Newly Founded Firm. Journal of Business Venturing. Vol. 7, 323–340.
Luoma, M. and Laitinen, E.K. (1991). Survival Analysis as a Tool for Company Failure Prediction. Omega. Vol. 19(6), 673–678.
Messier, W.F. and Hansen, J.V. (1988). Inducing Rules for Expert System Development: An Example Using Default and Bankruptcy Data. Management Science. Vol. 34(12), 1403–1415.
Ohlson, J.A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research. Vol. 18, 109–131.
Scapens, R.W., Ryan, R.J. and Flecher, L. (1981). Explaining Corporate Failure: A Catastrophe Theory Approach. Journal of Business Finance and Accounting. Vol. 8(1), 1–26.
Slowinski, R. and Zopounidis, C. (1995). Application of the Rough Set Approach to Evaluation of Bankruptcy Risk. International Journal of Intelligent Systems in Accounting, Finance and Management. Vol. 4, 27–41.
Theodossiou, P., Kahya, E., Saidi, R. and Philippatos, G. (1996). Financial Distress and Corporate Acquisitions: Further Empirical Evidence. Journal of Business Finance and Accounting. Vol. 23(5–6), 699–719.
Vranas, A.S. (1992). The Significance of Financial Characteristics in Predicting Business Failure: An Analysis in the Greek Context. Foundations of Computing and Decision Sciences. Vol. 17(4), 257–275.
Zavgren, C.V. (1985). Assessing the Vulnerability to Failure of American Industrial Firms: A Logistic Analysis. Journal of Business Finance and Accounting. Vol. 12(1), 19–45.
Zmijewski, M.E. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Studies on current Econometric Issues in Accounting Research, 59–82.
Zopounidis, C. (1987). A Multicriteria Decision Making Methodology for the Evaluation of the Risk of Failure and an Application. Foundations of Control Engineering. Vol. 12(1), 45–67.
Zopounidis, C. (1995). Evaluation du Risque de Défaillance de l’Entreprise: Méthodes et Cas d’Application. Economica, Paris.
Zopounidis, C. and Dimitras, A.I. (1998). Multicriteria Decision Aid Methods for the Prediction of Business Failure. Kluwer Academic Publishers, Dordrecht.
Zopounidis, C. and Doumpos, M. (1999a). Business Failure Prediction Using UTADIS Multicriteria Analysis. Journal of the Operational Research Society. Vol. 50(11), 1138–1148.
Zopounidis, C. and Doumpos, M. (1999b). A Multicriteria Decision Aid Methodology for Sorting Decision Problems: The Case of Financial Distress. Computational Economics. Vol. 14(3), 197–218.
Zopounidis, C. and Doumpos, M. (2000). Building Additive Utilities for Multi-group Hierarchical Discrimination: The M.H.DIS Method. Optimization Methods and Software. Vol. 14(3), 219–240.
Zopounidis, C. and Doumpos, M. (2002). Multicriteria Classification and Sorting Methods: A Literature Review. European Journal of Operational Research. Vol. 138(2), 229–246.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Doumpos, M., Zopounidis, C. Business failure prediction: A comparison of classification methods. Oper Res Int J 2, 303–319 (2002). https://doi.org/10.1007/BF02936387
Issue Date:
DOI: https://doi.org/10.1007/BF02936387