Consideration of Uneven Misclassification Cost and Group Size for Bankruptcy Prediction


Despite a larger number of approaches developed for predicting bankruptcy over the past three decades, rare research has considered the effects of misclassification cost and group size. Uneven cost of misclassification results from Type I (misclassify a healthy company as a failure) and Type II errors (misclassify a failed company as healthy), which are seldom considered. Without accounting for unevenness in misclassification cost, the classifier is developed based on minimizing total misclassification errors to improve the hit-ratio for classification performance. This not only results in poor decision capability, but also causes bias towards the larger group. This paper explores the issues of uneven misclassification costs and imbalanced group size by applying an asymmetric-stratified data envelopment analysis to bankruptcy prediction. The results show a tradeoff between hit-ratio and misclassification cost when Type II error cost is ten times over that of Type I, that is, the higher the hit-ratio is, the greater the resulting misclassification costs are. By incorporating different proportions of Type II error costs to Type I into the classification procedures, the proposed approach provides greater flexibility to decision makers for credit evaluation or bankruptcy prediction based on different risk attitudes and situations.


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Y. Kuo, "Consideration of Uneven Misclassification Cost and Group Size for Bankruptcy Prediction," American Journal of Industrial and Business Management, Vol. 3 No. 8, 2013, pp. 708-714. doi: 10.4236/ajibm.2013.38080.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] E. I. Altman, “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy,” The Journal of Finance, Vol. 23, No. 4, 1968, pp. 589-609.
[2] A. I. Dimitras, C. Zopounidis and C. Hurson, “A Multicriteria Decision Aid Method for the Assessment of Business Failure Risk,” Foundations of Computing and Decision Sciences, Vol. 20, No. 2, 1995, pp. 99-112.
[3] A. I. Dimitras, S. H. Zanakis and C. Zopounidis, “A Survey of Business Failures with an Emphasis on Prediction Methods and Industrial Applications,” European Journal of Operation Research, Vol. 90, No. 3, 1996, pp. 487-513.
[4] B. E. Deakin, “A Discriminant Analysis of Predictors of Business Failure,” Journal of Accounting Research Spring, Vol. 10, No. 1, 1976, pp.167-179.
[5] M. D. Troutt, A. Rai and A. Zhang, “The Potential Use of DEA for Credit Applicant Acceptance Systems,” Computers Operations Research, Vol. 23, No. 4, 1996, pp. 405-408.
[6] D. Retzlaff-Roberts and R. Puelz, “Classification in Automobile Insurance Using a DEA and Discriminant Analysis Hybrid,” Journal of Productivity Analysis, Vol. 7, No. 4, 1996, pp. 417-427.
[7] L. M. Seiford and J. Zhu, “An Acceptance System Decision Rule with Data Envelopment Analysis,” Computers Operations Research, Vol. 25, No. 4, 1998, pp. 329-332.
[8] P. C. Pendharkar, “A Potential Use of Data Envelopment Analysis for the Inverse Classification Problem,” Omega, Vol. 30, No. 3, 2002, pp. 243-248.
[9] A. Cielen, L. Peeters and K. Vanhoof, “Bankruptcy Prediction Using a Data Envelopment Analysis,” European Journal of Operational Research, Vol. 154, No. 2, 2004, pp. 526-532.
[10] J. C. Paradi, M. Asmild and P. C. Simak, “Using DEA and Worst Practice DEA in Credit Risk Evaluation,” Journal of Productivity Analysis, Vol. 21, No. 2, 2004, pp. 153-165.
[11] T. Sueyoshi and M. Goto, “DEA-Discriminant Analysis: Methodological Comparison Among Eight Discriminant Analysis Approaches,” European Journal of Operational Research, Vol. 169, No. 1, 2006, pp. 247-272.
[12] T. Sueyoshi and M. Goto, “DEA-DA for BankruptcyBased Performance Assessment: Misclassification Analysis of Japanese Construction Industry,” European Journal of Operational Research, Vol. 199, No. 2, 2009, pp. 576-594.
[13] M. Psillaki, I. E. Tsolas and D. Margaritis, “Evaluation of Credit Risk Based on Firm Performance,” European Journal of Operational Research, Vol. 201, No. 3, 2010, pp. 873-881.
[14] A. Charnes and W. W. Cooper, “Goal Programming and Multiple Objective Optimization,” European Journal of Operational Research, Vol. 1, No. 1, 1977, pp. 39-54.
[15] A. Charnes, W. W. Cooper and E. Rhode, “Measuring the Efficiency of Decision Making Units,” European Journal of Operational Research, Vol. 2, No. 6, 1978, pp. 429-444.
[16] J. Zhu, “Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets and DEA Excel Solver,” Kluwer Academic Publishers, Boston, 2003.
[17] D. S. Chang and Y. I. Kuo, “A Novel Procedure to Identify the Minimized Overlap Boundary of Two Groups by DEA Model,” Lecture Notes in Computer Science, Vol. 3483, 2005, pp. 577-586.
[18] D. S. Chang and Y. I. Kuo, “An Approach for the TwoGroup Discriminant Analysis: An Application of DEA,” Mathematic & Computer Modelling, Vol. 47, No. 9-10, 2008, pp. 970-981.

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