Journal of Productivity Analysis

, Volume 21, Issue 2, pp 153–165 | Cite as

Using DEA and Worst Practice DEA in Credit Risk Evaluation

  • Joseph C. Paradi
  • Mette Asmild
  • Paul C. Simak
Article

Abstract

The purpose of this paper is to introduce the concept of worst practice DEA, which aims at identifying worst performers by placing them on the frontier. This is particularly relevant for our application to credit risk evaluation, but this also has general relevance since the worst performers are where the largest improvement potential can be found. The paper also proposes to use a layering technique instead of the traditional cut-off point approach, since this enables incorporation of risk attitudes and risk-based pricing. Finally, it is shown how the use of a combination of normal and worst practice DEA models enable detection of self-identifiers. The results of the empirical application on credit risk evaluation validate the method. The best combination of layered normal and worst practice DEA models yields an impressive 100% bankruptcy and 78% non-bankruptcy prediction accuracy in the calibration data set, and equally convincing 100% and 67% out-of-sample classification accuracies.

data envelopment analysis credit risk worst practice DEA layering or peeling technique 

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References

  1. Ali, A. I. and L. M. Seiford. (1990). “Translation Invariance in Data Envelopment Analysis.” Journal of Operations Research 9(99), 403–405.Google Scholar
  2. Altman, E. (1993). Corporate Financial Distress and Bankruptcy. Second edition, John Wiley & Sons, 1993.Google Scholar
  3. Barr, R. S., L. M. Seiford and T. F. Siems. (1993). “An Envelopment-Analysis Approach to Measuring the Managerial Efficiency of Banks.” Annals of Operations Research 45, 1–19.Google Scholar
  4. Barr, R. S., L. M. Seiford and T. F. Siems. (1994). “Forcasting Bank Failure: A Non-Parametric Frontier Estimation Approach.” Recherches Economiques de Louvain 60(4), 417–429.Google Scholar
  5. Beaver, W. (1967). “Financial Ratios as Predictors of Failure.” Empirical Research in Accounting, 1996 Supplement to Journal of Accounting Research, 1967, 71–111.Google Scholar
  6. Charnes, A., W. W. Cooper and E. Rhodes. (1978). “Measuring the Efficiency of Decision Making Units.” European Journal of Operational Research 2(6), 429–444.Google Scholar
  7. Deakin, E. B. (1972). “A Discriminant Analysis of Predictors of Business Failure.” Journal of Accounting Research, Spring 1972, 167–179.Google Scholar
  8. Divine, J. D. (1986). “Efficiency Analysis and Management of Not for Profit and Governmentally Regulated Organizations.” Ph.D. dissertation, Graduate School of Business, University of Texas, Austin.Google Scholar
  9. Edminster, R. O. (1972). “An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction.” Journal of Finance and Quantitative Analysis March 1992, 1477–1498.Google Scholar
  10. Falkenstein, E. G. (1998). “Commercial Credit Risk and the C Score.” http:/www.efalken.com/banking/default.htmGoogle Scholar
  11. Farrell, M. J. (1957). “The Measurement of Productive Efficiency.” Journal of the Royal Statistical Society 120(3), 253–281.Google Scholar
  12. Hull, J. (1998). “Financial Risk Management.” Course Notes, University of Toronto, MGT2315, Winter 1998.Google Scholar
  13. Paradi, J. C., M. Asmild and P. C. Simak. (2001). “DEA Based Analysis of Corporate Failure.” Working Paper, CMTE, Department of Chemical Engineering, University of Toronto.Google Scholar
  14. Paradi, J. C. and R. Chehade. (1999). “Mutual Fund Performance Using DEA.” Working Paper, CMTE, Department of Chemical Engineering, University of Toronto.Google Scholar
  15. Seballos, L. D. and J. B. Thompson. (1990). “Understanding Causes of Commercial Bank Failures in the 1980s.” Economic Commentary, Federal Reserve Bank of Cleveland, September.Google Scholar
  16. Thanassoulis, E. (1999). “Setting Achievements Targets for School Children.” Education Economics 7(2), 101–119.Google Scholar
  17. Ward, T. (1995). “Using Information from the Statement of Cash Flows to Predict Insolvency.” The Journal of Commercial Lending 77(7), March 1995, 29–34.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Joseph C. Paradi
    • 1
  • Mette Asmild
    • 2
  • Paul C. Simak
    • 3
  1. 1.Department of Chemical Engineering and Applied ChemistryCMTE, University of TorontoTorontoCanada
  2. 2.CMTE, University of TorontoCanada
  3. 3.McKinsey & CompanyCanada

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