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DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique

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  • Premachandra, I.M.
  • Bhabra, Gurmeet Singh
  • Sueyoshi, Toshiyuki

Abstract

This paper proposes data envelopment analysis (DEA) as a quick-and-easy tool for assessing corporate bankruptcy. DEA is a non-parametric method that measures weight estimates (not parameter estimates) of a classification function for separating default and non-default firms. Using a recent sample of large corporate failures in the United States, we examine the capability of DEA in assessing corporate bankruptcy by comparing it with logistic regression (LR). We find that DEA outperforms LR in evaluating bankruptcy out-of-sample. This feature of DEA is appealing and has practical relevance for investors. Another advantage of DEA over LR is that it does not have assumptions associated with statistical and econometric methods. Furthermore, DEA does not need a large sample size for bankruptcy evaluation, usually required by such statistical and econometric approaches. The need for such a large sample size is a significant disadvantage to practitioners when investment decisions are made using small samples. DEA can bypass such a difficulty related to a sample size. Thus, DEA is a practically appealing method for bankruptcy assessment.

Suggested Citation

  • Premachandra, I.M. & Bhabra, Gurmeet Singh & Sueyoshi, Toshiyuki, 2009. "DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique," European Journal of Operational Research, Elsevier, vol. 193(2), pages 412-424, March.
  • Handle: RePEc:eee:ejores:v:193:y:2009:i:2:p:412-424
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    References listed on IDEAS

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    1. Sueyoshi, Toshiyuki & Sekitani, Kazuyuki, 2007. "The measurement of returns to scale under a simultaneous occurrence of multiple solutions in a reference set and a supporting hyperplane," European Journal of Operational Research, Elsevier, vol. 181(2), pages 549-570, September.
    2. Cooper, W.W. & Huang, Zhimin & Li, Susan X. & Parker, Barnett R. & Pastor, Jesus T., 2007. "Efficiency aggregation with enhanced Russell measures in data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 41(1), pages 1-21, March.
    3. Sueyoshi, Toshiyuki & Sekitani, Kazuyuki, 2005. "Returns to scale in dynamic DEA," European Journal of Operational Research, Elsevier, vol. 161(2), pages 536-544, March.
    4. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    5. Sueyoshi, Toshiyuki & Sekitani, Kazuyuki, 2007. "Measurement of returns to scale using a non-radial DEA model: A range-adjusted measure approach," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1918-1946, February.
    6. Altman, Edward I, 1984. " A Further Empirical Investigation of the Bankruptcy Cost Question," Journal of Finance, American Finance Association, vol. 39(4), pages 1067-1089, September.
    7. Appa, Gautam & Williams, H. Paul, 2006. "A new framework for the solution of DEA models," European Journal of Operational Research, Elsevier, vol. 172(2), pages 604-615, July.
    8. Yang, Z. R. & Platt, Marjorie B. & Platt, Harlan D., 1999. "Probabilistic Neural Networks in Bankruptcy Prediction," Journal of Business Research, Elsevier, vol. 44(2), pages 67-74, February.
    9. Warner, Jerold B, 1977. "Bankruptcy Costs: Some Evidence," Journal of Finance, American Finance Association, vol. 32(2), pages 337-347, May.
    10. Kahya, Emel & Theodossiou, Panayiotis, 1999. "Predicting Corporate Financial Distress: A Time-Series CUSUM Methodology," Review of Quantitative Finance and Accounting, Springer, vol. 13(4), pages 323-345, December.
    11. Altman, Edward I. & Rijken, Herbert A., 2004. "How rating agencies achieve rating stability," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2679-2714, November.
    12. Grice, John Stephen & Ingram, Robert W., 2001. "Tests of the generalizability of Altman's bankruptcy prediction model," Journal of Business Research, Elsevier, vol. 54(1), pages 53-61, October.
    13. Charnes, A. & Cooper, W. W. & Golany, B. & Seiford, L. & Stutz, J., 1985. "Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 91-107.
    14. Sueyoshi, Toshiyuki, 2004. "Mixed integer programming approach of extended DEA-discriminant analysis," European Journal of Operational Research, Elsevier, vol. 152(1), pages 45-55, January.
    15. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    16. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    17. Cielen, Anja & Peeters, Ludo & Vanhoof, Koen, 2004. "Bankruptcy prediction using a data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 154(2), pages 526-532, April.
    18. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    19. Laitinen, Erkki K. & Laitinen, Teija, 2000. "Bankruptcy prediction: Application of the Taylor's expansion in logistic regression," International Review of Financial Analysis, Elsevier, vol. 9(4), pages 327-349.
    20. Asmild, Mette & Paradi, Joseph C. & Reese, David N., 2006. "Theoretical perspectives of trade-off analysis using DEA," Omega, Elsevier, vol. 34(4), pages 337-343, August.
    21. Sueyoshi, Toshiyuki, 2006. "DEA-Discriminant Analysis: Methodological comparison among eight discriminant analysis approaches," European Journal of Operational Research, Elsevier, vol. 169(1), pages 247-272, February.
    22. repec:bla:joares:v:18:y:1980:i:1:p:109-131 is not listed on IDEAS
    23. Johnsen, Thomajean & Melicher, Ronald W., 1994. "Predicting corporate bankruptcy and financial distress: Information value added by multinomial logit models," Journal of Economics and Business, Elsevier, vol. 46(4), pages 269-286, October.
    24. Kao, Chiang & Liu, Shiang-Tai, 2004. "Predicting bank performance with financial forecasts: A case of Taiwan commercial banks," Journal of Banking & Finance, Elsevier, vol. 28(10), pages 2353-2368, October.
    25. Manski, Charles F & Lerman, Steven R, 1977. "The Estimation of Choice Probabilities from Choice Based Samples," Econometrica, Econometric Society, vol. 45(8), pages 1977-1988, November.
    26. Eisenbeis, Robert A, 1977. "Pitfalls in the Application of Discriminant Analysis in Business, Finance, and Economics," Journal of Finance, American Finance Association, vol. 32(3), pages 875-900, June.
    27. Collins, Robert A. & Green, Richard D., 1982. "Statistical methods for bankruptcy forecasting," Journal of Economics and Business, Elsevier, vol. 34(4), pages 349-354.
    28. Lo, Andrew W., 1986. "Logit versus discriminant analysis : A specification test and application to corporate bankruptcies," Journal of Econometrics, Elsevier, vol. 31(2), pages 151-178, March.
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    Cited by:

    1. Llano Monelos Pablo De & Piñeiro Sánchez Carlos & Rodríguez López Manuel, 2014. "DEA as a business failure prediction tool. Application to the case of galician SMEs," Contaduría y Administración, Accounting and Management, vol. 59(2), pages 65-96, abril-jun.
    2. Sueyoshi, Toshiyuki & Goto, Mika, 2009. "DEA-DA for bankruptcy-based performance assessment: Misclassification analysis of Japanese construction industry," European Journal of Operational Research, Elsevier, vol. 199(2), pages 576-594, December.
    3. du Jardin, Philippe, 2012. "The influence of variable selection methods on the accuracy of bankruptcy prediction models," MPRA Paper 44383, University Library of Munich, Germany.
    4. Sueyoshi, Toshiyuki & Goto, Mika, 2009. "Methodological comparison between DEA (data envelopment analysis) and DEA-DA (discriminant analysis) from the perspective of bankruptcy assessment," European Journal of Operational Research, Elsevier, vol. 199(2), pages 561-575, December.
    5. repec:spr:fininn:v:2:y:2016:i:1:d:10.1186_s40854-016-0026-9 is not listed on IDEAS
    6. Sevim, Cuneyt & Oztekin, Asil & Bali, Ozkan & Gumus, Serkan & Guresen, Erkam, 2014. "Developing an early warning system to predict currency crises," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1095-1104.
    7. repec:eee:spacre:v:15:y:2012:i:1:p:7-58 is not listed on IDEAS
    8. Perko, Igor, 2017. "Behaviour-based short-term invoice probability of default evaluation," European Journal of Operational Research, Elsevier, vol. 257(3), pages 1045-1054.
    9. repec:eee:apmaco:v:251:y:2015:i:c:p:431-441 is not listed on IDEAS
    10. Mousavi, Mohammad M. & Ouenniche, Jamal & Xu, Bing, 2015. "Performance evaluation of bankruptcy prediction models: An orientation-free super-efficiency DEA-based framework," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 64-75.
    11. Qunfeng LIAO & Seyed MEHDIAN, 2016. "Measuring Financial Distress And Predicting Corporate Bankruptcy: An Index Approach," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 17, pages 33-51, June.
    12. Hui Li & Jie Sun, 2010. "Forecasting business failure in China using case-based reasoning with hybrid case respresentation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(5), pages 486-501.
    13. Premachandra, I.M. & Chen, Yao & Watson, John, 2011. "DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment," Omega, Elsevier, vol. 39(6), pages 620-626, December.

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