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DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment

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  • Premachandra, I.M.
  • Chen, Yao
  • Watson, John

Abstract

Using an additive super-efficiency data envelopment analysis (DEA) model, this paper develops a new assessment index based on two frontiers for predicting corporate failure and success. The proposed approach is applied to a random sample of 1001 firms, which is composed of 50 large US bankrupt firms randomly selected from Altman's bankruptcy database and 901 healthy matching firms. This sample represents the largest firms that went bankrupt over the period 1991-2004 and represents a full spectrum of industries. Our findings demonstrate that the DEA model is relatively weak in predicting corporate failures compared to healthy firm predictions, and the assessment index improves this weakness by giving the decision maker various options to achieve different precision levels of bankrupt, non-bankrupt, and total predictions.

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Bibliographic Info

Article provided by Elsevier in its journal Omega.

Volume (Year): 39 (2011)
Issue (Month): 6 (December)
Pages: 620-626

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Handle: RePEc:eee:jomega:v:39:y:2011:i:6:p:620-626

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Keywords: Data envelopment analysis (DEA) Bankruptcy Corporate failure Corporate success Bankruptcy;

References

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  1. 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.
  2. Avkiran, Necmi K., 2011. "Association of DEA super-efficiency estimates with financial ratios: Investigating the case for Chinese banks," Omega, Elsevier, vol. 39(3), pages 323-334, June.
  3. Warner, Jerold B, 1977. "Bankruptcy Costs: Some Evidence," Journal of Finance, American Finance Association, vol. 32(2), pages 337-47, May.
  4. Yuliya Demyanyk & Iftekhar Hasan, 2009. "Financial crises and bank failures: a review of prediction methods," Working Paper 0904, Federal Reserve Bank of Cleveland.
  5. 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.
  6. Collins, Robert A. & Green, Richard D., 1982. "Statistical methods for bankruptcy forecasting," Journal of Economics and Business, Elsevier, vol. 34(4), pages 349-354.
  7. 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 323-45, December.
  8. 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.
  9. Charnes, A. & Cooper, W. W. & Seiford, L. & Stutz, J., 1982. "A multiplicative model for efficiency analysis," Socio-Economic Planning Sciences, Elsevier, vol. 16(5), pages 223-224.
  10. 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.
  11. 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.
  12. Altman, Edward I, 1984. " A Further Empirical Investigation of the Bankruptcy Cost Question," Journal of Finance, American Finance Association, vol. 39(4), pages 1067-89, September.
  13. 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.
  14. Tam, KY, 1991. "Neural network models and the prediction of bank bankruptcy," Omega, Elsevier, vol. 19(5), pages 429-445.
  15. Du, Juan & Liang, Liang & Zhu, Joe, 2010. "A slacks-based measure of super-efficiency in data envelopment analysis: A comment," European Journal of Operational Research, Elsevier, vol. 204(3), pages 694-697, August.
  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, 09.
  17. Shanmugam, Ramalingam & Johnson, Charles, 2007. "At a crossroad of data envelopment and principal component analyses," Omega, Elsevier, vol. 35(4), pages 351-364, August.
  18. Molinero, C Mar & Ezzamel, M, 1991. "Multidimensional scaling applied to corporate failure," Omega, Elsevier, vol. 19(4), pages 259-274.
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Citations

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Cited by:
  1. Carlos Serrano-Cinca & Y. Fuertes-Callén & Begoña Gutiérrez-Nieto & B. Cuéllar-Fernández, 2011. "Path modeling to bankruptcy: causes and symptoms of the banking crisis," Working Papers CEB 11-007, ULB -- Universite Libre de Bruxelles.
  2. Chen, Yao & Du, Juan & Huo, Jiazhen, 2013. "Super-efficiency based on a modified directional distance function," Omega, Elsevier, vol. 41(3), pages 621-625.
  3. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min & Lin, Bruce J.Y., 2013. "A survey of DEA applications," Omega, Elsevier, vol. 41(5), pages 893-902.
  4. Sahoo, Biresh K. & Tone, Kaoru, 2013. "Non-parametric measurement of economies of scale and scope in non-competitive environment with price uncertainty," Omega, Elsevier, vol. 41(1), pages 97-111.
  5. Edelstein, Barak & Paradi, Joseph C., 2013. "Ensuring units invariant slack selection in radial data envelopment analysis models, and incorporating slacks into an overall efficiency score," Omega, Elsevier, vol. 41(1), pages 31-40.

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