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Performance evaluation of bankruptcy prediction models: An orientation-free super-efficiency DEA-based framework

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  • Mousavi, Mohammad M.
  • Ouenniche, Jamal
  • Xu, Bing

Abstract

Prediction of corporate failure is one of the major activities in auditing firms risks and uncertainties. The design of reliable models to predict bankruptcy is crucial for many decision making processes. Although a large number of models have been designed to predict bankruptcy, the relative performance evaluation of competing prediction models remains an exercise that is unidimensional in nature, which often leads to reporting conflicting results. In this research, we overcome this methodological issue by proposing an orientation-free super-efficiency data envelopment analysis model as a multi-criteria assessment framework. Furthermore, we perform an exhaustive comparative analysis of the most popular bankruptcy modeling frameworks for UK data including our own models. In addition, we address two important research questions; namely, do some modeling frameworks perform better than others by design? and to what extent the choice and/or the design of explanatory variables and their nature affect the performance of modeling frameworks?, and report on our findings.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:finana:v:42:y:2015:i:c:p:64-75
    DOI: 10.1016/j.irfa.2015.01.006
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    1. 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.
    2. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
    3. Arturo Bris & Ivo Welch & Ning Zhu, 2006. "The Costs of Bankruptcy: Chapter 7 Liquidation versus Chapter 11 Reorganization," Journal of Finance, American Finance Association, vol. 61(3), pages 1253-1303, June.
    4. Lim, Sungmook & Oh, Kwang Wuk & Zhu, Joe, 2014. "Use of DEA cross-efficiency evaluation in portfolio selection: An application to Korean stock market," European Journal of Operational Research, Elsevier, vol. 236(1), pages 361-368.
    5. 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.
    6. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    7. Martin Bemmann, 2005. "Improving the comparability of insolvency predictions," Finance 0506017, University Library of Munich, Germany.
    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. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    10. Joe Zhu, 2014. "DEA Cross Efficiency," International Series in Operations Research & Management Science, in: Quantitative Models for Performance Evaluation and Benchmarking, edition 3, chapter 4, pages 61-92, Springer.
    11. Joseph Paradi & Mette Asmild & Paul Simak, 2004. "Using DEA and Worst Practice DEA in Credit Risk Evaluation," Journal of Productivity Analysis, Springer, vol. 21(2), pages 153-165, March.
    12. Heinz Ahn & Ludmila Neumann, 2014. "Measuring Effectiveness: A DEA Approach Under Predetermined Targets," International Journal of Business Analytics (IJBAN), IGI Global, vol. 1(1), pages 16-28, January.
    13. 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.
    14. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    15. Sergei A. Davydenko & Ilya A. Strebulaev & Xiaofei Zhao, 2012. "A Market-Based Study of the Cost of Default," The Review of Financial Studies, Society for Financial Studies, vol. 25(10), pages 2959-2999.
    16. Agarwal, Vineet & Taffler, Richard, 2008. "Comparing the performance of market-based and accounting-based bankruptcy prediction models," Journal of Banking & Finance, Elsevier, vol. 32(8), pages 1541-1551, August.
    17. Branch, Ben, 2002. "The costs of bankruptcy: A review," International Review of Financial Analysis, Elsevier, vol. 11(1), pages 39-57.
    18. Banker, Rajiv D. & Cooper, William W. & Seiford, Lawrence M. & Thrall, Robert M. & Zhu, Joe, 2004. "Returns to scale in different DEA models," European Journal of Operational Research, Elsevier, vol. 154(2), pages 345-362, April.
    19. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    20. Lozano, Sebastián & Gutiérrez, Ester, 2008. "Data envelopment analysis of mutual funds based on second-order stochastic dominance," European Journal of Operational Research, Elsevier, vol. 189(1), pages 230-244, August.
    21. Kelly Rae Chi, 2010. "A systems approach," Nature, Nature, vol. 464(7291), pages 1090-1091, April.
    22. Taffler, Richard J., 1984. "Empirical models for the monitoring of UK corporations," Journal of Banking & Finance, Elsevier, vol. 8(2), pages 199-227, June.
    23. Arindam Bandyopadhyay, 2006. "Predicting probability of default of Indian corporate bonds: logistic and Z-score model approaches," Journal of Risk Finance, Emerald Group Publishing, vol. 7(3), pages 255-272, May.
    24. Psillaki, Maria & Tsolas, Ioannis E. & Margaritis, Dimitris, 2010. "Evaluation of credit risk based on firm performance," European Journal of Operational Research, Elsevier, vol. 201(3), pages 873-881, March.
    25. Zhang, Jianhua & Jiang, Chunxia & Qu, Baozhi & Wang, Peng, 2013. "Market concentration, risk-taking, and bank performance: Evidence from emerging economies," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 149-157.
    26. Hernandez Tinoco, Mario & Wilson, Nick, 2013. "Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 394-419.
    27. 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.
    28. 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.
    29. Daniel McFadden, 1976. "A Comment on Discriminant Analysis "Versus" Logit Analysis," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 511-523, National Bureau of Economic Research, Inc.
    30. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    31. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    32. Elkamhi, Redouane & Ericsson, Jan & Parsons, Christopher A., 2012. "The cost and timing of financial distress," Journal of Financial Economics, Elsevier, vol. 105(1), pages 62-81.
    33. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    34. 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.
    35. Chortareas, Georgios E. & Garza-García, Jesús G. & Girardone, Claudia, 2012. "Competition, efficiency and interest rate margins in Latin American banking," International Review of Financial Analysis, Elsevier, vol. 24(C), pages 93-103.
    36. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    37. Sreedhar T. Bharath & Tyler Shumway, 2008. "Forecasting Default with the Merton Distance to Default Model," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1339-1369, May.
    38. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    39. 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.
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    5. Jakub Horak & Jaromir Vrbka & Petr Suler, 2020. "Support Vector Machine Methods and Artificial Neural Networks Used for the Development of Bankruptcy Prediction Models and their Comparison," JRFM, MDPI, vol. 13(3), pages 1-15, March.
    6. Kulabutr Komenkul & Mohamed Sherif & Bing Xu, 2017. "IPOs’ signalling effects for speculative stock detection: evidence from the Stock Exchange of Thailand," Applied Economics, Taylor & Francis Journals, vol. 49(31), pages 3067-3085, July.
    7. Eling, Martin & Jia, Ruo, 2018. "Business failure, efficiency, and volatility: Evidence from the European insurance industry," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 58-76.
    8. Almaskati, Nawaf & Bird, Ron & Yeung, Danny & Lu, Yue, 2021. "A horse race of models and estimation methods for predicting bankruptcy," Advances in accounting, Elsevier, vol. 52(C).
    9. Arazmuradov, Annageldy, 2016. "Assessing sovereign debt default by efficiency," The Journal of Economic Asymmetries, Elsevier, vol. 13(C), pages 100-113.
    10. Campa, Domenico & Camacho-Miñano, María-del-Mar, 2015. "The impact of SME’s pre-bankruptcy financial distress on earnings management tools," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 222-234.
    11. Siekelova Anna & Kliestik Tomas & Adamko Peter, 2018. "Predictive Ability of Chosen Bankruptcy Models: A Case Study of Slovak Republic," Economics and Culture, Sciendo, vol. 15(1), pages 105-114, June.
    12. Jamal Ouenniche & Kaoru Tone, 2017. "An out-of-sample evaluation framework for DEA with application in bankruptcy prediction," Annals of Operations Research, Springer, vol. 254(1), pages 235-250, July.
    13. Katarina Valaskova & Tomas Kliestik & Lucia Svabova & Peter Adamko, 2018. "Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities Using Regression Analysis," Sustainability, MDPI, vol. 10(7), pages 1-15, June.
    14. Ioannis E. Tsolas, 2021. "Firm Credit Scoring: A Series Two-Stage DEA Bootstrapped Approach," JRFM, MDPI, vol. 14(5), pages 1-12, May.
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    16. Eduardo Acosta-González & Fernando Fernández-Rodríguez & Hicham Ganga, 2019. "Predicting Corporate Financial Failure Using Macroeconomic Variables and Accounting Data," Computational Economics, Springer;Society for Computational Economics, vol. 53(1), pages 227-257, January.
    17. Salvatore Loprevite & Domenico Raucci & Daniela Rupo, 2020. "KPIs Reporting and Financial Performance in the Transition to Mandatory Disclosure: The Case of Italy," Sustainability, MDPI, vol. 12(12), pages 1-24, June.
    18. Nawaf Almaskati & Ron Bird & Yue Lu & Danny Leung, 2019. "The Role of Corporate Governance and Estimation Methods in Predicting Bankruptcy," Working Papers in Economics 19/16, University of Waikato.
    19. Podhorska Ivana & Kovacova Maria & Valaskova Katarina, 2018. "Searching for Key Factors in Enterprise Bankrupt Prediction: A Case Study in Slovak Republic," Economics and Culture, Sciendo, vol. 15(1), pages 78-87, June.
    20. Tingting Yang & Xuefeng Guan & Yuehui Qian & Weiran Xing & Huayi Wu, 2019. "Efficiency Evaluation of Urban Road Transport and Land Use in Hunan Province of China Based on Hybrid Data Envelopment Analysis (DEA) Models," Sustainability, MDPI, vol. 11(14), pages 1-18, July.
    21. Róbert Štefko & Jarmila Horváthová & Martina Mokrišová, 2021. "The Application of Graphic Methods and the DEA in Predicting the Risk of Bankruptcy," JRFM, MDPI, vol. 14(5), pages 1-19, May.

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    More about this item

    Keywords

    Bankruptcy prediction; Performance criteria; Performance measures; Data envelopment analysis; Slacks-based measure;
    All these keywords.

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other

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