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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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    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.
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    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.
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    14. 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.
    15. 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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    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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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