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An out-of-sample evaluation framework for DEA with application in bankruptcy prediction

Author

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  • Jamal Ouenniche

    (University of Edinburgh, Business School)

  • Kaoru Tone

    (National Graduate Institute for Policy Studies)

Abstract

Nowadays, data envelopment analysis (DEA) is a well-established non-parametric methodology for performance evaluation and benchmarking. DEA has witnessed a widespread use in many application areas since the publication of the seminal paper by Charnes, Cooper and Rhodes in 1978. However, to the best of our knowledge, no published work formally addressed out-of-sample evaluation in DEA. In this paper, we fill this gap by proposing a framework for the out-of-sample evaluation of decision making units. We tested the performance of the proposed framework in risk assessment and bankruptcy prediction of companies listed on the London Stock Exchange. Numerical results demonstrate that the proposed out-of-sample evaluation framework for DEA is capable of delivering an outstanding performance and thus opens a new avenue for research and applications in risk modelling and analysis using DEA as a non-parametric frontier-based classifier and makes DEA a real contender in industry applications in banking and investment.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:254:y:2017:i:1:d:10.1007_s10479-017-2431-5
    DOI: 10.1007/s10479-017-2431-5
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    Cited by:

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    6. Jamal Ouenniche & Kais Bouslah & Blanca Perez-Gladish & Bing Xu, 2021. "A new VIKOR-based in-sample-out-of-sample classifier with application in bankruptcy prediction," Annals of Operations Research, Springer, vol. 296(1), pages 495-512, January.
    7. Jinghai Shao & Sovan Mitra & Andreas Karathanasopoulos, 2022. "Optimal feedback control of stock prices under credit risk dynamics," Annals of Operations Research, Springer, vol. 313(2), pages 1285-1318, June.
    8. Philippe Jardin, 2021. "Forecasting bankruptcy using biclustering and neural network-based ensembles," Annals of Operations Research, Springer, vol. 299(1), pages 531-566, April.
    9. 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.
    10. Mohammad Mahdi Mousavi & Jamal Ouenniche, 2018. "Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions," Annals of Operations Research, Springer, vol. 271(2), pages 853-886, December.
    11. Eric Séverin & David Veganzones, 2021. "Can earnings management information improve bankruptcy prediction models?," Annals of Operations Research, Springer, vol. 306(1), pages 247-272, November.
    12. Yan He & Yung-ho Chiu & Bin Zhang, 2020. "Prevaluating Technical Efficiency Gains From Potential Mergers and Acquisitions in China’s Coal Industry," SAGE Open, , vol. 10(3), pages 21582440209, July.

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