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

Jamal Ouenniche () and Kaoru Tone
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Jamal Ouenniche: University of Edinburgh, Business School
Kaoru Tone: National Graduate Institute for Policy Studies

Annals of Operations Research, 2017, vol. 254, issue 1, No 12, 235-250

Abstract: 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.

Keywords: Data envelopment analysis; Out-of-sample evaluation; K-Nearest neighbor; Bankruptcy prediction; Risk assessment (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (12)

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DOI: 10.1007/s10479-017-2431-5

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