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

Jamal Ouenniche, Kaoru Tone

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Pages (from-to)235–250
JournalAnnals of Operations Research
Issue number1-2
Early online date17 Feb 2017
Publication statusPublished - Jul 2017

Keywords / Materials (for Non-textual outputs)

  • data envelopment analysis
  • out-of-sample evaluation
  • K-nearest neighbor
  • bankruptcy prediction
  • risk assessment


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