Ranking of bankruptcy prediction models under multiple criteria

Jamal Ouenniche, Mohammad M. Mousavi, Bing Xu, Kaoru Tone

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

1 Citation (Scopus)


Prediction of corporate failure is one of the major activities in auditing firms’ risks and uncertainties. In practice, the design of reliable models to predict bankruptcy is crucial in many decision-making processes. In this chapter we address two research questions related to the design of bankruptcy prediction models: namely, do some modelling frameworks perform better than others by design? and to what extent do the choice and/or the design of explanatory variables and their nature affect the performance of modelling frameworks? Elements of answers to these questions are devised through an exhaustive comparative analysis of the most popular bankruptcy-modelling frameworks, including our own models. Our comparative analysis is performed using a multicriteria performance evaluation framework based on data envelopment analysis, namely, an orientation-free super-efficiency slacks-based measure model. The proposed performance evaluation framework delivers a multicriteria ranking of bankruptcy prediction models, which overcomes the methodological issues related to the commonly used monocriterion framework. The performance of bankruptcy prediction models is assessed under a set of commonly used criteria and is tested on a sample that consists of all UK firms listed on the London Stock Exchange during an 18 year period.
Original languageEnglish
Title of host publicationAdvances in DEA Theory and Applications
Subtitle of host publicationWith Extensions to Forecasting Models
EditorsKaoru Tone
PublisherJohn Wiley and Sons Ltd
ISBN (Electronic)9781118946688
ISBN (Print)9781118945629
Publication statusPublished - 6 May 2017


  • Performance evaluation
  • Data Envelopment Analysis
  • super-efficiency
  • Bankruptcy prediction;
  • performance criteria and measures


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