Performance evaluation of bankruptcy prediction models: An orientation-free super-efficiency DEA-based framework

Mohammad M. Mousavi, Jamal Ouenniche, Bing Xu

    Research output: Contribution to journalArticlepeer-review

    37 Citations (Scopus)


    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.
    Original languageEnglish
    Pages (from-to)64-75
    Number of pages12
    JournalInternational Review of Financial Analysis
    Early online date21 Jan 2015
    Publication statusPublished - Dec 2015


    • Bankruptcy prediction;
    • Performance criteria
    • Performance measures
    • DEA
    • Slacks-based measure


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