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

- A methodological issue in performance evaluation of bankruptcy models is overcome.
- We propose SBM super-efficiency DEA model as a multicriteria assessment framework.
- We evaluate bankruptcy modeling frameworks under multiple criteria and measures.
- We demonstrate that some modeling frameworks perform better than others by design.
- We demonstrate that choice of predictors affect modeling frameworks' performance.

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.