A comparative analysis of two-stage distress prediction models

Abstract

On feature selection, as one of the critical steps to develop a distress prediction model (DPM), a variety of expert systems and machine learning approaches have analytically supported developers. Data envel- opment analysis (DEA) has provided this support by estimating the novel feature of managerial efficiency, which has frequently been used in recent two-stage DPMs. As key contributions, this study extends the application of expert system in credit scoring and distress prediction through applying diverse DEA mod- els to compute corporate market efficiency in addition to the prevailing managerial efficiency, and to estimate the decomposed measure of mix efficiency and investigate its contribution compared to Pure Technical Efficiency and Scale Efficiency in the performance of DPMs. Further, this paper provides a com- prehensive comparison between two-stage DPMs through estimating a variety of DEA efficiency measures in the first stage and employing static and dynamic classifiers in the second stage. Based on experimen- tal results, guidelines are provided to help practitioners develop two-stage DPMs; to be more specific, guidelines are provided to assist with the choice of the proper DEA models to use in the first stage, and the choice of the best corporate efficiency measures and classifiers to use in the second stage.