No / Conflicting rankings corresponding to alternative performance criteria and measures are mostly reported in the mono-criterion evaluation of competing distress prediction models (DPMs). To overcome this issue, this study extends the application of the expert system to corporate credit risk and distress prediction through proposing a Multi-criteria Decision Aid (MCDA), namely PROMETHEE II, which provides a multi-criteria evaluation of competing DPMs. In addition, using data on Chinese firms listed on Shanghai and Shenzhen stock exchanges, we perform an exhaustive comparative analysis of the most popular DPMs; namely, statistical, artificial intelligence and machine learning models under both mono-criterion and multi-criteria frameworks. Further, we address two prevailing research questions; namely, "which DPM performs better in predicting distress?" and "will training models with corporate governance indicators (CGIs) enhance the performance of models?”; and discuss our findings. Our multi-criteria ranking suggests that non-parametric DPMs outperform parametric ones, where random forest and bagging CART are among the best machine learning DPMs. Further, models fed with CGIs as features outperform those fed without CGIs.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/17917 |
| Date | 2020 May 1922 |
| Creators | Mousavi, Mohammad M., Lin, J. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, No full-text in the repository |
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