The application of PROMETHEE multi-criteria decision aid in financial decision making: case of distress prediction models evaluation
dc.contributor.author | Mousavi, Mohammad M. | |
dc.contributor.author | Lin, J. | |
dc.date.accessioned | 2020-07-15T10:32:42Z | |
dc.date.available | 2020-07-15T10:32:42Z | |
dc.date.issued | 2020-11-30 | |
dc.identifier.citation | Mousavi MM and Lin J (2020) The application of PROMETHEE multi-criteria decision aid in financial decision making: case of distress prediction models evaluation. Expert Systems with Applications. 159: 113438. | en_US |
dc.identifier.uri | http://hdl.handle.net/10454/17917 | |
dc.description | No | en_US |
dc.description.abstract | 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. | en_US |
dc.language.iso | en | en_US |
dc.relation.isreferencedby | https://doi.org/10.1016/j.eswa.2020.113438 | en_US |
dc.subject | Corporate distress prediction models | en_US |
dc.subject | Multi-criteria decision aid | en_US |
dc.subject | PROMETHEE II | en_US |
dc.subject | Corporate governance indicators | en_US |
dc.subject | Machine learning | en_US |
dc.title | The application of PROMETHEE multi-criteria decision aid in financial decision making: case of distress prediction models evaluation | en_US |
dc.status.refereed | Yes | en_US |
dc.date.Accepted | 2020-04-04 | |
dc.date.application | 2020-05-22 | |
dc.type | Article | en_US |
dc.type.version | No full-text in the repository | en_US |