Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets
| Autor: | Vladimír Gazda, Martin Zoričak, Peter Drotar, Peter Gnip |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Economics and Econometrics
Government 050208 finance Process (engineering) business.industry 05 social sciences Machine learning computer.software_genre Support vector machine Bankruptcy 0502 economics and business Economics Bankruptcy prediction Classification methods Anomaly detection Isolation (database systems) Artificial intelligence 050207 economics business computer |
| Zdroj: | Economic Modelling. 84:165-176 |
| ISSN: | 0264-9993 |
| DOI: | 10.1016/j.econmod.2019.04.003 |
| Popis: | Bankruptcy prediction is still important topic receiving notable attention. Information about an imminent bankruptcy threat is a crucial aspect of the decision-making process of managers, financial institutions, and government agencies. In this paper, we utilize a newly acquired dataset comprising financial parameters derived from the annual reports of small- and medium-sized companies. The data, which reveal the true ratio between bankrupt and non-bankrupt companies, are severely imbalanced and only contain a small fraction of bankrupt companies. Our solution to overcome this challenging scenario of imbalanced learning was to adopt three one-class classification methods: a least-squares approach to anomaly detection, an isolation forest, and one-class support vector machines for comparison with conventional support vector machines. We provide a comprehensive analysis of the financial attributes and identify those that are most relevant to bankruptcy prediction. The highest prediction performance in terms of the geometric mean score is 91%. The results are validated on two datasets from the manufacturing and construction industries. |
| Databáze: | OpenAIRE |
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