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• W2939367930 abstract "Abstract 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." @default.
• W2939367930 created "2019-04-25" @default.
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• W2939367930 date "2020-01-01" @default.
• W2939367930 modified "2023-09-29" @default.
• W2939367930 title "Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets" @default.
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• W2939367930 doi "https://doi.org/10.1016/j.econmod.2019.04.003" @default.
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