Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets |
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Institution: | 1. Digital Contents Research Institute, Sejong University, Seoul, South Korea;2. Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Viet Nam;3. Faculty of Software and Information Science, Iwate Prefectural University, Iwate, Japan;4. Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Poland |
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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. |
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Keywords: | Bankruptcy Imbalanced learning Anomaly detection Annual reports 00-01 99-00 |
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