Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets

Martin Zoričák, Peter Gnip, Peter Drotár and Vladimír Gazda ()

Economic Modelling, 2020, vol. 84, issue C, 165-176

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.

Keywords: Bankruptcy; Imbalanced learning; Anomaly detection; Annual reports (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:84:y:2020:i:c:p:165-176

DOI: 10.1016/j.econmod.2019.04.003

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