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

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  • Zoričák, Martin
  • Gnip, Peter
  • Drotár, Peter
  • Gazda, Vladimír

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.

Suggested Citation

  • Zoričák, Martin & Gnip, Peter & Drotár, Peter & Gazda, Vladimír, 2020. "Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets," Economic Modelling, Elsevier, vol. 84(C), pages 165-176.
    • Handle: RePEc:eee:ecmode:v:84:y:2020:i:c:p:165-176
    DOI: 10.1016/j.econmod.2019.04.003
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    Cited by:

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    2. Nabeel Al-Milli & Amjad Hudaib & Nadim Obeid, 2021. "Population Diversity Control of Genetic Algorithm Using a Novel Injection Method for Bankruptcy Prediction Problem," Mathematics, MDPI, vol. 9(8), pages 1-18, April.
    3. Lucia Svabova & Lucia Michalkova & Marek Durica & Elvira Nica, 2020. "Business Failure Prediction for Slovak Small and Medium-Sized Companies," Sustainability, MDPI, vol. 12(11), pages 1-14, June.
    4. Zhao, Shuping & Xu, Kai & Wang, Zhao & Liang, Changyong & Lu, Wenxing & Chen, Bo, 2022. "Financial distress prediction by combining sentiment tone features," Economic Modelling, Elsevier, vol. 106(C).
    5. Giuseppe Arbia & Vincenzo Nardelli, 2024. "Using Web-Data to Estimate Spatial Regression Models," International Regional Science Review, , vol. 47(2), pages 204-226, March.
    6. Oleksandr Melnychenko, 2020. "Is Artificial Intelligence Ready to Assess an Enterprise’s Financial Security?," JRFM, MDPI, vol. 13(9), pages 1-19, August.
    7. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
    8. Katarina Valaskova & Pavol Durana & Peter Adamko & Jaroslav Jaros, 2020. "Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities," JRFM, MDPI, vol. 13(5), pages 1-16, May.
    9. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    10. Michaela Staňková, 2023. "Threshold Moving Approach with Logit Models for Bankruptcy Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 61(3), pages 1251-1272, March.
    11. Vadlamani Ravi & Vadlamani Madhav, 2020. "Optimizing the reliability of a bank with Logistic Regression and Particle Swarm Optimization," Papers 2004.11122, arXiv.org.
    12. Antonio Pelaez-Verdet & Pilar Loscertales-Sanchez, 2021. "Key Ratios for Long-Term Prediction of Hotel Financial Distress and Corporate Default: Survival Analysis for an Economic Stagnation," Sustainability, MDPI, vol. 13(3), pages 1-17, January.

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