Corporate Bankruptcy Forecasting before and after the COVID-19 Pandemic in Poland
DOI:
https://doi.org/10.15678/krem.18776Keywords:
bankruptcy forecasting, crisis, COVID-19, machine learningAbstract
Objective: The main objective of the study is to analyse the impact of the economic crisis caused by the COVID-19 pandemic on the forecasting of corporate bankruptcy in Poland. The analysis verifies the accuracy of models used to make forecasts and examines the determinants of corporate bankruptcy before and after the pandemic.
Research Design & Methods: The study used financial data from 121,000 companies for the 2015–2022 period. Five variable selection methods, eight classification methods and 1,000 different random learning and testing samples were used to perform the study.
Findings: The results indicate different determinants of corporate bankruptcy before and after the outbreak of the COVID-19 pandemic. Models constructed and tested before the pandemic had lower classification accuracy than models constructed and tested after the outbreak.
Implications / Recommendations: The results confirm the need to reconstruct and test models for forecasting corporate bankruptcy during periods of dynamic changes in capital markets, such as those triggered by the COVID-19 pandemic.
Contribution: The considerations presented in the article deepen knowledge of the impact of the economic crisis on the forecasting of corporate bankruptcy in Poland. To date, no research has been conducted with such a wide range of research methodology used in the context of the crisis caused by the COVID-19 pandemic.
Downloads
References
Almamy, J., Aston, J., & Ngwa, L. N. (2016). An Evaluation of Altman’s Z-score Using Cash Flow Ratio to Predict Corporate Failure Amid the Recent Financial Crisis: Evidence from the UK. Journal of Corporate Finance, 36, 278–285. https://doi.org/10.1016/j.jcorpfin.2015.12.009 DOI: https://doi.org/10.1016/j.jcorpfin.2015.12.009
Berkson, J. (1944). Application of the Logistic Function to Bio-Assay. Journal of the American Statistical Association, 39(227), 357–365. https://doi.org/10.1080/01621459.1944.10500699 DOI: https://doi.org/10.1080/01621459.1944.10500699
Breiman, L. (1996). Bagging Predictors. Machine Learning, 24, 123–140. https://doi.org/10.1007/BF00058655 DOI: https://doi.org/10.1023/A:1018054314350
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324 DOI: https://doi.org/10.1023/A:1010933404324
Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. KDD ’16. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939785 DOI: https://doi.org/10.1145/2939672.2939785
Chernozhukov, V., Kasahara, H., & Schrimpf, P. (2021). Causal Impact of Masks, Policies, Behavior on Early COVID-19 Pandemic in the U.S. Journal of Econometrics, 220(1), 23–62. https://doi.org/10.1016/j.jeconom.2020.09.003 DOI: https://doi.org/10.1016/j.jeconom.2020.09.003
Cho, S.-W. (Stanley). (2020). Quantifying the Impact of Nonpharmaceutical Interventions during the COVID-19 Outbreak: The Case of Sweden. Econometrics Journal, 23(3), 323–344. https://doi.org/10.1093/ECTJ/UTAA025 DOI: https://doi.org/10.1093/ectj/utaa025
Coccia, M. (2020). The Relation between Length of Lockdown, Numbers of Infected People and Deaths of COVID-19, and Economic Growth of Countries: Lessons Learned to Cope with Future Pandemics Similar to COVID-19 and to Constrain the Deterioration of Economic System. Science of the Total Environment, 775, 145801. https://doi.org/10.1016/j.scitotenv.2021.145801 DOI: https://doi.org/10.1016/j.scitotenv.2021.145801
Cortes, C., & Vapnik, V. (1995). Support-vector Networks. Machine Learning, 20, 273–297. https://doi.org/10.1007/BF00994018 DOI: https://doi.org/10.1023/A:1022627411411
Gopalakrishnan, B., Jacob, J., & Mohapatra, S. (2022). COVID-19 Pandemic and Debt Financing by Firms: Unravelling the Channels. Economic Modelling, 114, 105929. https://doi.org/10.1016/j.econmod.2022.105929 DOI: https://doi.org/10.1016/j.econmod.2022.105929
Gourinchas, P.-O., Kalemli-Özcan, Ṣ., Penciakova, V., & Sander, N. (2020). SME Failures under Large Liquidity Shocks: An Application to the COVID-19 Crisis (NBER Working Paper No. 27877). National Bureau of Economic Research. Retrieved from: https://www.nber.org/papers/w27877.pdf (accessed: 5.05.2024). DOI: https://doi.org/10.3386/w27877
Guo, G., Wang, H., Bell, D., Bi, Y., & Greer, K. (2003). KNN Model-based Approach in Classification. In: R. Meersman, Z. Tari, D. C. Schmidt (Eds), On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE. OTM 2003. Lecture Notes in Computer Science (Vol. 2888). Springer. https://doi.org/10.1007/978-3-540-39964-3_62 DOI: https://doi.org/10.1007/978-3-540-39964-3_62
Hadasik, D. (1998). Upadłość przedsiębiorstw w Polsce i metody jej prognozowania. Wydawnictwo Akademii Ekonomicznej w Poznaniu.
Herman, S., & Lach, B. (2024). Czy warto budować modele prognozowania upadłości przedsiębiorstw uwzględniające kryterium przynależności sektorowej? Wiadomości Statystyczne. The Polish Statistician, 69(10), 41–58. https://doi.org/10.59139/ws.2024.10.3 DOI: https://doi.org/10.59139/ws.2024.10.3
Hołda, A. (2006). Zasada kontynuacji działalności i prognozowanie upadłości w polskich realiach gospodarczych. Wydawnictwo Akademii Ekonomicznej w Krakowie.
Hołda, A., & Strojny, K. (2019). Determinants of Entrepreneurs’ Bankruptcy in Poland – Systematics and Comparative Analysis of the Opinion of Management and Trustees. Zeszyty Teoretyczne Rachunkowości, 101(157), 9–34. https://doi.org/10.5604/01.3001.0013.0753 DOI: https://doi.org/10.5604/01.3001.0013.0753
Kanitkar, T. (2020). The COVID-19 Lockdown in India: Impacts on the Economy and the Power Sector. Global Transitions, 2, 150–156. https://doi.org/10.1016/j.glt.2020.07.005 DOI: https://doi.org/10.1016/j.glt.2020.07.005
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2019). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In: 31st Conference on Neural Information Processing Systems (NIPS 2017) (pp. 3149–3157). NIPS.
Ke, X., & Hsiao, C. (2022). Economic Impact of the Most Drastic Lockdown during COVID-19 Pandemic – the Experience of Hubei, China. Journal of Applied Econometrics, 37(1), 187–209. https://doi.org/10.1002/jae.2871 DOI: https://doi.org/10.1002/jae.2871
Ke, Y. (2022). The Impact of COVID-19 on Firms’ Cost of Equity Capital: Early Evidence from U.S. Public Firms. Finance Research Letters, 46, 102242. https://doi.org/10.1016/j.frl.2021.102242 DOI: https://doi.org/10.1016/j.frl.2021.102242
Kira, K., & Rendell, L. A. (1992). A Practical Approach to Feature Selection. In: D. Sleeman, P. Edwards (Eds), Machine Learning Proceedings (pp. 249–256). Morgan Kaufmann. https://doi.org/10.1016/B978-1-55860-247-2.50037-1 DOI: https://doi.org/10.1016/B978-1-55860-247-2.50037-1
Korol, T., & Prusak, B. (2005). Upadłość przedsiębiorstw a wykorzystanie sztucznej inteligencji. CeDeWu.
Lai, S., Ruktanonchai, N. W., Zhou, L., Prosper, O., Luo, W., Floyd, J. R., Wesolowski, A., Santillana, M., Zhang, C., Du, X., Yu, H., & Tatem, A. J. (2020). Effect of Non-pharmaceutical Interventions to Contain COVID-19 in China. Nature, 585(7825), 410–413. https://doi.org/10.1038/s41586-020-2293-x DOI: https://doi.org/10.1038/s41586-020-2293-x
Liu, Y., Zeng, Q., Li, B., Ma, L., & Ordieres-Meré, J. (2022). Anticipating Financial Distress of High-tech Startups in the European Union: A Machine Learning Approach for Imbalanced Samples. Journal of Forecasting, 41(6), 1131–1155. https://doi.org/10.1002/for.2852 DOI: https://doi.org/10.1002/for.2852
Makni, M. S. (2023). Analyzing the Impact of COVID-19 on the Performance of Listed Firms in Saudi Market. Technological Forecasting and Social Change, 187, 122171. https://doi.org/10.1016/j.techfore.2022.122171 DOI: https://doi.org/10.1016/j.techfore.2022.122171
Mann, H. B., & Whitney, D. R. (1947). On a Test of Whether One of Two Random Variables Is Stochastically Larger Than the Other. The Annals of Mathematical Statistics, 18(1), 50–60. https://doi.org/10.1214/aoms/1177730491 DOI: https://doi.org/10.1214/aoms/1177730491
Miller, A. J. (1984). Selection of Subsets of Regression Variables. Journal of the Royal Statistical Society. Series A: General, 147(3), 389–425. https://doi.org/10.2307/2981576 DOI: https://doi.org/10.2307/2981576
Mitze, T., Kosfeld, R., Rode, J., & Wälde, K. (2020). Face Masks Considerably Reduce COVID-19 Cases in Germany. Proceedings of the National Academy of Sciences, 117(51), 32293–32301. https://doi.org/10.1073/pnas.2015954117 DOI: https://doi.org/10.1073/pnas.2015954117
Nam, J.-H., & Jinn, T. (2000). Bankruptcy Prediction: Evidence from Korean Listed Companies during the IMF Crisis. Journal of International Financial Management and Accounting, 11(3), 178–197. https://doi.org/10.1111/1467-646X.00061 DOI: https://doi.org/10.1111/1467-646X.00061
Papík, M., & Papíková, L. (2023). Impacts of Crisis on SME Bankruptcy Prediction Models’ Performance. Expert Systems with Applications, 214, 119072. https://doi.org/10.1016/j.eswa.2022.119072 DOI: https://doi.org/10.1016/j.eswa.2022.119072
Peng, H., Long, F., & Ding, C. (2005). Feature Selection Based on Mutual Information Criteria of Max-dependency, Max-relevance, and Min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226–1238. https://doi.org/10.1109/TPAMI.2005.159 DOI: https://doi.org/10.1109/TPAMI.2005.159
Pociecha, J., Pawełek, B., Baryła, M., & Augustyn, S. (2014). Statystyczne metody prognozowania bankructwa w zmieniającej się koniunkturze gospodarczej. Fundacja Uniwersytetu Ekonomicznego w Krakowie.
Qin, X., Huang, G., Shen, H., & Fu, M. (2020). COVID-19 Pandemic and Firm-level Cash Holding – Moderating Effect of Goodwill and Goodwill Impairment. Emerging Markets Finance and Trade, 56(10), 2243–2258. https://doi.org/10.1080/1540496X.2020.1785864 DOI: https://doi.org/10.1080/1540496X.2020.1785864
Rumelhart, D., Hinton, G., & Williams, R. (1986). Learning Representations by Back-propagating Errors. Nature, 323, 533–536. https://doi.org/10.1038/323533a0 DOI: https://doi.org/10.1038/323533a0
Shen, H., Fu, M., Pan, H., Yu, Z., & Chen, Y. (2020). The Impact of the COVID-19 Pandemic on Firm Performance. Emerging Markets Finance and Trade, 56(10), 2213–2230. https://doi.org/10.1080/1540496X.2020.1785863 DOI: https://doi.org/10.1080/1540496X.2020.1785863
Srebro, B., Mavrenski, B., Bogojević Arsić, V., Knežević, S., Milašinović, M., & Travica, J. (2021). Bankruptcy Risk Prediction in Ensuring the Sustainable Operation of Agriculture Companies. Sustainability, 13(14), 7712. https://doi.org/10.3390/su13147712 DOI: https://doi.org/10.3390/su13147712
Veganzones, D., & Séverin, E. (2018). An Investigation of Bankruptcy Prediction in Imbalanced Datasets. Decision Support Systems, 112, 111–124. https://doi.org/10.1016/j.dss.2018.06.011 DOI: https://doi.org/10.1016/j.dss.2018.06.011
WHO. (2024). Coronavirus Disease (COVID-2019) Situation Reports. Retrieved from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports (accessed: 7.06.2024).
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Krakow University of Economics
This work is licensed under a Creative Commons Attribution 4.0 International License.
