Sergiusz Herman https://orcid.org/0000-0002-2753-1982
ARTYKUŁ

(Angielski) PDF

STRESZCZENIE

Deteriorating financial condition of a company may lead to insolvency. As a result, the company may be declared bankrupt or undergo restructuring. The first goal of the study described in the paper is to compare the financial condition of Poland-based non-financial companies undergoing restructuring and bankruptcy processes. In the empirical study, a tool for forecasting the future financial situation of a company was constructed. The second goal is the assessment of the effectiveness of restructuring processes on the basis of a comparative analysis of companies subjected to various forms of this procedure. An attempt was made to identify the determinants of the success or failure of the restructuring process.
The study was based on the information from the Coface Poland, EMIS Professional and the Court and Commercial Gazette (Pol. ‘Polski Monitor Sądowy i Gospodarczy’) databases. The empirical research was conducted on a random sample of financial data of 1740 non-financial companies (580 companies that were declared bankrupt, 580 companies undergoing a restructuring, and 580 companies in a good financial condition) in 2015–2019. The Kruskal-Wallis test, Dunn’s test, Mann-Whitney’s test and the random forest classifier were used for the purpose of the study.
Companies that were declared bankrupt or underwent a restructuring process have more in common with each other than with companies efficiently operating in the market. It was possible to create a classifier which enabled forecasting whether a company will have financial problems. The results of the study demonstrated that the efficiency of the restructuring process does not depend on financial factors. Moreover, restructuring often fails to protect companies from bankruptcy and does not have a significant impact on the financial condition of restructured entities.

SŁOWA KLUCZOWE

corporate bankruptcy, restructuring proceedings, financial condition

JEL

G33, G34, G38, C38

BIBLIOGRAFIA

Ahmad, A. H. (2019). What factors discriminate reorganized and delisted distressed firms: Evidence from Malaysia. Finance Research Letters, 29, 50–56. https://doi.org/10.1016/j.frl.2019.03.010 .

Ahmad, A. H., Abdullah, N. A. H., & Taufil Mohd, K. N. (2018). Long-run performance of firms emerging from financial distress: Empirical evidence from Malaysia. Economics and Business Letters, 7(1), 47–54. https://doi.org/10.17811/ebl.7.1.2018.47-54 .

Aivazian, V. A., & Zhou, S. (2012). Is Chapter 11 Efficient?. Financial Management, 41(1), 229– 253. https://doi.org/10.1111/j.1755-053X.2012.01196.x.

Alderson, M. J., & Betker, B. L. (1999). Assessing Post-Bankruptcy Performance: An Analysis of Reorganized Firms’ Cash Flows. Financial Management, 28(2), 68–82. https://doi.org/10.2307/3666196 .

Alessi, L., & Detken, C. (2018). Identifying excessive credit growth and leverage. Journal of Financial Stability, 35, 215–225. https://doi.org/10.1016/j.jfs.2017.06.005 .

Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. https://doi.org/10.2307/2978933 .

Altman, E. I., & Branch, B. (2015). The bankruptcy system’s chapter 22 recidivism problem: How serious is it?. Financial Review, 50(1), 1–26. https://doi.org/10.1111/fire.12058 .

Altman, E. I., Kant, T., & Rattanaruengyot, T. (2009). Post-Chapter 11 Bankruptcy Performance: Avoiding Chapter 22. Journal of Applied Corporate Finance, 21(3), 53–64. https://doi.org/10.1111/j.1745-6622.2009.00239.x .

Antulov-Fantulin, N., Lagravinese, R., & Resce, G. (2021). Predicting bankruptcy of local government: A machine learning approach. Journal of Economic Behavior and Organization, 183, 681– 699. https://doi.org/10.1016/j.jebo.2021.01.014 .

Ayadi, R., Abid, I., & Guesmi, K. (2021). Survival of reorganized firms in France. Finance Research Letters, 38, 1–6. https://doi.org/10.1016/j.frl.2020.101434 .

Balcaen, S., & Ooghe, H. (2006). 35 years of studies on business failure: An overview of the classic statistical methodologies and their related problems. British Accounting Review, 38(1), 63–93. https://doi.org/10.1016/j.bar.2005.09.001 .

Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405–417. https://doi.org/10.1016/j.eswa.2017.04.006 .

Beaver, W. H. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research, 4, 71–111. https://doi.org/10.2307/2490171 .

Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324 .

Calderoni, L., Ferrara, M., Franco, A., & Maio, D. (2015). Indoor localization in a hospital environment using Random Forest classifiers. Expert Systems with Applications, 42(1), 125–134. https://doi.org/10.1016/j.eswa.2014.07.042 .

Cepec, J., & Grajzl, P. (2020). Debt-to-equity conversion in bankruptcy reorganization and postbankruptcy firm survival. International Review of Law and Economics, 61, 1–13. https://doi.org/10.1016/j.irle.2019.105878 .

Coface. (2021). Raport roczny Coface: Niewypłacalności firm w Polsce w 2020 roku. Warszawa. https://www.coface.pl/Aktualnosci-i-media/Biuro-prasowe/Roczny-raport-upadlosciowy-Coface-Niewyplacalnosci-przedsiebiorstw-w-Polsce-w-2020-roku .

Coface Poland. (2021). Ogólnopolski Informator Upadłości i Restrukturyzacji Coface. https://www-1emis-1com-10000f34800e7.han3.ue.poznan.pl/localui/interiu/index/ .

Denis, D. K., & Rodgers, K. J. (2007). Chapter 11: Duration, Outcome, and Post-Reorganization Performance. The Journal of Financial and Quantitative Analysis, 42(1), 101–118. https://doi.org/10.1017/S0022109000002209 .

EMIS Professional. (2021). https://www.emis.com/pl.

Fitzpatrick, F. (1932). A Comparison of Ratios of Successful Industrial Enterprises with Those of Failed Firm. Certified Public Accountant, 6, 727–731.

Geng, R., Bose, I., & Chen, X. (2015). Prediction of financial distress: An empirical study of listed Chinese companies using data mining. European Journal of Operational Research, 241(1), 236– 247. https://doi.org/10.1016/j.ejor.2014.08.016 .

Gepp, A., & Kumar, K. (2015). Predicting Financial Distress: A Comparison of Survival Analysis and Decision Tree Techniques. Procedia Computer Science, 54, 396–404. https://doi.org/10.1016/j.procs.2015.06.046 .

Hotchkiss, E. S., John, K., Mooradian, R. M., & Thorburn, K. S. (2008). Chapter 14 – Bankruptcy and the Resolution of Financial Distress. In B. Espen Eckbo (Ed.), Handbook of Empirical Corporate Finance (pp. 235–287). Amsterdam: Elsevier B.V. https://doi.org/10.1016/B978-0-444-53265-7.50006-8 .

du Jardin, P. (2018). Failure pattern-based ensembles applied to bankruptcy forecasting. Decision Support Systems, 107, 64–77. https://doi.org/10.1016/j.dss.2018.01.003 .

Kalay, A., Singhal, R., & Tashjian, E. (2007). Is Chapter 11 costly?. Journal of Financial Economics, 84(3), 772–796. https://doi.org/10.1016/j.jfineco.2006.04.001 .

Kim, S. Y. (2018). Predicting hospitality financial distress with ensemble models: the case of US hotels, restaurants, and amusement and recreation. Service Business, 12(3), 483–503. https://doi.org/10.1007/s11628-018-0365-x .

Kim, S. Y., & Upneja, A. (2021). Majority voting ensemble with a decision trees for business failure prediction during economic downturns. Journal of Innovation and Knowledge, 6(2), 112–123. https://doi.org/10.1016/j.jik.2021.01.001 .

Komera, S., & Jijo Lukose, P. J. (2013). No longer sick: what does it convey? An empirical analysis of post-bankruptcy performance. International Journal of Emerging Markets, 8(2), 182–202. https://doi.org/10.1108/17468801311307055 .

Martin, D. (1977). Early warning of bank failure: A logit regression approach. Journal of Banking and Finance, 1(3), 249–276. https://doi.org/10.1016/0378-4266(77)90022-X .

Monitor Sądowy i Gospodarczy. (2021). https://ems.ms.gov.pl/msig/przegladaniemonitorow.

Odom, M. D., & Sharda, R. (1990). A neural network model for bankruptcy prediction. In 1990 IJCNN International Joint Conference on Neural Networks. https://doi.org/10.1109/ijcnn.1990.137710 .

Petropoulos, A., Siakoulis, V., Stavroulakis, E., & Vlachogiannakis, N. E. (2020). Predicting bank insolvencies using machine learning techniques. International Journal of Forecasting, 36(3), 1092–1113. https://doi.org/10.1016/j.ijforecast.2019.11.005 .

Prusak, B., & Potrykus, M. (2021). Short-Term Price Reaction to Filing for Bankruptcy and Restructuring Proceedings—The Case of Poland. Risks, 9(3), 1–14. https://doi.org/10.3390/risks9030056 .

Shin, K. S., & Lee, Y. J. (2002). A genetic algorithm application in bankruptcy prediction modeling. Expert Systems with Applications, 23(3), 321–328. https://doi.org/10.1016/S0957-4174(02)00051-9 .

Smith, R. F., & Winakor, A. H. (1935). Changes in the financial structure of unsuccessful industrial corporations. Urbana: University of Illinois.

Tanaka, K., Higashide, T., Kinkyo, T., & Hamori, S. (2019). Analyzing Industry-Level Vulnerability by Predicting Financial Bankruptcy. Economic Inquiry, 57(4), 2017–2034. https://doi.org/10.1111/ecin.12817 .

Ustawa z dnia 28 lutego 2003 r. Prawo upadłościowe (Dz.U. 2003 nr 60 poz. 535).

Ustawa z dnia 15 maja 2015 r. – Prawo restrukturyzacyjne (Dz.U. 2015 poz. 978).

Ustawa z dnia 19 czerwca 2020 r. o dopłatach do oprocentowania kredytów bankowych udzielanych przedsiębiorcom dotkniętym skutkami COVID-19 oraz o uproszczonym postępowaniu o zatwierdzenie układu w związku z wystąpieniem COVID-19 (Dz.U. 2020 poz. 1086).

Wang, C. A. (2012). Determinants of the Choice of Formal Bankruptcy Procedure: An International Comparison of Reorganization and Liquidation. Emerging Markets Finance and Trade, 48(2), 4–28. https://doi.org/10.2753/REE1540-496X480201 .

Zmijewski, M. E. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research, 22, 59–82. https://doi.org/10.2307/2490859 .

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