Assessment of bankruptcy risks in Czech companies using regression analysis
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DOIhttp://dx.doi.org/10.21511/ppm.19(3).2021.05
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Article InfoVolume 19 2021, Issue #3, pp. 46-55
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Bankruptcy is an important topic in academic research and practice. It is a burning issue worldwide in the current COVID-19 situation. The aim of this study is to examine the financial risks of Czech companies. By employing the stepwise regression technique to estimate the financial risks, the p-values of all selected 15 financial ratios (explanatory variables) were calculated. If the p-value of the variable is more than the level of significance, the particular variable is removed from the model and another regression model is calculated. The findings from the stepwise regression revealed that return on capital, current ratio, net working capital turnover rate, and current assets turnover rate have a positive influence on company’s financial health. On the contrary, return on capital employed, asset turnover rate, inventory turnover rate, fixed assets turnover rate, and debt to equity ratio negatively impact the company’s financial health. The findings of this study will be fruitful for managers, policymakers, and investors of the companies to estimate and assess financial risks.
Acknowledgments
This study is supported by the Internal Grant Agency (IGA) in Tomas Bata University in Zlin, the Czech Republic, under the projects No IGA/FAME/2021/008 and IGA/FAME/2021/014.
- Keywords
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JEL Classification (Paper profile tab)G32, G33
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References49
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Tables7
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Figures0
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- Table 1. Sector-wise data of Czech companies
- Table 2. List of the selected ratios and their measurements
- Table 3. Descriptive statistics of the selected variables
- Table 4. Variance inflation factor (VIF)
- Table 5. Regression analysis of all selected financial ratios
- Table 6. Regression analysis of all selected financial ratios
- Table 7. Results of the stepwise regression analysis
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- Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4), 589-609.
- Andrea, R. (2014). Financial performance analysis and bankruptcy prediction in Hungarian dairy sector. The Annals of the University of Oradea. Economic Sciences, 23, 936-945.
- Andrea, R., & Pető, D. (2015). Financial future prospect investigation using bankruptcy forecasting models in Hungarian meat processing industry. Annals of the University of Oradea, Economic Sciences, 25(1), 801-809.
- Bărbuță-Mișu, N., & Madaleno, M. (2020). Assessment of Bankruptcy Risk of Large Companies: European Countries Evolution Analysis. Journal of Risk and Financial Management, 13(3), 58.
- Belyaeva, E. (2014). On a new logistic regression model for bankruptcy prediction in the IT branch (Project Report 2014:35). Uppsala University.
- Ben Jabeur, S. (2017). Bankruptcy prediction using Partial Least Squares Logistic Regression. Journal of Retailing and Consumer Services, 36, 197-202.
- Bose, I. (2006). Deciding the financial health of dot-coms using rough sets. Information & Management, 43(7), 835-846.
- Cepel, M., Dvorsky, J., Gregova, E., & Vrbka, J. (2020). Business environment quality model in the SME segment. Transformations in Business & Economics, 9(1), 262-283.
- Donthu, N., & Gustafsson, A. (2020). Effects of COVID-19 on business and research. Journal of Business Research, 117, 284-289.
- Dorgai, K., Fenyves, V., & Sütő, D. (2016). Analysis of commercial enterprises’ solvency by means of different bankruptcy models. Gradus, 3(1), 341-349.
- Durica, M., Valaskova, K., & Janoskova, K. (2019). Logit business failure prediction in V4 countries. Engineering Management in Production and Services, 11(4), 54-64.
- Dvorský, J., Kljucnikov, A., & Polách, J. (2020). Business risks and their impact on business future concerning the entrepreneur’s experience with business bankruptcy: Case of Czech Republic. Problems and Perspectives in Management, 18(2), 418-430.
- Faltus, S. (2014). Firm Default Prediction Model for Slovak Companies. Proceedings of the 11th International Conference on European Financial Systems, 173-177. Lednice, Czech Republic.
- Fenyves, V., Dajnoki, K., Máté, D., & Kata, B-G. (2016). Examination of the solvency of enterprises dealing with accommodation service providing in the northern great plain region. SEA: Practical Application of Science, 11, 197-203.
- Habibi, A., & Iqbal, M. (2021). Benefits of financial ratios for financing sharia banking Indonesia. Jurnal Ekonomi Dan Keuangan Syariah, 5(1), 1-12.
- Holienka, M., Pilková, A., & Kubišová, M. (2016). The influence of intellectual capital performance on value creation in Slovak SMEs. In T. Dudycz, G. Osbert-Pociecha & B. Brycz (Eds.), The Essence and Measurement of Organizational Efficiency (pp.65-77). Springer Proceedings in Business and Economics.
- Hosaka, T. (2019). Bankruptcy prediction using imaged financial ratios and convolutional neural networks. Expert Systems with Applications, 117, 287-299.
- Hua, Z., Wang, Y., Xu, X., Zhang, B., & Liang, L. (2007). Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Systems with Applications, 33(2), 434-440.
- Jenčová, S., Vašaničová, P., & Litavcová, E. (2019). Financial indicators of the company from electrical engineering industry: The case study of Tesla, Inc. Serbian Journal of Management, 14(2), 361-371.
- Karas, M., & Režňáková, M. (2012). Financial Ratios as Bankruptcy Predictors: The Czech Republic Case. Proceedings of the 1st WSEAS International Conference on Finance, Accounting and Auditing, 86-91.
- Kliestik, T., Misankova, M., Valaskova, K., & Svabova, L. (2018). Bankruptcy Prevention: new effort to reflect on legal and social changes. Science and Engineering Ethics, 24(2), 791-803.
- Kliestik, T., Valaskova, K., Lazaroiu, G., Kovacova, M., & Vrbka, J. (2020a). Remaining financially healthy and competitive: the role of financial predictors. Journal of Competitiveness, 12(1), 74-92.
- Kliestik, T., Valaskova, K., Nica, E., Kovacova, M., & Lazaroiu, G. (2020b). Advanced methods of earnings management: Monotonic trends and change-points under spotlight in the Visegrad countries. Oeconomia Copernicana, 11(2), 371-400.
- Knot, O., & Vychodil, O. (2006). Czech Bankruptcy Procedures: Ex-post Efficiency View (Working Papers IES 2006/03). Charles University Prague.
- Kotaskova, A., Lazanyi, K., Amoah, J., & Belás, J. (2020). Financial risk management in the V4 Countries’ SMEs segment. Investment Management and Financial Innovations, 17(4), 228-240.
- Kral, P., Svabova, L., & Durica, M. (2018). Overview of selected bankruptcy prediction models applied in V4 countries. Second International Scientific Conference on Economics and Management. Ljubljana, Slovenia.
- Kristóf, T., & Virág, M. (2020). A Comprehensive Review of Corporate Bankruptcy Prediction in Hungary. Journal of Risk and Financial Management, 13(2), 35.
- Lin, W.-C., Lu, Y.-H., & Tsai, C.-F. (2019). Feature selection in single and ensemble learning-based bankruptcy prediction models. Expert Systems, 36(1), e12335.
- Mai, F., Tian, S., Lee, C., & Ma, L. (2019). Deep learning models for bankruptcy prediction using textual disclosures. European Journal of Operational Research, 274(2), 743-758.
- Nachane, M. D. (2006). Econometrics: Theoretical foundations and empirical perspectives. Oxford University Press.
- Nyitrai, T. (2019). Dynamization of bankruptcy models via indicator variables. Benchmarking: An International Journal, 26(1), 317-332.
- Nyitrai, T., & Virág, M. (2019). The effects of handling outliers on the performance of bankruptcy prediction models. Socio-Economic Planning Sciences, 67, 34-42.
- Ohlson, J. A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), 109-131.
- Ozturk, H., & Karabulut, T. A. (2020). Impact of financial ratios on technology and telecommunication stock returns: evidence from an emerging market. Investment Management and Financial Innovations, 17(2), 76-87.
- Popescu, M. E., & Dragotă, V. (2018). What do post-communist countries have in common when predicting financial distress? Prague Economic Papers, 27(6), 637-653.
- Premachandra, I. M., Bhabra, G. S., & Sueyoshi, T. (2009). DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique. European Journal of Operational Research, 193(2), 412-424.
- Prusak, B. (2018). Review of Research into Enterprise Bankruptcy Prediction in Selected Central and Eastern European Countries. International Journal of Financial Studies, 6(3), 60.
- Reizinger-Ducsai, A. (2016). Bankruptcy prediction and financial statements. The reliability of a financial statement for the purpose of modelling. Prace Naukowe Uniwersytetu Ekonomicznego We Wrocławiu, 441, 202-213.
- Sharma, R. K., Bakshi, A., Chhabra, Sh., & McMillan, D. (Rev. ed.). (2020). Determinants of behaviour of working capital requirements of BSE listed companies: An empirical study using co-integration techniques and generalised method of moments. Cogent Economics & Finance, 8(1).
- Simon, S., Sawandi, N., & Abdul-Hamid, M. A. (2017). The quadratic relationship between working capital management and firm performance: Evidence from the Nigerian economy. Journal of Business and Retail Management Research, 12(1).
- Springate, G. L. (1978). Predicting the possibility of failure in a Canadian firm: A discriminant analysis. Simon Fraser University.
- Štefko, R., Jenčová, S., Litavcová, E., & Vašaničová, P. (2017). Management and funding of the healthcare system. Polish Journal of Management Studies, 16(2), 266-277.
- Valaskova, K., Kliestik, T., & Kovacova, M. (2018a). Management of financial risks in Slovak enterprises using regression analysis. Oeconomia Copernicana, 9(1), 105-121.
- Valaskova, K., Kliestik, T., Svabova, L., & Adamko, P. (2018b). Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities Using Regression Analysis. Sustainability, 10(7), 2144.
- Vochozka, M., Vrbka, J., & Suler, P. (2020). Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM. Sustainability, 12(18), 7529.
- Wang, T. C., & Chen, Y. H. (2006). Applying rough sets theory to corporate credit ratings. IEEE International Conference on Service Operations and Logistics, and Informatics, 132-136.
- Zaini, B. J., & Mahmuddin, M. (2019). Classifying Firms’ Performance using Data Mining Approaches. International Journal Supply Chain Management, 8(1), 690.
- Zmijewski, M. E. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research, 22, 59-82.
- Zoričák, M., Gnip, P., Drotár, P., & Gazda, V. (2020). Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets. Economic Modelling, 84, 165-176.