Management of Financial Risks in Slovak Enterprises Using Regression Analysis

• Autorzy: Katarina Valaskova , Tomas Kliestik , Maria Kovacova
• Języki publikacji: EN

Abstrakty

EN

Research background: Financial risk management is the task of monitoring financial risks and managing their impact. Financial risk is often perceived as the risk that a company may default on its debt payments. The issue of the debt, default or prosperity of the company are presented in the article as one of the ways of the risk management. A prediction of corporate default is an inseparable element of the risk management. Mainly the consequences of risk are the engine of research and development of methods and models, which enable to predict economic and financial situation in specific conditions of global economies.
Purpose of the article: The main aim of the presented article is to assess financial risks of Slovak entities, realized by the identification of significant factors and determinants affecting the prosperity of Slovak companies.
Methods: To conduct the research we have used the data of Slovak enterprises, obtained from annual financial reports covering the year 2015 and the calculated financial ratios of profitability, activity, liquidity and indebtedness that may affect the financial health of the company were applied in the regression analysis. Realizing the multiple regression analysis, the statistically significant determinants that affect the future financial development of the company are identified, as well as the regression model of the bankruptcy prediction.
Findings & Value added: In the research aimed at the management of financial risks in Slovak enterprises, we focused on the revelation of significant economic risk factors using multiple regression. The results suggest that the most significant predictors are net return on capital, cash ratio, quick ratio, current ratio, net working capital, RE/TA ratio, current debt ratio, financial debt ratio and current assets turnover based on which the decision about the future company default can be made. These factors are significant enough to manage financial risks and to affect the profitability and prosperity of the company. (original abstract)

Słowa kluczowe

EN

Financial risk; Bankruptcy; Regression models

PL

Ryzyko finansowe; Bankructwo; Modele regresji

• Czasopismo: Oeconomia Copernicana
• Rocznik: 2018
• Tom: 9
• Numer: nr 1
• Strony: 105--121

Twórcy

autor

Katarina Valaskova

• University of Zilina, Slovakia

autor

Tomas Kliestik

• University of Zilina, Slovak Republic

autor

Maria Kovacova

• University of Zilina, Slovak Republic

Bibliografia

• Adamko P. (2016). Analysis of the modifications of the global Altman´s model 2014. In: T. Kliestik (Ed.). Proceedings of the 16th international scientific conference on globalization and its socio-economics consequences. Rajecke Teplice: University of Zilina.
• Altman E.I. (1968). Financial ratios. Discriminant analysis and prediction of corporate bankruptcy. Journal of Finance, 23(4). doi: 10.2307/2978933.
• Antunes F., Ribeiro B., & Pereira F. (2017). Probabilistic modelling and visualization for bankruptcy prediction. Applied Soft Computing, 60. doi: 10.1016/j.asoc.2017.06.043.
• Argenti J. (1976). Corporate collapse: the causes and symptoms. London: McGraw-Hill.
• Barboza F., Kimura H., & Altman E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83. doi: 10.1016/j.eswy.2017.04.006.
• Barreda A.A., Kageyama Y., Singh D., & Zubieta S. (2017). Hospitality bankruptcy in United States of America: a multiple discriminant analysis - Logit model comparison. Journal of Quality Assurance in Hospitality & Tourism, 18(1). doi: 10.1080/1528008X.2016.1169471.
• Beaver W.H. (1966). Financial ratios as predictors of failure. Empirical research in accounting: selected studies. Journal of Accounting Research, 4. doi: 10.2307/2490171.
• Belas J., & Cipovova E. (2011). Internal model of commercial bank as an instrument for measuring credit risk of the borrower in relation to financial performance (credit scoring and bankruptcy models). Journal of Competitiveness, 3(4).
• Ben Jabeur S. (2017). Bankruptcy prediction using partial least squares logistic regression. Journal of Retailing and Consumers Services, 36. doi: 10.1016/j.jretconser.2017.02.005.
• Berent T., Bławat B., Dietl M., Krzyk P., & Rejman R. (2017). Firm's default - new methodological approach and preliminary evidence from Poland. Equilibrium. Quarterly Journal of Economics and Economic Policy, 12(4). doi: 10.24136/eq.v12i4.39.
• Bohdalova M., & Klempaiova N. (2017). Bankruptcy model IN05 and private Slovak civil engineering companies. In: J. Nesleha, T. Plihal & K. Urbanovsky (Eds.). Proceedings of the 14th international scientific conference on European financial systems. Brno: Masarykova university.
• Boratyńska K. (2016). Corporate bankruptcy and survival on the market: lessons from evolutionary economics. Oeconomia Copernicana, 7(1). doi: 12775/OeC.2016.008.
• Camska D. (2016). Accuracy of models predicting corporate bankruptcy in a selected industry branch. Journal of Economics, 64(4).
• Cisko S., & Kliestik T. (2013). Financial management II. Žilina: EDIS.
• De Andres J., Lorca P., de Cos Juez F.J., & Sánchez-Lasheras F. (2011). Bankruptcy forecasting: a hybrid approach using fuzzy c-means clustering and multivariate adaptive regression splines (MARS). Expert Systems with Applications, 38(3). doi: 10.1016/j.eswa.2010.07.117.
• Durica M., & Adamko P. (2016). Verification of MDA bankruptcy prediction models for enterprises in Slovak Republic. In: T. Loster & T. Pavelka (Eds.). Proceedings of the 10th international days of statistics and economics. Praha: Melandrium.
• Ékes K.S., & Koloszár L. (2014). The efficiency of bankruptcy forecast models in the Hungarian SME sector. Journal of Competitiveness, 6(2). doi: 10.7441/joc.2014.02.05.
• Faltus S. (2014). Firm default prediction model for Slovak companies. In: Proceedings of the 11th international conference on European financial systems. Praha.
• Fedorova E., Gilenko E., & Dovzhenko S. (2013). Bankruptcy prediction for Russian companies: application of combined classifiers. Expert Systems with Application, 40(18). doi: 10.1016/j.eswa.2013.07.032.
• Fitzpatrik P.J. (1931). A comparison of the ratios of successful industrial enterprises with those of failed companies. Certified Public Accountant, 6.
• Fulmer J.G., Moon J.E., Gavin T.A., & Erwin M.J. (1984). A bankruptcy classification model for small firms. Journal of Commercial Bank Lending, 66(11).
• Gavurova B., Packova M., Misankova M., & Smrcka L. (2017a). Predictive potential and risks of selected bankruptcy prediction models in the Slovak business environment. Journal of Business Economics and Management, 18(6). doi: 10.3846/16111699.2017.1400461.
• Gavurova B., Janke F., & Packova M. (2017b). Analysis of impact of using the trend variables on bankruptcy prediction models performance. Journal of Economics, 65(4).
• Gundova P. (2015). Verification of the selected prediction methods in Slovak companies. Acta academica karviniensia, 4.
• Horrigan J.O. (1996). The determination of long-term credit standing with financial ratios. Journal of Accounting Research, 62. doi: 10.2307/2490168.
• Jílek J. (2000). Financial risks. Praha: Grada.
• Kamenikova K. (2005). Determination of the use of the financial development prediction models in conditions of Slovakia. Acta Montanistica Slovaca, 10(3).
• Kiaupaite-Grushniene V. (2016). Altman Z-score model for bankruptcy forecasting of the listed Lithuanian agricultural companies. In: J. Alver (Ed.). Proceedings of the 5th international conference on accounting, auditing, and taxation. Tallinn: Atlantis Press.
• Kim S.Y. (2011). Prediction of hotel bankruptcy using support vector machine, artificial neural network, logistic regression and multivariate discriminant analysis. Service Industries Journal, 31(3). doi: 10.1080/02642060802712848.
• Kostrzewska J., Kostrzewski M., & Pawelek B. (2016). The classical and Bayesan logistic regression in the research on the financial standing of enterprises after bankruptcy in Poland. In: M. Papiez & S. Smiech (Eds.). Proceedings of the 10th professor Aleksander Zelias international conference on modelling and forecasting of socio-economic phenomena. Zakopane: Foundation Cracow Univ Economics.
• Kovacova M., & Kliestik T. (2017). Logit and probit application for the prediction of bankruptcy in Slovak companies. Equilibrium. Quarterly Journal of Economics and Economic Policy, 12(4). doi: 10.24136/eq.v12i4.40.
• Kral P., Fleischer M., & Stachova M. (2016). Corporate financial distress prediction of Slovak companies: Z-score models vs. alternatives. In: M. Boda & V. Mendelova (Eds.). Proceedings of the 19th conference on applications of mathematics and statistics in economics. Banska Stiavnica: University of Banska Bystrica.
• Kral P., & Janoskova K. (2016). Evaluation of prediction ability of bankruptcy prediction models applying logistic regression (LOGIT). In: H. Zhang (Ed.). Proceedings of the international conference of information, communication and social sciences. United Arab Emirates: Singapore Management & Sports Science institution Pte Ltd.
• Kubickova D., & Nulicek V. (2017). Bankruptcy model construction and its limitation in input data quality. In: P. Jedlicka, P. Maresova & I. Soukal (Eds.). Proceedings of the 15th international scientific conference on Hradec economic days. Hradec Kralove: University of Hradec Kralove.
• Kubickova D. (2011). Financial statements according to IFRS and the bankruptcy model Z-score. Journal of Competitiveness, 3(1).
• Lalbakhsh P., & Chen Y.P. (2017). TACD: a transportable ant colony discrimination model for corporate bankruptcy prediction. Enterprise Information Systems, 11(5). doi: 10.1080/17517575.2015.1090630.
• Lawrence K., Pai D.R. & Kleinman G. (2009). Bankruptcy prediction in retail industry using logistic regression. Financial Modelling Applications and Data Envelopment Applications, 13.
• Li M.Y.L., & Miu P. (2010). A hybrid bankruptcy prediction model with dynamic loadings on accounting-ratio-based and market-based information: a binary quantile regression approach. Journal of Empirical Finance, 17(4). doi: 10.1108/S0276-8976(2009)0000013006.
• Lorca P., Landajo M., & De Andres J. (2014). Nonparametric quantile regression- based classifiers for bankruptcy forecasting. Journal of Forecasting, 33(2). doi: 10.1002/for.2280.
• Mendelova V., & Bielikova T. (2017). Diagnosing of the corporate financial health using DEA: an application to companies in the Slovak Republic. Politicka Ekonomie, 65(1). doi: 10.18267/j.pep.1125.
• Merton R.C. (1973). Theory of rational option pricing. Bell journal of Economic and Management Science, 4(1). doi: 10.2307/3003143.
• Mihalovic M. (2016). Performance comparison of multiple discriminant analysis and logit models in bankruptcy prediction. Economics & Sociology, 9(4). doi: 10.14254/2017-789X.2016/9-4/6.
• Moles P. (1998). Financial risk management. Sources of financial risk and risk assessment. Edinburgh: Heriot- Watt University.
• Neumaierova I., & Neumaier I. (2002). Efficiency and corporate market value. Praha: GRADA Publishing.
• Ohlson J.A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1). doi: 10.2307/2490395.
• Onofrei M., & Lupu D. (2014). The modeling of forecasting the bankruptcy risk in Romania. Economic Computation and Economic Cybernetics Studies and Research, 48(3).
• Patakyova M., & Gramblickova B. (2016). Bankruptcy and liquidation: current legal situation in European and international context, solutions under the European model company act (EMCA). European Company and Financial Law Review, 13(2). doi: 10.1515/ecfr-2016-0322.
• Pawelek B., Pociecha J., & Baryla M. (2016). Dynamic aspects of bankruptcy prediction logit model for manufacturing firms in Poland. In: A. Wilhelm & H. Kestler (Eds.). Proceedings of the 2nd European conference on data analysis (ECDA) /workshop on classification and subject indexing in library and information science. Bremen: Springer-Verlag Berlin.
• Rimarčík M. (2007). Statistics for practice. Bratislava: Marián Rimarčík Publisher.
• Singh B.P., & Mishra A.K. (2016). Re-estimation and comparisons of alternative accounting based bankruptcy prediction models for Indian companies. Financial Innovations, 2(1). doi: 10.1186/s40854-016-0026-9.
• Sofrankova B. (2014). Analysis of prediction model in conditions of accommodation sector in Slovakia. Trends in Entrepreneuring, 3.
• Sofrankova B., & Matkova S. (2016). Analytical view of using prediction models in conditions of accommodation facilities in Slovakia. In: Proceedings of the 3rd international multidisciplinary scientific conference on social sciences and arts. Albena: Stef 92 Technology Ltd.
• Springate G.I.L (1978). Predicting the possibility of failure in a Canadian firm. Unpublished M.B.A. Research Project, Simon Fraser University.
• Spuchlakova E., & Michalikova K.F. (2016). The selected global bankruptcy models. In: T. Kliestik (Ed.). Proceedings of the 16th international scientific conference on globalization and its socio-economic consequences. Rajecke Teplice: University of Zilina.
• Stachova M., Kral P., Sobisek L., & Kakascik M. (2015). Analysis of financial distress of Slovak companies using repeated measurements. Applications of Mathematics and Statistics in Economics.
• Svabova L., & Durica M. (2016). A closer view of the statistical methods globally used in bankruptcy prediction of companies. In: T. Kliestik (Ed.). Proceedings of the 16th international scientific conference on globalization and its socioeconomic consequences. Rajecke Teplice: University of Zilina.
• Svabova L., & Kral P. (2016). Selection of predictors in bankruptcy prediction models for Slovak companies. In: T. Loster & T. Pavelka (Eds.). Proceedings of the 10th international days of statistics and economics. Praha: Melandrium.
• Taffler R.J. (1983). The assessment of company solvency and performance using a statistical model. Accounting & Business Research, 52. doi: 10.1080/00014788 .1983.9729767.
• Taffler R.J., & Tisshawa H. (1984). The audit going- concern in Practise. Accountant ´s Magazine, 88.
• Weissova I. (2016). Applicability of selected predictive models in the Slovak companies. In: D. Prochazka (Ed.). Proceedings of the 17th annual conference on finance and accounting. Prague: Springer International Publishing Ag.
• Wilcox J.W. (1971). A simple theory of financial ratios as predictors of failure. Journal of Accounting Research, 9(2). doi: 10.2307/2489944.
• Wilson N., Ochotnický P., & Kacer M. (2016). Creation and destruction in transition economies: the SME sector in Slovakia. International Small Business Journal, 34(5). doi: 10.1177/0266242614558892.
• Zhao D., Huang Ch., & Wei Y. (2017). An effective computational model for bankruptcy prediction using kernel extreme learning machine approach. Computational Economics, 49(2). doi: 10.1007/s.10614-016-9562-7.
• Zmijewski M. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22. doi: 10.2307/2490859.
• Zvarikova K., Spuchlakova E., & Sopkova G. (2017). International comparison of the relevant variables in the chosen bankruptcy models used in the risk management. Oeconomia Copernicana, 8(1). doi: 10.24136/oc.v8i1.10.

Identyfikatory

DOI

https://doi.org/10.24136/oc.2018.006