The practice of use of models predicting financial distress in Slovak companies
DOI:
https://doi.org/10.15549/jeecar.v7i1.369Keywords:
financial analysis, models predicting financial distress, Slovak companiesAbstract
The aim of the paper is to present the results of the research focused on the identification of the current situation concerning the knowledge and use of the models predicting financial distress in Slovak companies. In the paper three partial goals are formulated. On grounds of the goals of this paper four hypotheses were formulated. Their validity was verified by means of the primary data gained by the questionnaire research with the use of the statistical software. The research results confirmed that Slovak companies did not know the term “models predicting financial distress” neither applied them in practice. The main reasons why they do not apply them involve not knowing them, the company size (too small company) and the use of some own prediction methods. The most often used models are simple methods of point evaluation in business practice. Companies prefer simple methods not demanding of much time.
References
Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Kumar, V., Ajayi, O., Akinade, O. O., Bilal, M. (2018). Systematic review of bankruptcy prediction models: Towards a framework for tool selection, Expert Systems with Applications, 94(March 2018), 164-184. DOI: https://doi.org/10.1016/j.eswa.2017.10.040
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, The Journal of Finance, 23(4), 589–609. DOI: https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
Altman, E. I. (1983). Corporate financial distress: a complete guide to predicting and avoiding distress and profiting from bankruptcy, 2nd edition, New York: Wiley.
Altman, E. I. (2002). Bankruptcy, credit risk, and high yield junk bonds, Malden: Blackwell.
Altman, E. I. (2013). Predicting inancial distress of companies: revisiting the Z-score and ZETA ® models, Chapter 17 in A. R. Bell, C. Brooks, M. Prokopczuk (Eds.). Handbook of research methods and applications in empirical inance. Glos, UK: Edward Elgar Publishing, doi: http://doi.org/10.4337/ 9780857936097
Altman, E. I. et al. (1977). ZETA™ analysis: a new model to identify bankruptcy risk of corporations, Journal of Banking and Finance, 1(1), 29–54, doi: http://doi.org/10. 1016/0378-4266(77)90017-6
Ansoff, H. I. (1965). Corporate Strategy, New York: McGraw-Hill.
Antunes, F., Ribeiro, B., Pereira, F. (2017). Probabilistic modeling and visualization for bankruptcy prediction, Applied Soft Computing, 60, 831-843. DOI: https://doi.org/10.1016/j.asoc.2017.06.043
Azayite, F. Z., Achchab, S. (2016). Hybrid Discriminant Neural Networks for bankruptcy prediction and risk scoring, Procedia Computer Science, 83, 670-674. DOI: https://doi.org/10.1016/j.procs.2016.04.149
Balcaen, S., Ooghe, H. (2006). 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems, The British Accounting Review, 38(1), 63–93. DOI: https://doi.org/10.1016/j.bar.2005.09.001
Beaver, W. H. (1966). Financial Ratios as Predictors of Failure. Empirical Research in Accounting, The Journal of Accounting Research, 4, 71–111. DOI: https://doi.org/10.2307/2490171
Binkert, Ch. H. (1999). Fruher kennung von Unternehmenskrisen mit Hilfe geeigneter Methoden im deutschen und slowakischen Wurtschaftsraum, Disertation tesis.
Bo?a, M., Úradní?ek, V. (2016). The portability of Altman’s Z-score model to predicting corporate financial distress of Slovak companies, Technological and Economic Development of Economy, 22(4), 532–553, doi: https://doi.org/10.3846/20294913.2016.1197165 DOI: https://doi.org/10.3846/20294913.2016.1197165
Chrastinová, Z. (1998). Metódy hodnotenia ekonomickej bonity a predikcie finan?nej situácie po?nohospodárskych podnikov, Bratislava: VÚEPP.
Deakin, E. B. (1972). A discriminant analysis of predictors of business failure, Journal of Accounting Research, 10(1), 167–179. DOI: https://doi.org/10.2307/2490225
Delina, R., Packová, M. (2013). Prediction bankruptcy models validation in Slovak business environment, E+M Economics and Management, 16(3), 101-110.
Eklund, J., Levratto, N., Ramello, G.B. (2018). Entrepreneurship and failure: two sides of the same coin?, Small Business Economics, 1-10, doi: https://doi.org/10.1007/s40804-017-0067-1 DOI: https://doi.org/10.1007/s40804-017-0067-1
European Commision, (2003). Commission Recommendation 2003/361/, from https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2003:124:0036:0041:EN:PDF
Fitzpatrick, F. (1932). A Comparison of Ratios of Successful Industrial Enterprises with Those of Failed Firm, Certified Public Accountant, 6, 727-731.
Gajdka J., Stos D. (1996). Wykorzystanie analizy dyskryminacyjnej w ocenie kondycji finansowej przedsi?biorstw, Krakow: Wydawnictwo Akademii Ekonomicznej.
Gavúrová, B. et al. (2017). Predictive potential and risks of selected bankruptcy prediction models in the Slovak business environment, Journal of Business Economics and Management, 18(6), 1156-1173, doi: https://doi.org/10.3846/16111699.2017.1400461 DOI: https://doi.org/10.3846/16111699.2017.1400461
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. DOI: https://doi.org/10.1016/j.ejor.2014.08.016
Gissel, J. L. et al. (2007). A review of bankruptcy prediction studies: 1930 to present, Journal of Financial Education, 33(winter 2007), 1–42.
Grice, J. S., Dugan, M. T. (2001). The limitations of bankruptcy prediction models: Some cautions for the researcher, Review of Quantitative Finance and Accounting, 17, 151–166. DOI: https://doi.org/10.1023/A:1017973604789
Grice, J. S., Ingram, R. W. (2001). Tests of the generalizability of Altman´s bankruptcy prediction model, Journal of Business Research, 54(1), 53–61. DOI: https://doi.org/10.1016/S0148-2963(00)00126-0
Gur?ík, ?. (2002). G-index-metóda predikcie fi nan?ného stavu po?nohospodárskych podnikov, Agricultural economics, 48(8), 373–378. DOI: https://doi.org/10.17221/5338-AGRICECON
Holda, A. (2006). Zasada kontynuacji dzia?alno?ci i prognozowanie upad?o?ci w polskich realiach gospodarczych, Krakow: Zeszyty Naukowe/Akademia Ekonomiczna w Krakowie.
Horváthová, J., Mokrišová, M. (2018). Risk of Bankruptcy, Its Determinants and Models, Risks, 6(117), 1-22. DOI: https://doi.org/10.3390/risks6040117
Hosaka, T. (2019). Bankruptcy prediction using imaged financial ratios and convolutional neural networks, Expert Systems with Application, 117, 287-299. DOI: https://doi.org/10.1016/j.eswa.2018.09.039
Jabeur, S. (2017). Bankruptcy prediction using Partial Least Squares Logistic Regression, Journal of Retailing and Consumer Services, 36, 197-202. DOI: https://doi.org/10.1016/j.jretconser.2017.02.005
James, H. (2010). The creation and destruction of value, Cambridge: Harvard University Press.
Karas, M., Rež?áková, M. (2018). Building a bankruptcy prediction model: Could information about past development increase model accuracy? Polish Journal of Management Studies, 17(1), 116-130, doi: https://dx.doi.org/10.17512/pjms.2018.17.1.10 DOI: https://doi.org/10.17512/pjms.2018.17.1.10
Kim, K., Lee, K., Ahn, H. (2019). Predicting Corporate Financial Sustainability using Novel Business Analytics, Sustainability, 11(64), 1-17, doi: https://doi.org/10.3390/su11010064 DOI: https://doi.org/10.3390/su11010064
Klieštik, T., Ko?išová, K., Mišanková, M. (2015). Logit and Probit Model used for Prediction of Financial Health of Company, Procedia Economics and Finance, 23, 850-855. DOI: https://doi.org/10.1016/S2212-5671(15)00485-2
Klieštik, T., Vrbka, J., Rowland, Z. (2018). Bankruptcy prediction in Visegrad group countries using multiple discriminant analysis, Equilibrium. Quarterly Journal of Economics and Economic Policy, 13(3), 569-593, doi: https://doi.org/10.24136/eq.2018.028 DOI: https://doi.org/10.24136/eq.2018.028
Kononiuk, A., Sacio-Szyma?ska, A., Gáspár, J. (2017). How do companies envisage the future? Functional foresight approaches, Engineering Management in Production and Services, 9(4), 21-23. DOI: https://doi.org/10.1515/emj-2017-0028
Ková?ová, M., Klieštik, T., Kubala, P. Valášková, K. Radiši?, M., Borocki, J. (2018). Bankruptcy models: Verifying their validity as a predictor of corporate failure, Polish Journal of Management Studies, 18(1), 167-179, doi: https://doi.org/ 10.17512/pjms.2018.18.1.13
Lee, S. H. et al. (2011). How do bankruptcy laws affect entrepreneurship development around the world? Journal of Business Venturing, 26(5), 505-520, doi: https://doi.org/10.1016/j.jbusvent.2010.05.001 DOI: https://doi.org/10.1016/j.jbusvent.2010.05.001
Lee, T. S., Yeh, Y. H. (2004). Corporate Governance and Financial Distress: evidence from Taiwan, Corporate Governance. An International Review, 12(3), 378-388.
Lesáková, ?. (2014). Small and medium enterprises in the new world of globalization, Forum Scientiae Oeconomia, 2(3), 111-122.
Li L., Faff, R. (2019). Predicting corporate bankruptcy: What matters? International Review of Economics and Finance, 62(July2019), 1-19. DOI: https://doi.org/10.1016/j.iref.2019.02.016
Merwin, C. L. (1942). Financial Small Corporations in Five Manufactoring Industries, 1926-1936, National Bureau of Economic Research, from https://www.nber.org/books/merw42-1
Mihalovi?, M. (2018). Využitie skóringových modelov pri predikcii úpadku ekonomických subjektov v Slovenskej republike, Politická ekonomie, 66(6), 689-708, doi: https://doi.org/10.18267/j.polek.1226 DOI: https://doi.org/10.18267/j.polek.1226
Nieman, M., Schmidt, J. H., Neukirchen, M. (2008). Improving performance of corporate rating prediction models by reducing heterogeneity, Journal of Banking and Finance, 32, 434–446. DOI: https://doi.org/10.1016/j.jbankfin.2007.05.015
Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy, Journal of Accounting Research, 18(1), 109–131. DOI: https://doi.org/10.2307/2490395
Pereira, J., Basto, M., da Silva, A. (2016). The Logistic Lasso and Ridge Regression in Predicting Corporate Failure, Procedia Economics and Finance, 39, 634-641. DOI: https://doi.org/10.1016/S2212-5671(16)30310-0
Peres, C., Antao, M. (2016). The use of multivariate discriminant analysis to predict corporate bankruptcy: A review, AESTIMATIO IEB Int. J. Finance, 14, 108-131, doi: https://doi.org/ 10.5605/IEB.14.6
Pozzoli, M., Paolone, F. (2017).Corporate Financial Distress, Cham: SpringerBriefs in Finance. DOI: https://doi.org/10.1007/978-3-319-67355-4
Prusak B. (2005). Nowoczesne metody prognozowania zagro?enia finansowego przedsi?biorstw, Warszawa: Difin.
Schönfeld J., Kud?j, M., Smr?ka, L. (2018). Financial health of enterprises introducing safeguard procedure based on bankruptcy models, Journal of Business Economics and Management, 19(5), 692-705, doi: https://doi.org/10.3846/jbem.2018.7063 DOI: https://doi.org/10.3846/jbem.2018.7063
Shpak, N.O., Stanusiak, N.S., Hlushko, O.V., Sroka, W. (2018). Assessment of the social and labor components of industrial potential in the context of corporate social responsibility, Polish Journal of Management Studies, 17(1), 209-220, doi: http://doi.org/10.17512/pjms.2018.17.1.17 DOI: https://doi.org/10.17512/pjms.2018.17.1.17
Sroka W., Szántó R. (2017). Business ethics in CEE: analysis of research results, Proceedings of the 5th International Scientific Conference “Innovation Management, Entrepreneurship and Sustainability”, 942-951.
Statistical Office of the Slovak Republic, from https://slovak.statistics.sk
Stoklasová, R. (2018). Econometric Analysis of SMEs in Eurozone, Forum Scientiae Oeconomia, 6(1), 19-29.
Sulub, S. A. (2014). Testing the predictive power of Altman´s revised Z´model: the case of 10 multinational companies, Research Journal of Finance and Accounting, 5(21), 174-184.
Sun, J. et al. (2014). Predicting financial distress and corporate failure: a review from the state-of-the-art definitions, modeling, sampling, and featuring approaches, Knowledge-Based Systems, 57, 41–56, doi: http://doi.org/10.1016/j.knosys.2013.12.006 DOI: https://doi.org/10.1016/j.knosys.2013.12.006
Veganzones, D., Séverin, E. (2018). An investigation of bankruptcy prediction in imbalanced datasets, Decision Support Systems, 112, 111-124. DOI: https://doi.org/10.1016/j.dss.2018.06.011
Virag, M., Kristof, T. (2005). Neural networks in bankruptcy prediction – a comparative study on the basis of the first Hungarian bankruptcy model, Acta Oeconomica, 55(4), 403–425. DOI: https://doi.org/10.1556/aoecon.55.2005.4.2
Wu, Y., Gaunt, C., Gray, S. (2010). A comparison of alternative bankruptcy prediction models, Journal of Contemporary Accounting and Economics, 6(1), 34–45. DOI: https://doi.org/10.1016/j.jcae.2010.04.002
Zalai, K. (2000). Metódy predvídania finan?nej situácie podnikov (a ich uplatnenie v SR), Finan?ný radca, 2(14-15), 106-128.
Zmijewski, M. E. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models, Journal of Accounting Research, 24, 59–82. DOI: https://doi.org/10.2307/2490859
Downloads
Published
How to Cite
Issue
Section
License
The JEECAR journal allows the author(s) to hold the copyright and publishing rights of their own manuscript without restrictions.
This journal applies the Creative Attribution Common License to works we publish, and allows reuse and remixing of its content, in accordance with a CC-BY 4.0 license.
Authors are free to: Share — copy and redistribute the material in any medium or format and Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
No additional restrictions — The author may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
The JEECAR Journal is committed to the editorial principles of all aspects of publication ethics and publication malpractice as assigned by the Committee on Public Ethics.