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Logit business failure prediction in V4 countries

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents the creation of the model that predicts the business failure of companies operating in V4 countries. Based on logistic regression analysis, significant predictors are identified to forecast potential business failure one year in advance. The research is based on the data set of financial indicators of more than 173 000 companies operating in V4 countries for the years 2016 and 2017. A stepwise binary logistic regression approach was used to create a prediction model. Using a classification table and ROC curve, the prediction ability of the final model was analysed. The main result is a model for business failure prediction of companies operating under the economic conditions of V4 countries. Statistically significant financial parameters were identified that reflect the impending failure situation. The developed model achieves a high prediction ability of more than 88%. The research confirms the applicability of the logistic regression approach in business failure prediction. The high predictive ability of the created model is comparable to models created by especially sophisticated artificial intelligence approaches. The created model can be applied in the economies of V4 countries for business failure prediction one year in advance, which is important for companies as well as all stakeholders.
Rocznik
Strony
54--64
Opis fizyczny
Bibliogr. 59 poz., rys., tab.
Twórcy
autor
  • The University of Zilina, Slovakia
  • The University of Zilina, Slovakia
  • The University of Zilina, Slovakia
Bibliografia
  • Agrawal, K., & Maheshwari, Y. (2016). Predicting financial distress: revisiting the option-based model. South Asian Journal of Global Business Research, 5(2), 268-284. doi: 10.1108/sajgbr-04-2015-0030
  • Alaka, H.A., Oyedele, L.O., Owolabi, H.A., Kumar, V., Ajayi, S.O., Akinade, O.O., & Bilal, M. (2018). Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Systems with Applications, 94, 164-184. doi: 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: 10.1111/j.1540-6261.1968.tb00843.x
  • Bandyopadhyay, A. (2006). Predicting probability of default of Indian corporate bonds: logistic and Z-score model approaches. The Journal of Risk Finance, 7(3), 255-272.
  • Bauer, P., & Endresz, M. (2016). Modelling Bankruptcy Using Hungarian Firm-Level Data. MNB Occasional Papers, 122.
  • Bewick, V., Cheek, L., & Ball, J. (2005). Statistics review 14: logistic regression. Critical Care, 9(1), 112-118. doi: 10.1186/cc3045
  • Brożyna, J., Grzegorz, M., & Pisula, T. (2016). Statistical methods of the bankruptcy prediction in the logistics sector in Poland and Slovakia. Transformations in Business & Economics, 15(1(37)), 80-96.
  • Čamska, D. (2016). Accuracy of models predicting corporate bankruptcy in a selected industry branch. Ekonomicky Casopis, 64(4), 353-366.
  • Chrastinova, Z. (1998). Metody hodnotenia ekonomickej bonity a predikcie finančnej situacie poľnohospodarskych podnikov [Methods of economic creditworthiness evaluation and prediction of financial situation of agricultural companies]. Bratislava, Slovakia: VUEPP.
  • Durica, M., Frnda, J., & Svabova, L. (2019). Decision tree based model of business failure prediction for Polish companies. Oeconomia Copernicana, 10(3), 453-469. doi: 10.24136/oc.2019.022
  • Ekes, K.S., & Koloszar, L. (2014). The Efficiency of Bankruptcy Forecast Models in the Hungarian SME Sector. Journal of Competitiveness, 6(2), 56-73. doi: 10.7441/joc.2014.02.05
  • Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. doi: 10.1016/j.patrec.2005.10.010
  • Fitzpatrick, P.J. (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). The use of discriminant analysis in assessing the financial condition of enterprises. In R. Borowiecki (Ed.), Restructuring in the Process of Transformation and Development of Enterprises. Krakow: Wydawnictwo Akademii Ekonomicznej w Krakowie.
  • Gavurova, B., Janke, F., Packova, M., & Pridavok, M. (2017). Analysis of Impact of Using the Trend Variables on Bankruptcy Prediction Models Performance. Ekonomicky Casopis, 65(4), 370-383.
  • Gruszczyński, M. (2003). Models of microeconometrics in the analysis and forecasting of the financial risk of enterprises. Zeszyty Polskiej Akademii Nauk, 23.
  • Gulka, M. (2016). Predictive Model of Corporate Failure in the Slovak Business Environment. Forum Statisticum Slovacum, 12(1), 16-22.
  • Gurčik, Ľ. (2012). G-index - the financial situation prognosis method of agricultural enterprises. Agricultural Economics (Zemědělska Ekonomika), 48(8), 373-378. doi: 10.17221/5338-agricecon
  • Hadasik, D. (1998). Upadłość przedsiębiorstw w Polsce i metody jej prognozowania [Bankruptcy of enterprises in Poland and methods of its forecasting]. Zeszyty Naukowe. Seria 2, Prace Habilitacyjne, Akademia Ekonomiczna w Poznaniu, 153.
  • Hajdu, O., & Virag, M. (2001). A Hungarian Model for Predicting Financial Bankruptcy. Society and Economy in Central and Eastern Europe, 23, 28-46.
  • Hamrol, M., Czajka, B., & Piechocki, M. (2004). Enterprise bankruptcy - discriminant analysis model. Przegląd Organizacji, 6, 35-39.
  • Hołda, A. (2001). Forecasting the bankruptcy of an enterprise in the conditions of the Polish economy using the discriminatory function ZH. Rachunkowość, 5, 306-310.
  • Hosmer, D.W., & Lemeshow, S. (2000). Applied Logistic Regression. New York, United States: John Wiley & Sons.
  • Hu, B., Palta, M., & Shao, J. (2006). Properties of R2 statistics for logistic regression. Statistics in Medicine, 25(8), 1383-1395. doi: 10.1002/sim.2300
  • Hurtošova, J. (2009). Konštrukcia ratingoveho modelu, nastroja hodnotenia uverovej sposobilosti podniku [Construction of the rating model as a tool for assessing the creditworthiness of a company] (Dissertation thesis). Bratislava, Slovakia: The University of Economics in Bratislava.
  • Jakubik, P., & Teply, P. (2011). The JT Index as an Indicator of Financial Stability of Corporate Sector. Prague Economic Papers, 20(2), 157-176. doi: 10.18267/j.pep.394
  • Jones, S., Johnstone, D., & Wilson, R. (2016). Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks. Journal of Business Finance & Accounting, 44(1-2), 3-34. doi: 10.1111/jbfa.12218
  • Kalouda, F., & Vaniček, R. (2013). Alternative bankruptcy models – First results. In O. Deev, V. Kajurova, & J. Krajiček (Eds.), European Financial Systems 2013 – Proceedings of the 10th International Scientific Conference (pp. 164-168). Brno, Czech Republic: Masaryk University.
  • Karas, M., & Režňakova, M. (2013). Bankruptcy Prediction Model of Industrial Enterprises in the Czech Republic. International Journal of Mathematical Models and Methods in Applied Sciences, 5, 519-531.
  • Karas, M., & Režňakova, M. (2017). Predicting the Bankruptcy of Construction Companies: A CART-Based Model. Engineering Economics, 28(2), 145-154. doi: 10.5755/j01.ee.28.2.16353
  • Karas, M., & Režňakova. M. (2014). A parametric or nonparametric approach for creating a new bankruptcy prediction model: The Evidence from the Czech Republic. International Journal of Mathematical Models and Methods in Applied Sciences, 8, 214-223.
  • Kliestik, T., Kliestikova, J., Kovacova, M., Svabova, L., Valaskova, K., Vochozka, M., & Olah, J. (2018a). Prediction of financial health of business entities in transition economies. New York, United States: Addleton Academic Publishers.
  • Kliestik, T., Misankova, M., Valaskova, K., & Svabova, L. (2018b). Bankruptcy prevention: new effort to reflect on legal and social changes. Science and Engineering Ethics, 24(2). doi: 10.1007/s11948-017-9912-4
  • Kliestik, T., Vrbka, J., & Rowland, Z. (2018c). Bankruptcy prediction in Visegrad group countries using multiple discriminant analysis. Equilibrium. Quarterly Journal of Economics and Economic Policy,13 (3), 569-593. doi: 10.24136/eq.2018.028
  • 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), 775-791. doi: 10.24136/eq.v12i4.40
  • Kumar, P.R., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques – a review. European Journal of Operational Research, 180(1), 1-28. doi: 10.1016/j.ejor.2006.08.043
  • Mączyńska, E. (1994). Assessment of the condition of the enterprise. Simplified methods. Życie Gospodarcze, 38, 42-45.
  • Mihalovič, M. (2016). Performance Comparison of Multiple Discriminant Analysis and Logit Models in Bankruptcy Prediction. Economics & Sociology, 9(4), 101-118. doi: 10.14254/2071-789x.2016/9-4/6
  • Němec, D., & Pavlik, M. (2016). Predicting insolvency risk of the Czech companies. In M. Reiff, & P. Gežik (Eds.), Proceedings of the International Scientific Conference Quantitative Methods in Economics: Multiple Criteria Decision Making XVIII (pp. 258-263). Bratislava, Slovakia: The University of Economics in Bratislava.
  • Neumaierova, I., & Neumaier, I. (2002). Vykonnost a tržni hodnota firmy [Efficiency and market value of the company]. Prague, Czech Republic: Grada Publishing.
  • Ohlson, J.A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), 109-131. doi: 10.2307/2490395
  • Pawelek, B., Galuszka, K., Kostrzewska, J., & Kostrzewski, M. (2017). Classification methods in the research on the financial standing of construction enterprises after bankruptcy in Poland. In F. Palumbo, A. Montanari, & M. Vichi (Eds.), Data Science Studies in Classification, Data Analysis, and Knowledge Organization. doi: 10.1007/978-3-319-55723-6_3
  • Pisula, T., Mentel, G., & Brożyna, J. (2013). Predicting Bankruptcy of Companies from the Logistics Sector Operating in the Podkarpacie Region. Modern Management Review, 20(3), 113-134. doi: 10.7862/rz.2013.mmr.33
  • Pisula, T., Mentel, G., & Brożyna, J. (2015). Non-Statistical Methods of Analysing of Bankruptcy Risk. Folia Oeconomica Stetinensia, 15(1), 7-21. doi: 10.1515/ foli-2015-0029
  • Pociecha, J., Pawełek, B., Baryła, M., & Augustyn, S. (2014). Statistical Methods of Forecasting Bankruptcy in the Changing Economic Situation. Krakow, Poland: Fundacja Uniwersytetu Ekonomicznego w Krakowie.
  • Pociecha, J., Pawelek, B., Baryla, M., & Augustyn, S. (2018). Classification models as tools of bankruptcy prediction - Polish experience. In W. Gaul, M. Vichi, & C. Weihs (Eds.), Studies in Classification, Data Analysis, and Knowledge Organization. doi: 10.1007/978-3-319-55708-3_18
  • 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. doi: 10.3390/ijfs6030060
  • Režňakova, M., & Karas, M. (2014). Identifying bankruptcy prediction factors in various environments: A contribution to the discussion on the transferability of bankruptcy models. International Journal of Mathematical Models and Methods in Applied Sciences, 8(1), 69-74.
  • Rybarova, D., Braunova, M., & Jantošova, L. (2016). Analysis of the Construction Industry in the Slovak Republic by Bankruptcy Model. Procedia – Social and Behavioral Sciences, 230, 298-306. doi: 10.1016/j.sbspro.2016.09.038
  • Shumway, T. (2001). Forecasting Bankruptcy More Accurately: A Simple Hazard Model. The Journal of Business, 74(1), 101-124. doi: 10.1086/209665
  • Springate, G.L.V. (1978). Predicting the Possibility of Failure in a Canadian Firm. Burnaby, Canada: Simon Fraser University.
  • Tokarski, A. (2018). The phenomenon of bankruptcy of enterprises in the polish economy in the years 2008-2015. In E. Lotko, U.K. Zawadzka-Pak, & M. Radvan (Eds.), Optimization of organization and legal solutions concerning public revenues and expenditures in public interest (Conference proceedings) (pp. 403-420). doi: 10.15290/oolscprepi.2018.30
  • 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), 403-426. doi: 10.1556/aoecon.55.2005.4.2
  • Virag, M., & Nyitrai, T. (2014). Is there a trade-off between the predictive power and the interpretability of bankruptcy models? The case of the first Hungarian bankruptcy prediction model. Acta Oeconomica, 64(4), 419-440. doi: 10.1556/aoecon.64.2014.4.2
  • Visegrad Group (2019, September). About the Visegrad Group. Retrieved from http://www.visegradgroup.eu/about
  • Vochozka, M., Strakova, J., & Vachal, J. (2015). Model to Predict Survival of Transportation and Shipping Companies. Naše More, 62(3), 109-113. doi: 10.17818/nm/2015/si4
  • Waqas, H., & Md-Rus, R. (2018). Predicting financial distress: Applicability of O-score model for Pakistani firms. Business and Economic Horizons, 14(2), 389-401. doi: 10.15208/beh.2018.28
  • Wyrobek, J., & Kluza, K. (2018). Efficiency of gradient boosting decision trees technique in Polish companies’ bankruptcy prediction. In L. Borzemski, J. Swiątek, & Z. Wilimowska (Eds.), Advances in Intelligent Systems and Computing Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology – ISAT 2018 (pp. 24-35). doi: 10.1007/978-3-319-99993-7_3
  • Zmijewski, M.E. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research, 22, 59-82. doi: 10.2307/2490859
Typ dokumentu
Bibliografia
Identyfikator YADDA
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