Politická ekonomie
Politická ekonomie
Prague Economic Papers
University of Economics, Prague

Prague Economic Papers - Articles first published online

What Do Post-Communist Countries Have in Common When Predicting Financial Distress?

DOI: https://doi.org/10.18267/j.pep.664

[full text (PDF)]

Madalina Ecaterina Popescu, Victor Dragotă

Published online: 20. 8. 2018

Business failure prediction is an important issue in corporate finance. Different prediction models are proposed by financial theory and are often used in practice. Their application is effortless, selecting only few key inputs with the greatest informative power from the large list of possible indicators. Our paper identifies the financial distress predictors for 5 post-communist countries (Bulgaria, Croatia, the Czech Republic, Hungary and Romania) based on information collected from the Amadeus database for the period 2011–2013 using CHAID decision trees and neural networks. We propose a short list of indicators, which can offer a synthetic perspective on corporate distress risk, adapted for these countries. The best prediction models are substantially different from country to country: in the Czech Republic, Hungary and Romania the flow-approach indicators perform better, while in Bulgaria and Croatia – the stock-approach indicators. The results suggest that the extrapolation of such models from one country to another should be made cautiously. One interesting finding is the presence of the ratios per employee as predictors of financial distress.

Keywords: CHAID decision trees, financial distress, neural networks, post-communist countries, prediction models, predictors

JEL Classification: C53, G33, L25


Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, 23(4), 589–609, https://doi.org/10.2307/2978933

Altman, E. I., Iwanicz-Drozdowska, M., Laitine, E. K., Suvas, A. (2014). Distressed Firm and Bankruptcy Prediction in an International Context: A Review and Empirical Analysis

of Altman’s Z-Score Model. [Retrieved 2016-07-03] Available at: http://doi.org/10.2139/


Anghel, I. (2002). Falimentul (Bankruptcy). Bucharest: Editura Economica. ISBN 973-590-678-3.

Buus, T. (2015). A General Free Cash Flow Theory of Capital Structure. Journal of Business

Economics and Management, 16(3), 675–695, https://doi.org/10.3846/16111699.2013.770787

Dragotă, V., Dragotă, M. I. (2009). Models and Indicators for Risk Valuation of Investment

Projects. Economic Computation and Economic Cybernetics Studies and Research,

43(3), 69–75.

Ekinci, A. (2016). Rethinking Credit Risk under the Malinvestment Concept: The Case

of Germany, Spain and Italy. European Financial and Accounting Journal, 11(1), 39–64,


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

Harris, M., Raviv, A. (1991). The Theory of Capital Structure. Journal of Finance, 46(1), 297–355, https://doi.org/10.1111/j.1540-6261.1991.tb03753.x

Hsu, J. C. (1996). Multiple Comparisons: Theory and Methods. London, UK: Chapman and Hall.

ISBN 978-0412982811.

Jaba, E., Robu, I. B., Istrate, C., Balan, C. B., Roman, M. (2016). Statistical Assessment of the Value Relevance of Financial Information Reported by Romanian Listed Companies. Romanian Journal of Economic Forecasting, 19(2), 27–42.

Jain, B. A., Nag, B. N. (1998). A Neural Network Model to Predict Long-Run Operating

Performance of New Ventures. Annals of Operations Research, 78, 83–110,


Jakubík, P., Teplý, P. (2011). The JT Index as an Indicator of Financial Stability of Corporate Sector. Prague Economic Papers, 20(2), 157–176, https://doi.org/10.18267/j.pep.394

Keasey, K., Watson, R. (1991). Financial Distress Prediction Models: A Review of Their Usefulness. British Journal of Management, 2(2), 89–102, https://doi.org/10.1111/j.1467-8551.1991.tb00019.x

Kumar, K., Tan, C. (2004). Artificial Intelligence in Financial Distress Prediction. [Retrieved 2016-09-10] Available at: www.niitcrcs.com/iccs/papers/2005_37.pdf

Myers, S. (2001). Capital Structure. Journal of Economic Perspectives, 15(2), 81–102,


Ohlson, J. A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), 109–131, https://doi.org/10.2307/2490395

Olson, D. L., Delen, D., Meng, Y. (2012). Comparative Analysis of Data Mining Methods

for Bankruptcy Prediction. Decision Support Systems, 52(2), 464–473, https://doi.


Reznakova, M., Karas, M. (2015). The Prediction Capabilities of Bankruptcy Models in a Different Environment: An Example of the Altman Model under the Conditions in the Visegrad

Group Countries. Ekonomický časopis, 63, 617–633.

Ross, S. (1977). The Determination of Financial Structure: The Incentive-Signalling Approach. The Bell Journal of Economics, 8(1), 23–40, https://doi.org/10.2307/3003485

Ross, S., Westerfield, R., Jaffe, J. (2010). Corporate Finance, 9th Edition. Irwin: The McGraw-Hill. ISBN 978-0073382333.

Šarlija, N., Jeger, M. (2011). Comparing Financial Distress Prediction Models before and during Recession. Croatian Operational Research Review (CRORR), 2, 133–142.

Scarlat, E., Chiriță, N., Bradea, I. A. (2012). Indicators and Metrics Used in the Enterprise Risk Management (ERM). Economic Computation and Economic Cybernetics Studies and Research Journal, 46(4), 5–18.

Shumway, T. (2001). Forecasting Bankruptcy More Accurately: A Simple Hazard Model. Journal

of Business, 74(1), 101–124, https://doi.org/10.2139/ssrn.171436

Smith, K., Gupta, J. (2002). Neural Networks in Business: Techniques and Applications. Hershey: Idea Group Publishing. ISBN 1-930708-31-9.

Tudor, L., Popescu, M. E., Andreica, M. (2015). A Decision Support System to Predict Financial Distress. The Case of Romania. Romanian Journal of Economic Forecasting, 18(4), 170–179.

Virág, M., Hajdu, O. (1996). Pénzügyi mutatószámokon alapuló csõdmodell-számítások

(Financial Ratio Based Bankruptcy Model Calculations). Bankszemle, 15(5), 42–53.

Virág, M., Kristóf, 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, https://doi.org/10.1556/aoecon.55.2005.4.2

Welc, J. (2016). Empirical Safety Thresholds for Liquidity and Indebtedness Ratios on the Polish Capital Market. European Financial and Accounting Journal, 11(3), 37–50, https://doi.org/10.18267/j.efaj.161

Wruck, K. (1990). Financial Distress: Reorganization and Organizational Efficiency. Journal of Financial Economics, 27(2), 419–444, https://doi.org/10.1016/0304-405x(90)90063-6

Zheng, Q., Yanhui, J. (2007). Financial Distress Prediction on Decision Tree Models. IEEE, 16, 80–120, https://doi.org/10.1109/soli.2007.4383925, http://data.worldbank.org