Predicting financial distress for Romanian companies
Abstract
Using a moderately large number of financial ratios, we tried to build models for classifying the companies listed on the Bucharest Stock Exchange into low and high risk classes of financial distress. We considered four classification techniques: Support Vector Machines, Decision Trees, Bayesian logistic regression and Fisher linear classifier, out of which the first two proved to have the highest prediction accuracy. Classifiers were trained and tested on randomly drown samples and on four different databases built starting from the initial financial indicators. As the literature related to the topic on Romanian data is very scarce, our study, by using a variety of methods and combining feature selection and principal components analysis, brings a new approach to predicting financial distress for Romanian companies.
Keyword : Support Vector Machines, Bayesian Analysis, financial distress prediction, data mining, discriminant analysis, logistic regression
How to Cite
Ruxanda, G., Zamfir, C., & Muraru, A. (2018). Predicting financial distress for Romanian companies. Technological and Economic Development of Economy, 24(6), 2318-2337. https://doi.org/10.3846/tede.2018.6736
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Tian, S., Yu, Y., & Zhou, M. (2015). Data sample selection issues for bankruptcy prediction. Risk, Hazards & Crisis in Public Policy, 6(1), 91-116. https://doi.org/10.1002/rhc3.12071
Vapnik, V. N. (1998). Statistical Learning Theory. John Wiley & Sons.
Wagle, M., Yang, Z., & Benslimane, Y. (2017, May 7-9). Bankruptcy prediction using data mining techniques. In 8th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES), Chonburi, Thailand (pp. 1-4). IEEE. https://doi.org/10.1109/ICTEmSys.2017.7958771
Wang, N. (2017). Bankruptcy prediction using machine learning. Journal of Mathematical Finance, 7, 908-918. https://doi.org/10.4236/jmf.2017.74049
Altman, E. I. (1968). Financial ratios, discriminant analysis and the presiction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
Barak, S., Dahooie, J. H., & Tichý, T. (2015). Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick. Expert Systems with Applications 42(23), 9221-9235. https://doi.org/10.1016/j.eswa.2015.08.010
Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405-417. https://doi.org/10.1016/j.eswa.2017.04.006
Bishop, C. (2006). Pattern recognition and machine learning. New York: Springer-Verlag.Bucharest Stock Exchange. (n.d.). Retrieved from http://www.bvb.ro/
Burda, A., & Pancerz, K. (2014). Clustering and visualization of bankruptcy patterns using the self-organizing maps. Barometr Regionalny, Analizy i Prognozy, 13(3), 133-138.
Dellepiane, U., Marcantonio, M. D., Laghi, E., & Renzi, S. (2015). Bankruptcy prediction using support vector machines and feature selection during the recent financial crisis. International Journal of Economics and Finance, 7(8), 182-195.
Drugowitsch, J. (2017). Variational Bayesian inference for linear and logistic regression. Retrieved from https://arxiv.org/pdf/1310.5438v3.pdf
du Jardin, P. (2009). Bankruptcy prediction models: How to choose the most relevant variables? (MPRA Paper No. 44380). Munich Personal RePEc Archive.
du Jardin, P. (2017). Dynamics of firm financial evolution and bankruptcy prediction. Expert Systems With Applications, 75, 25-43. https://doi.org/10.1016/j.eswa.2017.01.016
Erdogan, B. E., & Akyüz, S. Ö. (2018). A weighted ensemble learning by SVM for longitudinal data: Turkish Bank bankruptcy. In M. Tez & D. von Rosen D. (Eds.), Trends and perspectives in linear statistical inference, contributions to statistics. Cham: Springer.
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
Gepp, A., & Kumar, K. (2015). Predicting financial distress: A Comparison of survival analysis and decision tree techniques. Procedia Computer Science, 54, 396-404. https://doi.org/10.1016/j.procs.2015.06.046
GitHub. (n.d.). MATLAB code by Jan Drugowitsch. Retrieved from https://github.com/DrugowitschLab/vb_logit
Jaakkola, T., S., & Jordan, M. (2000). Bayesian parameter estimation via variational methods. Statistics and Computing, 10, 25-37. https://doi.org/10.1023/A:1008932416310
Koklu, M., & Tutuncu, K. (2014, July 17–18). Qualitative bankruptcy prediction rules using artificial intelligence techniques. Paper presented at International Conference on challenges in IT, Engineering and Technology (ICCIET2014), Phuket, Thailand.
Kostopoulos, G., Karlos, S., Kotsiantis, S., & Tampakas, V. (2017). Evaluating active learning methods for bankruptcy prediction. In C. Frasson, G. Kostopoulos. (Eds.), Brain Function Assessment in Learning. BFAL 2017. Lecture Notes in Computer Science, (vol. 10512, pp. 57-66). Cham: Springer. https://doi.org/10.1007/978-3-319-67615-9_5
Lopez-Iturriaga, F., & Sanz, I. P. (2015). Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks. Expert Systems with Applications, 42, 2857-2869. https://doi.org/10.1016/j.eswa.2014.11.025
National Trade Register Office. (n.d.). Retrieved from https://www.onrc.ro/index.php/ro/
Ohlson, J. A. (1980). Financial ratios and probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131. https://doi.org/10.2307/2490395
Pena, T., Martinez, S., & Abudu, B. (2009). Bankruptcy prediction: A comparison of some statistical and machine learning techniques. (Banco de Mexico Working Papers, 2009-18). Banco de Mexico.
Pereira, J., M., Basto, M., & da Silva, A. F. (2016). The logistic lasso and ridge regression in predicting corporate failure. Procedia Economics and Finance 39, 634-641. https://doi.org/10.1016/S2212-5671(16)30310-0
Pervan, I., Pervan, M., & Vukoja, B. (2011). Prediction of company bankruptcy using statistical techniques – case of Croatia. Croatian Operational Research Review (CRORR), 2, 158-167.
Prodan-Palade, D. (2017). Bankruptcy risk prediction models based on artificial neural networks. Audit Financiar, XV(3), 418-429. https://doi.org/10.20869/AUDITF/2017/147/418
Safavian, S., R., & Landgrebe, D. (1991). A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics, 21(3), 660-674. https://doi.org/10.1109/21.97458
Stádník, B., Raudeliūnienė, J., & Davidavičienė, V. (2016). Fourier analysis for stock price forecasting: assumption and evidence. Journal of Business Economics and Management, 17(3), 365-380. https://doi.org/10.3846/16111699.2016.1184180
Succuro, M. (2017). Financial bankruptcy across European Countries. International Journal of Econom-cs and Finance, 9(7), 132-146. https://doi.org/10.5539/ijef.v9n7p132
Sun, J., Li, H., Huang, Q., & He, K. (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. https://doi.org/10.1016/j.knosys.2013.12.006
Tian, S., Yu, Y., & Zhou, M. (2015). Data sample selection issues for bankruptcy prediction. Risk, Hazards & Crisis in Public Policy, 6(1), 91-116. https://doi.org/10.1002/rhc3.12071
Vapnik, V. N. (1998). Statistical Learning Theory. John Wiley & Sons.
Wagle, M., Yang, Z., & Benslimane, Y. (2017, May 7-9). Bankruptcy prediction using data mining techniques. In 8th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES), Chonburi, Thailand (pp. 1-4). IEEE. https://doi.org/10.1109/ICTEmSys.2017.7958771
Wang, N. (2017). Bankruptcy prediction using machine learning. Journal of Mathematical Finance, 7, 908-918. https://doi.org/10.4236/jmf.2017.74049