Predicting bankruptcy of manufacturing companies in EU

DSpace Repository

Show simple item record Klepáč, Václav Hampel, David
dc.contributor.other Ekonomická fakulta cs 2018-03-29 2018-03-29 2018-03-29
dc.identifier.issn 1212-3609
dc.description.abstract Article focuses on the prediction of bankruptcy of the 1,000 medium-sized retail business companies in EU from which 170 companies gone bankrupt in 2014 with respect to lag of the used features. In recent times, bankruptcy of manufacturing companies rapidly increased due to the impact of the recession, which produces economic and social problems accordingly. Therefore, the need for bankruptcy prediction models is very high. From various types of classification models we chose Support vector machines method with spline, hyperbolic tangent and RBF ANOVA kernels, Decision trees, Random forests and Adaptive boosting to acquire best results. Pre-processing is enhanced with filter based feature selection like Gain ratio and Relief algorithm to acquire attributes with the best information value. As we can see both filtering methods offers different variables to be used in the classification and Decision trees wrapper algorithm chose less number than its competitors. Suitable attributes as ROA, Interest cover, Solvency ratio based on assets and Operating revenues were mostly used but it also changes across the time, which are probably very obtainable. It is apparent that inappropriate theoretical value of one variable does not necessarily lead to bankruptcy, so it is better to use combinations of these variables. From the results it is obvious that with the rising distance to the bankruptcy there drops precision of bankruptcy prediction. The last year (2013) with avaible financial data offers best total prediction accuracy, thus we also infer both the Error I and II types for better recognizance of misclassification rates. The Random forest and Decision trees offer better accuracy for bankruptcy prediction than SVM method, both method offers prediction accuracy which is comparable to previous empirical studies. en
dc.format text
dc.format.extent 16 stran cs
dc.language.iso en
dc.publisher Technická Univerzita v Liberci cs
dc.publisher Technical university of Liberec, Czech Republic en
dc.relation.ispartof Ekonomie a Management cs
dc.relation.ispartof Economics and Management en
dc.relation.isbasedon Alfaro, E., Gamez, M., & Garcia, N. (2008). Linear discriminant analysis versus adaboost for failure forecasting. Spanish Journal of Finance and Accounting, 37(137), 13-32.
dc.relation.isbasedon Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.
dc.relation.isbasedon Assaad, M., Bone, R., & Cardot, H. (2008). A new boosting algorithm for improved time-series forecasting with recurrent neural networks. Information Fusion, 9(1), 41-55.
dc.relation.isbasedon Aziz, M. A., & Dar, H. A. (2006). Predicting corporate bankruptcy: where we stand? Corporate Governance: The international journal of business in society, 6(1), 18-33.
dc.relation.isbasedon Beaver, W. H. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research, 4, 71-102.
dc.relation.isbasedon 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.
dc.relation.isbasedon Breiman, L., & Cutler, A. (1993). A deterministic algorithm for global optimization. Mathematical Programming, 58(1-3), 179-199.
dc.relation.isbasedon Breiman, L. (1993). Classification and regression trees. Boca Raton, Fla.: Chapman & Hall.
dc.relation.isbasedon Breiman, L. (2004). Population theory for boosting ensembles. The Annals of Statistics, 32(1), 1-11.
dc.relation.isbasedon Ding, Y., Song, X., & Zen, X. (2008). Forecasting financial condition of Chinese listed companies based on support vector machine. Expert Systems with Applications, 34(4), 3081-3089.
dc.relation.isbasedon Doumpos, M., & Zopounidis, C. (1996). A multicriteria discrimination method of the prediction of financial distress: The case of Greece. Multionational Finance Journal, 3(2), 71-101.
dc.relation.isbasedon Fawcet, T. (2004). ROC Graphs: Notes and Practical Considerations for Researchers. Technical report HP Laboratories. Kluwer Academic Publishers. Retrieved October 21, 2015, from
dc.relation.isbasedon Freund, Y., & Schapire, R. (1996). Experiments with a new boosting algorithm. In Machine Learning: Proceedings of the Thirteenth International Conference (pp. 148-156).
dc.relation.isbasedon Frydman, H., Altman, E. I., & Kao, D.-L. (1985). Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress. The Journal of Finance, 40(1), 269-291.
dc.relation.isbasedon Gepp, A., Kumar, K., & Bhattacharya, S. (2010). Business failure prediction using decision trees. Journal of Forecasting, 29(6), 536-555.
dc.relation.isbasedon Jain, A., & Zongker, D. (1997). Feature selection: Evaluation, application and small sample performance. IEEE Transactions on Pattern Analysis Machine Intelligence, 19(2), 153-158.
dc.relation.isbasedon Kim, S. Y., & Upneja, A. (2014). Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models. Economic Modelling, 36, 354-362.
dc.relation.isbasedon Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(1-2), 273-324.
dc.relation.isbasedon Kononenko, I. (1994). Estimating attributes: Analysis and extensions of RELIEF. In F. Bergadano & De Raedt L. (Eds.), Machine Learning: ECML-94. ECML 1994. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence), vol. 784 (pp. 171-182). Berlin, Heidelberg: Springer.
dc.relation.isbasedon Li, H., Sun, J., & Wu, J. (2010). Predicting business failure using classification and regression tree: an empirical comparison with popular classical methods and top classification mining methods. Expert Systems with Applications, 37(8), 5895-5904.
dc.relation.isbasedon Lin, F., & McClean, S. (2001). A data mining approach to the prediction of corporate failure. Knowledge-Based Systems, 14(3-4), 189-195.
dc.relation.isbasedon Huarng, K., Yu, H., & Chen, C. (2005). The application of decision trees to forecast financial distress companies. In Proceedings of International Conference on Intelligent Technologies and Applied Statistics, Taipei, Taiwan.
dc.relation.isbasedon Min, S.-H., Lee, J., & Han, I. (2006). Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Systems with Applications, 31(3), 652-660.
dc.relation.isbasedon Min, J. H., & Lee, Y. C. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28(4), 603-614.
dc.relation.isbasedon Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131.
dc.relation.isbasedon Romanski, P., & Kotthoff, L. (2015). Selecting attributes. R vignette of the R-package FSelector. Retrieved October 21, 2015, from
dc.relation.isbasedon Therneau, E., & Atkinson, J. (2015). (Technical Report 61). Retrieved from
dc.relation.isbasedon Tsai, C. F. (2009). Feature selection in bankruptcy prediction. Knowledge-Based Systems, 22(2), 120-127.
dc.relation.isbasedon Quinlan, J. R. (1993). Programs for Machine Learning. Morgan Kaufmann Publishers.
dc.relation.isbasedon Vapnik, V. M. (1995). The Nature of Statistical Learning Theory. New York: Springer.
dc.relation.isbasedon Wu, C. (2004). Using non-financial information to predict bankruptcy: a study of public companies in Taiwan. International Journal of Management, 21(2), 194-201.
dc.relation.isbasedon Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Liu, B. et al. (2007). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1),
dc.relation.isbasedon Witten, I., & Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques (2nd ed.). Burlington, MA: Morgan Kaufmann Publishers in an imprint of Elsevier.
dc.relation.isbasedon Zavgren, C. V. (1985). Assessing the vulnerability to failure of American industrial firms: A logistic analysis. Journal of Business Finance & Accounting, 12(1), 19-45.
dc.relation.isbasedon Zmijewski, M. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59-86.
dc.rights CC BY-NC
dc.subject bankruptcy prediction en
dc.subject classification en
dc.subject decision tree en
dc.subject feature selection en
dc.subject.classification C52
dc.subject.classification C44
dc.title Predicting bankruptcy of manufacturing companies in EU en
dc.type Article en
dc.publisher.abbreviation TUL
dc.relation.isrefereed true
dc.identifier.doi 10.15240/tul/001/2018-1-011
dc.identifier.eissn 2336-5604
local.relation.volume 21
local.relation.issue 1
local.relation.abbreviation E+M cs
local.relation.abbreviation E&M en
local.faculty Faculty of Economics
local.citation.spage 159
local.citation.epage 174
local.access open
local.fulltext yes
local.filename EM_1_2018_11

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace

Advanced Search


My Account