Acta Univ. Agric. Silvic. Mendelianae Brun. 2016, 64(2), 627-634 | DOI: 10.11118/actaun201664020627

Prediction of Bankruptcy with SVM Classifiers Among Retail Business Companies in EU

Václav Klepáč, David Hampel
Department of Statistics and Operation Analysis, Mendel University in Brno, Zemědělská 1, 613 00 Brno, Czech Republic

Article focuses on the prediction of bankruptcy of the 850 medium-sized retail business companies in EU from which 48 companies gone bankrupt in 2014 with respect to lag of the used features. From various types of classification models we chose Support vector machines method with linear, polynomial and radial kernels to acquire best results. Pre-processing is enhanced with filter based feature selection like Gain ratio, Chi-square and Relief algorithm to acquire attributes with the best information value. On this basis we deal with random samples of financial data to measure prediction accuracy with the confusion matrices and area under curve values for different kernel types and selected features. 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. The 3rd order polynomial kernel offers better accuracy for bankruptcy prediction than linear and radial versions. But in terms of the total accuracy we recommend to use radial kernel without feature selection.

Keywords: bankruptcy prediction, classification, feature selection, support vector machines
Grants and funding:

The paper was supported by the Internal Grant Agency FBE MENDELU under project: Testování modelů pro vícerozměrnou analýzu a predikci kreditního rizika (No. 19/2015).

Prepublished online: May 4, 2016; Published: May 1, 2016  Show citation

ACS AIP APA ASA Harvard Chicago IEEE ISO690 MLA NLM Turabian Vancouver
Klepáč, V., & Hampel, D. (2016). Prediction of Bankruptcy with SVM Classifiers Among Retail Business Companies in EU. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 64(2), 627-634. doi: 10.11118/actaun201664020627
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. 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 Go to original source...
    2. AK, J. and ZONGKER, D. 1997. Feature selection: Evaluation, application and small sample performance. IEEE Transactions on Pattern Analysis Machine Intelligence, 19(2): 153-158. Go to original source...
    3. AZIZ, M. A. and DAR, H. A. 2006. Predicting corporate bankruptcy: where we stand? Corporate Governance: The international journal of business in society, 6(1): 18-33. Go to original source...
    4. DING, Y., SONG, X. and ZEN, X. 2008. Forecasting financial condition of Chinese listed companies based on support vector machine. Expert Systems with Applications, 34(4): 3081-3089. DOI: 10.1016/j.eswa.2007.06.037 Go to original source...
    5. HOLČÍK, J. 2012. Analýza a klasifikace dat. Brno: Akademické nakladatelství CERM.
    6. MIN, J. H. and LEE, Y. C. 2005. Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28: 603-614. DOI: 10.1016/j.eswa.2004.12.008 Go to original source...
    7. KONONENKO, I. 1994. Estimating attributes: Analysis and extensions of RELIEF, in Machine Learning. Lecture Notes in Computer Science, 784: 171-182. DOI: 10.1007/3-540-57868-4_57 Go to original source...
    8. MICHÁLEK, J., SEDLAČÍK, M. and DOUDOVÁ, L. 2005. A comparison of two parametric ROC curves estimators in binormal model. In: Proceedings of the 23rd International Conference Mathematical methods in Economics 2005. Hradec Králové: GAUDEAMUS Univerzita Hradec Králové, 256-261.
    9. MIN, S.-H., LEE, J. and HAN, I. 2006. Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Systems with Applications, 31(3): 652-660. DOI: 10.1016/j.eswa.2005.09.070 Go to original source...
    10. NIKNYA, A., DARABI, R. and VAKILI, F. 2013. Financial Distress Prediction of Tehran Stock Exchange Companies Using Support Vector Machines. European Online Journal of Natural and Social Science, 2(3).
    11. SHIN, K. S., LEE, T. S. and KIM, H. J. 2005. An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28(1): 127-135. DOI: 10.1016/j.eswa.2004.08.009 Go to original source...
    12. TSAI, C. F. 2009. Feature selection in bankruptcy prediction. Knowledge-Based Systems, 22(2): 120-127. DOI: 10.1016/j.knosys.2008.08.002 Go to original source...
    13. OHLSON, J. A. 1980. Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1): 109-131. DOI: 10.2307/2490395 Go to original source...
    14. QUINLAN, J. R. 1993. Programs for Machine Learning. Morgan Kaufmann Publishers.
    15. VAPNIK, V. M. 1995. The Nature of Statistical Learning Theory. New York: Springer. Go to original source...
    16. ZMIJEWSKI, M. 1984. Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22: 59-86. DOI: 10.2307/2490859 Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY NC ND 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content