Efficiency of Random Decision Forest Technique in Polish Companies’ Bankruptcy Prediction

  • Joanna WyrobekEmail author
  • Krzysztof Kluza
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10842)


The purpose of the paper was to compare the accuracy of traditional bankruptcy prediction models with the Random Forest method. In particular, the paper verifies 2 research hypotheses (verification was based on the representative sample of Polish companies): [H1]: The Random Forest algorithm (trained on a representative set of companies) is more accurate than traditional bankruptcy prediction methods: logit and linear discriminant models, and [H2]: The Random Forest algorithm efficiently uses normalized financial statement data (there is no need to calculate financial ratios).


  1. 1.
    Barboza, F., Kimura, H., Altman, E.: Machine learning models and bankruptcy prediction. Expert Syst. Appl. 83, 405–417 (2017)CrossRefGoogle Scholar
  2. 2.
    Jabeur, S., Fahmi, Y.: Forecasting financial distress for french firms: a comparative study. Empir. Econ. 3, 1–14 (2017)Google Scholar
  3. 3.
    Nagaraj, K., Sridhar, A.: A predictive system for detection of bankruptcy using machine learning techniques. Int. J. Data Min. Knowl. Manag. Process (IJDKP) 5, 29–40 (2015)CrossRefGoogle Scholar
  4. 4.
    Liao, J.J., Shih, C.H., Chen, T.F., Hsu, M.F.: An ensemble-based model for two-class imbalanced financial problem. Econ. Model. 37, 175–183 (2014)CrossRefGoogle Scholar
  5. 5.
    Huang, J., Wang, H., Kochenberger, G.: Distressed chinese firm prediction with discretized data. Manag. Decis. 55, 786–807 (2017)CrossRefGoogle Scholar
  6. 6.
    Pociecha, J., Pawelek, B., Baryla, B.: Statystyczne metody prognozowania bankructwa w zmieniajacej sie koniunkturze gospodarczej. Wydawnictwo UEK (2014)Google Scholar
  7. 7.
    Korol, T.: Systemy ostrzegania przedsiebiorstw przed ryzykiem upadlosci. Oficyna Wolters Kluwer Business (2010)Google Scholar
  8. 8.
    Pawelek, B., Grochowina, D.: Podejscie wielomodelowe w prognozowaniu zagrozenia przedsiebiorstw upadloscia w polsce. Prace Naukowe Uniwersytetu Ekonomicznego we Wroclawiu, pp. 171–179 (2017)Google Scholar
  9. 9.
    Jardin, P.: A two-stage classification technique for bankruptcy prediction. Eur. J. Oper. Res. 254, 236–252 (2016)CrossRefGoogle Scholar
  10. 10.
    Min, J., Jeong, C.: A binary classification method for bankruptcy prediction. Expert Syst. Appl. 36, 5256–5263 (2009)CrossRefGoogle Scholar
  11. 11.
    Alfaro, E., Garcia, N., Games, M., Elizondo, D.: Bankruptcy forecasting: an empirical comparison of ada boost and neural networks. Decis. Support Syst. 45, 110–122 (2008)CrossRefGoogle Scholar
  12. 12.
    Anandarajan, M., Lee, P., Anandarajan, A.: Bankruptcy prediction of financially stressed firms: an examination of the predictive accuracy of artificial neural networks. Int. J. Intell. Syst. Acc. 10, 69–81 (2001)CrossRefGoogle Scholar
  13. 13.
    Cho, S., Hong, H., Ha, B.: A hybrid approach based on the combination of variable selection using decision trees and case-based reasoning using the mahalanobis distance: for bankruptcy prediction. Expert Syst. Appl. 37, 3482–3488 (2010)CrossRefGoogle Scholar
  14. 14.
    Cho, S., Kim, J., Bae, J.K.: An integrative model with subject weight based on neural network learning for bankruptcy prediction. Expert Syst. Appl. 10, 403–410 (2009)CrossRefGoogle Scholar
  15. 15.
    Fedorova, E., Gilenko, E., Dovzhenko, S.: Bankruptcy prediction for russian companies: application of combined classifiers. Expert Syst. Appl. 40, 7285–7293 (2013)CrossRefGoogle Scholar
  16. 16.
    Ghodselahi, A., Amirmadhi, A.: Application of artificial intelligence techniques for credit risk evaluation. Int. J. Model. Optim. 1, 243–249 (2011)CrossRefGoogle Scholar
  17. 17.
    Hu, Y.C., Tseng, F.M.: Functional-link net with fuzzy integral for bankruptcy prediction. Neurocomputing 3, 2959–2968 (2007)CrossRefGoogle Scholar
  18. 18.
    Sun, J., Li, H.: Financial distress prediction based on serial combination of multiple classifiers. Expert Syst. Appl. 18, 8659–8666 (2009)CrossRefGoogle Scholar
  19. 19.
    Li, H., Sun, J.: Business failure prediction using hybrid2 case-based reasoning. Comput. Oper. Res. 37, 137–151 (2010)CrossRefGoogle Scholar
  20. 20.
    Li, H., Sun, J.: Principal component case-based reasoning ensemble for business failure prediction. Inf. Manag. 48, 220–227 (2009)CrossRefGoogle Scholar
  21. 21.
    Li, H., Lee, Y.C., Zhou, Y.C., Sun, J.: The random subspace binary logit (RSBL) model for bankruptcy prediction. Knowl.-Based Syst. 24, 1380–1388 (2011)CrossRefGoogle Scholar
  22. 22.
    Min, J., Lee, Y.: Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Syst. Appl. 28, 603–614 (2005)CrossRefGoogle Scholar
  23. 23.
    Pena, T., Martinez, S., B., A.: Bankruptcy prediction: a comparison of some statistical and machine learning techniques. SSRN’s eLibrary (18) (2009)Google Scholar
  24. 24.
    Tseng, F., Hu, Y.: Comparing four bankruptcy prediction models: logit, quadratic interval logit, neural and fuzzy neural networks. Expert Syst. Appl. 37, 1846–1853 (2010)CrossRefGoogle Scholar
  25. 25.
    Lewis, N.: Machine Learning Made Easy with R: Intuitive Step by Step Blueprint for Beginners. CreateSpace (2017)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Corporate Finance DepartmentCracow University of EconomicsKrakówPoland
  2. 2.AGH University of Science and TechnologyKrakówPoland

Personalised recommendations