Skip to main content
SpringerLink
Log in

Efficiency of Gradient Boosting Decision Trees Technique in Polish Companies’ Bankruptcy Prediction

  • Conference paper
  • First Online:
Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology – ISAT 2018 (ISAT 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 854))

Abstract

The goal of the research was to compare the selected traditional bankruptcy prediction models, namely linear discriminant analysis and logit (logistic) models, with the technique called Gradient Boosting. In particular, the paper verifies two research hypotheses (verification was based on the balanced sample of Polish companies): [H1]: Gradient Boosted Decision Trees algorithm is more accurate than traditional bankruptcy prediction models: logit and discriminant analysis; [H2]: Boosted Decision Trees use both: financial ratios and normalized data from financial statements, but the same accuracy one can achieve only with the normalized data and the bigger number of weak learners.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Barboza, F., Kimura, H., Altman, E.: Machine learning models and bankruptcy prediction. Expert Syst. Appl. 83, 405–417 (2017)

    Article  Google Scholar 

  2. Johnson, R., Zhang, T.: Learning nonlinear functions using regularized greedy forest. Technical report 29 (2012)

    Google Scholar 

  3. Altman, E.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J. Financ. 47, 589–609 (1968)

    Article  Google Scholar 

  4. Altman, E.: Predicting railroad bankruptcies in America. Bell J. Econ. Manag. Sci. 4, 184–211 (1973)

    Article  Google Scholar 

  5. Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2010)

    MATH  Google Scholar 

  6. Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proceedings of the Thirteenth International Conference on Machine Learning, pp. 148–156 (1996)

    Google Scholar 

  7. Witten, I., Eibe, F.: Practical Machine Learning Tools and Techniques. Morgan Kaufman, Burlington (2005)

    MATH  Google Scholar 

  8. Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. Stat. 28, 337–407 (2000)

    Article  MathSciNet  Google Scholar 

  9. Friedman, J.: Greedy boosting approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)

    Article  Google Scholar 

  10. Mueller, A., Guido, S.: Introduction to Machine Learning with Python for Data Scientists. O’Reilly Media, Newton (2016)

    Google Scholar 

  11. Grzybowska, U., Karwański, M.: Szacowanie parametrów ryzyka kredytowego przy uźyciu rodzin klasyfikatorów. Zeszyty Naukowe Uniwersytetu Ekonomicznego w Katowicach 248, 107–120 (2015)

    Google Scholar 

  12. Jardin, P.: A two-stage classification technique for bankruptcy prediction. Eur. J. Oper. Res. 254, 236–252 (2016)

    Article  Google Scholar 

  13. Min, J., Jeong, C.: A binary classification method for bankruptcy prediction. Expert Syst. Appl. 36, 5256–5263 (2009)

    Article  Google Scholar 

  14. 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)

    Article  Google Scholar 

  15. Hu, Y.C., Tseng, F.M.: Functional-link net with fuzzy integral for bankruptcy prediction. Neurocomputing 3, 2959–2968 (2007)

    Article  Google Scholar 

  16. Huang, J., Wang, H., Kochenberger, G.: Distressed Chinese firm prediction with discretized data. Manag. Decis. 55, 786–807 (2017)

    Article  Google Scholar 

  17. Jabeur, S., Fahmi, Y.: Forecasting financial distress for french firms: a comparative study. Empir. Econ. 3, 1–14 (2017)

    Google Scholar 

  18. Min, J., Lee, Y.: Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Syst. Appl. 28, 603–614 (2005)

    Article  Google Scholar 

  19. Nagaraj, K., Sridhar, A.: A predictive system for detection of bankruptcy using machine learning techniques. Int. J. Data Min. Knowl. Manag. Process 5, 29–40 (2015)

    Article  Google Scholar 

  20. Sun, J., Li, H.: Financial distress prediction based on serial combination of multiple classifiers. Expert Syst. Appl. 18, 8659–8666 (2009)

    Article  Google Scholar 

  21. 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)

    Article  Google Scholar 

  22. 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)

    Article  Google Scholar 

  23. 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. Account. 10, 69–81 (2001)

    Article  Google Scholar 

  24. 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)

    Article  Google Scholar 

  25. Fedorova, E., Gilenko, E., Dovzhenko, S.: Bankruptcy prediction for russian companies: application of combined classifiers. Expert Syst. Appl. 40, 7285–7293 (2013)

    Article  Google Scholar 

  26. Ghodselahi, A., Amirmadhi, A.: Application of artificial intelligence techniques for credit risk evaluation. Int. J. Model. Optim. 1, 243–249 (2011)

    Article  Google Scholar 

  27. Li, H., Sun, J.: Business failure prediction using hybrid2 case-based reasoning. Comput. Oper. Res. 37, 137–151 (2010)

    Article  Google Scholar 

  28. 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)

    Article  Google Scholar 

  29. Pena, T., Martinez, S., Abudu, B.: Bankruptcy prediction: a comparison of some statistical and machine learning techniques. In: SSRN’s eLibrary, vol. 18 (2009)

    Google Scholar 

  30. Heo, J., Yang, J.Y.: Adaboost based bankruptcy forecasting of korean construction companies. Appl. Soft Comput. 24, 494–499 (2014)

    Article  Google Scholar 

  31. Kim, S.Y., Upneja, A.: Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models. Econ. Model. 36, 354–362 (2014)

    Article  Google Scholar 

  32. Marques, A.I., Garcia, V., Sanchez, J.S.: Exploring the behavior of base classifiers in credit scoring ensembles. Expert Syst. Appl. 39, 10244–10250 (2012)

    Article  Google Scholar 

  33. Sun, J., Jia, M.Y., Li, H.: Adaboost ensemble for financial distress prediction: an empirical comparison with data from chinese listed companies. Expert Syst. Appl. 38, 9305–9312 (2011)

    Article  Google Scholar 

  34. Kim, M.J., Kang, D.K.: Ensemble with neural networks for bankruptcy prediction. Expert Syst. Appl. 37, 3373–3379 (2010)

    Article  Google Scholar 

  35. Pociecha, J., Pawelek, B., Baryla, B.: Statystyczne metody prognozowania bankructwa w zmieniajacej sie koniunkturze gospodarczej. Wydawnictwo UEK (2014)

    Google Scholar 

  36. Korol, T.: Systemy ostrzegania przedsiebiorstw przed ryzykiem upadlosci. Oficyna Wolters Kluwer Business (2010)

    Google Scholar 

  37. Pawelek, B., Grochowina, D.: Podejscie wielomodelowe w prognozowaniu zagrozenia przedsiebiorstw upadloscia w polsce. Prace Naukowe Uniwersytetu Ekonomicznego we Wroclawiu 171–179 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Corporate Finance Department, Cracow University of Economics, ul. Rakowicka 27, 31-510, Kraków, Poland

    Joanna Wyrobek

  2. AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059, Krakow, Poland

    Krzysztof Kluza

Authors
  1. Joanna Wyrobek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Krzysztof Kluza
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joanna Wyrobek .

Editor information

Editors and Affiliations

  1. University of Applied Sciences in Nysa, Nysa, Poland

    Zofia Wilimowska

  2. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Leszek Borzemski

  3. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Jerzy Świątek

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wyrobek, J., Kluza, K. (2019). Efficiency of Gradient Boosting Decision Trees Technique in Polish Companies’ Bankruptcy Prediction. In: Wilimowska, Z., Borzemski, L., Świątek, J. (eds) Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology – ISAT 2018. ISAT 2018. Advances in Intelligent Systems and Computing, vol 854. Springer, Cham. https://doi.org/10.1007/978-3-319-99993-7_3

Download citation

Publish with us

Policies and ethics

Search

Navigation