Advertisement

Company Bankruptcy Prediction with Neural Networks

  • Jolanta PozorskaEmail author
  • Magdalena Scherer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10841)

Abstract

Bankruptcy prediction is a very important issue in business financing. Raising availability of financial data makes it more and more viable. We use large data concerning the health of Polish companies to predict their possible bankruptcy in a relatively short period. To this end, we utilize feedforward neural networks.

Keywords

Bankruptcy prediction Neural networks 

References

  1. 1.
    Altman, E.I., Hotchkiss, E.: Corporate Financial Distress and Bankruptcy: Predict and Avoid Bankruptcy, Analyze and Invest in Distressed Debt, vol. 289. Wiley, Hoboken (2010)Google Scholar
  2. 2.
    Atiya, A.F.: Bankruptcy prediction for credit risk using neural networks: a survey and new results. IEEE Trans. Neural Networks 12(4), 929–935 (2001)CrossRefGoogle Scholar
  3. 3.
    Bartczuk, Ł., Łapa, K., Koprinkova-Hristova, P.: A new method for generating of fuzzy rules for the nonlinear modelling based on semantic genetic programming. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS (LNAI), vol. 9693, pp. 262–278. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-39384-1_23CrossRefGoogle Scholar
  4. 4.
    Bioch, J., Popova, V.: Bankruptcy prediction with rough sets. ERIM Report Series Research in Management ERS-2001-11-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam, February 2001Google Scholar
  5. 5.
    Bologna, G., Hayashi, Y.: Characterization of symbolic rules embedded in deep dimlp networks: a challenge to transparency of deep learning. J. Artif. Intell. Soft Comput. Res. 7(4), 265–286 (2017)CrossRefGoogle Scholar
  6. 6.
    de Andrés, J., Landajo, M., Lorca, P.: Forecasting business profitability by using classification techniques: a comparative analysis based on a Spanish case. Eur. J. Oper. Res. 167(2), 518–542 (2005)zbMATHCrossRefGoogle Scholar
  7. 7.
    du Jardin, P.: A two-stage classification technique for bankruptcy prediction. Eur. J. Oper. Res. 254(1), 236–252 (2016)CrossRefGoogle Scholar
  8. 8.
    Galkowski, T., Pawlak, M.: Nonparametric estimation of edge values of regression functions. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J. (eds.) ICAISC 2016. LNCS (LNAI), vol. 9693, pp. 49–59. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-39384-1_5CrossRefGoogle Scholar
  9. 9.
    Gorzalczany, M.B., Piasta, Z.: Neuro-fuzzy approach versus rough-set inspired methodology for intelligent decision support. Inf. Sci. 120(1), 45–68 (1999)CrossRefGoogle Scholar
  10. 10.
    Greco, S., Matarazzo, B., Slowinski, R.: A new rough set approach to multicriteria and multiattribute classification. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 60–67. Springer, Heidelberg (1998).  https://doi.org/10.1007/3-540-69115-4_9zbMATHCrossRefGoogle Scholar
  11. 11.
    Jensen, R., Cornelis, C.: Fuzzy-rough nearest neighbour classification and prediction. Theor. Comput. Sci. 412(42), 5871–5884 (2011). Rough Sets and Fuzzy Sets in Natural ComputingMathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Kumar, P.R., Ravi, V.: Bankruptcy prediction in banks and firms via statistical and intelligent techniques - a review. Eur. J. Oper. Res. 180(1), 1–28 (2007)zbMATHCrossRefGoogle Scholar
  13. 13.
    Łapa, K., Cpałka, K., Galushkin, A.I.: A new interpretability criteria for neuro-fuzzy systems for nonlinear classification. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS (LNAI), vol. 9119, pp. 448–468. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-19324-3_41CrossRefGoogle Scholar
  14. 14.
    Lin, F.Y., McClean, S.: A data mining approach to the prediction of corporate failure. Knowl.-Based Systems 14(3–4), 189–195 (2001)CrossRefGoogle Scholar
  15. 15.
    Marquardt, D.W.: An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 11(2), 431–441 (1963)MathSciNetzbMATHCrossRefGoogle Scholar
  16. 16.
    Mckee, T.E.: Developing a bankruptcy prediction model via rough sets theory. Int. J. Intell. Syst. Account. Financ. Manag. 9(3), 159–173 (2000)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Min, J.H., Lee, Y.C.: Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Syst. Appl. 28(4), 603–614 (2005)CrossRefGoogle Scholar
  18. 18.
    Olson, D.L., Delen, D., Meng, Y.: Comparative analysis of data mining methods for bankruptcy prediction. Decis. Support Syst. 52(2), 464–473 (2012)CrossRefGoogle Scholar
  19. 19.
    Scherer, M.: Waste flows management by their prediction in a production company. J. Appl. Math. Comput. Mech. 16, 135–144 (2017)CrossRefGoogle Scholar
  20. 20.
    Scherer, M.: Multi-layer neural networks for sales forecasting. J. Appl. Math. Comput. Mech. 17, 61–68 (2018)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Scherer, R.: Multiple Fuzzy Classification Systems. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-30604-4zbMATHCrossRefGoogle Scholar
  22. 22.
    Shin, K.S., Lee, T.S., Kim, H.J.: An application of support vector machines in bankruptcy prediction model. Expert Syst. Appl. 28(1), 127–135 (2005)CrossRefGoogle Scholar
  23. 23.
    Tinoco, M.H., Wilson, N.: Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. Int. Rev. Financ. Anal. 30, 394–419 (2013)CrossRefGoogle Scholar
  24. 24.
    Villmann, T., Bohnsack, A., Kaden, M.: Can learning vector quantization be an alternative to SVM and deep learning? - recent trends and advanced variants of learning vector quantization for classification learning. J. Artif. Intell. Soft Comput. Res. 7(1), 65–81 (2017)CrossRefGoogle Scholar
  25. 25.
    Zikeba, M., Tomczak, S.K., Tomczak, J.M.: Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction. Expert Syst. Appl. 58, 93–101 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Mechanical Engineering and Computer Science, Institute of MathematicsCzȩstochowa University of TechnologyCzȩstochowaPoland
  2. 2.Faculty of ManagementCzȩstochowa University of TechnologyCzȩstochowaPoland

Personalised recommendations