Three-State Financial Distress Prediction Based on Support Vector Machine

  • Hongshan Yao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5552)


This paper examines the three-state financial distress prediction using support vector machine (SVM) and compares the classification results with the one using multinominal logit analysis(MLA).The results show that SVM provides better three-state classification than MLA. The model using SVM has better generalization than the model using MLA.


Financial state multinomial logit analysis(MLA) support vector machine(SVM) 


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  1. 1.
    Altman, E.I., Hotchkiss, E.: Corporate Financial Distress and Bankruptcy. John Wiley & Sons Inc., New York (2005)CrossRefGoogle Scholar
  2. 2.
    Beaver, W.H.: Financial Ratios as Predictors of Failure, Empirical Research in Accounting: Selected Studies. Journal of Accounting Research 4(suppl.1), 79–111 (1966)Google Scholar
  3. 3.
    Altman, E.I.: Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance 9, 589–690 (1968)CrossRefGoogle Scholar
  4. 4.
    Ohlson, J.: Financial Ratio and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research 18, 109–131 (1980)CrossRefGoogle Scholar
  5. 5.
    Odom, M., Sharda, R.A.: Neural Networks Model for Bankruptcy Prediction. In: IEEE International Conference on Neural Network, vol. 2, pp. 163–168. IEEE Press, New York (1990)Google Scholar
  6. 6.
    Lau, H.: A Five State Financial Distress Prediction Model. Journal of Accounting Research 25, 127–138 (1987)CrossRefGoogle Scholar
  7. 7.
    Ward, T.J.: An Empirical Study of the Incremental Predictive Ability of Beaver’s Naive Operating Flow Measure Using Four-State Ordinal Models of Financial Distress. Journal of Business Finance and Accounting 7, 547–561 (1994)CrossRefGoogle Scholar
  8. 8.
    Wu, S.N., Lu, X.Y.: The Financial Distress Prediction Research of Chinese Listed Corporations. Economic Research Journal 6, 46–55 (2001)Google Scholar
  9. 9.
    Yang, S., Huang, L.: Firms Warning Model Based on BP Neural Networks. Systems Engineering Theory and Practice 1, 12–18 (2005)Google Scholar
  10. 10.
    Vapnik, V.N.: Statistical Learning Theory. Electric Industrial Publishing House, Beijing (2004)zbMATHGoogle Scholar
  11. 11.
    Hearst, M.A., Dumais, S.T., Osman, E., Platt, J., Schölkopf, B.: Support Vector Machines. IEEE Intelligent Systems 13(4), 18–28 (1998)CrossRefGoogle Scholar
  12. 12.
    Hsu, C.W., Lin, C.J.: A Comparison of Methods for Multi-class Support Vector Machines, Technical report, National Taiwan University, Taiwan (2001)Google Scholar
  13. 13.
    Hensher, D.A., Rose, D., Greene, W.: Applied Choice Analysis: A Primer. Cambridge University Press, Cambridge (2005)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Hongshan Yao
    • 1
  1. 1.Zhongnan University of Economics and LawWuhanChina

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