Skip to main content

Neural and Wavelet Network Models for Financial Distress Classification

Abstract

This work analyzes the use of linear discriminant models, multi-layer perceptron neural networks and wavelet networks for corporate financial distress prediction. Although simple and easy to interpret, linear models require statistical assumptions that may be unrealistic. Neural networks are able to discriminate patterns that are not linearly separable, but the large number of parameters involved in a neural model often causes generalization problems. Wavelet networks are classification models that implement nonlinear discriminant surfaces as the superposition of dilated and translated versions of a single “mother wavelet” function. In this paper, an algorithm is proposed to select dilation and translation parameters that yield a wavelet network classifier with good parsimony characteristics. The models are compared in a case study involving failed and continuing British firms in the period 1997–2000. Problems associated with over-parameterized neural networks are illustrated and the Optimal Brain Damage pruning technique is employed to obtain a parsimonious neural model. The results, supported by a re-sampling study, show that both neural and wavelet networks may be a valid alternative to classical linear discriminant models.

This is a preview of subscription content, access via your institution.

References

  1. Alici, Y. 1996. Neural networks in corporate failure prediction: The UK experience. In Neural Networks in Financial Engineering. A. Refenes, Y. Abu-Mostafa, and J. Moody (Eds.): London: World Scientific.

    Google Scholar 

  2. Altman, E. 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4):589–609.

    Google Scholar 

  3. Altman, E., Marco, G., and Varetto, F. 1994. Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking and Finance, 18:505–529.

    Article  Google Scholar 

  4. Ash, T. 1989. Dynamic node creation. Connection Sci., 1(4):365–375.

    Google Scholar 

  5. Beaver, W. 1966. Financial ratios as predictors of failure. Empirical Research in Accounting: Selected Studies, 5:71–111.

    Google Scholar 

  6. Bjorck, A. 1994. Numerics of Gram-Schmidt orthogonalization. Linear Algebra Applicat, 197.

  7. Cannon, M. and Slotine, J.-J.E. 1995. Space-frequency localized basis function networks for nonlinear system estimation and control. Neurocomputing, 9:293–342.

    Article  Google Scholar 

  8. Chen, S., Cowanss, C., and Grant, P. 1991. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks, 2(2):302–309.

    Article  Google Scholar 

  9. Coats, P. and Fant, L. 1993. Recognizing financial distress patterns using a neural network tool. Financial Management, 22:142–155.

    Google Scholar 

  10. Daubechies, I. 1992. Ten Lectures on Wavelets. Philadelphia: SIAM.

    Google Scholar 

  11. Ezekiel, M. and Fox, K.A. 1959. Methods of Correlation and Regression Analysis, 3rd edition, New York: John Wiley.

    Google Scholar 

  12. Foster, G. 1986. Financial Statement Analysis, London: Prentice-Hall.

    Google Scholar 

  13. Galvao, R.K.H., Becerra, V.M. and Abou-Seada, M. 2004. Ratio selection for classification models. Data Mining and Knowledge Discovery, 8(2):151–170.

    Article  MathSciNet  Google Scholar 

  14. Galvao, R.K.H. and Yoneyama, T. 1999. Improving the discriminatory capabilities of a neural classifier by using a biased-wavelet layer. International Journal of Neural Systems, 9(3): 167–174.

    Article  PubMed  Google Scholar 

  15. Gill, P., Murray, W. and Wright, M. 1981. Practical Optimization. London: Academic Press.

    Google Scholar 

  16. Gomm, J. and Yu, D. 2000. Selecting radial basis function network centers with recursive orthogonal least squares training. IEEE Transactions on Neural Networks, 11(2):306–314.

    Article  Google Scholar 

  17. Hassibi, B., Stork, D., and Wolff, G. 1993. Optimal brain surgeon and general network pruning. In IEEE International Conference on Neural Networks, pp. 293–299.

  18. Haykin, S. 1999. Neural Networks: A Comprehensive Foundation. London: Prentice-Hall.

    Google Scholar 

  19. Kohonen, T. 1995. Self-Organizing Maps. Berlin: Springer-Verlag.

    Google Scholar 

  20. Kun, Y., Denker, J., and Solla, S. 1990. Optimal Brain Damage. In Advances in Neural Information Processing Systems, Touretzky D. (Ed.), San Mateo, Calif.: Morgan Kaufmann, pp. 598–605.

  21. Lawson, C.L. and Hanson, R.J. 1974. Solving Least Squares Problems. Englewood Cliffs: Prentice-Hall.

    Google Scholar 

  22. Mao, K. 2002. RBF neural network center selection based on Fisher ratio class separability measure. IEEE Transactions on Neural Networks, 13(5):1211–1217.

    Article  Google Scholar 

  23. Moody, J. and Darken, C. 1989. Fast learning in network of locally-tuned processing units. Neural Computing, 1:281–294.

    Google Scholar 

  24. Morrison, D. 1990. Multivariate Statistical Methods, New York: McGraw-Hill.

    Google Scholar 

  25. Naes, T. and Mevik, B.H. 2001. Understanding the collinearity problem in regression and discriminant analysis. Journal of Chemometrics, 15(4):413–426.

    Article  Google Scholar 

  26. Norgaard, M. 2000. Neural network based system identification toolbox. Technical Report 00-E-891, Technical University of Denmark, Department of Automation.

  27. Odom, M. and Sharda, R. 1990. A neural network model for bankruptcy prediction. In IJCNN International Joint Conference on Neural Networks, Vol. II. San Diego, California, pp. 163–167.

  28. Pedrycz, W. 1998. Conditional fuzzy clustering in the design of radial basis function neural networks. IEEE Transactions on Neural Networks, 9(4):601–612.

    Article  Google Scholar 

  29. Scholkopf, B., Sung, K.-K., Burges, C., Girosi, F., Niyogi, P., Poggio, T., and Vapnik, V. 1997. Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Transactions on Signal Processing, 45:2758–2765.

    Article  Google Scholar 

  30. Setiono, R. and Hui, L.C.K. 1995. Use of a quasi-newton method in a feedforward neural network construction algorithm. IEEE Trans. Neural Networks, 6(1):273–277.

    Article  Google Scholar 

  31. Sherstinsky, A. and Picard, R. 1996. On the efficiency of the orthogonal least squares training method for radial basis function networks. IEEE Transactions on Neural Networks, 7(1):195–200.

    Article  Google Scholar 

  32. Szu, H.H., Telfer, B., and Kadambe, S. 1992. Neural network adaptive wavelets for signal representation and classification. Optical Engineering, 31(9):1907–1916.

    Google Scholar 

  33. Tam, K. and Kiang, M.Y. 1990. Predicting bank failures: A neural network approach. Applications of Artificial Intelligence, 4:265–282.

    Google Scholar 

  34. Tam, K. and Kiang, M.Y. 1992. Managerial applications of neural networks. Management Science, 38, 926–947.

    Google Scholar 

  35. Taylor, J.S. and Cristianini, N. 2004. Kernel Methods for Pattern Analysis. Cambridge: Cambridge University Press.

    Google Scholar 

  36. The Mathworks: 2004. Statistics Toolbox Users Guide, Version 5. Natick, Massachussetts: The Mathworks.

    Google Scholar 

  37. Trigueiros, D. and Taffler, R. 1996. Neural networks and empirical research in accounting. Accounting and Business Research, 26:347–355.

    Google Scholar 

  38. Tyree, E. and J. Long: 1996, Assessing financial distress with probabilistic neural networks. In Neural Networks in Financial Engineering, A. Refenes, Y. Abu-Mostafa, and J. Moody (Eds.), London: World Scientific.

    Google Scholar 

  39. Wilson, R.L. and Sharda, R. 1994. Bankruptcy prediction using neural networks. Decision Support Systems, 11:545–557.

    Article  Google Scholar 

  40. Yao, X. 1999. Evolving artificial neural networks. Proceedings of the IEEE, 87(9):1423–1447.

    Article  Google Scholar 

  41. Zhang, Q. 1997. Using wavelet network in nonparametric estimation. IEEE Trans. Neural Networks, 8(2):227–236.

    Article  Google Scholar 

  42. Zhang, Q. and Benveniste, A. 1992. Wavelet Networks. IEEE Trans. Neural Networks, 3(6):889–898.

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Victor M. Becerra.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Becerra, V.M., Galvão, R.K.H. & Abou-Seada, M. Neural and Wavelet Network Models for Financial Distress Classification. Data Min Knowl Disc 11, 35–55 (2005). https://doi.org/10.1007/s10618-005-1360-0

Download citation

Keywords

  • financial distress
  • neural networks
  • wavelets
  • finance
  • classification