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.
Similar content being viewed by others
References
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.
Altman, E. 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4):589–609.
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.
Ash, T. 1989. Dynamic node creation. Connection Sci., 1(4):365–375.
Beaver, W. 1966. Financial ratios as predictors of failure. Empirical Research in Accounting: Selected Studies, 5:71–111.
Bjorck, A. 1994. Numerics of Gram-Schmidt orthogonalization. Linear Algebra Applicat, 197.
Cannon, M. and Slotine, J.-J.E. 1995. Space-frequency localized basis function networks for nonlinear system estimation and control. Neurocomputing, 9:293–342.
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.
Coats, P. and Fant, L. 1993. Recognizing financial distress patterns using a neural network tool. Financial Management, 22:142–155.
Daubechies, I. 1992. Ten Lectures on Wavelets. Philadelphia: SIAM.
Ezekiel, M. and Fox, K.A. 1959. Methods of Correlation and Regression Analysis, 3rd edition, New York: John Wiley.
Foster, G. 1986. Financial Statement Analysis, London: Prentice-Hall.
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.
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.
Gill, P., Murray, W. and Wright, M. 1981. Practical Optimization. London: Academic Press.
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.
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.
Haykin, S. 1999. Neural Networks: A Comprehensive Foundation. London: Prentice-Hall.
Kohonen, T. 1995. Self-Organizing Maps. Berlin: Springer-Verlag.
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.
Lawson, C.L. and Hanson, R.J. 1974. Solving Least Squares Problems. Englewood Cliffs: Prentice-Hall.
Mao, K. 2002. RBF neural network center selection based on Fisher ratio class separability measure. IEEE Transactions on Neural Networks, 13(5):1211–1217.
Moody, J. and Darken, C. 1989. Fast learning in network of locally-tuned processing units. Neural Computing, 1:281–294.
Morrison, D. 1990. Multivariate Statistical Methods, New York: McGraw-Hill.
Naes, T. and Mevik, B.H. 2001. Understanding the collinearity problem in regression and discriminant analysis. Journal of Chemometrics, 15(4):413–426.
Norgaard, M. 2000. Neural network based system identification toolbox. Technical Report 00-E-891, Technical University of Denmark, Department of Automation.
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.
Pedrycz, W. 1998. Conditional fuzzy clustering in the design of radial basis function neural networks. IEEE Transactions on Neural Networks, 9(4):601–612.
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.
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.
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.
Szu, H.H., Telfer, B., and Kadambe, S. 1992. Neural network adaptive wavelets for signal representation and classification. Optical Engineering, 31(9):1907–1916.
Tam, K. and Kiang, M.Y. 1990. Predicting bank failures: A neural network approach. Applications of Artificial Intelligence, 4:265–282.
Tam, K. and Kiang, M.Y. 1992. Managerial applications of neural networks. Management Science, 38, 926–947.
Taylor, J.S. and Cristianini, N. 2004. Kernel Methods for Pattern Analysis. Cambridge: Cambridge University Press.
The Mathworks: 2004. Statistics Toolbox Users Guide, Version 5. Natick, Massachussetts: The Mathworks.
Trigueiros, D. and Taffler, R. 1996. Neural networks and empirical research in accounting. Accounting and Business Research, 26:347–355.
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.
Wilson, R.L. and Sharda, R. 1994. Bankruptcy prediction using neural networks. Decision Support Systems, 11:545–557.
Yao, X. 1999. Evolving artificial neural networks. Proceedings of the IEEE, 87(9):1423–1447.
Zhang, Q. 1997. Using wavelet network in nonparametric estimation. IEEE Trans. Neural Networks, 8(2):227–236.
Zhang, Q. and Benveniste, A. 1992. Wavelet Networks. IEEE Trans. Neural Networks, 3(6):889–898.
Author information
Authors and Affiliations
Corresponding author
Rights 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
Received:
Revised:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/s10618-005-1360-0