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Bayesian kernel based classification for financial distress detection. (English) Zbl 1111.90330

Summary: Corporate credit granting is a key commercial activity of financial institutions nowadays. A critical first step in the credit granting process usually involves a careful financial analysis of the creditworthiness of the potential client. Wrong decisions result either in foregoing valuable clients or, more severely, in substantial capital losses if the client subsequently defaults. It is thus of crucial importance to develop models that estimate the probability of corporate bankruptcy with a high degree of accuracy. Many studies focused on the use of financial ratios in linear statistical models, such as linear discriminant analysis and logistic regression. However, the obtained error rates are often high. In this paper, Least Squares Support Vector Machine (LS-SVM) classifiers, also known as kernel Fisher discriminant analysis, are applied within the Bayesian evidence framework in order to automatically infer and analyze the creditworthiness of potential corporate clients. The inferred posterior class probabilities of bankruptcy are then used to analyze the sensitivity of the classifier output with respect to the given inputs and to assist in the credit assignment decision making process. The suggested nonlinear kernel based classifiers yield better performances than linear discriminant analysis and logistic regression when applied to a real-life data set concerning commercial credit granting to mid-cap Belgian and Dutch firms.

MSC:

90B50 Management decision making, including multiple objectives
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[1] Altman, E., Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, Journal of Finance, 23, 589-609 (1968)
[2] Altman, E., Corporate Financial Distress and Bankruptcy: A Complete Guide to Predicting and Avoiding Distress and Profiting from Bankruptcy (1993), Wiley Finance Edition
[3] Beaver, W., Financial ratios as predictors of failure, empirical research in accounting selected studies, Journal of Accounting Research, 5, Suppl., 71-111 (1966)
[4] Bishop, C., Neural Networks for Pattern Recognition (1995), Oxford University Press
[5] Altman, E.; Marco, G.; Varetto, F., Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience), Journal of Banking and Finance, 18, 505-529 (1994)
[6] Atiya, A., Bankruptcy prediction for credit risk using neural networks: A survey and new results, IEEE Transactions on Neural Networks, 12, 4, 929-935 (2001)
[7] Baestaens, D.-E.; van den Bergh, W.-M.; Wood, D., Neural Network Solutions for Trading in Financial Markets (1994), Pitman: Pitman London
[8] Lee, K.; Han, I.; Kwon, Y., Hybrid neural network models for bankruptcy predictions, Decision Support Systems, 18, 63-72 (1996)
[9] Piramuthu, S.; Ragavan, H.; Shaw, M., Using feature construction to improve the performance of neural networks, Management Science, 44, 3, 416-430 (1998) · Zbl 0988.90522
[10] Serrano Cinca, C., Self organizing neural networks for financial diagnosis, Decision Support Systems, 17, 227-238 (1996)
[11] Wong, B.; Bodnovich, T.; Selvi, Y., Neural network applications in business: A review and analysis of the literature (1988-1995), Decision Support Systems, 19, 4, 301-320 (1997)
[12] MacKay, D., Bayesian interpolation, Neural Computation, 4, 415-447 (1992)
[13] MacKay, D., Probable networks and plausible predictions—A review of practical Bayesian methods for supervised neural networks, Network: Computation in Neural Systems, 6, 469-505 (1995) · Zbl 0834.68098
[14] Cristianini, N.; Shawe-Taylor, J., An Introduction to Support Vector Machines (2000), Cambridge University Press
[15] Schölkopf, B.; Smola, A., Learning with Kernels (2002), MIT Press: MIT Press Cambridge, MA
[16] Suykens, J. A.K.; Van Gestel, T.; De Brabanter, J.; De Moor, B.; Vandewalle, J., Least Squares Support Vector Machines (2002), World Scientific: World Scientific New Jersey · Zbl 1003.68146
[17] Vapnik, V., Statistical Learning Theory (1998), Wiley: Wiley New York · Zbl 0935.62007
[18] Suykens, J. A.K.; Vandewalle, J., Least squares support vector machine classifiers, Neural Processing Letters, 9, 3, 293-300 (1999) · Zbl 0958.93042
[19] Baudat, G.; Anouar, F., Generalized discriminant analysis using a kernel approach, Neural Computation, 12, 2385-2404 (2000)
[20] Van Gestel, T.; Suykens, J. A.K.; Lanckriet, G.; Lambrechts, A.; De Moor, B.; Vandewalle, J., A Bayesian framework for least squares support vector machine classifiers, Gaussian processes and kernel Fisher discriminant analysis, Neural Computation, 14, 1115-1147 (2002) · Zbl 1003.68146
[21] Van Gestel, T.; Suykens, J. A.K.; Baestaens, D.-E.; Lambrechts, A.; Lanckriet, G.; Vandaele, B.; De Moor, B.; Vandewalle, J., Predicting financial time series using least squares support vector machines within the evidence framework, IEEE Transactions on Neural Networks (Special Issue on Financial Engineering), 12, 809-821 (2001)
[22] Eisenbeis, R., Pitfalls in the application of discriminant analysis in business, The Journal of Finance, 32, 3, 875-900 (1977)
[23] Duda, R.; Hart, P., Pattern Classification and Scene Analysis (1973), John Wiley: John Wiley New York · Zbl 0277.68056
[24] Ripley, B., Pattern Classification and Neural Networks (1996), Cambridge University Press · Zbl 0853.62046
[25] Fisher, R., The use of multiple measurements in taxonomic problems, Annals of Eugenics, 7, 179-188 (1936)
[26] McCullagh, P.; Nelder, J., Generalized Linear Models (1989), Chapman & Hall: Chapman & Hall London · Zbl 0744.62098
[27] Ohlson, J., Financial ratios and the probabilistic prediction of bankruptcy, Journal of Accounting Research, 18, 109-131 (1980)
[28] Baesens, B.; Setiono, R.; Mues, C.; Vanthienen, J., Using neural network rule extraction and decision tables for credit-risk evaluation, Management Science, 49, 3, 312-329 (2003) · Zbl 1232.91684
[29] Baesens, B.; Van Gestel, T.; Viaene, S.; Stepanova, M.; Suykens, J. A.K.; Vanthienen, J., Benchmarking state of the art classification algorithms for credit scoring, Journal of the Operational Research Society, 54, 6, 627-635 (2003) · Zbl 1097.91516
[30] B. Baesens, Developing intelligent systems for credit scoring using machine learning techniques, Ph.D. thesis, Department of Applied Economic Sciences, Katholieke Universiteit Leuven, 2003.; B. Baesens, Developing intelligent systems for credit scoring using machine learning techniques, Ph.D. thesis, Department of Applied Economic Sciences, Katholieke Universiteit Leuven, 2003.
[31] Van Gestel, T.; Suykens, J. A.K.; Baesens, B.; Viaene, S.; Vanthienen, J.; Dedene, G.; De Moor, B.; Vandewalle, J., Benchmarking least squares support vector machine classifiers, Machine Learning, 54, 5-32 (2004) · Zbl 1078.68737
[32] Vapnik, V., Statistical Learning Theory (1998), John Wiley: John Wiley New York · Zbl 0935.62007
[33] Evgeniou, T.; Pontil, M.; Poggio, T., Regularization networks and support vector machines, Advances in Computational Mathematics, 13, 1-50 (2001) · Zbl 0939.68098
[34] Hutchinson, J.; Lo, A.; Poggio, T., A nonparametric approach to pricing and hedging derivative securities via learning networks, Journal of Finance, 49, 851-889 (1994)
[35] Vapnik, V.; Lerner, A., Pattern recognition using generalized portrait method, Automation and Remote Control, 24, 774-780 (1963)
[36] V. Vapnik, A.J. Chervonenkis, On the one class of the algorithms of pattern recognition, Automation and Remote Control 25 (6).; V. Vapnik, A.J. Chervonenkis, On the one class of the algorithms of pattern recognition, Automation and Remote Control 25 (6). · Zbl 0137.10601
[37] Fletcher, R., Practical Methods of Optimization (1987), John Wiley: John Wiley Chichester and New York · Zbl 0905.65002
[38] Cortes, C.; Vapnik, V., Support vector networks, Machine Learning, 20, 273-297 (1995) · Zbl 0831.68098
[39] Jeffreys, H., Theory of Probability (1961), Oxford University Press · Zbl 0116.34904
[40] Baestaens, D.-E., Credit risk modelling strategies: The road to serfdom, International Journal of Intelligent Systems in Accounting, Finance & Management, 8, 225-235 (1999)
[41] Van der Vaart, A., Asymptotic Statistics (1998), Cambridge University Press · Zbl 0910.62001
[42] Egan, J., Signal Detection Theory and ROC analysis. Series in Cognition and Perception (1975), Academic Press: Academic Press New York
[43] Everitt, B., The Analysis of Contingency Tables (1977), Chapman & Hall: Chapman & Hall London · Zbl 0777.62060
[44] De Long, E.; De Long, D.; Clarke-Pearson, D., Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach, Biometrics, 44, 837-845 (1988) · Zbl 0715.62207
[45] Soberhart, J.; Keenan, S.; Stein, R., Validation methodologies for default risk models, Credit Magazine, 1, 4, 51-56 (2000)
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