Bankruptcy prediction with least squares support vector machine classifiers

IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, Proceedings (CIFEr)

Volumn 2003-January, Issue , 2003, Pages 1-8

  Van Gestel, T ^a   Baesens, B ^a   Suykens, J ^a   Espinoza, M ^a   Baestaens, D E ^b   Vanthienen, J ^a   De Moor, B ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b Fortis   (Belgium)

Author keywords

Classification algorithms; Input variables; Kernel; Least squares methods; Linear discriminant analysis; Logistics; Predictive models; Risk analysis; Support vector machine classification; Support vector machines

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTATION THEORY; DISCRIMINANT ANALYSIS; FACE RECOGNITION; FISHER INFORMATION MATRIX; FORECASTING; IMAGE RETRIEVAL; LEARNING ALGORITHMS; LEAST SQUARES APPROXIMATIONS; LOGISTICS; NONLINEAR ANALYSIS; RISK ANALYSIS; RISK ASSESSMENT; VECTORS;

CLASSIFICATION ALGORITHM; INPUT VARIABLES; KERNEL; LEAST SQUARES METHODS; LINEAR DISCRIMINANT ANALYSIS; PREDICTIVE MODELS; SUPPORT VECTOR MACHINE CLASSIFICATION;

SUPPORT VECTOR MACHINES;

EID: 84941367932     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIFER.2003.1196234     Document Type: Conference Paper

References (36)

1. Altman, E.I. Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, 23:589-609, 1968.
2. Altman, E.I., Marco, G., and 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.
3. Atiya, A.F. Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Transactions on Neural Networks, 12(4):929-935, 2001.
4. Baesens, B., Viaene, S., Van den Poel, D., Vanthienen, J., and Dedene, G. Using bayesian neural networks for repeat purchase modelling in direct marketing. European Journal of Operational Research, 138(1):191-211, 2002.
5. Baestaens, D.-E. Credit Risk Modelling Strategies: The Road to Serfdom. International Journal of Intelligent Systems in Accounting, Finance & Management, 8:225-235, 1999.
6. Baudat, G. and Anouar, F. Generalized Discriminant Analysis Using a Kernel Approach. Neural Computation, 12:2385-2404, 2000.
7. Beaver, W.H. Financial Ratios as Predictors of Failure. Empirical Research in Accounting Selected Studies, supplement to the Journal of Accounting, 5:71-111, 1966.
8. Bishop, C.M. Neural Networks for Pattern Recognition. Oxford University Press, 1995.
9. Cristianini, N., and Shawe-Taylor, J. An Introduction to Support Vector Machines. Cambridge University Press, 2000.
10. Cucker, F., and Smale, S. On the mathematical foundations of learning theory. Bulleting of the AMS, 39:1-49, 2002.
11. De Long, E.R., De Long, D.M. and Clarke-Pearson, D.L. Comparing the areas under two or more correlated receiver operating characteristic curves: a non-parametric approach. Biometrics, 44:837-845, 1988.
12. Duda, R.O., and Hart, P.E. Pattern Classification and Scene Analysis. John Wiley, New York, 1973.
13. Eisenbeis, R. Pitfalls in the application of discriminant analysis in business. The Journal of Finance, 32(3):875-900, 1977.
14. Everitt, B.S. The analysis of contingency tables. Chapman and Hall, London, 1977.
15. Evgeniou, T., Pontil, M., and Poggio, T. Regularization Networks and Support Vector Machines. Advances in Computational Mathematics, 13:1-50, 2001.
16. Fisher, R.A. The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7:179-188, 1936.
17. Hanley, J.A. and McNeil, B.J. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology, 148:839-843, 1983.
18. Hoerl, A.E. Application of ridge analysis to regression problems. Chemical Engineering Progress, 58:54-59, 1962.
19. Hutchinson, J.M., Lo, A.W., and Poggio, T. A Non-parametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks. Journal of Finance, 49:851-889, 1994.
20. MacKay, D.J.C. Bayesian Interpolation. Neural Computation, 4:415-447, 1992.
21. Mercer, J. Functions of positive and negative type and their connection with the theory of integral equations. Philos. Trans. Roy. Soc. London, 209:415-446, 1909.
22. Mika, S., Rätsch, G., Weston, J., Schölkopf, B., and Müller, K.-R. Fisher discriminant analysis with kernels. In Proceedings Neural Networks for Signal Processing Workshop IX (NNSP'99), Hu, Y.-H., Larsen, J., Wilson, E., Douglas, S. (Eds.), pp. 41-48, 1999. IEEE.
23. Ohlson, J.A. Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18:109-131, 1980.
24. Poddig, T. Neural networks in the Capital Markets (Editor: Apostolos-Paul Refenes), chapter Bankruptcy prediction: a comparison with discriminant analysis, pages 311-323. John Wiley and Sons, 1995.
25. Ripley, B.D. Pattern Classification and Neural Networks. Cambridge University Press, 1996.
26. Schölkopf, B., and Smola, A. Learning with Kernels. MIT Press, Cambridge, MA, 2002.
27. Suykens, J.A.K., and Vandewalle, J. Least squares support vector machine classifiers. Neural Processing Letters, 9:293-300, 1999.
28. Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B., and Vandewalle J. Least Squares Support Vector Machines. World Scientific, in press, 2002.
29. Tam, K.Y., and Kiang, M.Y. Managerial applications of neural networks: the case of bank failure predictions. Management Science, 38(7):926-947, 1992.
30. Van Gestel, T., Baesens, B., Suykens, J., Willekens, M., Baestaens, D.E., Vanthienen, J., and De Moor, B. From Linear to Nonlinear Kernel Discriminant Analysis for Bankruptcy Prediction, Working Paper, Dept. Electrical Engineering, K.U.Leuven, Belgium, 2000.
31. Van Gestel, T., Suykens, J.A.K., Baesens, B., Viaene, S., Vanthienen, J., Dedene, G., De Moor, B., and Vandewalle, J. Benchmarking Least Squares Support Vector Machine Classifiers. Machine Learning, in press.
32. Van Gestel, T., Suykens, J.A.K., Baestaens, D.-E., Lambrechts, A., Lanckriet, G., Vandaele, B., De Moor, B., and 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.
33. Van Gestel, T., Suykens, J.A.K., Lanckriet, G., Lambrechts, A., De Moor, B., and 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.
34. Vapnik, V. Statistical Learning Theory. Wiley, New-York, 1998.
35. Williams, C.K.I. Prediction with Gaussian Processes: from Linear Regression to Linear Prediction and Beyond. In Learning and Inference in Graphical Models, Jordan, M.I. (Ed.), pp. 599-621, 1998. Kluwer Academic Press.
36. Wilson, R.L., and Sharda, R. Bankruptcy prediction using neural networks. Decision Support Systems, 11:545-557, 1994.