Application of support vector machines to corporate credit rating prediction

Expert Systems with Applications

Volumn 33, Issue 1, 2007, Pages 67-74

  Lee, Young Chan ^a  

^a Dongguk University   (South Korea)

Author keywords

BPN; CBR; Credit rating; MDA; SVM

Indexed keywords

BACKPROPAGATION; FUNCTIONS; NEURAL NETWORKS; PROBLEM SOLVING; STATISTICAL METHODS;

BACK-PROPAGATION NEURAL NETWORKS (BPN); CASE-BASED REASONING (CBR); CREDIT RATING; MULTIPLE DISCRIMINANT ANALYSIS (MDA); SUPPORT VECTOR MACHINES (SVM);

LEARNING SYSTEMS;

EID: 33845665258     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2006.04.018     Document Type: Article

References (39)

1. Altman E., and Katz S. Statistical bond rating classification using financial and accounting data. In: Schiff M., and Sorter G. (Eds). Topical research in accounting (1976), Springer
2. Ang J., and Patel S.K.A. Bond rating methods: Comparison and validation. Journal of Finance 30 2 (1975) 631-640
3. Baran A., Lakonishok J., and Ofer A.R. The value of general price level adjusted data to bond rating. Journal of Business Finance and Accounting 7 (1980) 135-149
4. Belkaoui A. Industrial bond ratings: A new look. Financial Management 9 (1980) 44-51
5. Bhandari S.B., Soldofsky R.M., and Boe W.J. Bond quality rating changes for electric utilities: A multivariate analysis. Financial Management 8 1 (1979) 74-81
6. Butta P. Mining for financial knowledge with CBR. AI Expert 9 2 (1994) 34-41
7. Chang, C.-C., & Lin, C.-J. (2004). LIBSVM: A library for support vector machines. Technical Report, Department of Computer Science and Information Engineering, National Taiwan University. Available from http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf.
8. Cristianini N., and Shawe-Taylor J. An introduction to support vector machines (2000), Cambridge University Press, Cambridge, England
9. Drucker H., Wu D., and Vapnik V.N. Support vector machines for spam categorization. IEEE Transactions on Neural Networks 10 5 (1999) 1048-1054
10. Dutta S., and Shekhar S. Bond rating: A non-conservative application of neural networks. In: Trippi R.R., and Turban E. (Eds). Neural networks in finance and investing (1996), IRWIN
11. Fan, A., & Palaniswami, M. (2000). Selecting bankruptcy predictors using a support vector machine approach. In Proceedings of the international joint conference on neural networks.
12. Gestel T.V., Suykens J.A.K., Baestaens D.-E., Lambrechts A., Lanckriet G., Vandaele B., et al. Financial time series prediction using least squares support vector machines within the evidence framework. IEEE Transactions on Neural Networks 12 4 (2001) 809-821
13. Gunn, S. R. (1998). Support vector machines for classification and regression. Technical Report, University of Southampton.
14. Hair J.F., Anderson R.E., Tatham R.E., and Black W.C. Multivariate data analysis with readings (1998), Prentice-Hall
15. Hearst M.A., Dumais S.T., Osman E., Platt J., and Scholkopf B. Support vector machines. IEEE Intelligent System 13 4 (1998) 18-28
16. Horrigan, J. L. (1966). The determination of long term credit standing with financial ratios, empirical research in accounting: Selected studies. Supplement to V.4. Journal of Accounting Research.
17. Hsu, C.-W., Chang, C.-C., & Lin, C.-J. (2004). A practical guide to support vector classification. Technical Report, Department of Computer Science and Information Engineering, National Taiwan University. Available from http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.
18. Huang Z., Chen H., Hsu C.-J., Chen W.-H., and Wu S. Credit rating analysis with support vector machine and neural networks: A market comparative study. Decision Support Systems 37 (2004) 543-558
19. Kamstra M., Kennedy P., and Suan T.-K. Combining bond rating forecasts using logit. The Financial Review 37 (2001) 75-96
20. Kim, J. (1992). A comparative study of rule-based, neural networks, and statistical classification systems for the bond rating problem. Unpublished doctoral dissertation, Richmond, VA: Virginia Common Wealth University.
21. Kim K.J. Financial time series forecasting using support vector machines. Neurocomputing 55 (2003) 307-319
22. Kim K.S. Predicting bond ratings using publicly available information. Expert Systems with Applications 29 (2005) 75-81
23. Kim K.S., and Han I. The clustering-indexing method for case-based reasoning using self-organizing maps and learning vector quantization for bond rating cases. Expert Systems with Applications 12 (2001) 147-156
24. Kwon Y.S., Han I., and Lee C. Ordinal pairwise partitioning (OPP) approach to neural networks training in bond rating. International Journal of Intelligent Systems in Accounting, Finance and Management 6 1 (1997) 23-40
25. Maher J.J., and Sen T.K. Predicting bond ratings using neural networks: A comparison with logistic regression. Intelligent Systems in Accounting, Finance and Management 6 (1997) 59-72
26. Martin, L., Henderson, G., Perry, L., & Cronan, T. (1984). Bond ratings: Predictions using rating agency criteria. Working paper 85-3, Arizona State University, Tempe, AZ.
27. McAdams L. How to anticipate utility bond rating changes. Journal of Portfolio Management 7 1 (1980) 56-60
28. Moody J.E., and Utans J. Architecture selection strategies for neural networks application to corporate bond rating. In: Refens A. (Ed). Neural networks in the capital markets (1995), John Wiley
29. Pinches G.E., and Mingo K.A. A multivariate analysis of industrial bond rating. Journal of Finance 28 (1973) 1-18
30. Pogue T.F., and Soldofsky R.M. What's a bond rating?. Journal of financial and Quantitative Analysis 4 (1969) 201-228
31. Salchenberger L.M., Cinar E.M., and Las N.A. Neural networks: A new tool for predicting thrift failures. Decision Sciences 23 4 (1992) 899-915
32. Shaw M., and Gentry J. Inductive learning for risk classification. IEEE Expert: Intelligent Systems and Their Applications 5 1 (1990) 47-53
33. Shin K.S., and Han I. Case-based reasoning supported by genetic algorithms for corporate bond rating. Expert Systems with Applications 16 (1999) 85-95
34. Shin K.S., and Han I. A case-based approach using inductive indexing for corporate bond rating. Decision Support Systems 32 (2001) 41-52
35. Singleton J.C., and Surkan A.J. Bond rating with neural networks. In: Refens A. (Ed). Neural networks in the capital markets (1995), John Wiley
36. Tam K.Y., and Kiang M.Y. Managerial applications of neural networks: The case of bank failure predictions. Management Science 38 7 (1992) 926-947
37. Tay F.E.H., and Cao L. Application of support vector machines in financial time series forecasting. Omega 29 (2001) 309-317
38. Vapnik V. Statistical learning theory (1998), Springer, New York
39. Viaene S., Derrig R.A., Baesens B., and Dedene G. A comparison of state-of-the-art classification techniques for expert automobile insurance claim fraud detection. Journal of Risk & Insurance 69 3 (2002) 373-421