Asia pacific journal of information systems

The Korea Society of Management Information Systems (한국경영정보학회)
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Corporate Bond Rating Using Various Multiclass Support Vector Machines

다양한 다분류 SVM을 적용한 기업채권평가

  • Published : 2009.06.30
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Abstract

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

Keywords

References

  1. Ahn, H., Kim, K-j., and Han, I. 'Intelligent Credit Rating Model for Korean Companies using Multiclass Support Vector Machines,' Korean Management Review, Vol. 35, No. 5, 2006, pp. 1479-1496
  2. Altman, E.I., Marco, G., and Varetto, F. 'Corporate Distress Diagnosis Compatisons Using Linear Discriminant Analysis and Neural Networks,' Journal of Banking and Finance, Vol. 18, No. 3, 1994, pp. 505-529 https://doi.org/10.1016/0378-4266(94)90007-8
  3. Cao, L., Guan, L.K., and Jingqing, Z. 'Bond rating using support vector machine,' Intelligent Data Analysis, Vol. 10, No. 3, 2006, pp. 285-296
  4. Chang, C.-C. and Lin, C.-J. LIBSVM : a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm/.
  5. Chaveesuk, R., Srivaree-Ratana, C., and Smith, A.E. 'Alternative neural network approaches to corporate bond rating,' Journal of Engineering Valuation and Cost Analysis, Vol. 2, No.2, 1999, pp. 117-131
  6. Chen, W.-H. and Shih, J.-Y. 'A study of Taiwan's issuer credit rating systems using support vector machines,' Expert Systems with Applications, Vol. 30, No. 3, 2006, pp. 427-435 https://doi.org/10.1016/j.eswa.2005.10.003
  7. Crammer, K. and Singer, Y. 'On the Learnability and Design of Output Codes for Multiclass Problems,' Proceedings of the $13^{th}$ Annial Conference on Computational Learning Theory, 2000, pp. 35-46 https://doi.org/10.1023/A:1013637720281
  8. Dietterich, T.G., and Bakiri, G. 'Solving multiclass learning problems via error correcting output codes,' Journal of Artificial Intelligence, Vol. 2, 1995, pp. 263-286
  9. Dutta, S. and Shekhar, S. 'Bond rating: a non-conservative application of neural networks,' Proceedings of IEEE International Conference on Neural Networks, 1988, pp. II443-II450
  10. Ederington, H.L. 'Classification models and bond ratings,' Financial Review, Vol. 20, No. 4, 1985, pp. 237-262 https://doi.org/10.1111/j.1540-6288.1985.tb00306.x
  11. Fisher, L. 'Determinants of risk premiums on corporate bonds,' Journal of Political Economy, Vol. 67, 1959, pp. 217-237 https://doi.org/10.1086/258172
  12. Friedman, J. 'Another Approach to Polychtomous Classification,' Technical Report, Stanford Univ., 1996
  13. Hsu, C-W. and Lin, C.-J. 'A Comparison of Methods for Multiclass Support Vector Machines,' IEEE Transactions on Neural Networks, Vol. 13, No. 2, 2002a, pp. 415-425 https://doi.org/10.1109/72.991427
  14. Hsu, C-W. and Lin, C.-J. 'A Simple Decomposition Method for Support Vector Machines,' Machine Learning, Vol. 46, 2002b, pp. 291-314 https://doi.org/10.1023/A:1012427100071
  15. Hsu, C-W. and Lin, C.-J. BSVM: a SVM library for the solution of large classification and regression problems, 2006. Software available at http://www.csie.ntu. edu.tw/-cjlin/bsvm/
  16. Huang, Z., Chen, H., Hsu, C.-J., Chen, W-H. and Wu, S. 'Credit Rating Analysis with Support Vector Machines and Neural Networks: A Market Comparative Study,' Decision Support Systems, Vol. 27, 2004, pp. 543-558 https://doi.org/10.1016/S0167-9236(03)00086-1
  17. Jo, H., and Han, I. 'Integration of casebased forecasting, neural network, and discriminant analysis for bankruptcy prediction,' Expert Systems with Applications, Vol. 11, 1996, pp. 415-422 https://doi.org/10.1016/S0957-4174(96)00056-5
  18. Kim, J.W. 'Expert systems for bond rating: a comparative analysis of statistical, rule-based and neural network systems,' Expert Systems, Vol. 10, 1993, pp. 167-171 https://doi.org/10.1111/j.1468-0394.1993.tb00093.x
  19. Kim, K. 'Financial time series forecasting using support vector machines,' Neurocomputing, Vol. 55, No. 1-2, 2003, pp. 307-319 https://doi.org/10.1016/S0925-2312(03)00372-2
  20. Klautau, A., Jevtic, N., and Orlitsky, A. 'On nearest-neighbor error-correcting output codes with application to allpairs multiclass support vector machines,' Journal of Machine Learning Research, Vol. 4, No. 1, 2003, pp. 1-15 https://doi.org/10.1162/153244304322765612
  21. Kre$\beta$el, U. 'Pairwise Classification and Support Vector Machines,' In Scholkopf, B., Burges, C. and Smola, A.J.: Advances in Kernal Methods: Support Vector Learning, Chapter 15. MIT Press. Cambridge, MA, 1999, pp. 255-268
  22. Kwon, Y.S., Han, I.G., and Lee, K.C. 'Ordinal Pairwise Partitioning (OPP) approach to neural networks training in bond rating,' Intelligent Systems in Accounting, Finance and Management, Vol. 6, 1996, pp. 23-40 https://doi.org/10.1002/(SICI)1099-1174(199703)6:1<23::AID-ISAF113>3.0.CO;2-4
  23. Lee, K.C., Han, I., and Kim, M.J. 'Study on the Credit Evaluation Model Integrating Statistical Model and Artificial Intelligence Model,' Journal of the Korean Operations Research and Management Science Society, Vol. 21, No. 1, 1996, pp. 81-100
  24. Lee, Y.-C. 'Application of support vector machines to corporate credit rating prediction,' Expert Systems with Applications, Vol. 33, No. 1, 2007, pp. 67-74 https://doi.org/10.1016/j.eswa.2006.04.018
  25. Lorena, A.C., and de Carvalho, A.C.P.L.F. 'Investigation of Strategies for the Generation of Multiclass Support Vector Machines,' In Nguyen, N.T., and Katarzyniak, R.: New Challenges in Applied Intelligence Techniques, Springer-Verlag. Berlin, Germany, 2008, pp. 319-328
  26. Park, K.-N., Lee, H.-Y., and Park, S.-K. 'A Hybrid Credit Rating System using Rough Set Theory,' Journal of the Korean Operations Research and Management Science Society, Vol. 25, No. 3, 2000, pp. 125-135
  27. Pinches, G.E., and Mingo, K.A. 'A multivariate analysis of industrial bond ratings,' Journal of Finance, Vol. 28, No. 1, 1973, pp. 1-18 https://doi.org/10.2307/2978164
  28. Pinches, G.E., and Mingo, K.A. 'The role of subordination and industrial bond ratings,' Journal of Finance, Vol. 30, No. 1, 1975, pp. 201-206 https://doi.org/10.2307/2978442
  29. Platt, J.C., Cristianini, N. and ShaweTaylor, J. 'Large Margin DAG's for multiclass classification,' In Solla, S.A., Leen, T.K. and Muller, K.-R.: Advances in Neural Information Prcessing Systems, Vol. 12. MIT Press. Cambridge, MA, 2000, pp. 547-553
  30. Shin, K.S., and Han. I. 'Case-based reasoning supported by genetic algorithms for corporate bond rating,' Expert Systems with Applications, Vol. 16, No. 2, 1999, pp. 85-95 https://doi.org/10.1016/S0957-4174(98)00063-3
  31. Shin, K.S., and Han, I. 'A case-based approach using inductive indexing for corporate bond rating,' Oecision Support Systems, Vol. 32, 2001, pp. 41-52 https://doi.org/10.1016/S0167-9236(01)00099-9
  32. Statnikov, A., Aliferis, C.F., Tsamardinos, I., Hardin, D. and Levy, S. 'A Comprehensive Evaluation of Multicategory Classification Methods for Microarray Gene Expression Cancer Diagnosis,' Bioinformatics, Vol. 21, No. 5, 2005, pp. 631-543 https://doi.org/10.1093/bioinformatics/bti033
  33. Tay, F.E.H. and Cao, L.J. 'Application of Support Vector Machines in Financial Time Series Forecasting,' Omega, Vol. 29, 2001, pp. 309-317 https://doi.org/10.1016/S0305-0483(01)00026-3
  34. Ubeyli, E.D. 'Multiclass support vector machines for diagnosis of erythematosquamous disease,' Expert Systems with Applications, Vol. 35, No. 4, 2008, pp. 1733-1740 https://doi.org/10.1016/j.eswa.2007.08.067
  35. Vapnik, V. The Nature of Statistical Learning Theory. New York, NY: Springer-Verlag, 1995
  36. Weston, J. and Watkins, C. 'Support Vector Machines for Multiclass Pattern Recognition,' Proceedings of the Seventh European Symposium on Artificial Neural Networks, 1999, pp. 219-224
  37. Wu, Y.-C., Lee, Y.-S., and Yang, J.-C. 'Robust and efficient multiclass SVM models for phrase pattern recognition,' Pattern Recognition, Vol. 41, No. 9, 2008, pp. 2874-2889 https://doi.org/10.1016/j.patcog.2008.02.010