Corporate Credit Rating using Multiclass Classification Models with order Information

Authors: Hyunchul Ahn, Kyoung-Jae Kim

Abstract:

Corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has been one of the attractive research topics in the literature. In recent years, multiclass classification models such as artificial neural network (ANN) or multiclass support vector machine (MSVM) have become a very appealing machine learning approaches due to their good performance. However, most of them have only focused on classifying samples into nominal categories, thus the unique characteristic of the credit rating - ordinality - has been seldom considered in their approaches. This study proposes new types of ANN and MSVM classifiers, which are named OMANN and OMSVM respectively. OMANN and OMSVM are designed to extend binary ANN or SVM classifiers by applying ordinal pairwise partitioning (OPP) strategy. These models can handle ordinal multiple classes efficiently and effectively. To validate the usefulness of these two models, we applied them to the real-world bond rating case. We compared the results of our models to those of conventional approaches. The experimental results showed that our proposed models improve classification accuracy in comparison to typical multiclass classification techniques with the reduced computation resource.

Keywords: Artificial neural network, Corporate credit rating, Support vector machines, Ordinal pairwise partitioning

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330557

References:

[1] L. Cao, L. K. Guan, and Z. Jingqing, "Bond rating using support vector machine," Intell. Data Anal., vol. 10, no. 3, pp. 285-296, 2006.
[2] V. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
[3] K. Crammer, and Y. Singer, "On the learnability and design of output codes for multiclass problems," in Proc. 13th Annu. Conf. Computational Learning Theory, Palo Alto, CA, 2000, pp. 35-46.
[4] J. C. Platt, N. Cristianini, and J. Shawe-Taylor, "Large margin DAG-s for multiclass classification," in Advances in Neural Information Processing Systems, vol. 12, S. A. Solla, T. K. Leen, and K. -R. Muller, Eds. Cambridge, MA: MIT Press, 2000, pp. 547-553.
[5] C. -W. Hsu, and C. -J. Lin, "A comparison of methods for multiclass support vector machines," IEEE Trans. Neural Networks, vol. 13, no. 2, pp. 415-425, 2002.
[6] Z. Shuibo, T. Houjun, H. Zhengzhi, and Z. Haoran, "Solving large-scale multiclass learning problems via an efficient support vector classifier," J. Syst. Eng. Electron., vol. 17, no. 4, pp. 910-915, 2006.
[7] A. Navia-Vázquez, "Compact multi-class support vector machine," Neurocomputing, vol. 71, nos. 1-3, pp. 400-405, 2007.
[8] E. D. Übeyli, "Multiclass support vector machines for diagnosis of erythemato-squamous disease," Expert Syst. Appl., vol. 35, no. 4, pp. 1733-1740, 2008.
[9] Z. Huang, H. Chen, C. -J. Hsu, W. -H. Chen, and S. Wu, "Credit rating analysis with support vector machines and neural networks: A market comparative study," Decis. Support Syst., vol. 37, no. 4, pp. 543-558, 2004.
[10] W. -H. Chen, and J. -Y. Shih, "A study of Taiwan-s issuer credit rating systems using support vector machines," Expert Syst. Appl., vol. 30, no. 3, pp. 427-435, 2006.
[11] Y. -C. Lee, "Application of support vector machines to corporate credit rating prediction," Expert Syst. Appl., vol. 33, no. 1, pp. 67-74, 2007.
[12] Y. S. Kwon, I. Han, and K. C. Lee, "Ordinal pairwise partitioning (OPP) approach to neural networks training in bond rating," Intell. Syst. Account. Finance Manag., vol. 6, no. 1, pp. 23-40, 1997.
[13] H. Ahn, J. J. Ahn, H. W. Byun, and K. J. Oh, "A novel customer scoring model to encourage the use of mobile value added services," Expert Syst. Appl., vol. 38, no. 9, pp. 11693-11700, 2011.
[14] Y. -C. Wu, Y. -S. Lee, and J. -C. Yang, "Robust and efficient multiclass SVM models for phrase pattern recognition," Pattern Recogn., vol. 41, no. 9, pp. 2874-2889, 2008.
[15] A. C. Lorena, and A. C. P. L. F. de Carvalho, "Investigation of strategies for the generation of multiclass support vector machines," in New Challenges in Applied Intelligence Techniques, N. T. Nguyen, and R. Katarzyniak, Eds. Berlin, Germany: Springer-Verlag, 2008, pp. 319-328.
[16] J. Friedman, "Another approach to polychtomous classification," Technical Report, Stanford University, 1996.
[17] U. Kreβel, "Pairwise classification and support vector machines," in Advances in Kernal Methods: Support Vector Learning, ch. 15, B. Schölkopf, C. Burges, and A. J. Smola, Eds. Cambridge, MA: MIT Press, 1999, pp. 255-268.
[18] J. Weston, and C. Watkins, "Support vector machines for multiclass pattern recognition," in Proc. 7th European Symp. Artificial Neural Networks, Bruges, Belgium, 1999, pp. 219-224.
[19] C. -W. Hsu, and C. -J. Lin, "A simple decomposition method for support vector machines," Mach. Learn., vol. 46, nos. 1-3, pp. 291-314, 2002.
[20] F. E. H. Tay, and L. J. Cao, "Application of support vector machines in financial time series forecasting," Omega, vol. 29, no. 4, pp. 309-317, 2001.
[21] C. -C. Chang, and C. -J. Lin, LIBSVM : a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm/