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http://dx.doi.org/10.5351/KJAS.2018.31.3.367

Comparative study of prediction models for corporate bond rating  

Park, Hyeongkwon (Department of Statistics, Inha University)
Kang, Junyoung (Department of Statistics, Inha University)
Heo, Sungwook (Department of Statistics, Inha University)
Yu, Donghyeon (Department of Statistics, Inha University)
Publication Information
The Korean Journal of Applied Statistics / v.31, no.3, 2018 , pp. 367-382 More about this Journal
Abstract
Prediction models for a corporate bond rating in existing studies have been developed using various models such as linear regression, ordered logit, and random forest. Financial characteristics help build prediction models that are expected to be contained in the assigning model of the bond rating agencies. However, the ranges of bond ratings in existing studies vary from 5 to 20 and the prediction models were developed with samples in which the target companies and the observation periods are different. Thus, a simple comparison of the prediction accuracies in each study cannot determine the best prediction model. In order to conduct a fair comparison, this study has collected corporate bond ratings and financial characteristics from 2013 to 2017 and applied prediction models to them. In addition, we applied the elastic-net penalty for the linear regression, the ordered logit, and the ordered probit. Our comparison shows that data-driven variable selection using the elastic-net improves prediction accuracy in each corresponding model, and that the random forest is the most appropriate model in terms of prediction accuracy, which obtains 69.6% accuracy of the exact rating prediction on average from the 5-fold cross validation.
Keywords
Corporate bond rating; linear regression; ordered logit; random forest; elastic-net;
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  • Reference
1 Akaike, H. (1974). A new look at the statistical model identification, IEEE Transactions on Automatic Control, 19, 716-723.   DOI
2 Altman, E. I. and Katz, S. (1976). Statistical bond rating classification using financial and accounting data. In Proceeding of the Conference on Topical Research in Accounting, NYU Press, 205-239.
3 Breiman, L. (1996). Bagging predictors, Machine Learning, 24, 123-140.
4 Ederington, L. H. (1986). Why split ratings occur, Financial Management, 15, 37-47.   DOI
5 Horrigan, J. O. (1966). The determination of long-term credit standing with financial ratios, empirical research in accounting: selected studies, Supplement to Journal of Accounting Research, 4, 44-62.   DOI
6 Huang, Z., Chen, H., Hsu, C. J., Chen, W. H., and Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: a market comparative study, Decision Support Systems, 37, 534-558.
7 Jeong, C. J. (2011). The empirical study on factors affecting corporate credit ratings (Unpublished master's thesis), Kyonggi University, Suwon, Korea.
8 Kaplan, R. S. and Urwitz, G. (1979). Statistical models of bond ratings: a methodological inquiry, Journal of Business, 52, 231-261.   DOI
9 Kim, J. S. and Choi, Y. M. (2006). Development of a bond rating prediction model based on financial and stock price-based variables, Study on Accounting, Taxation & Auditing, 43, 185-217.
10 Kim, K. J. and Kim, J. S. (2002). Development of bond rating prediction model for effective interest rate estimation, Korean Accounting Journal, 11, 81-100.
11 Kim, M. J. (2012). Ensemble learning with support vector machines for bond rating, Journal of Intelligence and Information System, 18, 29-45.
12 Kim, S. J. and Ahn, H. (2016). Application of random forests to corporate credit rating prediction, The Journal of Business and Economics, 32, 187-211.
13 Kim, S. T., Lee, J. J., and Hong, J. B. (2006). The prediction model of bond-rating with ordered logit analysis, Journal of the Korean Data Analysis Society, 8, 641-654.
14 Ko, D. P. and Kim, H. M. (2002). Using financial health index approach to credit analysis, Industrial Management Review, 25, 231-254.
15 Pinches, G. E. and Mingo, K. A. (1973). A multivariate analysis of industrial bond ratings, Journal of Finance, 28, 1-18.   DOI
16 Tibshirani, R. (1996). Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B: Statistical Methodology, 58, 267-288.
17 West, R. R. (1970). An alternative approach to predicting corporate bond ratings, Journal of Accounting Research, 8, 118-125.   DOI
18 Wurm, M. J., Rathouz, P. J., and Hanlon B. M. (2017). Regularized ordinal regression and the ordinalNet R package, arXiv preprint arXiv:1706.05003.
19 Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society. Series B, 67, 301-320.   DOI
20 Seo, Y. H. (2015). The effect of revenue-expense matching on corporate bond ratings (Unpublished master's thesis), Chung-Ang University, Seoul, Korea.