On multiple-class prediction of issuer credit ratings

  • Authors:

  • Ruey-Ching Hwang;K. F. Cheng;Cheng-Few Lee

  • Affiliations:

  • Department of Finance, National Dong Hwa University, Hualien, Taiwan;Biostatistical Center, China Medical University, Taichung, Taiwan;Department of Finance and Economics, Rutgers University, NJ, U.S.A. and Graduate Institute of Finance, National Chiao Tung University, Hsinchu, Taiwan

  • Venue:
  • Applied Stochastic Models in Business and Industry
  • Year:
  • 2009

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Abstract

For multiple-class prediction, a frequently used approach is based on ordered probit model. We show that this approach is not optimal in the sense that it is not designed to minimize the error rate of the prediction. Based upon the works by Altman (J. Finance 1968; 23:589–609), Ohlson (J. Accounting Res. 1980; 18:109–131), and Begley et al. (Rev. Accounting Stud. 1996; 1:267–284) on two-class prediction, we propose a modified ordered probit model. The modified approach depends on an optimal cutoff value and can be easily applied in applications. An empirical study is used to demonstrate that the prediction accuracy rate of the modified classifier is better than that obtained from usual ordered probit model. In addition, we also show that not only the usual accounting variables are useful for predicting issuer credit ratings, market-driven variables and industry effects are also important determinants. Copyright © 2008 John Wiley & Sons, Ltd.