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On multiple‐class prediction of issuer credit ratings

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  • Ruey‐Ching Hwang
  • K. F. Cheng
  • Cheng‐Few Lee

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.

Suggested Citation

  • Ruey‐Ching Hwang & K. F. Cheng & Cheng‐Few Lee, 2009. "On multiple‐class prediction of issuer credit ratings," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(5), pages 535-550, September.
    • Handle: RePEc:wly:apsmbi:v:25:y:2009:i:5:p:535-550
    DOI: 10.1002/asmb.735
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    References listed on IDEAS

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    1. Ederington, Louis H, 1985. "Classification Models and Bond Ratings," The Financial Review, Eastern Finance Association, vol. 20(4), pages 237-262, November.
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    Cited by:

    1. Ruey-Ching Hwang, 2013. "Forecasting credit ratings with the varying-coefficient model," Quantitative Finance, Taylor & Francis Journals, vol. 13(12), pages 1947-1965, December.
    2. Cheng Few Lee, 2020. "Financial econometrics, mathematics, statistics, and financial technology: an overall view," Review of Quantitative Finance and Accounting, Springer, vol. 54(4), pages 1529-1578, May.
    3. Golbayani, Parisa & Florescu, Ionuţ & Chatterjee, Rupak, 2020. "A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    4. Jaspreet Kaur & Madhu Vij & Ajay Kumar Chauhan, 2023. "Signals influencing corporate credit ratings—a systematic literature review," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 50(1), pages 91-114, March.
    5. Sermpinis, Georgios & Tsoukas, Serafeim & Zhang, Ping, 2018. "Modelling market implied ratings using LASSO variable selection techniques," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 19-35.

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