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Predicting Industrial Bond Ratings with a Probit Model and Funds Flow Components

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  • Gentry, James A
  • Whitford, David T
  • Newbold, Paul

Abstract

This study uses an n-chotomous multivariate probit model with cash-based funds flow components and financial ratios to predict industrial bond ratings. The n-chotomous probit model provides superior information for evaluating the bond classification process. The model determines the probabilities of a bond being rated in one o f three risk classes. New and reclassified bond rating by Moody's in 19 83 provide the information base for the model that is used to predict 1984 ratings. Initially, the classification and predictive results were slightly lower than previous studies. A careful analysis of the probability distributions showed that the results were close to being correct in over 90 percent of the cases. Five significant cash flow components in predictive bond ratings of reclassified issues were inventories, other current liabilities, dividends, long-term financing, and fixed coverage charges. Likelihood tests indicated that both ratios and funds flow components contributed information that significantly improved the ability of the n -chotomous multivariate probit model to classify new and revised bond ratings. Copyright 1988 by MIT Press.

Suggested Citation

  • Gentry, James A & Whitford, David T & Newbold, Paul, 1988. "Predicting Industrial Bond Ratings with a Probit Model and Funds Flow Components," The Financial Review, Eastern Finance Association, vol. 23(3), pages 269-286, August.
    • Handle: RePEc:bla:finrev:v:23:y:1988:i:3:p:269-86
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    Cited by:

    1. Chabowski, Brian & Chiang, Wen-Chyuan & Deng, Kailing & Sun, Li, 2019. "Environmental inefficiency and bond credit rating," Journal of Economics and Business, Elsevier, vol. 101(C), pages 17-37.
    2. Periklis Gogas & Theophilos Papadimitriou & Anna Agrapetidou, 2014. "Forecasting bank credit ratings," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 15(2), pages 195-209, March.
    3. Eleimon Gonis & Salima Paul & Jon Tucker, 2012. "Rating or no rating? That is the question: an empirical examination of UK companies," The European Journal of Finance, Taylor & Francis Journals, vol. 18(8), pages 709-735, September.
    4. Lee, Hei-Wai & Gentry, James A., 1995. "An empirical study of the corporate choice among common stock, convertible bonds and straight debt: A cash flow interpretation," The Quarterly Review of Economics and Finance, Elsevier, vol. 35(4), pages 397-419.
    5. Öğüt, Hulisi & Doğanay, M. Mete & Ceylan, Nildağ Başak & Aktaş, Ramazan, 2012. "Prediction of bank financial strength ratings: The case of Turkey," Economic Modelling, Elsevier, vol. 29(3), pages 632-640.
    6. Seyyide Doğan & Yasin Büyükkör & Murat Atan, 2022. "A comparative study of corporate credit ratings prediction with machine learning," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 32(1), pages 25-47.
    7. Chamroeun Sok, 2012. "Corporate Credit Rating Announcements: Information Content of Rating Announcements Models: Evidence from the Australian Financial Markets," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 4-2012.
    8. Huffman, Stephen P. & Ward, David J., 1996. "The prediction of default for high yield bond issues," Review of Financial Economics, Elsevier, vol. 5(1), pages 75-89.
    9. 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.
    10. Poon, Winnie P. H. & Firth, Michael & Fung, Hung-Gay, 1999. "A multivariate analysis of the determinants of Moody's bank financial strength ratings," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 9(3), pages 267-283, August.
    11. Hwang, Ruey-Ching & Chung, Huimin & Chu, C.K., 2010. "Predicting issuer credit ratings using a semiparametric method," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 120-137, January.
    12. repec:uts:finphd:36 is not listed on IDEAS
    13. Ken Hung & Hui Wen Cheng & Shih-shen Chen & Ying-Chen Huang, 2013. "Factors that Affect Credit Rating: An Application of Ordered Probit Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 94-108, December.
    14. Hirk, Rainer & Vana, Laura & Hornik, Kurt, 2022. "A corporate credit rating model with autoregressive errors," Journal of Empirical Finance, Elsevier, vol. 69(C), pages 224-240.
    15. Stephen P. Huffman & David J. Ward, 1996. "The prediction of default for high yield bond issues," Review of Financial Economics, John Wiley & Sons, vol. 5(1), pages 75-89, December.
    16. Poon, Winnie P. H., 2003. "Are unsolicited credit ratings biased downward?," Journal of Banking & Finance, Elsevier, vol. 27(4), pages 593-614, April.
    17. 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.

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