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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. 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.
    2. 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.
    3. 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.
    4. Poon, Winnie P. H., 2003. "Are unsolicited credit ratings biased downward?," Journal of Banking & Finance, Elsevier, vol. 27(4), pages 593-614, April.
    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. repec:uts:finphd:36 is not listed on IDEAS
    7. 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.
    8. 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.

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