PREDICTING INDUSTRIAL BOND RATINGS WITH A PROBIT MODEL AND FUNDS FLOW COMPONENTS

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Abstract
Since 1978, there has been a significant change in new bond offerings with a substantive increase in the number of nonconvertible high risk bonds. 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 of three risk classes. The distribution of the probabilities for each predicted bond rating provides a wealth of new information for evaluating the accuracy of the actual rating. New and reclassified bond ratings by Moody's in 1983 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 results were close to being correct in over 90 percent of the cases. The analysis found five cash flow components to be significant in predicting the bond ratings of reclassified issues. The significant components were inventories, other current liabilities, dividends, long‐term financing, and fixed coverage charges. The 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. The study provides valuable insight and nuances concerning the bond‐rating process.

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