Abstract
This paper investigates the incentive of credit rating agencies (CRAs) to bias ratings using a semiparametric, ordered-response model. The proposed model explicitly takes conflicts of interest into account and allows the ratings to depend flexibly on risk attributes through a semiparametric index structure. Asymptotic normality for the estimator is derived after using several bias correction techniques. Using Moody’s rating data from 2001 to 2016, I found that firms related to Moody’s shareholders were more likely to receive better ratings. Such favorable treatments were more pronounced in investment grade bonds compared with high yield bonds, with the 2007–2009 financial crisis being an exception. Parametric models, such as the ordered-probit, failed to identify this heterogeneity of the rating bias across different bond categories.
Highlights
The relaxed rating standard was largely attributable to conflicts of interest arising out of the credit rating agencies (CRAs)’s business model
I propose a semiparametric model to investigate to what extent Moody’s ratings are affected by the economic interests of its shareholders, which is pertinent for the regulation of credit rating agencies
Compared with extant bond rating models, the proposed model has two key features: (i) I explicitly consider the impact of conflicts of interest on ratings through common shareholders, (ii) the model imposes few distributional and functional form restrictions on the underlying rating process
Summary
Econometrics 2021, 9, 23 aforementioned studies, with the exception of Ichimura and Lee (1991), required estimating nonparametric conditional mean functions and/or derivatives in the original space of regressors. Such methods may not behave well when the sample size is small relative to the number of regressors. I estimate Moody’s rating model year-by-year and characterize the average partial effects (APE) of MFOI, the aforementioned shared-ownership index. As the employed model permits the partial effect of MFOI to flexibly depend on other characteristics in an interactive fashion, the contribution of this application to the pertaining empirical literature is to explore the heterogeneity of rating bias.
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