Split ratings

time when they make their cost of debt assessment, the authors estimate a regression model of spreads against the difference between actual firms’ ratings and predicted firms’

ratings, and some control variables. Their findings are similar with Alp’s result in the specific hypothesis. It seems that investors understand credit standards stringency and ask for lower yields, hence the spreads get tighter.

To sum up, Baghai, Servaes, and Tamayo results are consistent with Alp’s (2013) on credit rating agencies’ conservatism. In addition, they find that conservatism in credit ratings influence firms’ financial decisions, leading them to issue less debt compare to firms assigned with a rating consistent with their credit risk. Also, stricter ratings have a negative impact on firm’s growth and investments in acquisitions strategy.

that in split-rated firms, it’s possible that Moody’s will assign a higher rating in a low leveraged and profitable, supporting Moody’s conservatism hypothesis.

In addition, they incorporate governance variables such the amounts of shares held by institutions, the boards independence, and the G-score variable. They expect that poor governance will lead to split ratings, and that a G-score will be negatively related, and the other two variables a positive relation, with Moody’s conservatism on split-rated firms.

They also add firm’s measurement of systematic risk (equity market beta) and expect that higher systematic risk will more likely lead to split ratings.

As macroeconomic indicators the authors use the GDP growth to identify procyclicality, expecting a positively relation of GDP growth with the Moody’s conservatism hypothesis. Also, they assume that the increase in competition in credit ratings industry, and especially between major agencies (after the entry of Fitch) lead to inflated ratings, hence they create a binary variable coded 1 if a firm has three ratings, indicating resulting in a higher probability of receiving split ratings. Finally, they introduce a dummy variable to capture the impact of FD regulation, which is set equal to 1 for the post-FD period, and they expect a negatively relation with Moody’s conservatism hypothesis.

They estimate a bivariate probit model, which combines the likelihood of the split rating to occur and the likelihood Moody’s to assign a higher rating in the split as one interrelated event. More specifically, the authors describe a two-stage procedure. They use dummies as explained variables, hence in stage once the dummy coded one if a split ratings occurred, and in the second stage another dummy set equal to one if Moody’s assigned a higher rating than S&P.

Moreover, they present their results from the first stage of regression analysis. The variable of size, interest coverage, and profitability are significant and negative, supporting that agencies’ ratings are not differentiate on firms with good financial standing. Interestingly, both agencies tend to agree on their ratings for highly leveraged firms, since the leverage variable is negative. Also, all three governance-related variables are significant and negative, supporting the fact that poor governance may lead to split ratings. For the macroeconomic indicators and regulations factors, their findings support in post-FD regulation period the credit rating agencies will differentiate on firms’ credit ratings their ratings more often.

Additionally, Bowe, and Larik present their findings for the second stage analysis. The variables of size, coverage, profitability, and governance (except G-score) are significant and positive and leverage is negative and significant supporting the Moody’s conservatism hypothesis. The negative sign on G-score indicates that the decrease of the power of stakeholders, will lead to a lower rating assignment from Moody’s compare to S&P. Therefore, Moddy’s assumes that the effective monitoring over management decisions is an important factor for assigning a higher credit rating. Moreover, they conclude that the increased competency in major credit rating agencies, and the assignment of three ratings to a firm will more likely lead to higher rating by Moody’s.

Also, it seems that the conservatism of Moody’s increased during the post-FD period, since the dummy’s coefficient is significant and negative.

Furthermore, the study by Livingston, Wei, Zhou (2010), is an investigation of the split ratings between the aforementioned credit rating agencies from a bond-yields’, and investor’s preference perspective. They find that yields are lower when a split with a superior rating by Moody’s occur compare to splits with superior rating by S&P’s. The threat of two agencies’ ratings as equal by financial regulators and academics may create ambiguous results from their analysis. In general, the authors posit that investors prefer ratings from the most conservative agency. Hence as the agency’s rating standards become stricter, investors will use those ratings to estimate credit risk. Also, they postulate that credit rating agencies try to protect from reputational risk, becoming more conservative over time.

To begin with, the authors construct the foundations of their analysis: the rating equivalence hypothesis, and the systematic difference hypothesis. In the aforementioned studies we can observe that rating agencies use almost identical determinants for their firm credit rating assignment. The authors assume that the two major credit rating agencies’ use exactly the same factors and assign the same weights to each one of them, and they call this view rating equivalence hypothesis. Therefore, if this hypothesis is valid, which agency has assigned the highest rating in a split does not concern us anymore because the bond yields will be equal.

Also, they assume that there are systematic differences between the two rating agencies.

More specifically, they provide evidence that Moody’s ratings are slightly better at predicting default than S&P ratings and refer to this as the systematic difference hypothesis. Obviously, we are going to observe lower yields in splits ratings with a superior rating by Moody’s compare to splits with S&P’s superior rating. Overall, they examine, whether split ratings are a systematic event, and if the investors more likely prefer the ratings of one of the two agencies.

Livingston, Wei, and Zhou estimate multivariate regressions of the split-rated bonds yields minus the treasury yield against: thirty-three dummy variables one for each rating category (letter), and some control variables. In these control variables, they include bond characteristics such as the maturity, magnitude of the issue, the seniority of the debt security, the characteristic of the bond to be callable, variables trying to capture the effect of different registration methods in debt issues, and a dummy variable coded one if the issuer belongs in utility industry. The authors expect that magnitude of the issue and seniority will be negatively related with spreads. The callable characteristic of the issue, and the bonds aren’t shelf-registered and especially for Rule 144A issues is more likely to be positively related with the yields. They also use the excess return of the security with the market variable to capture the security’s systematic risk, and the coefficient of this variable is expected to be positive. Finally, as a test variable they construct a dummy coded one if Moody’s rating is the highest, and they expect a negative relation with spreads.

They find that most of the explanatory variables are significant and have the anticipated signs. More specifically, the variable that captures the seniority of the issue has a positive sign, but the coefficient on the test variable is negative and significant, indicating that indeed, the investors prefer the bonds assigned with higher rating by Moody’s.

In addition, to examine evolution of conservatism in rating standards over time they split the sample into two sub-samples based on period: 1983-97 and 1998-2008. Estimating their regression model over these two periods, they conclude that the test variable is highly significant for the 1998-2008 subsample, suggesting that Moody’s conservatism increased during this period. They also create an interaction term between the test dummy and a time trend variable and add it to the main regression model. They find that the interaction term is negative and significant, supporting the investors’ preferences are inherent with more conservative ratings.

To sum up, the major credit agencies systematically disagree on some of their credit rating assignments on specific firms. The likelihood of split ratings is explained by both financial and governance variables. The researchers identify that Moody’s apply stricter credit standards on its ratings, and the investors ask lower yields for split-rated bonds with Moody’s superior ratings during the 1998 to 2008 period.