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Journal of Financial Services Research

Erschienen in:

01.08.2013

Is There a Difference Between Solicited and Unsolicited Bank Ratings and, If So, Why?

verfasst von: Patrick Van Roy

Erschienen in: Journal of Financial Services Research | Ausgabe 1/2013

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Abstract

This paper analyses the effect of soliciting a rating on the actual outcome of bank ratings. Using two sample banks (one rated by Fitch and one rated by S&P), I find evidence that unsolicited ratings tend to be lower than solicited ones, after accounting for differences in observable bank characteristics. This downward bias does not seem to be explained by the fact that better-quality banks self-select into the solicited group. Rather, unsolicited ratings appear to be lower because they are based on public information and are therefore dependent on the quantity of public information disclosed by the banks. As a result, unsolicited ratings tend to be more conservative than solicited ratings, which incorporate both public and non-public information. While the latter result is also consistent with the fact that credit rating agencies may blackmail low-disclosure firms, the findings suggest that blackmailing—if it is actually used—is ineffective in making these firms start to pay for a rating.

Vorheriger Artikel Banks, Bears, and the Financial Crisis

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Golin (2001) insists that credit rating agencies conducting unsolicited ratings attempt to invite the participation of the rated entity, either through submission of questionnaires, informal visits, or informal reviews of the draft report. Fitch (2006) claims that some issuers choose to participate to the rating process following this initial contact.

Fight (2001) reports excerpts from a survey which indicates that more than 90 % of companies release either selected or substantial non-public information to their rating agency during these meetings.

Shimoda and Yuko (2007) look at S&P’s ratings of non-financial companies in Japan and confirm the existence of a downward bias in unsolicited ratings without however controlling for sample selection.

An additional and equally plausible reason why these studies find conflicting evidence about the existence of sample selection is obviously the use of different samples.

Interestingly, the findings for Fitch appear to contradict the figures in Fitch (2006), which tend to show that there is no difference in Fitch’s credit judgment of firms with unsolicited ratings. Note, however, that the solicited status of the Moody’s and S&P’s ratings used as control groups in Fitch (2006) is unknown.

One may also argue that credit rating agencies assign lower unsolicited ratings not because they are conservative but because they are rational. Indeed, as mentioned in Footnote 1, credit rating agencies often stress that issuers are given the opportunity to participate in the unsolicited ratings process. Therefore, issuers who refuse to participate in such process must have something to “hide” and a lower rating may be warranted. However, there have been cases where issuers with no negative inside information have refused to participate in the unsolicited ratings process (see, e.g., Wall Street Journal 2004) hence this argument may not be entirely convincing.

More specifically, the papers reviewed in the introduction report the following percentages of unsolicited ratings for the Asian (or the Pacific) region: 60 % (Poon 2003b), 61 % (Poon 2003b), 63 % (Poon and Firth 2005), 63 % (Fairchild et al. 2009), 47 % (Bannier et al. 2010), and 49 % (Poon and Chan 2010).

Fitch produces two types of bank credit reports: short-form reports called “credit updates” and long-form reports called “full rating reports”.

The banking and finance score estimated by the Heritage Foundation is used to capture aspects related to the market environment of banks. This index, which measures the relative openness of a country’s banking and financial system on a 0–100 scale, has been shown to be strongly correlated with a sample of bank individual ratings assigned by Fitch in Asia (Pasiouras et al. 2007). The banking and finance score was preferred to Fitch’s sovereign credit rating because the latter exhibits a low correlation with the bank individual ratings and is available for fewer countries.

As a robustness check, I also used the 40th and 75th percentiles of the sample distribution of disclosure indices to define the high-disclosure dummy variable (these percentiles are respectively equal to 65.7 and 71.4). The main results of this paper are robust to these alternative definitions of the high-disclosure dummy.

More specifically, the disclosure index averages 76.2 in North America (528 banks), 54.3 in Europe (2,204 banks), 61.9 in Asia (795 banks, including those in the sample), 64.8 in Australia and New Zealand (37 banks), 50.0 in Africa and the Middle East (668 banks) and 52.4 in Latin America (896 banks). The standard deviation of the index varies between 11.9 (North America) and 17.4 (Australia and New Zealand).

Throughout the paper, significance refers to a level of confidence of 95 % and marginal significance to a level of confidence of 90 %.

The findings of this paper are robust to the choice of alternative variables for each area of Fitch’s bank rating methodology. The results of Tables 4 and 5 also hold if Baumann and Nier’s original disclosure index is substituted for the high-disclosure dummy. A dummy variable capturing whether the bank is rated (or not) by a competitor of Fitch was also added among the regressors, but was found to be insignificant.

Other studies (e.g. Cantor and Packer 1997) have shown that the analysis of rating determinants is not very sensitive to the choice between OLS (or 2SLS) and ordered probit. One reason is that the range of values taken by the Rating variable in Eq. (1) is large enough so that it can be treated as continuous.

A Chow test does not reject the null hypothesis that the coefficients of the explanatory variables (except the constant and the high-disclosure dummy) are the same in the solicited and unsolicited groups (test statistic = 1.40, p-value = 0.19). The test of the first part of the public disclosure hypothesis is based on a regression which assumes different intercepts and coefficients of the high-disclosure dummy in both groups (see Section 5.3).

As in Table 4, the instruments for these two variables consist of the other exogenous variables and country dummies - one for all but one country to avoid perfect collinearity (bootstrapped t-statistics are in parentheses).

This means that a bank’s decision to request an individual rating is not necessarily determined by its need to raise funds on the public market, which restricts the choice of valid exclusion restrictions.

While the solicited status of the long-term debt ratings used in this paper is unknown, private correspondence with a Fitch Ratings analyst revealed that a majority of these ratings are actually solicited.

Contrary to what was found in Table 4 (2SLS results), a Chow test now rejects the null hypothesis that the coefficients of the explanatory variables are the same in the solicited and unsolicited groups of the switching regression (the value of the test statistic is 61.72 with an associated probability of 0.00).

According to Mercieca et al. (2007) and others, the Z-score of banks is calculated as Z = (ROA + EA)/SD (ROA), where ROA is the average return on assets, EA is the average equity-to-assets ratio and SD (ROA) is the standard deviation of the return on assets. All three variables are based on the same number of years (either 5 years, from 1999 to 2003, or 3 years, from 2001 to 2003). The Z-score thus measures the number of standard deviations below the mean by which bank profits would have to fall just to deplete equity capital.

In addition, the variance inflation factor of the sample selection term is close to 10 when this variable is added to either Eqs. (8) or (9), which is a sign that multicollinearity between this variable and others (in particular the interaction terms) is very high.

Specifically, the following variables are instrumented (in addition to equity/total assets): High-disclosure dummy and (Unsolicited-rating dummy × High-disclosure dummy) in Eq. (8); (Unsolicited-rating dummy × High-disclosure dummy), (Unsolicited-rating dummy × Low-disclosure dummy) and (Solicited-rating dummy × Low-disclosure dummy) in Eq. (9).

I am grateful to a referee for this point.

Due to a lack of data, the 5-year Z-score (instead of the full list of financial and non-financial variables) was used to control for credit quality in Table 8. Note that Table 9, which replicates the same type of analysis on S&P data, is based on the full list of control variables and follows trends in solicitation status up to 4 years after the initial rating date.

The sample, which is similar to that used in Poon (2003b), includes 114 banks from Africa (Egypt, Morocco, South Africa, and Tunisia), Asia (Hong Kong, Indonesia, Israel, Malaysia, the Philippines and Taiwan), Europe (Greece, Hungary, Poland, Slovakia, Slovenia and Turkey), and Latin America (Brazil and Mexico). It contains exactly the same number of solicited and unsolicited ratings (57). Some of the 57 banks who had an unsolicited rating in 2004 disappeared over the next 4 years, which explains the lower number of unsolicited ratings in Column 7 (48) and in Column 8 (32). Results of probit regressions (not reported here) indicate that the probability of leaving the sample between 2004 and 2008 is not related to the level of information disclosure in 2004.

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Titel: Is There a Difference Between Solicited and Unsolicited Bank Ratings and, If So, Why?
verfasst von: Patrick Van Roy
Publikationsdatum: 01.08.2013
Verlag: Springer US
Erschienen in: Journal of Financial Services Research / Ausgabe 1/2013
Print ISSN: 0920-8550
Elektronische ISSN: 1573-0735
DOI: https://doi.org/10.1007/s10693-012-0149-8