asymmetric disclosure to credit rating agencies/media/files/msb...in this paper, we consider the...

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Asymmetric Disclosure to Credit Rating Agencies Minkwan Ahn The University of Hong Kong School of Business K.K. Leung Building Pokfulam, Hong Kong Samuel Bonsall and Andrew Van Buskirk * The Ohio State University Fisher College of Business 2100 Neil Avenue Columbus, OH 43210 August 2014 Abstract Prior research documents that managers tend to withhold bad news and accelerate good news in their public disclosures. A separate line of research shows that firms provide significant non- public information to other parties, such as lenders and ratings agencies. We ask whether managers withhold bad news from ratings agencies in the same way they do from the public. To answer this question, we decompose observed credit ratings into the component explained by financial statement information and the remaining component presumably based on non-financial statement information (what we call the rating deviation). We find that negative deviations have significantly stronger associations (relative to positive deviations) with bid-ask spreads and investor reaction to ratings changes. We also find that negative rating deviations predict abnormal stock returns around subsequent earnings announcements, while positive deviations do not. Our results suggest that only negative non-financial information in credit ratings is perceived to be private, which implies that managers are more willing to provide bad news to ratings agencies than they are to the general public. Our results also indicate that rating agencies incorporate this negative information in ratings, which should comfort those who are concerned that the issuer-pay model leads to inflated ratings. * Corresponding author: [email protected] We thank Ryan Ball, Mei Cheng, Christine Cuny, Ed DaHaan, Rahul Vashishtha, and workshop participants at Ohio State University for helpful comments and suggestions.

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Page 1: Asymmetric Disclosure to Credit Rating Agencies/media/Files/MSB...In this paper, we consider the question of strategic disclosure to credit rating agencies (CRAs). Ratings agencies,

Asymmetric Disclosure to Credit Rating Agencies

Minkwan Ahn

The University of Hong Kong School of Business

K.K. Leung Building Pokfulam, Hong Kong

Samuel Bonsall and Andrew Van Buskirk*

The Ohio State University Fisher College of Business

2100 Neil Avenue Columbus, OH 43210

August 2014

Abstract

Prior research documents that managers tend to withhold bad news and accelerate good news in their public disclosures. A separate line of research shows that firms provide significant non-public information to other parties, such as lenders and ratings agencies. We ask whether managers withhold bad news from ratings agencies in the same way they do from the public. To answer this question, we decompose observed credit ratings into the component explained by financial statement information and the remaining component presumably based on non-financial statement information (what we call the rating deviation). We find that negative deviations have significantly stronger associations (relative to positive deviations) with bid-ask spreads and investor reaction to ratings changes. We also find that negative rating deviations predict abnormal stock returns around subsequent earnings announcements, while positive deviations do not. Our results suggest that only negative non-financial information in credit ratings is perceived to be private, which implies that managers are more willing to provide bad news to ratings agencies than they are to the general public. Our results also indicate that rating agencies incorporate this negative information in ratings, which should comfort those who are concerned that the issuer-pay model leads to inflated ratings.

* Corresponding author: [email protected] We thank Ryan Ball, Mei Cheng, Christine Cuny, Ed DaHaan, Rahul Vashishtha, and workshop participants at Ohio State University for helpful comments and suggestions.

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1. Introduction

A longstanding question in accounting research is whether managers disclose fully and

truthfully or whether they disclose strategically. Potentially strategic disclosure has been

investigated in many contexts, with researchers frequently concluding that managers withhold

bad news or emphasize good news (e.g., Kothari et al. 2009; Lougee and Marquardt 2004; Doyle

et al. 2013). 1 While prior research provides substantial evidence that managers disclose

strategically in a variety of situations, that research is generally concerned with the interaction

between managers and equity markets.

In this paper, we consider the question of strategic disclosure to credit rating agencies

(CRAs). Ratings agencies, as well as banks involved in private lending contracts, receive more

information than what is publicly disclosed to equity investors (see, for example, Jorion et al.

2005; Bushman et al. 2010; Plumlee et al. 2014). However, managers have the ability to bias

those private communications in the same way that they can bias their public disclosures.

Anecdotally, Moody’s has alleged that Enron’s “responses to [their] specific requests for

information were misleading and incomplete”.2 Further, the agencies’ own manuals assert that

they will drop coverage of an issuer on the basis of insufficient information. (Moody's Investors

2008; Standard and Poor's 2012). On a broader scale, whether managers tend to withhold bad

news in these quasi-private channels is an open question.

It is difficult to predict whether managers who withhold bad news in their public

disclosures would reveal that bad news to credit rating agencies. On one hand, a manager who

1 Two exceptions are Aboody and Kasznik (2000), who provide evidence that managers accelerate bad news prior to option grants, and Cheng and Lo (2006), who show that managers issue more bad news forecasts prior to planned purchases of shares. Of course, these examples are consistent with the broader notion that managers use discretion to personally benefit from voluntary disclosure. We discuss this literature in greater detail in Section 2. 2 Testimony of John Diaz Managing Director Moody's Investors Service before the Committee on Governmental Affairs United States Senate.

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wants to withhold bad news from the equity market may anticipate that full disclosure to a credit

analyst would lead to a credit rating that is lower than what outsiders would expect based on

publicly disclosed information. As a consequence, even if the credit analyst did not disclose the

precise nature of the bad news, investors would infer from the relatively low rating the likely

presence of bad news, and the manager’s efforts to withhold bad news would be futile.3 On the

other hand, credit ratings agencies have a powerful tool if they believe that the firm is

withholding bad news – they can assign lower credit ratings or even, in the extreme, withdraw

their rating altogether. A rating withdrawal could be quite costly to an issuer if rating triggers

exist in current bond indentures and require immediate repayment of principal upon loss of

rating. In addition, the liquidity of issuers’ bonds would likely decline dramatically as a result of

portfolio managers relinquishing their holdings of bonds without rating since portfolio

governance often relies on credit ratings (Cantor and Packer 1995).

Determining whether managers disclose truthfully in any context is difficult, as it

requires some assumption about what the manager actually knows (as opposed to what the

manager discloses publicly); by definition, private information is difficult to identify and

measure. Prior research in this area has relied upon inference, finding support for biased

disclosure based on things like asymmetric responses to good and bad news (Kothari et al. 2009),

the asymmetry of forecast news (Ajinkya and Gift 1984), or the ex post bias in management

forecasts (Rogers and Stocken 2005).

Because we cannot observe what is (and is not) disclosed to ratings agencies, we take a

similarly inferential approach. Conceptually, our approach is as follows. (A detailed discussion

can be found in Section 3.2.) We assume that credit rating agencies use a mix of information to 3 A similar argument applies to private lending contracts, where outsiders could infer privately-communicated information from an unexpectedly high or low observed interest rate.

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assess a firm’s credit risk, some of which is currently reflected in the firm’s financial statements

and some of which is not. We further assume that the rated firm publicly discloses at least a

portion of the “non-financial statement” information. (Obviously all of the information reflected

in the financial statements is public.) We believe that both of these assumptions are benign.

We argue that if a rated firm provides full information to the credit rating agency, but

withholds bad news in its public disclosures, then the CRA’s private information will be

asymmetrically negative; the positive information will have been disclosed and priced in the

equity market, but the negative information will not. This serves as the basis for our empirical

examination.

We estimate a firm’s observed credit rating as a function of observable financial

statement information and then focus on the non-financial statement information in the credit

rating, as reflected in the residual from this estimation.4 We examine how the ratings residual

affects investors, focusing in particular on whether the residual asymmetrically affects market

participants in terms of perceived information asymmetry. Specifically, we use bid-ask spreads

and investor response to ratings changes to investigate whether investors perceive CRAs as

having relatively more private information when the residual credit rating is large and negative

rather than positive. We then test whether equity prices fully reflect the non-financial statement

information in credit ratings, based on the association between residual ratings and abnormal

returns around subsequent quarterly earnings announcements.

We conclude that rated firms disclose both more information and more symmetric

information to the rating agencies compared to what firms disclose to the public at large, and that

4 This residual is not simply noise; as we discuss in Section 4.4, the residual credit rating predicts future changes in firm fundamentals such as leverage, size, and return on capital.

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this information is incorporated in the firm’s credit rating.5 We draw this conclusion based on

several empirical results: bid-ask spreads are larger when firms’ credit ratings are less explained

by financial statement information, and are larger when firms have unexpectedly low credit

ratings compared to when they have unexpectedly high ratings. Equity investors respond more

strongly to ratings changes when firms have credit ratings that are less explained by financial

statement information prior to the change. Finally, subsequent earnings announcement returns

are abnormally negative when credit ratings are unexpectedly low. All of these results suggest

that credit rating agencies receive both more information and more symmetric information than

what is publicly disclosed to investors.

To more directly assess the role of strategic disclosure to the equity markets, we then

consider firms’ relative optimism in their public disclosures. We classify firms based on the

level of optimism in their previous quarterly earnings announcements, after controlling for the

firm’s economic performance (i.e., the level of “justified” optimism); we consider the excess

optimistic tone as a measure of how much the firm disproportionately discloses good news in its

public disclosures. As expected, the asymmetric effect of negative credit rating information is

greatest when firms are publicly optimistic.

It is worth noting that we find significant results despite two features of our setting likely

to work against us. First, investors could infer the presence of bad news from the observed credit

rating, which presumably incorporates the full set of disclosed information. Second, even if rated

firms provide bad news to the rating agency, conflicts of interest stemming from the issuer-pay

model may lead to inflated ratings that do not incorporate that bad news. Thus, we not only

conclude that managers provide relatively unbiased information to ratings agencies, but also that 5 We cannot say whether managers disclose symmetrically in an absolute sense. This is because we do not observe the endowment of news they could disclose. Hence, our tests are designed to determine whether firms provide relatively more symmetric information to credit rating agencies than to the general public.

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the rating agencies use that information in their ratings and that investors fail to fully incorporate

the information in credit ratings in stock prices.

Our paper proceeds as follows. Section 2 discusses prior literature and develops our

hypotheses. Section 3 describes our data and our process for estimating the rating deviation, or

non-financial statement portion of firms’ credit ratings. In section 4, we describe several

properties of the calculated rating deviation that support our use of the deviation in our empirical

tests. Section 5 presents the results of our hypothesis tests. Section 6 concludes.

2. Prior Research and Hypothesis Development

2.1. Asymmetric disclosure behavior

Accounting researchers have long been interested in understanding whether managers

disclose strategically to investors. In some cases, researchers assume this strategic reporting has

a specific goal, such as avoiding debt covenant violations (Sweeney 1994), maximizing an

earnings-based bonus (Healy 1985; Guidry et al. 1999), avoiding earnings decreases or losses

(Burgstahler and Dichev 1997), or meeting/beating analyst expectations (McVay 2006). In other

cases, there is a more general assumption that managers want to maximize current share price or

avoid price declines (Rogers and Stocken 2005; Li 2008).

Regardless of why managers might choose to report strategically, extant literature

describes a wide variety of strategic reporting tools. These include manipulating reported

accounting numbers (Healy and Wahlen 1999), making real operating decisions for reporting

purposes (Roychowdhury 2006), withholding bad news (Kothari et al. 2009), using opaque

language to describe poor results (Li 2008), shifting income statement classification (McVay

2006) or issuing biased forecasts of future earnings (Rogers and Stocken 2005). Overall, there is

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substantial evidence that, rather than providing full and unbiased information, managers report

strategically.

2.2. Information disclosed to non-equity parties

The bulk of prior research involves managers’ strategic choices about what information

to publicly disclose. In many cases, the presumed target of (mis)information is the equity market.

But researchers have also investigated managers’ attempts to influence other parties, such as

credit rating agencies (Alissa et al. 2013; Jung et al. 2013), competitors (Berger and Hann 2007),

and labor unions (Liberty and Zimmerman 1986). However, even though the target audience

differs across these studies, the papers share a common theme – researchers presume managers

use public disclosures to achieve their goals.

While public disclosures have drawn the greatest amount of attention from researchers,

managers have discretion over more than their public disclosures; they also make decisions about

what to disclose in non-public venues. This non-public disclosure was common in the pre-

Regulation FD era. For example, managers chose what information to reveal privately to

analysts (Bamber and Cheon 1998; Hutton 2005) and in closed conference calls (Bushee et al.

2003).

In the post-FD period, researchers have shifted their focus to non-public disclosures in

other settings, frequently involving the transmission of non-public information in debt ratings or

lending relationships. Jorion et al. (2005) find that investors perceive that rating agencies had

more private information (relative to other market participants) after Regulation FD banned

selective disclosure (but excluded ratings agencies). Sufi (2007) documents that lead arrangers

of syndicated loans respond to opaque borrowers by choosing participant lenders that are closer

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to the borrower. Bushman et al. (2010) show that price discovery in the equity market is faster

when lenders have early access to private information. Ivashina and Sun (2011) show that

institutional investors with access to private loan information earn abnormal returns investing in

the borrowing firm’s equity. Massoud et al. (2011) find evidence that suggests hedge funds use

private information obtained in the syndicated loan process to take profitable short positions in

the borrowing firms. Finally, Chen and Martin (2011) show that bank-affiliated analysts issue

more accurate forecasts after the followed firms borrow from the affiliated bank.

In total, it seems well established that firms provide material information to private

lenders and ratings agencies beyond what they disclose to the public. Our broad question relates

to the nature of this non-public information: given that managers seem to disclose strategically

to the public, do they also disclose strategically in these non-public settings? In particular, do

they withhold bad news from ratings agencies, or do they disclose more symmetrically?

2.3. The incentive to disclose good vs. bad news to ratings agencies

If managers disproportionately disclose good news in their public statements, it seems

reasonable to expect they would do the same when communicating privately with ratings

agencies. Just as positive disclosures would benefit managers in the form of higher stock prices,

positive communications to the ratings agencies would benefit managers in the form of higher

credit ratings. In particular, higher credit ratings would gain the borrowing firm better terms at

loan inception and the possibility, through performance-based pricing, of lower interest

payments during the life of the loan (Asquith et al. 2005).

Another force that would lead to firms providing non-public good news to ratings

agencies is the threat of competition, or proprietary costs. Managers may be reluctant to publicly

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disclose positive information if the revelation of that information would generate more

competitive pressure (Verrecchia 1983). But those same managers would be willing to privately

disclose the information to a credit rating agency or private lender such that the firm would get

the benefit of the higher credit rating or lower spread without suffering the competitive cost of

revealing the detailed proprietary information. Plumlee et al. (2014) provide evidence for this in

the context of pending patent approvals, finding that private lenders charge lower rates for firms

with forthcoming patents.

It is less obvious why managers would willingly disclose bad news to ratings agencies

without disclosing the same news publicly. One possible explanation is that managers may

prefer to gradually release bad news over time to avoid a lawsuit-precipitating sudden stock price

decline, but be willing to fully disclose bad news to credit analysts bound by a confidentiality

agreement. A more likely explanation, though, is that if credit ratings agencies believe that

managers are withholding material news, they can threaten a lower credit rating or even decline

to issue a rating altogether. Unlike an individual equity analyst’s threat to withhold a stock

recommendation, a withheld or withdrawn credit rating would be extremely costly to the firm.

Both U.S. securities regulations and portfolio governance rules rely heavily on credit ratings to

determine whether a fund may invest (or continue to hold) a bond in its portfolio and the amount

of capital held by financial institutions (Cantor and Packer 1995).

It is difficult to estimate how frequently ratings agencies lower or withhold ratings due to

a perceived lack of information about the company, or concerns about the company’s disclosed

information. Nonetheless, there is evidence that lenders and ratings agencies do take into

account the perceived quality of firms’ financial reporting and disclosures. Jorion et al. (2009)

show that credit ratings, after controlling for fundamental economic determinants, are lower for

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firms with lower accounting quality. Sengupta (1998) finds a negative relation between

corporate disclosure quality and a firm’s cost of debt. And, anecdotally, S&P’s 2011 withdrawal

of Cheung Kong Holdings Ltd’s credit rating due to lack of access to the company indicates that

even firms’ non-public transparency can affect ratings (Wong 2011).

2.4. Hypotheses

Our first two hypotheses are straightforward and based on the expectation that equity

investors will protect themselves, through lower bids and higher asks, when they believe other

parties (in this case, the CRA) have non-public information about the firm. Since all financial

statement information is public, but only a portion of non-financial information is public, we

predict that investors will fear information asymmetry when ratings are based less on financial

statement information and more on non-financial information. In our empirical design, this

translates to the following hypothesis:

H1: Firms with larger (absolute) rating deviations will have greater bid-ask spreads in the equity market.

Our second hypothesis is directional. If managers, on average, disclose good news and

withhold bad news to the public, but provide unbiased information to rating agencies, then rating

agencies will have more private information when ratings are unexpectedly low than when they

are unexpectedly high. Our second hypothesis follows:

H2: Controlling for the magnitude of the ratings deviation, negative rating deviations are associated with larger bid-ask spreads than positive rating deviations.

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Our next hypothesis is based on the idea that investors are Bayesian – when revising their

beliefs after observing a signal, those revisions will be stronger when the signal is more precise.

This line of reasoning is discussed in analytical models in Holthausen and Verrecchia (1988) and

Verrecchia (2001), and also underlies the empirical design used by Jorion et al. (2005), who also

take on the question of rating agencies’ private information. Jorion et al. provide the following

argument: before Regulation FD, firms were able to disclose non-public information to both

equity analysts and credit rating agencies. After Regulation FD, firms were barred from

disclosing private information to equity analysts, but were able to continue disclosing such

information to credit rating agencies. Thus, credit rating agencies had relatively more precise

information about firms after Reg FD than they did before. As a consequence, Jorion et al.

predict and find that investors respond more strongly to CRA actions after Reg FD than they did

before.

Our hypothesis follows directly from Jorion et al., and is tested in a similar fashion.

H3: Equity investors will respond more strongly to a rating change when the prior rating deviation is large (i.e., when the prior credit rating was based less on financial statement information).

Our first three hypotheses are based on perception – do investors act as if they believe

that CRAs have private information? Our final hypothesis is different. We are interested in

testing whether CRAs’ private information is fully revealed to the equity market by the observed

rating. That is, even if investors do not know precisely what information the CRA has, do they

infer the nature of that information from the (unexplained) credit rating they observe?

H4: Firms with more negative ratings deviations experience more negative returns at subsequent earnings announcements.

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3. Data

3.1. Sample selection

We gather all firm-years from Compustat during the 1985 – 2012 period with S&P

issuer-level credit ratings and remove any observations for which we are unable to match a

CRSP PERMNO identifier. To estimate DEVIATION and RESIDUAL, our measures of non-

financial statement information, 6 we require credit ratings to be available in the month

immediately following the release of annual earnings and non-missing values for all regressors in

equation (1). After applying these criteria to the base Compustat sample, we retain 32,962 firm-

year observations. Panel A of Table 1 shows the breakdown of observations by year. Consistent

with increases in coverage of firms by S&P and publicly held bonds over time, the number of

sample firms begins at a low of 536 in 1985 and grows to nearly 1,500 by 2004 before tapering

off slightly by the end of the sample period in 2012.

3.2. Estimating non-financial statement information in firms’ credit ratings

Our empirical analysis starts with an estimate of the non-financial information used by

S&P in setting credit ratings. To estimate non-financial statement information in credit ratings,

we first predict firm-year credit ratings based on publicly available information at the

announcement of fiscal year end earnings. For each year in our sample, we employ the following

ordinary least squares model:

RATINGi = β0 + β1EBITDA_COVi + β2LEVERAGEi + β3ROCi + β4LNMVEi + εi (1)

6 Note that these measures are not our estimates of CRAs private information. Instead, these are simply proxies for non-financial information incorporated in the credit ratings. The non-financial information may have been publicly disclosed or privately communicated.

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where RATING is a firm’s S&P issuer-level credit rating available immediately after the

announcement of earnings for fiscal year t, converted from a letter ranking to a numerical rank.

(The numerical range ranges from 1 for a B- rating to 16 for a AAA rating.)

The regressors in equation (1) comprise the financial statement variables used by Blume

et al. (1998), and are consistent with Standard & Poor’s ratings process.7 EBITDA_COV is the

ratio of operating income before depreciation and amortization to interest expense for fiscal year

t. LEVERAGE is the ratio of total debt (long- and short-term) to the sum of total debt,

stockholders’ equity, and noncontrolling interest at the end of fiscal year t. ROC is the ratio of

earnings before interest and taxes to the average of beginning- and end-of-year capital for fiscal

year t, where we define capital as the sum of long- and short-term debt, stockholders’ equity, and

noncontrolling interest. LNMVE is the natural logarithm of market value of equity measured at

the end of fiscal year t. We estimate equation (1) on an annual basis using industry fixed effects

based on the twelve Fama and French categories.

We derive two proxies for non-financial statement information contained in firms’ credit

ratings. The first, DEVIATION, is a firm’s actual credit rating less its predicted rating. We

compute the predicted rating from equation (1) consistent with the method employed by Alissa et

al. (2013). Specifically, for each observation, we compute the fitted probability of that

observation being assigned to each rating category (e.g., AA-, BBB+) and select the category

with the highest fitted probability after adjusting for the distribution of actual ratings.8 The

second, RESIDUAL, is a firm’s actual credit rating (as translated to a quantitative ranking) less 7 A detailed description of Standard & Poor’s ratings criteria can be found at http://www.standardandpoors.com/ratings/criteria/en/us/?filtername=corporates. 8 We compute the fitted probability of each possible rating category following the estimation of an ordered logit version of equation (1). Then, we divide each fitted probability by the proportion of ratings in the sample having that rating. The financial statement based rating is the rating with the largest ratio. In general, this approach leads to the selection of the highest fitted probability rating but allows for the selection of fitted probabilities that are high relative to the rating’s frequency in the sample.

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the linear fitted value based on coefficients from estimating equation (1). Both proxies can take

on negative and positive values 9 , where positive (negative) values of DEVIATION and

RESIDUAL capture instances in which firms’ actual credit ratings exceed (fall below) the value

implied by the explanatory financial statement variables.

Panel B of Table 1 presents descriptive statistics regarding credit ratings and the

regressors for the estimation of equation (1). Firm-years in the sample have an average rating of

7.3, which translates to between BBB- and BBB on the S&P scale. These firm-years tend to

produce reasonably high cash flows and returns on capital, with average interest coverage of 10.5

and average return on capital of 18 percent. Firm-years in the sample have an average market

capitalization of approximately $1.7 billion.

Table 2 presents the average coefficients from the annual estimation of equation (1).

Consistent with our expectations, firms’ credit ratings tend to be higher when their interest

coverage, profitability, and market capitalization are greater and lower when leverage is greater.

Turning back to Panel B of Table 1, the means and medians of our two private information

proxies, DEVIATION and RESIDUAL, are close to zero, while the middle 50 percent of the

distributions of these variables lies between -1 and 1 and -1.25 and 1.25 respectively. 10

Importantly for our study, the regressors in our model explain a substantial amount of variation

in observed credit ratings: the average adjusted R2 value from the annual regressions is 0.66.

Table 3 provides a detailed breakdown of actual versus predicted credit ratings using

DEVIATION. Although the estimation of equation (1) leads to the correct prediction of only 24

percent of ratings, approximately 80 percent of predicted ratings lie within two notches of their

actual value. In addition, the number of predicted ratings per actual rating tends to decline as the 9 Firms with the top (bottom) rating category can only have non-negative (non-positive) DEVIATION values. 10 The means of Deviation and Residual are equal to zero before they are winsorized at 1 and 99 percent.

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magnitude of the deviation increases, suggesting that our estimation of predicted ratings does not

systematically lead to high frequencies of large magnitude deviations.

4. Properties of the rating deviations

In this section, we discuss the attributes of our calculated ratings deviation measures.

Our goal in discussing these properties is to provide comfort that the rating deviations represent

non-financial statement information used by the credit rating agency, rather than just noise in the

regression model.

4.1. Mean reversion of rating deviations

If our estimates of DEVIATION and RESIDUAL capture cross-sectional variation in the

non-financial statement information contained in credit ratings, we expect that each proxy will

exhibit mean reversion in its time series; we do not expect unusually high or low deviations to

persist indefinitely. This mean reversion could occur by a firm’s future fundamentals catching

up to the period t rating, the firm’s future rating catching up to the fundamentals, or a

combination of the two. To assess whether our rating deviations exhibit mean reversion, we

follow Alissa et al. (2013) and estimate a series of standard Dickey-Fuller unit root tests of the

form:

ΔDEVIATION/RESIDUALi,t+k = β0 + β1DEVIATION/RESIDUAL + εi,t (2)

Where k ∈ [1,5].

Table 4 presents the results from estimating equation (2). Both Panel A for DEVIATION

and Panel B for RESIDUAL reveal a strong negative statistical association between proxies for

current period non-financial statement information and future changes in those proxies for

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periods of up to five fiscal years ahead. Thus, both DEVIATION and RESIDUAL appear to

exhibit mean reversion, a necessary (but not sufficient) condition for validating our cross-

sectional proxies of non-financial statement information in credit ratings. In the next two

sections, we examine whether the mean reversion is driven by fundamentals catching up to

ratings, ratings catching up to fundamentals, or both. We examine each of these possibilities in

the following sections.

4.2. Changes in future fundamentals

A maintained assumption in our paper is that what we call “non-financial statement

information” is meaningful information about firms’ credit quality and future performance,

rather than noise from a regression model. If our proxies represent information about future

fundamentals that is not currently reflected in the financial statements, the proxies should exhibit

a statistical association with changes in those fundamentals. We examine the validity of this

assumption by testing whether DEVIATION and RESIDUAL are associated with future changes

in the four fundamental variables we use in the estimation of equation (1). Accordingly, we

estimate the following OLS model:

ΔFUNDAMENTALi,t+1 = β0 + β1DEVIATION/RESIDUALi,t + εi,t (3)

Where FUNDAMENTAL ∈{EBITDA_COV, LEVERAGE, ROC, LNMVE}.

Table 5 presents the results from estimating equation (3) in columns 1, 3, 5, and 7. Both

Panel A for DEVIATION and Panel B for RESIDUAL provide evidence of a statistically

significant positive (negative) association between our private information proxy and changes in

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ROC and LNMVE (LEVERAGE) between period t and t+1. (We are unable to detect a

statistically significant association between either DEVIATION or RESIDUAL and changes in

EBITDA_COV.)

The results in Table 5 support our assumption that our proxies for non-financial

information are meaningful – they represent forward-looking information that current financial

statement ratios do not fully capture.

4.3. Asymmetric prediction of future fundamentals

Our hypothesis tests, which we present in the next section, are based on the idea that the

non-financial statement information in credit ratings is informative about future fundamentals,

regardless of whether the deviation is positive or negative. To test that assumption, we modify

equation (3) to allow for asymmetry between positive and negative non-financial statement

information. In columns 2, 4, 6, and 8 of Table 5, we allow β1 in equation (4) to differ across

“good” news deviations (i.e., DEVIATION>0, RESIDUAL>0) and “bad” news deviations (i.e.,

DEVIATION<0, RESIDUAL<0). If positive and negative deviations are differentially predictive

of future performance, our assumption would be invalid.

The results of our test are shown in the even-numbered columns of Table 5, indicating in

each case whether the coefficient on the positive residual is statistically different from the

coefficient on the negative residual. Across the 8 comparisons in Table 6 (4 different

fundamental metrics predicted by 2 proxies for non-financial statement information), we find

only one statistically significant difference between positive and negative residuals: Panel A

shows that a bad deviation is asymmetrically associated with future Return on Capital (ROC).

Even this result is weak – the difference between the good residual and bad residual is significant

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only at the 10% level. Thus, we find very little evidence that the sign of the rating deviation

influences the association between rating deviations and future changes in accounting

fundamentals.

4.4. Changes in future credit ratings

An alternative explanation for the mean reversion documented in Table 4 is that future

ratings changes catch up to current financial statement information, perhaps due to inefficient or

non-timely credit analysts. Beaver et al. (2006) document the relatively less timely ratings of

Moody’s Investors Service compared to the smaller Egan-Jones Ratings, while Cheng and

Neamtiu (2009) highlight the criticism of rating agencies providing untimely ratings in the

aftermath of Enron’s bankruptcy. We test whether our proxies capture some extent of ratings at

period t+k moving toward the fundamentals disclosed at period t using the following OLS

model:

ΔRATINGi,t+k = β0 + β1DEVIATION/RESIDUALi,t + εi,t (4)

where k∈[1,5]. Table 6 presents the results from estimating equation (4). Both Panel A for

DEVIATION and Panel B for RESIDUAL provide statistical evidence of future rating changes

moving to reverse period t deviations. Thus, a positive value for DEVIATION or RESIDUAL is

associated, on average, with a rating downgrade in period t+k, and vice versa.

We should note that the results in Table 5 do not necessarily imply that credit analysts are

inefficient. Because credit ratings are discrete measures rather than continuous measures, each

credit grade will include firms of varying credit quality. If our rating model is sensible, the

lowest-quality firms in a particular rating grade will tend to have a positive residual (their rating

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is higher than expected), while the highest-quality firms will tend to have a negative residual

(their rating is lower than expected).

In this scenario, firms with positive and negative residuals will experience different

ratings outcomes in response to random shocks to their fundamentals. If a relatively low-

quality/positive residual firm experiences a negative shock, its credit rating will likely fall from

the low end of the current grade to the high end of the next-lowest grade.11 But if that firm

experiences a positive shock, it will keep its credit rating and simply move closer to the average

quality within that credit grade. Similarly, if a high-quality/negative residual firm experiences a

positive shock, its rating will likely increase from the high end of the current grade to the low

end of the next-highest grade. But a negative shock would result in no credit rating change. In

short, a combination of discrete ratings and random economic shocks to fundamentals would

lead to the negative correlation we document between unexpected ratings and future ratings

changes.

In any event, the results in Tables 4, 5, and 6 offer broad support for the assumptions

underlying our study: Ratings are based on both financial statement information and non-

financial statement information. The non-financial statement information (the residual) is mean

reverting, driven both by future ratings converging to the current fundamentals, and by future

fundamentals converging to current credit ratings. The latter fact implies that the non-financial

statement component of credit ratings represents forward-looking information that will be

recognized in future financial statements. Finally, the non-financial statement information seems

to predict future fundamentals fairly symmetrically – there is no strong statistical difference in

the predictive values of positive and negative residuals. 11 In this example, a “relatively low quality” firm is one with lower credit quality than the average firm in that same credit grade, rather than a firm with low absolute quality. Thus, there will be relatively high quality firms in the lowest credit grade and relatively low quality firms in the grade credit rating.

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5. Hypothesis tests

5.1. Reflection of private information in bid-ask spreads

Our first hypothesis is that investors will perceive greater information asymmetry when

credit ratings appear to be based less on financial statement information than on non-financial

statement information. If so, we expect that equity market bid-ask spreads will be positively

associated with the absolute magnitude of rating deviations (after controlling for other

determinants of bid-ask spreads).

Our second hypothesis is that investors will perceive even greater information asymmetry

when unexplained credit ratings are negative – they will fear that managers have publicly

disclosed the good news not reflected in financial statements, but withheld the bad news. If so,

we expect that the relation between bid-ask spreads and rating deviations will be stronger for

negative deviations than for positive deviations.

We test these hypotheses with the following OLS model: = + | / | + ++ + + (1 + )+ _ +

(5)

where SPREAD is the natural logarithm of the percentage bid-ask spread on the rating date using

daily data, computed as log ( ).

For our first hypothesis, we are interested in the coefficient on the absolute value of the

unexplained credit rating (either DEVIATION or RESIDUAL). For our second hypothesis, we

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allow the effect of the unexplained credit rating to vary by sign, and are interested in the relative

size of the coefficients on “good news” versus “bad news” deviations.

The remaining independent variables are similar to those used in prior research using bid-

ask spreads as measures of information asymmetry (e.g., Leuz and Verrecchia 2000). LNMVE is

the natural log of the firm’s market value of equity, measured prior to the rating date. PRICE is

the mean stock price over the three months prior to the rating date; TURNOVER is the mean

daily share volume deflated by the number of shares outstanding over the three months prior to

the rating date; VOLATILITY is the standard deviation of daily stock returns measured over the

three months prior to the rating date; NUMEST is the number of analysts providing one-year-

ahead EPS forecasts for fiscal year t at least once prior to the rating date; and NYSE_AMEX is a

binary variable equal to one if a firm trades on the NYSE or AMEX exchanges and zero

otherwise. We include industry, quarter, and rating level fixed effects and use standard errors

clustered by firm.

Table 7 presents the results from estimating equation (5), with Panel A and Panel B

showing the results using DEVIATION and RESIDUAL as the proxy for non-financial statement

information, respectively. In column 1, we test whether the absolute magnitude of non-financial

statement information is associated with bid-ask spreads. In column 2, we test whether good and

bad non-financial statement information are asymmetrically associated with bid-ask spreads.

For both measures of non-financial statement information, column 1 shows a positive

association between the unsigned residual credit rating (ABS_DEVIATION in Panel A,

ABS_RESIDUAL in Panel B) and bid-ask spreads; in both cases the coefficients are

significantly different from 0 at the p<0.01 level. These results support our first hypothesis – bid-

ask spreads are larger when the (unsigned) residual credit rating is larger. We interpret this result

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as evidence that investors believe credit analysts have more private information when the

observed credit rating deviates from what would be expected based on financial statement

information.

In column 2, we show the results of our second test, which relates to the asymmetric

effect of non-financial statement information in credit ratings, depending on whether that

information is good news or bad news. In both Panel A and Panel B, we find that the effect of

bad non-financial statement information is substantially larger than the effect of good news non-

financial statement information (t-stat=-21.9 in Panel A, t-stat=-25.9 in Panel B). We interpret

these results as evidence that investors believe that credit analysts have more private information

when the observed rating is unexpectedly low compared to when the observed rating is

unexpectedly high. These results support the idea that managers, on average, withhold bad news

from the equity markets, but provide relatively unbiased information to credit rating agencies.

5.2. Response to rating changes

Jorion et al. (2005) document larger equity market responses to rating upgrades and

downgrades following the enactment of Regulation FD in October 2000. Based on this result,

they conclude that rating agencies had more private information (relative to other parties like

sell-side analysts or institutional investors) after the enactment of Reg FD, which banned private

communication but excluded ratings agencies from this ban.

We extend the Jorion et al. (2005) analysis to test whether credit ratings agencies are

perceived to have more private information when credit ratings are based less on financial

statement information. To do so, we modify the research design employed by Jorion et al.

(2005): in their study, private information is identified based on post-Reg FD observations; in

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our study, private information is identified by the portion of the credit rating unexplained by

financial statement information. Specifically, we estimate the following OLS model:

_ = + + | / | +∗ | / | +

(6)

where ABN_RET is the three-day cumulative market-adjusted stock return centered on the rating

change announcement date and UPGRADE is a binary variables equal to one if the rating

changes is an upgrade and zero otherwise. In equation (6), captures the average abnormal

stock return for a downgrade where | / | = 0, while β0 + β1 captures the

average abnormal stock return for an upgrade for a similar firm. (Like Jorion et al. (2005) and

earlier studies, we distinguish between the responses to upgrades and downgrades.)

Table 8 presents the results from estimating equation (6). Column 1 uses DEVIATION as

our proxy for non-financial statement information, while column 2 uses RESIDUAL. As

expected, investors respond negatively to downgrades (t-statistics of 9.41 and 7.52) and

positively (albeit more weakly) to upgrades (t-statistics of 3.67 and 3.22, based on + ).

Regarding our hypothesis, we find a negative and significant (p<0.01) association

between abnormal returns and β2, the unsigned residual credit rating (ABS_DEVIATION in

column 1, ABS_RESIDUAL in column 2). The negative association indicates that investors

respond more strongly to ratings downgrades when pre-downgrade ratings deviate from what

would be expected based on financial statement information.

We find similar results for ratings upgrades. For both DEVIATION and RESIDUAL,

investors respond more strongly to upgrades for firms with larger deviations between predicted

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and actual credit ratings (t-stat=2.41 and t-stat=3.15, based on + ). Overall, we find support

our third hypothesis: investors respond more strongly to ratings changes when they perceive that

the ratings agency has more private information (i.e., when observed ratings deviate from

predicted ratings).

5.3. Expected vs. unexpected changes in credit ratings

The hypothesis tests discussed in the previous section assume that equity investors view

credit rating agencies as potentially informed parties, and that those investors draw inferences

about equity values from credit ratings and changes in those ratings. These assumptions do not

seem controversial - existing research documents the intuitive notion that equity investors

respond positively to ratings upgrades and negatively to ratings downgrades (Dichev and

Piotroski 2001; Jorion et al. 2005). However, our assumptions are slightly stronger than in prior

research – we assume that equity investors have a more nuanced understanding of credit ratings

than simply believing that upgrades are good news and downgrades are bad news.

In particular, we assume that equity investors can discern whether a credit rating is at the

level implied by financial statement information, or if that credit rating is unusually high or low.

It is difficult to test this assumption directly, but we can test an implication of this assumption: if

investors believe that a credit rating is unusually high and likely to revert (either because of the

discrete nature of credit ratings or because the ratings agency operates with a lag), we should see

a muted response to “expected” ratings changes compared to “unexpected” changes.

We test this assumption by testing whether investors respond more strongly to

unexpected (non-mean-reverting) ratings changes than to expected (mean-reverting) ratings

changes. We estimate an OLS model similar to that shown in Table 8, where the dependent

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variable is a firm’s market-adjusted stock return for the three-day window surrounding a credit

rating change for that firm. We again distinguish between upgrades and downgrades, but this

time also condition on whether the upgrades or downgrades are mean-reverting or non-mean-

reverting.

Table 9 shows the results of our regression. We find that the market responds more

negatively to non-reverting ratings changes. This result is evident for both downgrades (t-stat=-

4.88, based on the Non-Reverting coefficient) and for upgrades (t-stat=3.12, based on the sum of

Non_Reverting + Upgrade*Non_Reverting coefficients). These results confirm that investors are

aware of the wedge between firms’ actual and predicted credit ratings, and respond accordingly.

5.4. Do equity prices fully reflect the private information in credit ratings?

The first three hypotheses are based on investors’ perception of credit rating agencies’

private information. And our results indicate that investors do, in fact, believe that CRAs have

private information when observed ratings are unexplained by current fundamentals, and

especially when observed ratings are unexpectedly low relative to financial statement

information. Our final hypothesis deals with a different question – does that private information

remain private (unpriced) even after the credit rating is publicly observable? In other words, do

equity investors infer the nature of the private information from the wedge between the firms’

actual and predicted credit ratings?

We test whether equity prices incorporate the information in credit ratings by examining

whether unexplained credit ratings predict subsequent earnings announcement period returns.

Focusing on earnings announcement periods has two benefits relative to focusing on longer-

window returns after the issuance of the credit rating. First, adjusting for risk is less of an issue in

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short-window earnings announcement periods than in long-window periods. (This is especially

important in our settings because our measure of private information may be correlated with firm

risk.) Second, if managers strategically withhold news from the public, it is difficult to predict

when that withheld information would be revealed (and affect returns). We view quarterly

earnings announcements as the most likely identifiable (and anticipated) event where private

information would become public.

We examine whether the difference between actual and predicted credit ratings is

associated with abnormal stock returns surrounding the first earnings announcement following

the credit rating release date. We use the following OLS model for this test:

_ = + /+ / +

(7)

where ABN_EARET is the three-day cumulative size-adjusted stock return for firm i surrounding

its quarter t+1 earnings announcement relative to the credit rating release date; / is a variable equal to / if / >0 and zero otherwise; and / is a variable equal to /

if < 0 and zero otherwise.

Table 10 presents the results from estimating equation (7). The first and third columns

show that the signed deviation/residual predicts future earnings announcement returns; the

coefficient on Deviation has a t-statistic of 2.70, while the coefficient on Residual has a t-statistic

of 3.00. These results indicate that equity prices do not fully reflect the information in credit

ratings. This suggests that not only do ratings agencies have access to non-public information,

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but that investors cannot or do not infer the non-public information once the ratings are made

public.

Our main question, though, is whether there is an asymmetry between positive and

negative deviations. We predict that if managers withhold bad news from the public, but provide

symmetric information to the ratings agents, then negative residuals will reflect more private

information than positive residuals. We test this by again decomposing Deviation and Residual

into positive and negative components and examining their ability to predict future earnings

announcement returns. The results are shown in columns 2 and 4, and are consistent with our

prediction. Namely, Bad_Deviation and Bad_Residual are both associated with abnormal

earnings announcement returns (p<0.01 in each case), but Good_Deviation and Good_Residual

are not. Moreover, the differences between the good and bad measures are significant (at the 5%

level for Residual and at the 10% level for Deviation). These results are consistent with a world

in which managers withhold bad news publicly, but reveal at least some of that bad news to

ratings agencies, who subsequently incorporate that information in their published ratings.

5.5. Classifying firms based on the optimism in their public disclosures

A maintained assumption in our study is that managers tend to disclose good news and

withhold bad news in their public disclosures. If managers tend to disclose symmetrically to

ratings agencies, then we would expect our results to be strongest for those firms whose public

disclosures are most optimistically biased.

We test this prediction by measuring abnormal optimism in firms’ public disclosures, and

splitting our sample into relatively optimistic and relatively pessimistic disclosers. Specifically,

we measure the optimistic tone in firms’ most recent quarterly earnings announcement prior to

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the measured credit rating, and regress that tone on profitability, change in profitability, stock

returns from day t+2 relative to the previous earnings announcement through day t-2 relative to

the current earnings announcement, the book-to-market ratio, quarter fixed effect, and industry

fixed effects. We use the residual from this regression, abnormal optimism, as a proxy for how

much good news a firm discloses in excess of what would be justified by their economic

circumstances.

We use the residual tone to split our sample into terciles, focusing on the highest tercile

(most optimistic) compared to the lowest tercile. We re-perform all of our empirical tests to see

whether the results are stronger when firms are unusually optimistic. In all cases, we find

support for our prediction. In untabulated results, we show that bid ask spreads are

incrementally larger for optimistic firms when ratings deviations are large. This is particularly

true for negative deviations, as expected. Investor responses to ratings changes are larger when

firms are optimistic in their public disclosures, particularly for ratings downgrades. Finally,

equity returns around subsequent earnings announcements are more negative for negative credit

rating deviations when firms’ public disclosures are more optimistic.

Overall, these results bolster our maintained assumption that firms disproportionately

disclose good news in public and provide more symmetric news to credit ratings agencies.

6. Conclusion

We investigate firms’ disclosures to credit rating agencies, asking whether firms disclose

non-public information to the rating agencies and, more importantly, whether that non-public

information is unbiased. We do so by estimating the amount and sign of non-financial statement

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information embedded in published credit ratings, and inferring the nature of that information

based on how equity investors respond to credit ratings and ratings changes.

We measure the wedge between a firm’s actual and predicted credit rating (what we call

the rating deviation), and find that equity investors perceive credit rating agencies to have more

private information when that deviation is large. We also find that investors respond more

strongly to ratings changes – both upgrades and downgrades – when the deviation is large. Both

of these results are consistent with rating agencies having access to non-public information, and

investors reacting accordingly.

More importantly, we find that investors perceive rating agencies to have more private

information when the deviation is negative (when a firm’s rating is lower than expected) – after

controlling for the magnitude of the deviation, bid-ask spreads are larger for negative deviations

than for positive deviations, and investor response to rating changes is stronger when rating

deviations are larger (and more negative). These results suggest that managers not only provide

non-public information to rating agencies, but that they provide relatively more negative

information to the rating agencies than what they disclose publicly. Thus, in a world in which

firms withhold bad news from equity investors (Kothari et al. 2009), they seem to provide more

symmetric information to rating agencies.

We also show that, while investors understand that rating agencies have private

information, they do not fully incorporate that information in prices. We find that large rating

deviations explain subsequent abnormal stock returns around future earnings announcements,

and that the predictive ability is entirely contained in the negative residuals. Again, these results

are consistent with a world in which managers withhold bad news from investors, but provide

more symmetric news to rating agencies. Finally, we find that our results are strongest for the

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set of firms whose public disclosures appear to be most unusually optimistic – the firms that are

most likely to be disclosing asymmetrically good news in public.

Overall, our results speak to managers’ disclosure practices in public and private

channels, and how those practices differ. Our results also speak to the question of the issuer-pay

model in the corporate debt rating market. This model has raised the concern that credit ratings

agencies would be reluctant to assign low credit ratings to the firms that are paying them. Our

results indicate that not only do firms provide more bad news to ratings agencies than they do in

public disclosures, but that ratings agencies are willing to incorporate at least some of that bad

news in their credit ratings. This outcome should provide some comfort to those concerned that

the issuer-pay model yields systematically inflated credit ratings.

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Appendix I. Variable Definitions

Variables used to predict S&P credit ratings

RATING S&P credit ratings, coded as AAA=16,…,B-=1, available right after earnings announcement dates for fiscal year t.

EBITDA_COV three-year average of EBITDA interest coverage computed as operating income before depreciation divided by interest expense

LEVERAGE three-year average of leverage computed as total debt divided by the sum of total debt and a book value of equity

ROC three-year average of return on capital computed as EBIT divided by average capital, where average capital is computed as the average of beginning and ending book values of the sum of total debt and equity

LNMVE three-year average of natural logarithm of market value of equity, where market value of equity is computed as the closing stock price multiplied by shares outstanding at the end of fiscal year t

Dependent variables SPREAD daily bid-ask spread on the rating date, computed as log[(bid - ask) / (bid+ask)/2]

ABN_RET abnormal stock returns computed as three-day cumulative market-adjusted stock returns centered on the rating change announcement date

ABN_EARET abnormal earnings announcement returns defined as cumulative size-adjusted stock returns over the three days around the first earnings announcement date after the rating date

FERROR one-quarter ahead analyst EPS forecast error (actual EPS - forecasted EPS) for the first quarter of fiscal year t+1; EPS forecasts are issued within 30 days after the rating date.

∆Deviationt+k change in Deviation at t+k relative to fiscal year t

∆Residualt+k change in Residual at t+k relative to fiscal year t

∆EBITDA_COVt+k change in EBITDA_COV at t+k relative to fiscal year t

∆LEVERAGEt+k change in Leverage at t+k relative to fiscal year t

∆ROCt+k change in ROC at t+k relative to fiscal year t

∆LNMVEt+k change in LNMVE at t+k relative to fiscal year t

Experimental variables

Deviation

actual rating minus predicted rating. Predicted rating is the predicted value from the regression of RATING on the four accounting variables that S&P claims that it uses in determining its ratings. Each predicted value is ranked by each year and then assigned to each level of rating based on the distribution of actual rating for each year.

Good_Deviation equals Deviation if Deviation is greater than zero and otherwise equals 0

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34

Bad_Deviation equals Deviation if Deviation is less than zero and otherwise equals 0 ABS_Deviation absolute value of Deviation ABS_Good_Deviation equals ABS_Deviation if Deviation is greater than zero and otherwise equals 0 ABS_Bad_Deviation equals ABS_Deviation if Deviation is less than zero and otherwise equals 0

Residual actual rating minus predicted rating. Predicted rating is the predicted value from the regression of RATING on the four accounting variables that S&P claims that it uses in determining its ratings.

Good_Residual equals Residual if Residual is greater than zero and otherwise equals 0 Bad_Residual equals Residual if Residual is less than zero and otherwise equals 0 ABS_Residual absolute value of Residual ABS_Good_Residual equals ABS_Residual if Residual is greater than zero and otherwise equals 0 ABS_Bad_Residual equals ABS_Residual if Residual is less than zero and otherwise equals 0 Upgrade equals 1 if ∆Rating is greater than zero and otherwise equals 0

Non_Reverting equals 1 if RATING changes in the direction of Deviation or Residual and otherwise equals 0

Firm characteristics StockPrice mean stock price over the three months prior to the rating date

ShareTurnover mean daily share volumes deflated by the number of shares outstanding over the three months prior to the rating date

Volatility standard deviation of daily stock returns measured over the three months prior to the rating date

Log(1+Numest) natural logarithm of 1 plus # of analysts (Numest) providing one-year-ahead EPS forecast for fiscal year t at least once prior to the rating date

NYSE_AMEX equals to 1 if a firm trades on the NYSE or AMEX exchange during fiscal year t and otherwise equals 0

LNMVER natural logarithm of market value of equity measured prior to the rating date

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35

Table 1 Descriptive Statistics

Panel A. Sample Distribution

Year # of Obs. Year # of Obs. 1985 536 2000 1,468 1986 862 2001 1,434 1987 909 2002 1,411 1988 857 2003 1,443 1989 818 2004 1,491 1990 764 2005 1,470 1991 768 2006 1,463 1992 822 2007 1,418 1993 920 2008 1,343 1994 981 2009 1,328 1995 1,033 2010 1,356 1996 1,165 2011 1,367 1997 1,276 2012 1,374 1998 1,415 1999 1,470

Total 32,962

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36

Table 1 (continued) Panel B. Distribution of Variables

Variable N Mean 25th 50th 75th Std. Dev.

Variables used to predict S&P credit ratings RATING 32962 7.266 4 7 10 3.572EBITDA_COV 32962 10.47 2.735 5.080 9.862 19.85LEVERAGE 32962 0.486 0.319 0.461 0.604 0.251ROC 32962 0.184 0.123 0.171 0.230 0.106LNMVE 32962 7.444 6.271 7.426 8.595 1.746

Dependent variables SPREAD 23138 -5.696 -7.163 -5.500 -4.311 1.679ABN_RET 6826 -0.019 -0.036 -0.005 0.020 0.094ABN_EARET 29574 0.005 -0.025 0.002 0.032 0.068FERROR 17308 -0.023 -0.001 0.000 0.002 1.681

Experimental variables Deviation 32962 -0.003 -1 0 1 2.011Good_Deviation 32962 0.753 0 0 1 1.173Bad_Deviation 32962 -0.756 -1 0 0 1.237ABS_Deviation 32962 1.526 1 1 2 1.382Residual 32962 -0.003 -1.247 0.032 1.246 1.962Good_Residual 32962 0.769 0 0.032 1.246 1.145Bad_Residual 32962 -0.772 -1.247 0 0 1.162ABS_Residual 32962 1.551 0.585 1.247 2.210 1.243Upgrade 6826 0.389 0 0 1 0.488Non_Reverting 6826 0.300 0 0 1 0.458

Firm characteristics StockPrice 30270 31.73 15.17 26.77 42.47 23.02ShareTurnover 30270 0.674 0.252 0.464 0.857 0.644Volatility 30226 0.024 0.014 0.020 0.029 0.015Log(1+Numest) 29134 2.410 1.946 2.485 2.996 0.758NYSE_AMEX 30282 0.825 1 1 1 0.380

LNMVER 30266 7.337 6.209 7.370 8.465 1.723

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37

Table 1 reports descriptive statistics for the variables used in estimating S&P issuer credit ratings as a function of accounting ratios that S&P claims that it uses in determining its ratings as well as for the measures of public information and firm characteristics. All the variables are winsorized at 1% and 99%. See Appendix I for variable definitions.

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38

Table 2 Regression of Credit Ratings on Public Accounting Information

Dependent variable: RATING

EBITDA_COV 0.006 ***

(3.15)LEVERAGE -3.818 ***

(-5.58)ROC 4.280 ***

(15.6)LNMVE 1.358 ***

(46.0)Constant -1.423 **

(-2.59)

Industry FE YES

# of Obs. 32,962Adj. Rsq. 0.663

Table 2 reports regression results of S&P credit ratings (RATING) available after the earnings announcement dates for fiscal year t. RATING is regressed on the four accounting ratios that S&P claims that it uses in determining its ratings. The parameters are estimated by running the OLS regression each year, using a sample of 32,962 observations from 1985 through 2012. The estimated parameters shown in Table 2 are the mean yearly estimates computed using the Fama and Macbeth (1973) approach, and the standard errors of the estimates are adjusted using the Newey and West (1987) procedure to account for possible autocorrelation in the disturbances across time for individual firms. Please refer to Appendix I for description of all the variables. *, **, *** denotes significance at the p<0.10, p<0.05, and p<0.01 level respectively.

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39

Tab

le 3

Dis

trib

uti

on o

f A

ctu

al a

nd

Pre

dic

ted

Rat

ings

(19

85 -

201

2)

Pre

dic

ted

A

ctu

al

AA

A

AA

+

AA

A

A-

A+

A

A

- B

BB

+

BB

B

BB

B-

BB

+

BB

B

B-

B+

B

B

-

AA

A

167

47

105

37

29

19

7 5

8 2

2 3

2 2

AA

+

18

17

45

26

41

31

13

11

12

1 2

A

A

82

48

235

144

150

78

55

35

25

11

5 1

A

A-

38

31

143

136

215

173

47

45

26

10

7 3

3 2

A+

61

30

11

8 19

3 34

0 42

6 20

4 14

4 11

8 43

21

4

2 1

A

40

26

98

16

5 36

1 62

1 48

8 44

0 29

6 10

0 40

48

23

13

2

2 A

- 16

11

56

86

22

7 46

7 47

8 45

9 33

7 15

6 64

51

20

19

1

1 B

BB

+

13

4 28

46

16

7 37

9 45

2 62

7 67

4 28

9 10

2 11

4 60

9

1

BB

B

3

31

29

104

315

371

537

977

678

301

297

149

45

5

BB

B-

8

12

47

160

184

330

594

682

408

336

199

67

4

BB

+

2

8 50

85

13

9 27

2 33

9 27

5 30

9 30

8 10

8 11

6

BB

1 2

6 27

27

97

23

5 31

6 28

7 53

0 62

3 36

4 63

13

B

B-

6

9 16

56

13

0 20

6 21

3 45

5 85

3 89

8 26

2 58

B

+

1

5 17

24

10

0 13

8 11

7 28

6 61

1 11

14

666

269

B

1

1 2

2 13

30

43

47

10

1 21

5 47

9 53

2 34

9 B

-

1 2

1 3

3 8

18

22

53

97

227

265

279

Cor

rect

78

63

Cor

rect

wit

hin

1 no

tch

1954

1 C

orre

ct w

ithi

n 2

notc

hes

2633

7 In

corr

ect

2509

9 In

corr

ect b

eyon

d 1

notc

h 13

421

Inco

rrec

t bey

ond

2 no

tche

s 66

25

% C

orre

ct

24%

%

Cor

rect

wit

hin

1 no

tch

=

59%

%

Cor

rect

wit

hin

2 no

tche

s =

80

%

T

able

3 r

epor

ts a

dis

trib

utio

n of

fre

quen

cy o

f th

e S

&P

cre

dit

rati

ngs

(RA

TIN

G)

mat

ched

to

the

pred

icte

d ra

ting

s fo

r ea

ch f

irm

-yea

r. P

redi

cted

rat

ings

are

es

tim

ated

by

regr

essi

ng R

AT

ING

on

four

acc

ount

ing

ratio

s. %

Cor

rect

indi

cate

s th

e pe

rcen

tage

of

pred

icte

d ra

ting

s th

at a

re th

e sa

me

as th

e ac

tual

rat

ings

.

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40

Tab

le 4

R

atin

gs D

evia

tion

an

d F

utu

re C

han

ge in

Dev

iati

on

Pane

l A. R

egre

ssio

n of

Fut

ure

Chan

ge in

Dev

iatio

n on

Dev

iatio

n

∆D

evia

tion

t+1

∆D

evia

tion

t+2

∆D

evia

tion

t+3

∆D

evia

tion

t+4

∆D

evia

tion

t+5

(1)

(2)

(3)

(4)

(5)

Dev

iati

on

-0.1

07**

*-0

.185

***

-0.2

50**

* -0

.299

***

-0.3

40**

* (-

30.3

)(-

29.1

)(-

27.7

)(-

26.8

)(-

25.8

)C

onst

ant

0.21

7**

*0.

269

***

0.16

00.

030

-0.1

16(4

.92)

(3.1

1)(1

.24)

(0.1

7)(-

0.51

)

Rat

ing

FE

Y

ES

YE

SY

ES

YE

SY

ES

Fir

m C

lust

erin

g Y

ES

YE

SY

ES

YE

SY

ES

# of

Obs

. 28

353

2450

921

319

1867

016

388

Adj

. Rsq

. 0.

059

0.09

80.

129

0.15

30.

176

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41

Tabl

e 4

– Ra

ting

s D

evia

tion

and

Fut

ure

Chan

ge in

Dev

iati

on (c

onti

nued

)

Pane

l B. R

egre

ssio

n of

Fut

ure

Chan

ge in

Res

idua

l on

Resid

ual

∆R

esid

ual t+

1 ∆

Res

idua

l t+2

∆R

esid

ual t+

3 ∆

Res

idua

l t+4

∆R

esid

ual t+

5

(1)

(2)

(3)

(4)

(5)

Res

idua

l -0

.096

***

-0.1

87**

*-0

.259

***

-0.3

12**

*-0

.358

***

(-26

.7)

(-26

.9)

(-26

.0)

(-25

.2)

(-24

.3)

Con

stan

t 0.

276

***

0.25

0**

*0.

076

-0.0

58-0

.187

(5.6

2)(2

.69)

(0.5

8)(-

0.32

)(-

0.79

)

Rat

ing

FE

Y

ES

YE

SY

ES

YE

SY

ES

Fir

m C

lust

erin

g Y

ES

YE

SY

ES

YE

SY

ES

# of

Obs

. 28

353

2450

921

319

1867

016

388

Adj

. Rsq

. 0.

055

0.09

60.

126

0.15

00.

173

Tab

le 4

rep

orts

reg

ress

ion

resu

lts o

f th

e ch

ange

in

Dev

iatio

n or

Res

idua

l at

fis

cal

year

t+

1 th

roug

h t+

5 re

lati

ve t

o fi

scal

yea

r t.

Ple

ase

refe

r to

App

endi

x I

for

desc

ript

ion

of a

ll th

e va

riab

les.

*, *

*, *

** d

enot

es s

igni

fica

nce

at th

e p<

0.10

, p<

0.05

, and

p<

0.01

le

vel r

espe

ctiv

ely.

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42

Tab

le 5

D

evia

tion

an

d F

irm

Ch

arac

teri

stic

s

Pane

l A. R

egre

ssio

n of

Cha

nge

in A

ccou

ntin

g Ra

tios o

n D

evia

tion

∆E

BIT

DA

_CO

Vt+

1 ∆

LE

VE

RA

GE

t+1

∆R

OC

t+1

∆L

NM

VE

t+1

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Dev

iati

on

-0.2

48-0

.005

**0.

003

***

0.00

7**

*(-

0.17

)(-

2.32

)(3

.13)

(3.8

5)G

ood_

Dev

iati

on

0.96

0-0

.000

40.

0001

0.00

4(0

.47)

(-0.

11)

(0.1

2)(1

.31)

Bad

_Dev

iati

on

-1.3

11-0

.009

* 0.

006

**0.

010

***

(-0.

93)

(-1.

68)

(2.3

7)(2

.72)

Con

stan

t 1.

132

-0.5

46-0

.015

-0.0

220.

093

0.09

8-0

.126

-0.1

21(0

.44)

(-0.

22)

(-0.

08)

(-0.

11)

(1.3

3)(1

.35)

(-3.

16)

***

(-3.

00)

***

t-te

st:

Goo

d_D

evia

tion

= B

ad_D

evia

tion

2.

271

0.00

8-0

.006

* -0

.006

(1.2

7)(1

.07)

(-1.

71)

(-1.

08)

Rat

ing

FE

Y

ES

YE

SY

ES

YE

SY

ES

YE

SY

ES

YE

SF

irm

Clu

ster

ing

YE

SY

ES

YE

SY

ES

YE

SY

ES

YE

SY

ES

# of

Obs

. 30

100

3010

030

317

3031

730

276

3027

629

986

2998

6A

dj. R

sq.

0.00

10.

001

0.00

00.

000

0.00

20.

002

0.00

60.

007

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43

Tab

le 5

Con

t'd

D

evia

tion

an

d F

irm

Ch

arac

teri

stic

s

Pane

l B. R

egre

ssio

n of

Cha

nge

in A

ccou

ntin

g Ra

tios o

n Re

sidua

l

∆E

BIT

DA

_CO

Vt+

1 ∆

LE

VE

RA

GE

t+1

∆R

OC

t+1

∆L

NM

VE

t+1

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Res

idua

l -0

.304

-0.0

08**

0.00

4**

*0.

008

***

(-0.

16)

(-1.

97)

(3.2

7)(3

.29)

Goo

d_R

esid

ual

1.07

8-0

.004

0.00

10.

007

* (0

.26)

(-0.

59)

(0.5

1)(1

.82)

Bad

_Dev

iati

on

-1.4

87-0

.011

0.00

6**

0.01

0**

(-

1.02

)(-

1.38

)(2

.51)

(2.0

6)C

onst

ant

0.95

0-1

.726

-0.0

23-0

.029

0.09

50.

101

-0.1

21-0

.118

(0.2

6)(-

0.50

)(-

0.11

)(-

0.14

)(1

.35)

(1.3

8)(-

3.03

)**

*(-

2.85

)**

*

t-te

st:

Goo

d_R

esid

ual =

Bad

_Res

idua

l 2.

565

0.00

6-0

.005

-0.0

03(0

.53)

(0.4

7)(-

1.45

)(-

0.44

)

Rat

ing

FE

Y

ES

YE

SY

ES

YE

SY

ES

YE

SY

ES

YE

SF

irm

Clu

ster

ing

YE

SY

ES

YE

SY

ES

YE

SY

ES

YE

SY

ES

# of

Obs

. 30

100

3010

030

317

3031

730

276

3027

629

986

2998

6A

dj. R

sq.

0.00

10.

001

0.00

00.

000

0.00

20.

002

0.00

60.

006

Tab

le 5

rep

orts

the

ave

rage

cor

rela

tion

bet

wee

n D

evia

tion

or

Res

idua

l an

d fi

rm c

hara

cter

isti

cs.

Ple

ase

refe

r to

App

endi

x I

for

desc

ript

ion

of a

ll th

e va

riab

les.

*,

**,

***

deno

tes

sign

ific

ance

at t

he p

<0.

10, p

<0.

05, a

nd p

<0.

01 le

vel r

espe

ctiv

ely.

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44

Table 6 Deviation and Future Change in Credit Ratings

Panel A. Regression of Future Change in Ratings on Deviation

∆Ratingt+1 ∆Ratingt+2 ∆Ratingt+3 ∆Ratingt+4 ∆Ratingt+5

(1) (2) (3) (4) (5) Deviation -0.047 *** -0.078 *** -0.100 *** -0.118 *** -0.128 ***

(-16.1) (-13.9) (-12.2) (-11.0) (-9.76)Constant 0.325 *** 0.773 *** 1.129 *** 1.443 *** 1.628 ***

(9.07) (9.42) (8.41) (7.21) (6.01)

Rating FE YES YES YES YES YESFirm Clustering YES YES YES YES YES

# of Obs. 28353 24509 21319 18670 16388Adj. Rsq. 0.033 0.062 0.084 0.103 0.122

Panel B. Regression of Future Change in Ratings on Residual

∆Ratingt+1 ∆Ratingt+2 ∆Ratingt+3 ∆Ratingt+4 ∆Ratingt+5

(1) (2) (3) (4) (5) Residual -0.059 *** -0.100 *** -0.130 *** -0.155 *** -0.171 ***

(-17.0) (-14.7) (-13.0) (-11.8) (-10.5)Constant 0.284 *** 0.690 *** 1.009 *** 1.290 *** 1.451 ***

(7.82) (8.35) (7.47) (6.39) (5.32)

Rating FE YES YES YES YES YESFirm Clustering YES YES YES YES YES

# of Obs. 28353 24509 21319 18670 16388Adj. Rsq. 0.034 0.064 0.086 0.105 0.125

Table 6 reports regression results of the change in actual S&P credit ratings (∆Rating) at fiscal year t+1 through t+5 relative to fiscal year t. Please refer to Appendix I for description of all the variables. *, **, *** denotes significance at the p<0.10, p<0.05, and p<0.01 level respectively.

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45

Table 7 Relation between bid-ask spreads and non-financial statement information

Panel A: Credit Rating Deviation

Dependent variable: SPREAD (1) (2)

ABS_Deviation 0.078 ***(6.72)

ABS_Good_Deviation -0.191 ***(-11.6)

ABS_Bad_Deviation 0.291 ***(21.5)

LNMVE -0.601 *** -0.759 ***(-44.0) (-47.7)

StockPrice -0.004 *** -0.005 ***(-5.42) (-6.65)

ShareTurnover -0.964 *** -0.899 ***(-39.1) (-35.4)

Volatility 32.63 *** 30.20 ***(35.3) (32.3)

Log(1+Numest) 0.480 *** 0.362 ***(17.8) (12.5)

NYSE_AMEX 0.204 *** 0.192 ***(5.20) (5.23)

Constant -2.887 *** -2.286 ***(-9.82) (-7.39)

t-test: ABS_Good_Deviation = ABS_Bad_Deviation -0.482 ***

(-21.9)

Industry, Quarter & Rating FE YES YES Firm Clustering YES YES

# of Obs. 21419 21419 Adj. Rsq. 0.456 0.498

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Table 7 Relation between bid-ask spreads and non-financial statement information

Panel B: Credit Rating Residual

Dependent variable: SPREAD (1) (2)

ABS_Residual 0.096 ***(6.75)

ABS_Good_Residual -0.298 ***(-14.8)

ABS_Bad_Residual 0.388 ***(25.3)

LNMVE -0.599 *** -0.766 ***(-43.9) (-49.2)

StockPrice -0.004 *** -0.005 ***(-5.45) (-6.91)

ShareTurnover -0.973 *** -0.898 ***(-39.6) (-35.7)

Volatility 32.93 *** 30.96 ***(35.8) (33.6)

Log(1+Numest) 0.478 *** 0.310 ***(17.8) (10.6)

NYSE_AMEX 0.194 *** 0.154 ***(4.96) (4.27)

Constant -3.024 *** -2.636 ***(-10.2) (-8.77)

t-test: ABS_Good_Residual = ABS_Bad_Residual -0.685 ***

(-25.9)

Industry, Quarter & Rating FE YES YES Firm Clustering YES YES

# of Obs. 23138 23138 Adj. Rsq. 0.054 0.058

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Table 8 Deviation and Stock Market Reaction to Change in Ratings

Dependent variable: ABN_RET (1) (2)

Upgrade 0.0354 *** 0.0309 *** (9.98) (7.97)

ABS_Deviation -0.0021 *** (-2.68)

ABS_Residual -0.0044 *** (-3.11)

Upgrade*ABS_Deviation 0.0035 *** (2.65)

Upgrade*ABS_Residual 0.0071 *** (3.20)

Constant -0.0306 *** -0.0268 *** (-9.41) (-7.52)

t-test:

Constant + Upgrade = 0 0.0048 *** 0.0041 *** (3.67) (3.22)

ABS_Deviation + Upgrade*ABS_Deviation = 0 0.0014 ** (2.41)

ABS_Residual + Upgrade*ABS_Residual = 0 0.0027 *** (3.15)

# of Obs. 6826 6826 Adj. Rsq. 0.042 0.043

Table 8 reports results from the regression of abnormal stock returns (ABN_RET) on absolute value of deviation (ABS_Deviation or ABS_Residual) and rating changes (Upgrade or Downgrade). Please refer to Appendix I for description of all the variables. *, **, *** denotes significance at the p<0.10, p<0.05, and p<0.01 level respectively.

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Table 9 Non-Reverting Deviation and Stock Market Reaction to Change in Ratings

Dependent variable: ABN_RET

Upgrade 0.0310 ***(9.99)

Non_Reverting -0.0227 ***(-4.88)

Upgrade*Non_Reverting 0.0260 ***(4.73)

Constant -0.0258 ***(-8.94)

t-test:

Constant + Upgrade = 0 0.0051 ***(5.53)

Non_Reverting + Upgrade*Non_Reverting = 0 0.0033 ***(3.12)

# of Obs. 6826 Adj. Rsq. 0.049

Table 9 reports results from the regression of abnormal stock returns (ABN_RET) on non-reverting deviation (Non_Reverting) and rating changes (Upgrade or Downgrade). Please refer to Appendix I for description of all the variables. *, **, *** denotes significance at the p<0.10, p<0.05, and p<0.01 level respectively.

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Table 10 Deviation and Earnings Announcement Returns

Dependent variable: ABN_EARET (1) (2) (3) (4) Deviation 0.0005 ***

(2.70)Good_Deviation 0.0000

(0.00)Bad_Deviation 0.0010 ***

(2.72)Residual 0.0008 ***

(3.00) Good_Residual 0.0000

(-0.09)Bad_Residual 0.0014 ***

(3.09)Constant 0.0044 0.0051 0.0050 0.0065

(1.03) (1.19) (1.17) (1.48)

t-test:

Good_Deviation = Bad_Deviation -0.001 * (-1.72)

Good_Residual = Bad_Residual -0.001 ** (-2.15)

Rating FE YES YES YES YES Firm Clustering YES YES YES YES

Adj. Rsq. 0.001 0.001 0.001 0.001

# of Obs. 29574 29574 29574 29574

Table 10 reports results from the regression of abnormal earnings announcement returns (ABN_EARET) on Deviation or Residual. Please refer to Appendix I for description of all the variables. *, **, *** denotes significance at the p<0.10, p<0.05, and p<0.01 level respectively.