Initial Credit Ratings and Earnings Management

K. Ozgur DemirtasAssistant Professor of Finance

Zicklin School of BusinessBaruch College, City University of New York

Aloke Ghosh**Professor of AccountancyZicklin School of Business

Baruch College, City University of New York

Kimberly J. RodgersVisiting Assistant Professor Finance

Stern School of BusinessNew York University

Jonathan SokobinDeputy Chief Economist

Office of Economic AnalysisU.S. Securities and Exchange Commission

December 2006

**Corresponding authorBox B12-225, One Bernard Baruch WayNew York, NY 10010Ph.: 646.312.3184, E-mail: Aloke_Ghosh@baruch.cuny.edu

JEL classification: G14, G28, G32, M41Keywords: Corporate finance, Credit Ratings; Earnings management, Accounting accruals, Market efficiency

This paper is the outcome of a collaboration that began in 2004 when Aloke Ghosh and Kimberly Rodgers were visiting the U.S. Securities and Exchange Commission as Academic Fellows. We thank Moody’s Investors Service for providing us with the credit ratings data, Richard Cantor, Larry Harris, and Bernell Stone for insightful discussions and Sarah Rudasill for her able research assistance.

Initial Credit Ratings and Earnings Management

Abstract

Credit rating agencies assert that they rely on financial information provided by issuers and that they value rating stability as well as accuracy. In an environment where rating agencies depend on issuer-reported information and are reluctant to adjust ratings promptly, managers of issuing firms can utilize the discretion afforded by GAAP to obtain the most favorable credit ratings. Consistent with our expectations, we find that current accruals are unusually positive and high around initial credit ratings. The increase in abnormally high accruals leading up to the initial credit rating year is followed by a reversal in the subsequent years. Multivariate regression analyses suggest that accounting accruals, abnormal current accruals in particular, are significantly positively related to initial credit ratings after controlling for several issue- and issuer-related characteristics indicative of default risk. Our results are robust to additional tests that account for endogeneity between credit ratings and earnings management.

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There is compelling evidence suggesting that firms manage earnings around initial and

seasoned public equity offerings (e.g., Teoh, Wong and Rao (1998), Teoh, Wong and

Welch (1998a, 1998b), Rangan (1998)).1 Given that prior studies find that credit ratings

play a key role in determining bond yields (Campbell and Taksler (2003), John et al.

(2003), Bhojraj and Sengupta (2003)), our study investigates two related issues: (1)

whether managers manipulate earnings when obtaining initial credit ratings on publicly

issued debt and (2) the extent to which credit ratings are associated with earnings

management. Because credit ratings contribute to the cost of debt, serve as the basis for

regulation, and influence debt-covenant triggers, recognizing the potential influence of

earnings management on credit ratings is important for issuers, investors, raters and

regulators.

In the United States, debt markets are by far the primary source of corporate

financing. The total value of straight corporate debt underwritten in 2004 was $1,278.4

Billion.2 In contrast, common stock issues totaled $169.6 Billion.2 Given the prominent

role of debt financing, managers acting in the interest of current shareholders have

incentive to inflate earnings around the time of credit ratings using the accounting

discretion afforded by GAAP. Since more favorable credit ratings lower the cost of debt,

existing shareholders benefit, at least in the near term, from aggressive earnings

management if inflating earnings leads to superior debt ratings. We posit that the

incentives for earnings management should match or exceed those at equity issuance

primarily because the reliance on debt capital exceeds equity financing.

1Researchers typically conclude that this form of earnings manipulation leads investors to overvalue newly issued securities, which reduces the cost of equity capital for existing shareholders.2 Source: Thompson Financial 2005.

Public firms typically receive ratings from credit rating agencies (CRAs) around

the time of a public debt offering. Over time, existing ratings can be revised as rating

agencies collect new information. However, because credit rating agencies reportedly

value stability of credit ratings as well as accuracy (see Cantor and Mann (2003a, 2003b)

and Fons (2002)), ratings are not continuously updated. Obtaining the most favorable

initial credit rating is thus crucial because (1) initial ratings become the benchmark for

ratings of future debt issues, and (2) ratings are potentially ‘sticky’.3 We thus believe that

initial credit ratings provide the most powerful setting to test whether firms manage

earnings to obtain favorable ratings.4 Furthermore, because credit rating agencies

reportedly rely on issuer-reported financial information (see S&P congressional

testimony in section B.2 below and Blume et al. (1998)), issuers have reasonable

expectation of benefit to aggressive reporting around the credit rating.

Based on a sample of 1,257 U.S. industrial firms issuing regular corporate debt

between 1980 and 2003 (‘issuers’) and receiving credit ratings from Moody’s Investors

Service for the first time, we find evidence consistent with earnings management in the

period leading up to their initial credit ratings. Since increases in accruals may partly

arise because of industry- and firm-specific factors, we focus on abnormal accruals as a

proxy for earnings management. Following a large body of literature, we estimate

abnormal accruals from the cross-sectional version of the modified Jones (1991) model.

Because researchers argue that managers have greater discretion over current accruals

3Critics contend that because ratings are sticky, they are imperfect indicators of credit risk. The investment grade rating of Enron debt in the days prior to bankruptcy is a popular anecdote (Partnoy (1999, 2002), Borros et al. (2002)).

4Moreover, as the debt structure becomes more complex and issuers receive several credit ratings on various issues, the distinction between issue rating and issuer rating becomes a potential source of concern with seasoned issues.

3

than over long-term accruals (Guenther (1994)), we further decompose abnormal accruals

into current and long-term components. The abnormal accruals decomposition process

(current and long-term abnormal accruals) closely follows the approach used in Teoh et

al. (1998a, 1998b).

Our results indicate that issuers make accounting choices and reporting decisions

that lead to unusually high accounting accruals around the time of initial credit ratings.

Further, the increase in accounting accruals leading up to the initial credit rating is

followed by a reversal in the subsequent years. This evidence is consistent with firms

borrowing from future earnings to report the most favorable earnings pattern at the time

of the initial credit rating.

Specifically, we find strong evidence indicating that issuers use abnormal current

accruals to inflate reported earnings around initial credit ratings. Average abnormal

current accruals (as a percentage of total assets) for the three years leading up to one year

prior to the initial credit rating year are 0.54%, 1.20%, and 1.44%, respectively. Thus,

the increase in abnormal current accruals between years -3 and -1 is around 166%.

Further, this increase in accounting accruals around initial credit rating year is followed

by a reversal in the subsequent years. We find that abnormal current accruals decline

following initial credit ratings from 0.99% in year 1 to -0.11% in year 3.

Although a time-series analysis of annual numbers is insightful, more precise

information with respect to the timing of earnings management can be obtained from an

analysis of the quarterly numbers. We find that average abnormal current accruals for the

seven quarters leading up to the initial credit rating quarter are 2.84%, 3.59%, 3.91%,

4.06%, 4.16%, 4.28%, and 5.21%, respectively. Similar to the annual trend, there is a

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near monotonic decline in quarterly abnormal current accruals following the initial credit

ratings quarter. Thus, our analysis suggests that firms manage earnings such that the

increasing accruals pattern observed prior to the ratings date mean reverts following the

rating quarter and the rating year.

More importantly, our results suggest that initial credit ratings are strongly

associated with the degree of earnings management. Multivariate regression analyses

indicates that accounting accruals, abnormal current accruals in particular, are

significantly positively related to initial credit ratings (at the 1% level) after controlling

for several issue- and issuer-related characteristics. Our results suggest that, holding

other explanatory variables constant, firms moving from a group reporting conservatively

(i.e., abnormal current accruals are the least) to an aggressive group (i.e., abnormal

current accruals are the highest) improve their ratings from B1 to Ba2.

We recognize that the decision to manipulate earnings by issuers hoping to obtain

favorable credit ratings may be endogenous. Because issuers with the highest levels of

creditworthiness have a high likelihood of obtaining the most favorable credit ratings,

they have the least incentives to manage earnings around initial credit ratings. Similarly,

issuers with the lowest levels of creditworthiness may have the least ability to effectively

manage earnings. We address this form of endogeneity as follows. We re-estimate our

credit ratings model without including accounting accruals as an independent variable.

We then use the estimated coefficients to predict credit ratings for each firm-year

observation using issue and issuer specific characteristics at the time of initial credit

ratings. We then examine the relationship between initial credit ratings and accounting

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accruals after deleting firms with the highest and lowest predicted credit ratings. Our

results and conclusions remain unchanged with respect to this additional scrutiny.

Our study contributes to the recent debate surrounding ‘nationally recognized’

credit ratings agencies (see Frost (2006), SEC (2003)).5 We offer two possible

conclusions: (1) rating agencies are misled by the abnormally high accruals around initial

ratings year and believe that the economic performance of aggressive issuers is superior

and sustainable, and/or (2) credit ratings agencies recognize the accounting accruals

generating process, but rely on issuers reported numbers.6

We organize the remainder of the paper in the following manner: Section I

provides additional discussion of the credit ratings process, and the motivation for

earnings management. Section II describes the data sources and provides descriptive

statistics. We present our results in Section III and Section IV concludes.

I. Earnings Management Motivation

A. Earnings Management

Generally accepted accounting principles (GAAP) allow managers, who are privy

to more detailed and proprietary information, discretion in selecting reporting methods,

estimates and disclosures. The reporting flexibility is aimed at assisting managers’

communication with outsiders. However, agency theory suggests that managers have

incentives to use this discretion to obfuscate economic reality for their personal benefit.

Healy and Wahlen (1999) define earnings management as managerial judgments and

5Nationally Recognized Statistically Ratings Organization (NRSRO) designation was created by U.S. Securities and Exchange Commission (SEC) in 1975. As of March 2005, firms included in the NRSRO list include Moody’s, Standard & Poors, Fitch, Dominion Bond Rating Services, and A.M. Best. Section I.B.1 provides added details on the NRSRO designation.

6See SEC (2003) and Frost (2006) for a discussion of the potential conflicts of interest due to issuer-compensation of rating agencies.

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decisions in financial reporting to alter financial reports to either mislead some

stakeholders about the underlying economic performance of the company or to influence

contractual outcomes.

Depending on the objective, earnings management is accomplished by shifting

income between current and future periods. Firms can accelerate the recognition of

accounting earnings through the use of current accruals, for example, by accelerating the

recognition of revenues, deferring the recognition of expenses, by reducing the provisions

for bad debt expense, by delaying the recognition of expenses when cash is advanced to

suppliers, and by decreasing the provisions for restructuring charges. Firms can also

accelerate the recognition of accounting earnings through the use of long-term current

accruals such as delaying recognition of asset write-downs, decelerating the recognition

of depreciation expenses, and decreasing deferred taxes. In general, earnings

management is not synonymous to accounting fraud, which is outside the confines of

GAAP.

Assuming rational capital market participants cannot immediately recognize

earnings management, firms benefit from deploying aggressive accounting practices in

the short run around public offering of securities, negotiations between bidders and

potential targets, and issuance of executive stock options.7 Rangan (1998) provides

evidence consistent with aggressive earnings management around seasoned equity

offerings. He finds that the degree of earnings management predicts subsequent earnings

changes and stock returns. Similarly, Teoh, Welch, and Wong (1998a, 1998b) provide

7Benefits arise from the firms’ ability to artificially increase the stock price as external market participants cannot ‘see through’ earnings manipulation using accounting accruals in transactions that use stock as a currency. However, any short-term benefit is lost when the market imposes a penalty in subsequent transactions.

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evidence of earnings management around initial public offerings (IPO) and secondary

equity offering (SEO).8 These authors also report that aggressive accounting reporting

around offerings is associated with poor performance for a sustained period subsequent to

the offering. Collectively, these results suggest that market participants are unable to see

through earnings management.

B. Credit Ratings

Credit ratings reflect a rating agencies’ opinion as of a specific date about the

creditworthiness of a company or a particular obligation.9 In this section, we highlight

three key stylized aspects of the ratings industry: (1) the relevance of credit rating

agencies, (2) the reliance on financial information reported by issuers, and (3) timeliness

of credit ratings.

B.1 Relevance of Credit Rating Agencies

A vital, and arguably controversial, characteristic of the ratings industry is the

Nationally Recognized Statistically Ratings Organization (NRSRO) designation created

by U.S. Securities and Exchange Commission (SEC) in 1975. The same year, SEC

permitted the reliance on credit ratings for regulatory purposes with the adoption of Rule

15c3-1 (‘Net Capital Rule’). This rule requires broker-dealers, when computing net

capital, to deduct from their net worth certain percentages of the market value of their

proprietary securities (‘haircuts’).

8Additional earnings management literature includes DeAngelo (1988) and Perry and Williams (1994), who document evidence of earnings management around MBOs, and Erickson and Wang (1998) find similar evidence of earnings management around stock-financed acquisitions. See also Dechow et al (1996), Teoh, Wong and Rao (1998) and Kasznik (1999).

9Moody’s defines its credit rating as “an opinion of the future ability, legal obligation, and willingness of a bond issuer or other obligor to make full and timely payments on principal and interest due to investors” (Moody’s 2003a).

8

A primary purpose of these haircuts is to provide a margin of safety against

broker-dealer losses in their proprietary positions (SEC 1975). The SEC concluded that it

is appropriate to apply a lower haircut requirement for securities held by broker-dealers

that are rated investment grade by a credit rating agency designated as an NRSRO. The

differential treatment across securities is warranted because securities rated as investment

grade are typically less volatile and more liquid than those that are rated below

investment-grade.

Over time, the regulatory reliance on credit ratings has increased dramatically and

the use of the NRSRO concept has also become more widespread. For instance, the SEC

has extended its reliance on NRSRO ratings to exempt certain financial transactions from

disclosure requirements, to set capital requirements for financial institutions, and to set

minimum quality investment standards for money market funds. Virtually all financial

regulators including public authorities that oversee banks, thrifts, insurance companies,

securities firms, capital markets, mutual funds, and private pensions rely on the NRSRO

concept in setting capital requirements. In addition, the ability of pension funds, mutual

funds, and banks to hold certain types of financial securities often depends on the level of

rating (i.e., investment grade versus non-investment grade) assigned by a rating agency.1

Credit ratings have become increasingly more important in debt contracts because

they are viewed as efficient credit quality benchmarks (Frost (2006)). Ratings triggers, in

particular, are clauses designed to protect lenders against any increase in post-lending

credit risk. Such triggers are found in bank agreements and commercial paper facilities,

1Congress also incorporated the NRSRO concept into a wide range of financial legislation (SEC 2003). Some of the other federal and state laws also employ the NRSRO concept. For example, the U.S. Department of Education uses NRSRO ratings to set standards for financial responsibility for institutions that wish to participate in students financial assistance programs.

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bond indentures, commercial agreements, swaps, hedge and derivative agreements,

leases, which require compensatory action (immediate repayment of principle in the

extreme case) in the event of a downgrade.

Investment banks have also long required credit ratings from NRSROs as part of

their underwriting activities. More important, from the issuers’ perspective, there is

evidence to suggest that ratings provide market information about default risk which in

turn influences yields. Among others, Kliger and Sarig (2000) and Hand et al. (1992)

find that credit ratings explain cross-sectional differences in yields. Similarly,

Houlthausen and Leftwich (1986), Hand et al. (1992), and Dichev and Piotroski (2001)

provide evidence of ratings changes affecting debt price levels and changes in debt

prices.

B.2 Relevance of Financial Information in Credit Ratings

Several studies document that ratings are based on public and non-public

information.1 Public information includes financial ratios such as leverage, interest

coverage ratios, profitability ratios (earnings and cash flow based) and other information

contained in the financial statements (e.g., Ashbaugh-Skaife et al. (2006), Ghosh and

Moon (2005), Kaplan and Urwitz (1979)).

In addition to public information, rating agencies often meet with management

and have access to confidential information such as financial projections, detailed

financials by product line or division, capital spending plans and new product plans, and

minutes of board meetings (Jorion, et al. 2005). Because the SEC considers this private

1Firms commonly approach ratings agencies and request a rating in advance of issuing debt. Rating agencies report that, although a team is responsible for assessing the creditworthiness of a company, there is one primary analyst who takes the lead in making regular contact with the issuer and who oversees the rating process (Jorion et al. (2005)).

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information gathering as part of the ratings process which is valuable for investors, rating

agencies have been excluded from Regulation FD (rules prohibiting issuers from

selectively revealing materially valuable information).2 Once CRAs complete their

analyses, ratings are assigned by a committee and the issuer is provided with an

opportunity to respond. When ratings are made public, explanations accompanying such

ratings only refer to public information to ensure that sensitive information provided by

the issuer is kept in strict confidence.

Recent public discourse has focused on the CRAs reliance on information

provided by the issuer. In statements before Congress following the Enron bankruptcy,

representatives of the credit rating industry testified that they rely on information

provided by issuers and that their ratings are as accurate as the information provided by

issuers. The following excerpt testimony of Ronald M. Barone (Managing Director of

S&P Rating Services) before the Permanent Subcommittee on Investigations of the

Committee on Government Affairs, United States Senate (July 23, 2002) underscores the

above point.

“Our ratings opinions are based on public information provided by the issuer, audited financial information, and qualitative analysis of a company and its sector.…We are not auditors, we do not audit the auditors of the companies that we rate or repeat the auditors’ accounting work, and we have no subpoena power to obtain information that a company is not willing to provide.”

B.3 Issue of Credit Ratings Timeliness

2 The Commission concluded that “Ratings organizations, like the media, have a mission of public disclosure; the objective and result of the ratings process is a widely available publication of the rating when it is completed. And under this provision, for the exclusion to apply, the ratings organization must make its credit ratings publicly available. For these reasons, we believe it is appropriate to provide this exclusion from the coverage of Regulation FD.” (SEC (2000)).

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Moody’s claims that bond ratings are intended to be ‘accurate’ and ‘stable’

measures of relative credit risk, as determined by each issuer’s relative fundamental

creditworthiness and without reference to explicit time horizon (Moody’s 2003b).

According to Moody’s, through-the-cycle ratings are stable because they are intended to

measure default risk over longer investment horizons. Ratings are changed only when

rating agencies are confident that observed changes in the company’s risk profile are

likely to be permanent (Altman and Rijken (2004)). Because NRSRO ratings are

intended to be stable, they are less likely to be sensitive to short-term fluctuations in

credit quality which suggests reduced timeliness.3

Although several studies find that credit ratings influence bond yields and equity

prices (e.g., Ederington and Goh (1998), Goh and Ederington (1993), Hand et al. (1992),

Holthausen and Leftwich (1986), John et al. (2003)), there is less agreement as to

whether credit ratings provide timely information (see Zuckerman and Richard (2002),

Schroeder (2002)). Shumway (2001) shows that simple hazard-rate models employing

accounting ratios, based on publicly available information, and market variables are

superior to credit ratings in predicting default rates. Anecdotal evidence also suggests

that credit ratings may not be timely. Both S&P and Moody’s continued to rate Enron

bonds as investment grade even while market bond prices were falling dramatically

(Berenson (2001).4

Credit rating agencies report a twofold objective when providing credit ratings:

(1) accuracy of ratings (i.e., the ability to correctly gauge the relative default risk of the 3According to Moody’s, through-the-cycle methodology manages the tension between ratings timeliness and rating stability (Cantor and Mann 2003b).

4Other highly publicized cases include New York City’s default (1975), Washington Public Power Supply System (1983), Integrated Resources (1989), and First Executive Life (1991).

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issuer) and (2) maintaining ratings stability. In an environment where CRAs depend on

financial information provided by issuers and are reluctant to adjust ratings quickly,

managers of issuing firms rationally utilize the discretion afforded by GAAP to obtain

most favorable initial credit ratings.

C. Linkages Between Earnings Management and Initial Credit Ratings

Our fundamental hypothesis is that rational managers have incentives to

manage earnings by reporting aggressively around the time of the credit rating. By

inflating earnings using ‘discretionary’ accounting accruals, managers hope to obtain a

more favorable credit rating and thereby lower their cost of debt.5 Although earnings

management might allow managers to raise debt at more favorable terms, it would not

necessarily increase the overall gain to the firm (assuming fixed investment). Existing

shareholders of the issuing firm would benefit at the cost of the new debtholders, who get

a lower than required rate of return given the true risk of the investment.

Further, given that credit rating agencies assert that they value stability as well

as accuracy, management can benefit the most from this ‘stickiness’ in ratings by

borrowing from the future and inflating earnings around initial debt ratings. If ratings

were continuously updated, potential pay-offs from earnings management would be

mitigated. As firms report declining accruals, following a period of abnormally high

accruals, continuously updated ratings would be downgraded for the aggressive reporting

firms. In contrast, CRAs are reluctant to amend ratings possibly because of the fear of a

subsequent reversal in performance. Thus, issuers are not promptly penalized for inflating

earnings around the time of initial debt ratings.

5Several studies find that credit ratings play a key role in determining bond yields. For example, John et al. (2003) find that, on average, bond yields increase by 544 (58) basis points for credit ratings between Caa and C (Baa1 and Baa3).

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Abnormally high accruals cannot be sustained in the long run because of the

nature of the accrual accounting process. While current earnings might deviate from

current operating cash flows because of accounting adjustments, in the long run earnings

and cash flows must converge. Thus, the accrual-accounting process dictates that

abnormally high positive accruals leading up to the initial debt rating will reverse in

subsequent periods. Hence, our first hypothesis is:

Hypothesis 1: Corporate debt issuers report abnormally high accruals for the period leading up to the initial credit ratings with a subsequent decline in accruals.

The extant literature suggests that credit rating agencies rely on issuer-reported

accounting information in establishing credit ratings. A key empirical question is

whether credit rating agencies effectively penalize this type of earnings management.

Evidence from academic studies focusing on initial and secondary public

offerings suggests that investors are slow to recognize and unravel accounting

manipulations (Coles et al. (2006)). Sloan (1996) documents that firms with large

accruals have poor future performance, which suggests that investors do not fully

understand the implications of current accruals about future earnings. In a related study,

Teoh and Wong (2002) examine whether analysts efficiently process information about

future earnings that is contained in past accounting accruals. They find that analysts are

overly optimistic about firms with large past accruals. Further, the predictive power of

accruals lasts up to four years following public equity offerings, which coincides with the

period issuing firms systematically under-perform (Ritter (1991), Loughren and Ritter

(1997)). This result is especially puzzling because financial analysts are frequently

considered specialists in interpreting accounting information.

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Because ratings agencies claim that their debt ratings are only as accurate as the

information provided by issuers, one innate proposition is that firms with abnormally

high positive accruals have more favorable debt ratings. Our hypothesis is based on at

least two non-mutually exclusive reasons. First, similar to other capital market

participants such as investors and financial analysts, ratings agencies are unable to fully

understand and unravel the accounting accruals process. Therefore, when firms report

abnormally high accruals, rating agencies are misled into believing that economic

performance is ‘truly’ superior and that such performance is sustainable in the future.

Second, it is possible that credit ratings agencies comprehend the accounting

accruals process, but they ‘go along’ because of potential conflicts of interests (Frost

(2006)). Conflicts of interest could arise because issuers pay for their ratings analogous

to how registrants (public companies) pay public accountants to get independent

certification of their financial statements (SEC (2003)). Conflicts of interest could also

arise because rating agencies develop ancillary fee-based businesses with the issuer (SEC

(2003)).6 Whether CRAs are mislead or take the issuer-reported numbers at face value,

our second hypothesis is:

Hypothesis 2: Corporate debt issuers with abnormal high accruals have enhanced credit ratings.

II. Research Design

A. Construct for Earnings Management

Following a large body of work in accounting and finance, we use a cross-

sectional version of the modified Jones (1991) model to measure earnings management.

6Our objective is to examine whether abnormally high accruals (if any) around initial credit ratings are positively associated with credit ratings. We do not investigate either explanation for the association.

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Specifically, we decompose accounting accruals (Accruals) into normal and abnormal

components using the following specification.

Accruals = β0 + β1 (ΔSales − ΔAR) + β2 PPE + μ (1)

where Accruals are the difference between Income before Extraordinary Items

and Operating Cash Flow, AR is Accounts Receivable and PPE is Gross Property, Plant

and Equipment. Δ represents the difference operator. All the variables including the

intercept term in equation (1) are deflated by total assets at the beginning of the year. We

estimate this regression for each industry (defined by a two-digit standard industry

classification code) and each year. The basic premise of the model is that normal (or

non-discretionary) accruals that arise because of industry or firm specific factors are

captured by the three independent variables. The magnitude of the residual represents

Abnormal accruals. The sign of the residual indicates whether accruals management is

income-increasing (positive) or income-decreasing (negative).

As in Teoh et al. (1998a, 1998b), we also decompose accounting accruals into

current and long-term components. Each of the components is further decomposed into

normal and abnormal components. Current accruals are computed as follows.

Current accruals = Δ [AR + Inventory + Other current assets] – ∆ [accounts payable + Income tax payable + Other current liabilities] (2)

Abnormal current accruals are based on the following regression.

Current accruals = β0 + β1 (ΔSales − ΔAR) + υ (3)

We estimate this regression for each industry and each year. The magnitude of

the residual represents Abnormal current accruals. Abnormal long-term accruals are

then defined as follows.

Abnormal long-term accruals = Abnormal accruals − Abnormal current accruals (4)

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The first part of our investigation focuses on the time-series patterns of the

abnormal component around the rating year. Specifically, we investigate whether

Abnormal accruals, Abnormal current accruals, and Abnormal long-term accruals are

high during the period immediately surrounding initial credit ratings year.

B. Earnings Management and Initial Credit Ratings

In the second part of our empirical analysis, we investigate whether Abnormal

accruals are associated with the level of initial credit ratings in the cross-section. In

particular, we estimate the following regression.

Credit ratings = β0 + β1 Abnormal accruals + δi Control variablesi + ζ (5)

where Credit ratings are numeric transformations of Moody’s credit ratings.7 We

assign a value of one for the highest Moody’s credit rating (Aaa) and a value of 28 to the

lowest credit rating. Following prior studies (e.g., Bhojraj and Sengupta (2005), John et

al. (2003), Kaplan and Urwitz (1979)), we include as control variables several indicators

of credit risk such as Cash Flow (operating cash flow scaled by total assets), Leverage

(sum of short and long term debt scaled by the total assets), Growth (sum of the market

value of equity and the book value of liabilities deflated by total assets), R&D (deflated

by total assets), Issuer size (logarithmic transformation of total assets) Issue size

(logarithmic transformation of the face value of debt issued), Years to maturity

(logarithmic transformation of the number of years remaining to maturity), and Seniority

(a dummy variable that takes the value of 1 for senior debt and zero otherwise).

7Moody’s ratings can be assigned for an issuer or an issue. An issue credit rating is an opinion about the creditworthiness of an obligor with respect to specific financial obligations. An issuer credit rating is an opinion about the obligor’s overall financial creditworthiness to pay its financial obligations (Jorion et al. (2005)). Because we focus on initial credit ratings, this distinction is less important for our sample.

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Firms with high Cash flow have higher ratings because of lower bankruptcy risk.

Firms with high Leverage have low credit ratings because of high probability of

bankruptcy. Growth firms have higher credit risk and therefore lower credit ratings.

Larger and more established firms have higher credit ratings because larger firms are

better able to survive market volatility. Issue size and Seniority are typically positively

associated with credit ratings while Year to maturity is typically negatively associated

with credit ratings. Finally, we account for R&D following evidence reported by Franzen

et al. (2006) suggesting that accounting-based distress risk measures have previously

misclassified high R&D firms as distressed.

III. Sample Description

A. Sample Selection

Our study is based on a comprehensive proprietary credit ratings database

obtained from Moody’s Investors Service (Moody’s). We limit our investigation to U.S.

Industrial firms issuing straight debt with credit ratings from Moody’s for the first time

between 1980 and 2003 (i.e., firms with initial credit ratings). Accounting data is

obtained from annual and quarterly Compustat tapes. In addition to Compustat data,

firms included in our sample must have the following characteristics: (1) initial ‘rating’

date (the date Moody’s issued a credit rating for the company for the first time, (2) ‘issue’

date (the date firms issued corporate straight debt), and (3) the rating date and issue date

are not more than two years apart. This sample selection procedure yields 1,257 initial

issuers with requisite accounting data. Similar to Teoh et al. (1998a, 1998b), to avoid

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survivorship bias, we do not require that firms have accruals data for the entire event

window.8

Table I presents the distribution of issuers by rating year. Since the data provided

by Moody’s are believed to be comprehensive, the distribution reflects the time variation

in initial public debt offerings. We find that there is some clustering of initial ratings

during the period 1996 to 1998.

B. Sample characteristics

Panel A of Table II reports the distribution of initial credit ratings.9

Approximately 74% of our sample is initially rated Ba1 or below Ba1 by Moody’s, which

reflects the proportion of those with speculative grade classification.10 The percentage of

firms with speculative grade ratings is much higher for our sample compared to that of a

sample which includes subsequent rated issues. This higher percentage arises mainly

because a sample including both initial and subsequent credit ratings is affected by

survivorship bias.

Panel B of Table II reports some important issuer characteristics measured one

year prior to the rating year. The average (median) issuer size, measured using Total

assets, is $1,318 million ($411 million). Growth is defined as the ratio of the sum of the

market value of equity (fiscal year-end price times the number of shares outstanding) and

8In a sensitivity analysis, we also replicate our results using a constant sample where firms have the requisite data for the entire event window (six years or twelve quarters around the rating year/quarter).

9Because these are initial credit ratings, there are no default issues (i.e., firms with D ratings). The four provisional ratings displayed in Table II (P-1, (P)B3, (P)Baa1, and WR) are excluded from our empirical analyses.

10Ratings are broadly defined into two categories, (1) ‘investment grade’ for credits ratings that are Baa or above, and (2) ‘speculative grade’ for credit ratings that are below investment grade (i.e., Ba1 or below).

19

the book value of liabilities to total assets. The average (median) Growth for our sample

is 1.72 (1.40). Leverage is the sum of short-term and long-term debt deflated by total

assets. The mean (median) leverage is 31% (28%). We measure accounting performance

as income before extraordinary items (Income) deflated by total assets. The mean

(median) Income is approximately 4% (6%).

IV. Empirical Results

A. Performance and Leverage Patterns Around Initial Rating Year

Table III reports the time series profile of performance (Income and Cash flow)

and financial leverage (Leverage) for firms being rated for the first time. Mean and

median values are reported in event time starting three years prior to the initial rating year

(Year 0) and ending three years after. All numbers are industry adjusted by subtracting

the median values from the firm level values. Industry is defined using a four-digit

standard industry classification code (SIC).

The differences between pre-rating and post-rating performance measures are

stark. We find that the average Income increases over the pre-rating years and then

declines dramatically over the post-rating years. Specifically, Income for the years -3 to -

1 is increasing (0.78, 0.93, and 0.95) while for the post-rating years (years 1 to 3), Income

declines dramatically (-2.24, -1.39, and -1.48).

In contrast to the Income numbers, which are the sum of operating cash flow and

accounting accruals, the average Cash flow is declining over the years -3 to -1 (2.78,

1.91, and 1.36). The decline is even steeper for the three post-rating years (0.02, 0.20 and

0.65). Given that Income is increasing while Cash flow is declining, our preliminary

20

performance results suggest that issuers must be ‘booking’ more income-increasing

accounting accruals to increase reported income.

In the final two columns, we report the results of Leverage around the rating year.

Industry-adjusted financial leverage for the first-time-rated public-debt issuers increase

by more than four times following the initial rating year. The average (median) Leverage

increases from 5.56 (2.65) in Year –1 to 23.18 (19.45) in Year 1. This result is not

surprising because we require that firms being rated for the first time also issue debt

within two years of being rated. Even though we require that firms issue debt within two

years of the rating year, an overwhelming majority of the firms issue debt in the same

year as they are rated.

B. Accrual Patterns Around Initial Rating Year

Panel A of Table IV reports accounting accrual patterns around the initial credit

rating year (Year 0). Consistent with the first hypothesis, we find that current accruals

are unusually high around the initial credit rating year. The mean Abnormal current

accruals are 0.55% in year -3 (three years prior to the rating year), it jumps to 1.20% in

year -2 and peaks to 1.45% in year -1. For a ‘typical’ firm with average total assets of

$1,318.05 million, Abnormal current accruals increase from $7 million ($1318.05x

0.0055) in year -3 to $19 million ($1318.05x0.0145) in year -1. Thus, the increase in the

magnitude of earnings management around the ratings year is economically large.

For the subsequent years, we find a reversal in the accruals pattern. Abnormal

current accruals decline from 1.31% in year 0 to -0.11% in year 3. For a typical firm,

Abnormal current accruals decline from $17 million ($1318.05x 0.0131) to $-1 million

($1318.05x-0.0011). The median numbers also indicate a similar pattern, although the

21

magnitude is much smaller; median Abnormal current accruals increase from 0.19% in

year -3 to 0.35% in year 0. Subsequent to the rating year, median Abnormal current

accruals decline from 0.23 in year 1 to -0.09% in year 3.

On the other hand, Abnormal long-term accruals are negative for all the seven

years without any clear pattern of earnings manipulation. The median Abnormal long-

term accruals are -0.56% in year -3, but they increase to around -0.80% in years -2 and -1

but they again decline to -0.50% in year 0. Collectively, our results suggest that issuers

try to project a more favorable picture of the firms’ operating performance using current

or working capital accruals.

As in Teoh et al. (1998b), to avoid survivorship bias, we do not require that firms

have accruals data for the entire seven-year period (three years prior to three years after

the initial rating year). As a robustness check, we repeat our analysis in Panel B using a

constant sample of 510 firms with available accounting accruals data for the entire seven-

year event window. The results from Panel B are even stronger than those reported in

Panel A. The mean (median) Abnormal current accruals increase from 0.61% (-0.05%)

in year -1 to 1.66 (0.43%) in year 0. For a typical firm, Abnormal current accruals

increase from $8 million ($1318.05x 0.0061) to $22 million ($1318.05x-0.0161). As in

Panel A, we find that Abnormal current accruals reverse during the post-rating years.

Again, we find no systematic evidence of earnings management using long-term accruals

in Panel B.

Although a time-series analysis of accruals patterns based on annual observations

is insightful, more precise information with respect to the timing of earnings management

can be obtained from an analysis of the quarterly numbers. Therefore, as in Rangan

22

(1998), we report the results of Abnormal current accruals around a twelve-quarter event

window beginning with six quarters prior to the rating quarter (Quarter 0) for the full

sample and a constant sample (389 firms). For the Full sample, we find that Abnormal

current accruals monotonically increase from 2.84% in quarter -6 to 5.21% in Quarter 0.

The median numbers indicate a similar increase over Quarters -6 to 0 (1.31% to 1.75%).

As in Table IV, we find that both mean and median Abnormal current accruals decline

following the initial credit rating quarter. We get very similar but economically stronger

results using the constant sample with available data for the entire twelve quarters. Our

analysis of the quarterly results suggest that firms manage earnings around the rating date

such that increasing accruals patterns observed prior to the ratings date mean revert

following the rating quarter and the rating year. Thus, Abnormal current accruals nearly

monotonically decline both across as well as within the post rating years.

Overall, the patterns in reported accounting accruals are consistent with earnings

management around initial credit ratings. In the subsequent sub-section, we investigate

whether abnormal accruals influence initial credit ratings.

C. Initial Credit Ratings and Accounting Accruals

Tables VI to VIII report cross-sectional regressions of initial credit ratings on

accounting accruals and other issue/issuer characteristics demonstrated previously as

reliable indicators of default risk. Moody’s credit rating mnemonics Aaa through Ca

have been converted into a numerical scale ranging from one to twenty-eight such that an

increase in rating number is associated with an increase in credit risk. For the ease of

exposition, we multiply the numerical scores with negative one so that an increase in the

rating number is associated with an increase in credit worthiness (as opposed to an

23

increase in credit risk). Thus, positive (negative) coefficients indicate that higher

accruals are associated with more (less) favorable ratings.

Consistent with our second hypothesis which states that firms with high abnormal

accruals have superior credit ratings, we find in Regression 1 of Table VI that Total

accruals (defined as income before extraordinary items less operating cash flow deflated

by lagged totals assets) is positive and significant at the 5% level. We get similar results

when we decompose Total accruals into predicted and abnormal components.

Interestingly, only the abnormal component is significant; Abnormal total accruals are

positive and significant at the 5% level while Predicted total accruals are insignificant.

Since univariate results from Tables IV and V indicate evidence of earnings

management using current accruals, we report the results of the influence of the

components of accruals (current and long-term) on credit ratings in Table VII. We find

that only Abnormal current accruals are positive and significant at the 5% level in

Regression 1. All the other accrual components (Abnormal long-term accruals,

Predicted current accruals, Predicted long-term accruals) are insignificant at the 5%

level.

A more powerful test of the hypothesis that firms manage earnings around the

initial rating year to influence credit ratings is to examine whether current period accruals

(t) are more powerful in explaining initial ratings than lagged accruals (t-1). If managers

use current period accruals to obtain more favorable ratings, only Abnormal current

accruals in period t (the rating year) should be significant. If Abnormal current accruals

is a proxy for some omitted variables, both current and lagged Abnormal current accruals

should be significant. In regression 2 of Table VII, Abnormal current accruals continue

24

to be positive and significant at the 5% level. However, none of the other accruals

components is significant. Thus, our results suggest that ratings agencies rely on working

capital accruals for the current period in setting credit ratings.

The accounting based variables including the computation of accruals are based

on annual numbers in Table VII. In Table VIII, we replicate the regression results using

quarterly numbers. Since the quarterly numbers provide more timely information about

the company’s risk and performance to the users of financial statements, we expect a

stronger association between initial credit ratings and Abnormal current accruals.

Consistent with our expectations, the coefficient on Abnormal current accruals is

between two to three times larger when we use quarterly numbers. One important

difference between the annual and quarterly results is that the coefficient on Abnormal

long-term accruals is also positive and significant in Table VIII. Our quarterly results

suggest that ratings agencies rely on working capital and long-term accruals in setting

credit ratings.

The results of the control variables are mostly consistent with prior studies. For

instance, in Regression 1 of Table VII, Cash flow, Growth, Issuer size, Sales, and Years

to maturity are all positive and significant. Firms with superior performance, those that

are growing rapidly, bigger firms, and those with longer maturity have superior credit

ratings. On the other hand, Leverage and Issue size are negative and significant. Firms

that are levered and those raising larger amounts of debt from the public market have

worse credit ratings. The other control variables are generally insignificant.

D. Economic Significance

25

Overall, Tables VI to VIII results suggest that first-time issuers benefit from

earnings management around the rating year by obtaining more favorable credit ratings.

Table IX reports the economic significance of the impact of Abnormal current accruals

on Credit ratings.

We sort the sample into three portfolios based on the portion of abnormal current

accruals that is orthogonal to the issue and issuer characteristics, which explain credit

ratings. Specifically, in the first stage, Abnormal current accruals are regressed on Cash

flow, Capital expenditure, R&D, Leverage, Growth, Issuer size, Sales, Issue size, Years to

maturity and Seniority. The residuals from this regression (or the component of Abnormal

current accruals that is orthogonal to the other determinants of Credit ratings) are used to

sort our sample into three portfolios. The first portfolio consists of firms with the lowest

20 percentile of residuals (Conservative), the third portfolio contains firms with the

highest 20 percentile of residuals (Aggressive), and the second portfolio (Medium)

contains the rest of the sample.

We find that the mean (median) difference in Credit ratings between firms using

accruals conservatively (Conservative) and those using accruals aggressively

(Aggressive) is 1.65 (2.00). The mean and median differences are statistical significant at

less than one percent level.11 Holding all other explanatory variables constant, the mean

results indicate that firms moving from the conservative group to the aggressive group

improve their ratings from B1 to Ba2.

E. Endogenous Choice Variables

11Our results suggest that the relationship between credit ratings and accounting accruals may be non-linear. However, our statistically tests indicate no evidence of a non-linear relationship.

26

We recognize that the earnings management choice around initial credit ratings

may be endogenous. Potential gains from earnings management techniques are likely

vary with the creditworthiness of the issuer. Because issuers with the highest levels of

creditworthiness have a high likelihood of obtaining the most favorable credit ratings,

they have the least incentives to manage earnings around initial credit ratings. Similarly,

issuers with the lowest levels of creditworthiness may have the least ability to effectively

manage earnings.

We address this endogeneity concern using a multiple stage model of

creditworthiness. We first re-estimate our credit ratings model without including

accounting accruals and issue characteristics but after including all other issuer

characteristics (i.e., we include Cash flow, Capital expenditure, R&D, Leverage, Growth,

Issuer size, and Sales). Credit ratings are based on S&P ratings available from the

Compustat database over the period 1980 to 2003. We then use the estimated

coefficients to predict credit ratings (or implied credit ratings) for each firm at the time of

the initial credit rating. In the final stage, we examine the relationship between initial

Moody’s credit ratings and accounting accruals after deleting firms with the highest and

lowest predicted or implied credit ratings. The economic intuition is that firms with the

best/worst expected credit ratings have the lowest incentives/ability to manage earnings.

The results of the endogeneity tests are reported in Table X. In Regression 1, we

delete firms with the highest and lowest 1% of implied credit ratings. Consistent with the

prior regression tables, the coefficient on Abnormal current accruals continues to be

positive and significant. As a further robustness check, in Regression 2, we delete firms

27

with implied credit ratings better than Aa3 or worse than Caa and the tenor of the results

remain unchanged.

Thus, our results suggest that, even after accounting for the possibility of an

endogenous relationship between ratings and accruals, abnormally high accruals are

associated with better ratings.

V. Conclusion

Credit ratings play a fundamental role in capital markets and in contract law.

Ratings provide information about default risk, which determines issuers’ cost of debt

capital. Many institutional investors are limited or prohibited from investing in

speculative grade debt or holding debt downgraded to non-investment grades.

Additionally, bond covenants often contain ratings-dependent clauses. Considering that

credit rating agencies report that they rely on financial information provided by issuers

and that they are reluctant to adjust ratings quickly (Ashbaugh-Skaife et al. (2005),

Moody’s (2003b)), managers of issuing firms rationally utilize the discretion afforded by

GAAP to obtain the most favorable initial credit ratings. Issuing firms benefit from more

favorable credit ratings because superior ratings typically lower the cost of raising debt

capital (Campbell and Taksler (2003)).

Based on a comprehensive database obtained from Moody’s, we find strong

evidence consistent with the hypothesis that issuers engage in earnings management prior

to initial credit ratings. Our results indicate that issuers, around the time of initial credit

ratings, make accounting choices and reporting decisions that lead to unusually high

working capital (current) accruals. Further, the increase in accounting accruals leading

up to the initial credit rating is followed by a reversal in the subsequent years. This

28

evidence is consistent with ‘borrowing future earnings’ to obtain more favorable initial

credit ratings.

Multivariate regression analyses suggest that abnormal accruals are significantly

positively related to initial credit ratings after controlling for several issue- and issuer-

related characteristics. Our results suggest that, holding all other explanatory variables

constant, firms moving from the conservative group to the aggressive group improve

their ratings from B1 to Ba2.

Our study contributes to the debate surrounding credit ratings by documenting

evidence consistent with the hypothesis that the average ratings are influenced by

opportunistic earnings management. Considering that credit ratings affect the cost of

debt, serve as the basis for regulation, and influence debt-covenant triggers,

understanding the potential influence of earnings management on credit ratings is

valuable for issuers, investors, raters and regulators.

29

References

Altman, Edward, I., and Herbert A. Rijken, 2004, How rating agencies achieve rating stability, Journal of Banking & Finance 28, 2679-2714.

Ashbaugh-Skaife, Hollis, Daniel. W. Collins, and Ryan LaFond, 2006, The effects of corporate governance on firms’ credit ratings, Journal of Accounting and Economics 42, 203-243.

Berenson, Alex, 2001 Enron's collapse: The rating agencies; Debt rankings finally fizzle, but the deal fizzled first, New York Times, November 29.

Bhojraj, Sanjeev and Partha Sengupta, 2003, Effect of corporate governance on bond ratings and yields: The role of institutional investors and outside directors, Journal of Business 76 (3), 455-476.

Blume, Marshall E., Felix Lim and A. Craig MacKinlay, 1998, The declining credit quality of U.S. corporate debt: Myth or reality? Journal of Finance 53, 1389-1413.

Borrus, Amy, Mike McNamee, and Heather Timmons, 2002, The credit raters: How they work and how they might work better, BusinessWeek, April 8.

Campbell, John, Y., and Glenn B. Taksler, 2003, Equity volatility and corporate bond yields, Journal of Finance 58, 2321-2349.

Cantor, Richard and C. Mann, 2003a, Measuring the performance of corporate bond ratings,

Moody’s Special Comment, April 2003.

Cantor, Richard and C. Mann, 2003b, Are corporate bond ratings procyclical?, Moody’s Special Comment, October 2003.

Coles, Jeffry, L., Michael Hertzel, and Swaminathan Kalpathy, 2006, Earnings management around employee stock option reissues, Journal of Accounting and Economics 41, 173-200.

DeAngelo, Linda E., 1988, Managerial competition, information costs, and corporate governance: The use of accounting performance measures in proxy contests, Journal of Accounting and Economics, 3-36.

Dechow, Patricia. M., Richard G. Sloan, and Amy P. Sweeney, 1996, Causes and consequences of earnings manipulation: An analysis of firms subject to enforcement actions by the SEC, Contemporary Accounting Research: 1-36.

Dichev, Ilia D., and Joseph D. Piotroski, 2001, The long-run stock returns following bond rating changes, Journal of Finance 56, 173-203

Ederington, Louis H. and Jeremy C. Goh, 1998, Bond rating agencies and stock analysts: Who knows what when? Journal of Financial and Quantitative Analysis 33, 569-585.

Erickson, Merle, and Shiing-wu Wang, 1998, Earnings management by acquiring firms in stock for stock mergers, Journal of Accounting and Economics 27, 149-176.

30

Fons, Jerome S., 1992, Understanding Moody’s corporate bond ratings and rating process. Moody’s Investors Service, Global Credit Research Special Comment (May).

Franzen, Laurel A., Kimberly J. Rodgers and Timothy T. Simin, 2006, Measuring distress risk: The effect of R&D intensity, Journal of Finance, Forthcoming.

Frost, Carol Ann, 2006, Credit ratings agencies in capital markets: A review of research evidence on selected criticisms of the agencies. Journal of Accounting, Auditing and Finance, Forthcoming.

Ghosh, Aloke, and Doocheol Moon, 2005, Auditor tenure and perceptions of audit quality, The Accounting Review 80, 585-602.

Goh, Jeremy C., and Louis H. Ederington, 1993, Is a bond rating downgrade bad news, good news, or no news for stockholders? Journal of Finance 48, 2001-2008.

Guenther, David A., 1994, Earnings management in response to corporate tax rate changes: Evidence from the 1986 tax reform act, Accounting Review 69, 230-243.

Hand, John M., Robert W. Holthausen, Richard W. Leftwich, 1992, The effect of bond rating agency announcements on bond and stock prices, The Journal of Finance 47, 733-752.

Healy, Paul M. and James M. Wahlen, (1999), A review of the earnings management literature

and its implications for standard setting, Accounting Horizons 13 (4), 365-383.

Holthausen, Robert W., and Richard W. Leftwich, 1986, The effect of bond rating changes on common stock prices, Journal of Financial Economics 17, 57-89.

John, Kose, Anthony W. Lynch, and Manju Puri, 2003, Credit ratings and loan characteristics: implications for yield, Journal of Business 76 (3), 371-409.

Jones, Jennifer, 1991 Earning management during import relief investigation, Journal of Accounting Research 29, 193-228.

Jorion, Phillipe, Zhu Liu, and Charles Shi, 2005, Informational effects of Regulation FD: Evidence from rating agencies, Journal of Financial Economics 76, 309-30

Kaplan, Robert S. and Gabriel Urwitz, 1979, Statistical Models of Bond Ratings: A Methodological Inquiry, Journal of Business 52 (2), 231-261.

Kasznik, Ron, 1999, On the association between voluntary disclosure and earnings management, Journal of Accounting Research 37 (1).

Kliger, Doron, and Oded Sarig, 2000, The information value of bond ratings, Journal of Finance 55, 2879-2902.

Loughran, Tim, and Jay R. Ritter, 1997, The operating performance of firms conducting seasoned equity offerings, Journal of Finance 52, 1823-1850.

Moody’s Investor Service, 2003a, Ratings definitions, Moody’s, New York, NY.

31

Moody’s Investor Service, 2003b, Measuring the performance of corporate bond ratings, Special comment, Moody’s, New York, NY.

Partnoy, Frank, 1999, The Siskel and Ebert of financial markets: Two thumbs down for the credit rating agencies, Washington University Law Quarterly 77, 619-712.

Partnoy, Frank, 2002, The paradox of credit ratings, in Ratings, Rating Agencies and the Global Financial System, edited by Richard M. Levich, Giovanni Majnoni, and Carmen Reinhart. Boston: Kluwer.

Perry, Susan E. and Thomas H. Williams, 1994, Earnings management preceding management buyout offers, Journal of Accounting and Economics 18, 157-179.

Rangan, Srinivasan, 1998, Earnings management and the performance of seasoned equity offerings, Journal of Financial Economics 50, 101-122.

Ritter, Jay, 1991, The long run performance of initial public offerings, Journal of Finance 46, 3-27.

Schroeder, Michael, 2002, SEC weighs curbs on credit-rating firms, Wall Street Journal, p. A3, March 21.

Securities and Exchange Commission, 1975, Adoption of alternative net capital requirement for certain broker and dealers, Release No. 34-11497.

Securities and Exchange Commission, 2000, Selective disclosure and insider trading, Release No. 33-788.

Securities and Exchange Commission, 2003, Report on the role and function of credit rating agencies in the operations of the securities markets: As required by Section 702(b) of the Sarbanes-Oxley Act of 2002.

Shumway, Tyler, 2001, Forecasting bankruptcy more accurately: A simple hazard model, Journal of Business, 101-124.

Sloan, Richard, 1996, Do stock prices fully reflect information in accruals and cash flows about future earnings, The Accounting Review 71, 289-315.

Teoh, Siew Hong, Ivo Welch, and T.J. Wong, 1998a, Earnings management and the long-run market performance of initial public offerings, Journal of Finance 53 (6), 1935-1974.

Teoh, Siew Hong, Ivo Welch, and T.J. Wong, 1998b, Earnings management and the

underperformance of seasoned equity offerings, Journal of Financial Economics 50, 63-99.

Teoh, Siew Hong, and T.J. Wong, 2002, Why new issues and high-accrual firms underperform: The role of analysts' credulity, Review of Financial Studies 15 (3), 869-900.

Teoh, Siew Hong, T.J. Wong and Gita Rao, 1998, Are accruals during initial public offerings opportunistic? Review of Accounting Studies 3, (1-2).

32

Zuckerman, Gregory and Jathon Sapsford, 2001, Why credit agencies didn’t switch off Enron –S&P cries ‘junk,’ but the warning comes too late, The Wall Street Journal, November 29, C1.

33

Table IDistribution of Issuers with Initial Credit Ratings

This table shows the time distribution of firms with initial credit ratings. The sample consists of 1,257 U.S. firms that issued regular corporate debt for the first time between 1980 and 2003. Firms issuing corporate debt are required to accompany a Moody’s credit rating.

Year Frequency PercentageCumulativ

e Frequency

CumulativePercentage

1980 27 2.15 27 2.151981 14 1.11 41 3.261982 24 1.91 65 5.171983 25 1.99 90 7.161984 23 1.83 113 8.991985 65 5.17 178 14.161986 90 7.16 268 21.321987 57 4.53 325 25.861988 55 4.38 380 30.231989 34 2.70 414 32.941990 9 0.72 423 33.651991 14 1.11 437 34.771992 56 4.46 493 39.221993 76 6.05 569 45.271994 65 5.17 634 50.441995 66 5.25 700 55.691996 104 8.27 804 63.961997 177 14.08 981 78.041998 155 12.33 1136 90.371999 60 4.77 1196 95.152000 14 1.11 1210 96.262001 15 1.19 1225 97.452002 16 1.27 1241 98.732003 16 1.27 1257 100.00

34

Table IIDistribution of Initial Credit Ratings and Sample Characteristics

Panel A shows the distribution of initial credit ratings. “Aaa’ is the highest credit rating assigned by Moody’s, while the lowest credit rating for our sample is ‘Ca.’ There are four firms with provisional ratings (P-1, (P)B3, (P)Baa1, WR). Moody's assigns a provisional rating when it is highly likely that the rating will become final after all documents are received, or an obligation is issued into the market. Firms with provisional ratings are not included in our subsequent analyses. Panel B reports the sample characteristics. Total assets are measured in millions of dollars. Growth is the ratio of market value of equity plus book value of liabilities deflated by the book value of total assets. Leverage is the sum of short-term and long-term debt deflated by total assets. Income before extraordinary items (Income) is deflated by total assets. Firm characteristics are measured one year prior to the initial credit ratings year.

Panel ACredit ratings Frequency Percentage Cumulative

FrequencyCumulative Percentage

Investment gradeAaa 8 0.64 8 0.64Aa1 3 0.24 11 0.88Aa 2 0.16 13 1.03Aa2 6 0.48 19 1.51Aa3 9 0.72 28 2.23A1 19 1.51 47 3.74A 19 1.51 66 5.25A2 44 3.50 110 8.75A3 44 3.50 154 12.25

Baa1 40 3.18 194 15.43Baa 1 0.08 195 15.51Baa2 68 5.41 263 20.92Baa3 59 4.69 322 25.62

Speculative gradeBa1 26 2.07 348 27.68Ba 6 0.48 354 28.16Ba2 34 2.70 388 30.87Ba3 54 4.30 442 35.16B1 112 8.91 554 44.07B 15 1.19 569 45.27B2 301 23.95 870 69.21B3 323 25.70 1193 94.91

Caa1 24 1.91 1217 96.82Caa 26 2.07 1243 98.89Caa2 9 0.72 1252 99.60Caa3 0 0.00 1252 99.60Ca 1 0.08 1253 99.68

Provisional4 0.32 1257 100

Panel B

  Mean MedianStandard Deviation N

Total assets ($ millions) 1318.05 411.92 3510.40 819Growth 1.72 1.40 1.13 697Leverage (%) 31.23 27.72 22.09 819Income (% of total assets) 3.85 6.10 23.11 727

35

Table IIIPerformance and Leverage Patterns Around Initial Credit Ratings

This table reports Income, Cash Flow and Leverage numbers for six years around the initial credit rating year (Year 0). Income is the income before extraordinary items scaled by total assets. Cash Flow is the operating cash flow scaled by total assets. Leverage is the sum of short and long-term debt scaled by the total assets. All the three variables are industry adjusted. Industry adjustments are computed by subtracting industry medians from firm level values. Industry is defined using a four-digit standard industry classification code.

Performance and Leverage Patterns Around Initial Credit RatingsIncome Cash Flow Leverage

Year Observations Mean Median Mean Median Mean Median

-3 607 0.778 2.012 2.785 2.657 4.415 1.324-2 650 0.926 2.059 1.912 2.047 4.871 2.149-1 727 0.946 1.723 1.362 1.780 5.555 2.655

1 911 -2.239 0.331 0.024 1.146 23.175 19.4472 947 -1.389 -0.246 0.208 0.000 25.105 19.7233 927 -1.482 -0.202 0.645 0.000 24.991 18.545

36

Table IVAccrual Patterns Around Initial Credit Ratings

This table reports current and long-term abnormal accruals for seven years around the initial credit rating year (Year 0). Total accruals, defined as income before extraordinary items less operating cash flow, are decomposed into current and long-term components. Current accruals or working capital accruals are defined as the change in noncash current assets less the change in current liabilities. Long-term accruals are the difference between Total accruals and Current accruals. Current and long-term accruals are further decomposed into abnormal and predicted components. Predicted or normal total accruals arising because of industry- and firm-specific factors are estimated from a regression of total accruals on changes in sales less changes in accounts receivables and gross property, plant and equipment. Abnormal total accruals are the residuals from the above regression. Abnormal current accruals are the residuals from a regression of current accruals on changes in sales less changes in accounts receivables. Abnormal long-term accruals are the difference between Abnormal total accruals and Abnormal current accruals. In Panel B we restrict the sample to include firms with data for all seven years around the initial credit ratings (constant sample).

Panel A: Full SampleAbnormal Accruals

Current Long-termObservations Mean Median Year Mean Median

594 0.546 0.194 -3 -1.516 -0.562641 1.204 0.350 -2 -1.506 -0.750715 1.449 0.300 -1 -1.671 -0.779784 1.305 0.354 0 -1.754 -0.494

887 0.994 0.231 1 -2.249 -1.170923 0.472 -0.295 2 -2.028 -0.971913 -0.110 -0.090 3 -1.247 -0.681

Panel B: Constant Sample Abnormal Accruals

Current Long-termObservations Mean Median Year Mean Median

510 0.610 -0.046 -3 -1.205 -0.510510 0.884 0.351 -2 -1.405 -0.875510 0.918 0.097 -1 -1.372 -0.751510 1.663 0.427 0 -1.836 -0.587

510 0.910 0.340 1 -2.208 -1.051510 0.454 -0.229 2 -1.494 -0.550510 0.036 -0.010 3 -1.204 -0.490

37

Table VQuarterly Accrual Patterns Around Initial Credit Ratings

This table reports current and long-term abnormal accruals for twelve quarters around the initial credit rating year (Year 0). Total accruals, defined as income before extraordinary items less operating cash flow, are decomposed into current and long-term components. Current accruals or working capital accruals are defined as the change in noncash current assets less the change in current liabilities. Long-term accruals are the difference between Total accruals and Current accruals. Abnormal current accruals are the residuals from a regression of current accruals on changes in sales less changes in accounts receivables. Constant sample restricts the sample to include firms with data for all twelve quarters around the initial credit ratings.

Abnormal Current Accruals

Full sample Constant sample (389 firms)

Observations Mean Median Quarter Mean Median

529 2.844 1.315 -6 2.358 1.009538 3.592 1.306 -5 3.319 1.263573 3.919 1.575 -4 3.511 1.662589 4.061 1.302 -3 3.839 1.318603 4.160 1.639 -2 3.808 1.263624 4.281 1.618 -1 4.061 1.623625 5.214 1.752 0 5.199 1.805

668 4.622 1.559 1 4.797 1.480694 4.380 1.300 2 4.658 1.507715 3.096 1.129 3 3.561 1.277731 2.060 0.735 4 2.815 1.068794 2.161 0.715 5 2.354 0.723812 2.509 0.592 6 2.700 0.719

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Table VIInitial Credit Ratings and Accounting Accruals

This table reports parameter estimates from cross-sectional regressions of numeric transformations of credit ratings on accounting accruals and issue/issuer characteristics for the initial credit rating year. We transform Moody’s credit ratings into numeric values by assigning a value of one for the highest Moody’s credit rating (Aaa) and a value of 28 for the lowest credit rating. We multiply the numeric transformations with -1 for the ease of exposition. Total accruals, defined as income before extraordinary items less operating cash flow, are decomposed into current and long-term components. Total accruals are further decomposed into abnormal and predicted components. Predicted or normal total accruals arising because of industry- and firm-specific factors are estimated from a regression of total accruals on changes in sales less changes in accounts receivables and gross property, plant and equipment. Abnormal accruals are the residuals from the above regression. The control variables are defined as follows. Cash Flow is the operating cash flow scaled by total assets, Leverage is the sum of short and long term debt scaled by the total assets, Growth is the sum of the market value of equity and the book value of liabilities deflated by total assets, Capital expenditure is the capital expenditures deflated by total assets, R&D is research and development expense deflated by total assets, Issuer size (Sales) is the logarithmic transformation of total assets (sales), Issue size is the logarithmic transformation of the face value of debt issued, Years to maturity is the logarithmic transformation of the number of years remaining to maturity, and Seniority is a dummy variable that takes the value of 1 for senior debt and zero otherwise. The reported t-statistics are corrected for heteroscedasticity using White (1980) corrections.

Regression 1 Regression 2Coefficients t-statistic Coefficients t-statistic

Intercept -27.232 (-23.39)*** -27.392 (-22.94)***

Total Accruals 0.043 (2.07)**

Abnormal accruals

0.043 (1.97)**

Predicted accruals 0.035 (1.30)Cash flow 0.080 (3.82)*** 0.080 (3.78)***

Capital expenditure 0.005 (0.34) 0.003 (0.23)R&D 0.064 (0.98) 0.064 (0.99)Leverage -0.041 (-5.60)*** -0.041 (-5.56)***

Growth 0.010 (4.52) 0.010 (4.48)***

Issuer size 2.154 (9.94)*** 2.116 (9.31)***

Sales 0.399 (2.18) 0.446 (2.31)Issue size -1.495 (-7.06)*** -1.536 (-7.01)***

Years to maturity 1.048 (4.07)*** 1.043 (4.02)***

Seniority -2.834 (-12.18)*** -2.854 (-12.10)***

Adjusted R2 66% 66%Observations 615 602***, **, and * denote significance at the 1%, 5% and 10%, respectively for a two-tailed test.

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Table VIIInitial Credit Ratings and Working Capital Accruals

This table reports parameter estimates from cross-sectional regressions of numeric transformations of credit ratings on components of accounting accruals and issue/issuer characteristics for the initial rating year. We transform Moody’s credit ratings into numeric values by assigning a value of one for the highest Moody’s credit rating (Aaa) and a value of 28 for the lowest credit rating. We multiply the numeric transformations with -1 for the ease of exposition. Total accruals, defined as income before extraordinary items less operating cash flow, are decomposed into current and long-term components. Current accruals or working capital accruals are defined as the change in noncash current assets less the change in current liabilities. Long-term accruals are the difference between Total accruals and Current accruals. Current and long-term accruals are further decomposed into abnormal and predicted components. Predicted total accruals arising because of industry- and firm-specific factors are estimated from a regression of total accruals on changes in sales less changes in accounts receivables and gross property, plant and equipment. Abnormal total accruals are the residuals from the above regression. Abnormal current accruals are the residuals from a regression of current accruals on changes in sales less changes in accounts receivables. Abnormal long-term accruals are the difference between Abnormal total accruals and Abnormal current accruals. Cash Flow is the operating cash flow scaled by total assets, Leverage is the sum of short and long term debt scaled by the total assets, Growth is the sum of the market value of equity and the book value of liabilities deflated by total assets, Capital expenditure is the capital expenditures deflated by total assets, R&D is research and development expense deflated by total assets, Issuer size (Sales) is the logarithmic transformation of total assets (sales), Issue size is the logarithmic transformation of the face value of debt issued, Years to maturity is the logarithmic transformation of the number of years remaining to maturity, and Seniority is a dummy variable that takes the value of 1 for senior debt and zero otherwise. The reported t-statistics are corrected for heteroscedasticity using White (1980) corrections.

Regression 1 Regression 2Coefficients t-statistic Coefficients t-statistic

Intercept -26.420 (-22.66)*** -26.964 (-20.41)***

Abnormal accruals Current 0.059 (2.56)** 0.074 (2.54)**

Long-term 0.033 (1.51) 0.031 (1.19)Predicted accruals Current -0.003 (-0.11) 0.015 (0.35) Long-term 0.061 (2.21) 0.066 (1.74)Cash flow 0.087 (4.03)*** 0.087 (3.24)***

Capital expenditure 0.007 (0.46) 0.014 (0.82)R&D 0.054 (0.83) 0.032 (0.48)Leverage -0.041 (-5.51)*** -0.039 (-4.26)***

Growth 0.010 (4.50)*** 0.011 (4.16)***

Issuer size 1.968 (8.52)*** 1.850 (7.24)***

Sales 0.558 (2.82)*** 0.672 (2.99)***

Issue size -1.505 (-6.75)*** -1.450 (-5.98)***

Years to maturity 1.039 (4.06)*** 0.914 (3.54)***

Seniority -2.806 (-11.72)*** -2.823 (-10.54)**

Past accrualsAbnormal accruals Current(t-1) -0.006 (-0.36) Long-term(t-1) 0.027 (1.45)Predicted accruals Current(t-1) -0.048 (-1.65) Long-term(t-1) 0.017 (0.47)Adjusted R2 66% 66%Observations 602 534*** and ** denote significance at the 1%, and 5%, respectively for a two-tailed test.

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Table VIIIInitial Credit Ratings and Quarterly Working Capital Accruals

This table reports parameter estimates from cross-sectional regressions of numeric transformations of credit ratings on components of accounting accruals and issue/issuer characteristics for the initial rating quarter. We transform Moody’s credit ratings into numeric values by assigning a value of one for the highest Moody’s credit rating (Aaa) and a value of 28 for the lowest credit rating. We multiply the numeric transformations with -1 for the ease of exposition. Total accruals, defined as income before extraordinary items less operating cash flow, are decomposed into current and long-term components. Current accruals or working capital accruals are defined as the change in noncash current assets less the change in current liabilities. Long-term accruals are the difference between Total accruals and Current accruals. Current and long-term accruals are further decomposed into abnormal and predicted components. Predicted total accruals arising because of industry- and firm-specific factors are estimated from a regression of total accruals on changes in sales less changes in accounts receivables and gross property, plant and equipment. Abnormal total accruals are the residuals from the above regression. Abnormal current accruals are the residuals from a regression of current accruals on changes in sales less changes in accounts receivables. Abnormal long-term accruals are the difference between Abnormal total accruals and Abnormal current accruals. Cash Flow is the operating cash flow scaled by total assets, Leverage is the sum of short and long term debt scaled by the total assets, Growth is the sum of the market value of equity and the book value of liabilities deflated by total assets, Capital expenditure is the capital expenditures deflated by total assets, R&D is research and development expense deflated by total assets, Issuer size (Sales) is the logarithmic transformation of total assets (sales), Issue size is the logarithmic transformation of the face value of debt issued, Years to maturity is the logarithmic transformation of the number of years remaining to maturity, and Seniority is a dummy variable that takes the value of 1 for senior debt and zero otherwise. The reported t-statistics are corrected for heteroscedasticity using White (1980) corrections.

Regression 1 Regression 2Coefficients t-statistic Coefficients t-statistic

Intercept -24.688 (-16.58)*** -25.245 (-14.65)***

Abnormal accruals Current 0.114 (2.91)*** 0.143 (2.79)***

Long-term 0.106 (2.61)*** 0.146 (2.79)***

Predicted accruals Current 0.118 (2.22)** 0.145 (1.88) Long-term 0.061 (1.37) 0.091 (1.51)Cash flow 0.146 (3.54)*** 0.162 (3.09)***

Capital expenditure -0.043 (-1.81) -0.043 (-1.54)R&D -0.105 (-0.40) -0.158 (-0.54)Leverage -0.055 (-6.38)*** -0.055 (-4.92)***

Growth 0.012 (4.58)*** 0.013 (4.37)***

Issuer size 1.593 (5.93)*** 1.604 (5.76)***

Sales 0.629 (3.42)*** 0.593 (2.99)***

Issue size -1.385 (-5.09)*** -1.302 (-4.80)***

Years to maturity 1.248 (4.52)*** 1.299 (4.51)***

Seniority -3.158 (-13.14)*** -3.217 (-12.07)***

Past accrualsAbnormal accruals Current(t-1) -0.016 (-0.80) Long-term(t-1) -0.033 (-1.79)Predicted accruals Current(t-1) -0.038 (-0.67) Long-term(t-1) -0.029 (-0.93)Adjusted R2 70% 70%Observations 485 440*** and ** denote significance at the 1%, and 5%, respectively for a two-tailed test.

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Table IXEconomic Significance of the Influence of Accounting Accruals on Credit Ratings

We sort the sample into three portfolios based on the portion of abnormal current accruals that is orthogonal to the issue and issuer characteristics used to explain credit ratings. Abnormal current accruals are the residuals from a regression of current accruals on changes in sales less changes in accounts receivables. Abnormal current accruals are regressed on Cash flow, Capital expenditure, R&D, Leverage, Growth, Issuer size, Sales, Issue size, Years to maturity and Seniority. The residuals from this regression are sorted into three unequal portfolios. The first portfolio consists of firms with the lowest 20 percentile of residuals (Conservative), the third portfolio contains firms with the highest 20 percentile of residuals (Aggressive), and the second portfolio (Medium) contains the rest of the sample. For each portfolio, we report the mean and median values of the numeric transformations of credit ratings. We test for the difference in credit ratings between the mean and median values across the two extreme portfolios (Aggressive and Conservative). Statistical significance of differences of the means is measured using a paired t-statistics. Statistical significance of differences of the medians is measured using Wilcoxon test. We also report the p values associated with each of the test statistics.

Credit RatingsMean Median

Abnormal Current Accruals Conservative (0-20 percentile)

16.91 18

Medium (20-80 percentile) 15.27 17

Aggressive (80-100 percentile) 15.26 16

Difference Conservative - Aggressive 1.65 2.00

(t-/Wilcoxon test) (2.80) (2.81)

(p-values) (0.0059) (0.0059)

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Table XInitial Credit Ratings and Quarterly Accounting Accruals: Correcting For Endogeneity

We correct for endogeneity in two ways. Using firms in Compustat with available credit ratings (out-of-sample), we estimate implied credit ratings for our sample. We delete firms with the highest and lowest 1% of implied credit ratings in regression 1 and those with ratings better than Aa3 and worse than Caa in regression 2. Total accruals, defined as income before extraordinary items less operating cash flow, are decomposed into current and long-term components. Current accruals or working capital accruals are defined as the change in noncash current assets less the change in current liabilities. Long-term accruals are the difference between Total accruals and Current accruals. Current and long-term accruals are further decomposed into abnormal and predicted components. Predicted or normal total accruals arising because of industry- and firm-specific factors are estimated from a regression of total accruals on changes in sales less changes in accounts receivables and gross property, plant and equipment. Abnormal total accruals are the residuals from the above regression. Abnormal current accruals are the residuals from a regression of current accruals on changes in sales less changes in accounts receivables. Abnormal long-term accruals are the difference between Abnormal total accruals and Abnormal current accruals. Cash Flow is the operating cash flow scaled by total assets, Leverage is the sum of short and long term debt scaled by the total assets, Growth is the sum of the market value of equity and the book value of liabilities deflated by total assets, Capital expenditure is the capital expenditures deflated by total assets, R&D is research and development expense deflated by total assets, Issuer size (Sales) is the logarithmic transformation of total assets (sales), Issue size is the logarithmic transformation of the face value of debt issued, Years to maturity is the logarithmic transformation of the number of years remaining to maturity, and Seniority is a dummy variable that takes the value of 1 for senior debt and zero otherwise. The reported t-statistics are corrected for heteroscedasticity using White (1980) corrections.

Regression 1 Regression 2Coefficients t-statistic Coefficients t-statistic

Intercept -24.894 (-16.54)*** -23.969 (-17.01)***

Abnormal accruals Current 0.137 (2.71)*** 0.097 (2.74)***

Long-Term 0.123 (2.26)** 0.087 (2.35)**

Predicted accruals Current 0.105 (1.52) 0.099 (1.98)**

Long-Term 0.067 (1.14)** 0.062 (1.54)Cash flow 0.142 (2.80)*** 0.118 (3.20)***

Capital expenditure -0.043 (-1.69) -0.025 (-1.15)R&D -0.334 (-1.12) -0.194 (-0.86)Leverage -0.065 (-7.03)*** -0.052 (-6.17)***

Growth 0.016 (6.42)*** 0.010 (3.92)***

Issuer size 1.459 (5.16)*** 1.392 (5.36)***

Sales 0.609 (2.91)*** 0.670 (3.65)***

Issue size -1.210 (-4.41)*** -1.377 (-5.12)***

Years to maturity 1.389 (5.01)*** 1.508 (5.95)***

Seniority -3.180 (-11.99)*** -3.117 (-13.47)***

Adjusted R2 72% 69%*** and ** denote significance at the 1%, and 5%, respectively for a two-tailed test.

43