Initial Credit Ratings and Earnings Management
K. Ozgur Demirtas
Assistant Professor of Finance
Zicklin School of Business
Baruch College, City University of New York
Aloke Ghosh**
Professor of Accountancy
Zicklin School of Business
Baruch College, City University of New York
Kimberly J. Rodgers
Visiting Assistant Professor Finance
Stern School of Business
New York University
Jonathan Sokobin
Deputy Chief Economist
Office of Economic Analysis
U.S. Securities and Exchange Commission
December 2006
**Corresponding author
Box B12-225, One Bernard Baruch Way
New York, NY 10010
Ph.: 646.312.3184, E-mail: Aloke_Ghosh@baruch.cuny.edu
JEL classification:
Keywords:
G14, G28, G32, M41
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.
1
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.
1
Researchers 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.
3
Critics 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)).
4
Moreover, 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
Because researchers argue that managers have greater discretion over current accruals
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
4.06%, 4.16%, 4.28%, and 5.21%, respectively. Similar to the annual trend, there is a
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
5
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.
A.
Earnings Management Motivation
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
5
Nationally 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.
6
See SEC (2003) and Frost (2006) for a discussion of the potential conflicts of interest due to
issuer-compensation of rating agencies.
6
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
Benefits 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.
7
7
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’).
8
Additional 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).
Moody’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).
9
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.10
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,
10
Congress 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.
9
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.11 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
11
Firms 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)).
10
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).12
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
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)).
12
11
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.13
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).14
According to Moody’s, through-the-cycle methodology manages the tension between ratings
timeliness and rating stability (Cantor and Mann 2003b).
13
Other highly publicized cases include New York City’s default (1975), Washington Public
Power Supply System (1983), Integrated Resources (1989), and First Executive Life (1991).
14
12
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
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.15 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
15
Several 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).
13
subsequent reversal in performance. Thus, issuers are not promptly penalized for inflating
earnings around the time of initial debt ratings.
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
14
(1997)). This result is especially puzzling because financial analysts are frequently
considered specialists in interpreting accounting information.
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)).16 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.
A.
Research Design
Construct for Earnings Management
16
Our 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.
15
Following a large body of work in accounting and finance, we use a crosssectional version of the modified Jones (1991) model to measure earnings management.
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
16
(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)
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.17
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
Moody’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.
17
17
(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).
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.
A.
Sample Description
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
18
issuers with requisite accounting data. Similar to Teoh et al. (1998a, 1998b), to avoid
survivorship bias, we do not require that firms have accruals data for the entire event
window.18
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.19
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.20 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
18
In 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).
19
Because 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.
Ratings 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).
20
19
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
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.
A.
Empirical Results
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
20
0.65). Given that Income is increasing while Cash flow is declining, our preliminary
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
21
($1318.05x-0.0011). The median numbers also indicate a similar pattern, although the
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 longterm 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 sevenyear 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
22
can be obtained from an analysis of the quarterly numbers. Therefore, as in Rangan
(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
23
rating number is associated with an increase in credit worthiness (as opposed to an
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
24
should be significant. In regression 2 of Table VII, Abnormal current accruals continue
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.21 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
21
Our 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 issuerrelated 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
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33
Table I
Distribution 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
Percentage
Cumulative
Frequency
Cumulative
Percentage
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
27
14
24
25
23
65
90
57
55
34
9
14
56
76
65
66
104
177
155
60
14
15
16
16
2.15
1.11
1.91
1.99
1.83
5.17
7.16
4.53
4.38
2.70
0.72
1.11
4.46
6.05
5.17
5.25
8.27
14.08
12.33
4.77
1.11
1.19
1.27
1.27
27
41
65
90
113
178
268
325
380
414
423
437
493
569
634
700
804
981
1136
1196
1210
1225
1241
1257
2.15
3.26
5.17
7.16
8.99
14.16
21.32
25.86
30.23
32.94
33.65
34.77
39.22
45.27
50.44
55.69
63.96
78.04
90.37
95.15
96.26
97.45
98.73
100.00
34
Table II
Distribution 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 longterm 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.
Credit ratings
Investment grade
Aaa
Aa1
Aa
Aa2
Aa3
A1
A
A2
A3
Baa1
Baa
Baa2
Baa3
Speculative grade
Ba1
Ba
Ba2
Ba3
B1
B
B2
B3
Caa1
Caa
Caa2
Caa3
Ca
Provisional
Frequency
Panel A
Percentage
Cumulative
Frequency
Cumulative
Percentage
8
3
2
6
9
19
19
44
44
40
1
68
59
0.64
0.24
0.16
0.48
0.72
1.51
1.51
3.50
3.50
3.18
0.08
5.41
4.69
8
11
13
19
28
47
66
110
154
194
195
263
322
0.64
0.88
1.03
1.51
2.23
3.74
5.25
8.75
12.25
15.43
15.51
20.92
25.62
26
6
34
54
112
15
301
323
24
26
9
0
1
2.07
0.48
2.70
4.30
8.91
1.19
23.95
25.70
1.91
2.07
0.72
0.00
0.08
348
354
388
442
554
569
870
1193
1217
1243
1252
1252
1253
27.68
28.16
30.87
35.16
44.07
45.27
69.21
94.91
96.82
98.89
99.60
99.60
99.68
4
0.32
1257
100
Panel B
Total assets ($ millions)
Growth
Leverage (%)
Income (% of total assets)
Mean
1318.05
1.72
31.23
3.85
35
Median
411.92
1.40
27.72
6.10
Standard
Deviation
3510.40
1.13
22.09
23.11
N
819
697
819
727
Table III
Performance 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.
Year
Performance and Leverage Patterns Around Initial Credit Ratings
Income
Cash Flow
Leverage
Observations
Mean Median
Mean Median
Mean Median
-3
-2
-1
607
650
727
0.778
0.926
0.946
2.012
2.059
1.723
2.785
1.912
1.362
2.657
2.047
1.780
4.415
4.871
5.555
1.324
2.149
2.655
1
2
3
911
947
927
-2.239
-1.389
-1.482
0.331
-0.246
-0.202
0.024
0.208
0.645
1.146
0.000
0.000
23.175
25.105
24.991
19.447
19.723
18.545
36
Table IV
Accrual 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 Sample
Abnormal Accruals
Current
Median
Year
Long-term
Mean
Median
Observations
Mean
594
641
715
784
0.546
1.204
1.449
1.305
0.194
0.350
0.300
0.354
-3
-2
-1
0
-1.516
-1.506
-1.671
-1.754
-0.562
-0.750
-0.779
-0.494
887
923
913
0.994
0.472
-0.110
0.231
-0.295
-0.090
1
2
3
-2.249
-2.028
-1.247
-1.170
-0.971
-0.681
Mean
Long-term
Median
Panel B: Constant Sample
Abnormal Accruals
Current
Median
Year
Observations
Mean
510
510
510
510
0.610
0.884
0.918
1.663
-0.046
0.351
0.097
0.427
-3
-2
-1
0
-1.205
-1.405
-1.372
-1.836
-0.510
-0.875
-0.751
-0.587
510
510
510
0.910
0.454
0.036
0.340
-0.229
-0.010
1
2
3
-2.208
-1.494
-1.204
-1.051
-0.550
-0.490
37
Table V
Quarterly 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.
Observations
529
538
573
589
603
624
625
668
694
715
731
794
812
Abnormal Current Accruals
Full sample
Constant sample (389 firms)
Mean
Median
Quarter
Mean
Median
2.844
3.592
3.919
4.061
4.160
4.281
5.214
1.315
1.306
1.575
1.302
1.639
1.618
1.752
-6
-5
-4
-3
-2
-1
0
2.358
3.319
3.511
3.839
3.808
4.061
5.199
1.009
1.263
1.662
1.318
1.263
1.623
1.805
4.622
4.380
3.096
2.060
2.161
2.509
1.559
1.300
1.129
0.735
0.715
0.592
1
2
3
4
5
6
4.797
4.658
3.561
2.815
2.354
2.700
1.480
1.507
1.277
1.068
0.723
0.719
38
Table VI
Initial 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 firmspecific 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
Coefficients
t-statistic
Intercept
Total Accruals
Abnormal accruals
Predicted accruals
Cash flow
Capital expenditure
R&D
Leverage
Growth
Issuer size
Sales
Issue size
Years to maturity
Seniority
-27.232
0.043
0.080
0.005
0.064
-0.041
0.010
2.154
0.399
-1.495
1.048
-2.834
(-23.39)***
(2.07)**
(3.82)***
(0.34)
(0.98)
(-5.60)***
(4.52)
(9.94)***
(2.18)
(-7.06)***
(4.07)***
(-12.18)***
Regression 2
Coefficients
t-statistic
-27.392
(-22.94)***
0.043
0.035
0.080
0.003
0.064
-0.041
0.010
2.116
0.446
-1.536
1.043
-2.854
(1.97)**
(1.30)
(3.78)***
(0.23)
(0.99)
(-5.56)***
(4.48)***
(9.31)***
(2.31)
(-7.01)***
(4.02)***
(-12.10)***
Adjusted R2
66%
66%
Observations
615
602
***, **, and * denote significance at the 1%, 5% and 10%, respectively for a two-tailed test.
39
Table VII
Initial 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
Coefficients
t-statistic
Regression 2
Coefficients
t-statistic
Intercept
-26.420
(-22.66)***
-26.964
Abnormal accruals
Current
0.059
(2.56)**
0.074
Long-term
0.033
(1.51)
0.031
Predicted accruals
Current
-0.003
(-0.11)
0.015
Long-term
0.061
(2.21)
0.066
Cash flow
0.087
(4.03)***
0.087
Capital expenditure
0.007
(0.46)
0.014
R&D
0.054
(0.83)
0.032
Leverage
-0.041
(-5.51)***
-0.039
Growth
0.010
(4.50)***
0.011
Issuer size
1.968
(8.52)***
1.850
Sales
0.558
(2.82)***
0.672
Issue size
-1.505
(-6.75)***
-1.450
Years to maturity
1.039
(4.06)***
0.914
Seniority
-2.806
(-11.72)***
-2.823
Past accruals
Abnormal accruals
Current(t-1)
-0.006
Long-term(t-1)
0.027
Predicted accruals
Current(t-1)
-0.048
Long-term(t-1)
0.017
Adjusted R2
66%
Observations
602
*** and ** denote significance at the 1%, and 5%, respectively for a two-tailed test.
40
(-20.41)***
(2.54)**
(1.19)
(0.35)
(1.74)
(3.24)***
(0.82)
(0.48)
(-4.26)***
(4.16)***
(7.24)***
(2.99)***
(-5.98)***
(3.54)***
(-10.54)**
(-0.36)
(1.45)
(-1.65)
(0.47)
66%
534
Table VIII
Initial 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
Coefficients
t-statistic
Regression 2
Coefficients
t-statistic
Intercept
-24.688
(-16.58)***
-25.245
Abnormal accruals
Current
0.114
(2.91)***
0.143
Long-term
0.106
(2.61)***
0.146
Predicted accruals
Current
0.118
(2.22)**
0.145
Long-term
0.061
(1.37)
0.091
Cash flow
0.146
(3.54)***
0.162
Capital expenditure
-0.043
(-1.81)
-0.043
R&D
-0.105
(-0.40)
-0.158
Leverage
-0.055
(-6.38)***
-0.055
Growth
0.012
(4.58)***
0.013
Issuer size
1.593
(5.93)***
1.604
Sales
0.629
(3.42)***
0.593
Issue size
-1.385
(-5.09)***
-1.302
Years to maturity
1.248
(4.52)***
1.299
Seniority
-3.158
(-13.14)***
-3.217
Past accruals
Abnormal accruals
Current(t-1)
-0.016
Long-term(t-1)
-0.033
Predicted accruals
Current(t-1)
-0.038
Long-term(t-1)
-0.029
Adjusted R2
70%
Observations
485
*** and ** denote significance at the 1%, and 5%, respectively for a two-tailed test.
41
(-14.65)***
(2.79)***
(2.79)***
(1.88)
(1.51)
(3.09)***
(-1.54)
(-0.54)
(-4.92)***
(4.37)***
(5.76)***
(2.99)***
(-4.80)***
(4.51)***
(-12.07)***
(-0.80)
(-1.79)
(-0.67)
(-0.93)
70%
440
Table IX
Economic 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 tstatistics. 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 Ratings
Abnormal Current Accruals
Conservative (0-20 percentile)
Medium
(20-80 percentile)
Aggressive
(80-100 percentile)
Difference
Conservative - Aggressive
(t-/Wilcoxon test)
(p-values)
Mean
Median
16.91
15.27
15.26
18
17
16
1.65
(2.80)
(0.0059)
2.00
(2.81)
(0.0059)
42
Table X
Initial 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-ofsample), 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
Coefficients
t-statistic
Intercept
Abnormal accruals
Current
Long-Term
Predicted accruals
Current
Long-Term
Cash flow
Capital expenditure
R&D
Leverage
Growth
Issuer size
Sales
Issue size
Years to maturity
Seniority
-24.894
(-16.54)***
Regression 2
Coefficients
t-statistic
-23.969
(-17.01)***
0.137
0.123
(2.71)***
(2.26)**
0.097
0.087
(2.74)***
(2.35)**
0.105
0.067
0.142
-0.043
-0.334
-0.065
0.016
1.459
0.609
-1.210
1.389
-3.180
(1.52)
(1.14)**
(2.80)***
(-1.69)
(-1.12)
(-7.03)***
(6.42)***
(5.16)***
(2.91)***
(-4.41)***
(5.01)***
(-11.99)***
0.099
0.062
0.118
-0.025
-0.194
-0.052
0.010
1.392
0.670
-1.377
1.508
-3.117
(1.98)**
(1.54)
(3.20)***
(-1.15)
(-0.86)
(-6.17)***
(3.92)***
(5.36)***
(3.65)***
(-5.12)***
(5.95)***
(-13.47)***
Adjusted R2
72%
*** and ** denote significance at the 1%, and 5%, respectively for a two-tailed test.
43
69%