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Initial Credit Ratings and Earnings Management
 

Initial Credit Ratings and Earnings Management

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    Initial Credit Ratings and Earnings Management Initial Credit Ratings and Earnings Management Document Transcript

    • 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: G14, G28, G32, M41 Keywords: 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. 2
    • 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. Because researchers argue that managers have greater discretion over current accruals 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. 4
    • 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 5
    • 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 6
    • 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 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. 7
    • 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 7 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. 8
    • 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). 9 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
    • 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, 1 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. 10
    • 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 1 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)). 11
    • 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)). 12
    • 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 3 According to Moody’s, through-the-cycle methodology manages the tension between ratings timeliness and rating stability (Cantor and Mann 2003b). Other highly publicized cases include New York City’s default (1975), Washington Public 4 Power Supply System (1983), Integrated Resources (1989), and First Executive Life (1991). 13
    • 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. 5 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). 14
    • 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. 15
    • 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. 6 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. 16
    • 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) 17
    • 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). 7 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. 18
    • 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 19
    • 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 8 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). 9 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. 10 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
    • 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 21
    • 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 22
    • 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 23
    • (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 24
    • 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 25
    • 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 26
    • 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 Our results suggest that the relationship between credit ratings and accounting accruals may be 11 non-linear. However, our statistically tests indicate no evidence of a non-linear relationship. 27
    • 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 28
    • 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 29
    • 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. 30
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    • 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. Cumulativ Year Frequency Percentage e Cumulative Frequency Percentage 1980 27 2.15 27 2.15 1981 14 1.11 41 3.26 1982 24 1.91 65 5.17 1983 25 1.99 90 7.16 1984 23 1.83 113 8.99 1985 65 5.17 178 14.16 1986 90 7.16 268 21.32 1987 57 4.53 325 25.86 1988 55 4.38 380 30.23 1989 34 2.70 414 32.94 1990 9 0.72 423 33.65 1991 14 1.11 437 34.77 1992 56 4.46 493 39.22 1993 76 6.05 569 45.27 1994 65 5.17 634 50.44 1995 66 5.25 700 55.69 1996 104 8.27 804 63.96 1997 177 14.08 981 78.04 1998 155 12.33 1136 90.37 1999 60 4.77 1196 95.15 2000 14 1.11 1210 96.26 2001 15 1.19 1225 97.45 2002 16 1.27 1241 98.73 2003 16 1.27 1257 100.00 35
    • 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 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 A Credit ratings Frequency Percentage Cumulative Cumulative Frequency Percentage Investment grade Aaa 8 0.64 8 0.64 Aa1 3 0.24 11 0.88 Aa 2 0.16 13 1.03 Aa2 6 0.48 19 1.51 Aa3 9 0.72 28 2.23 A1 19 1.51 47 3.74 A 19 1.51 66 5.25 A2 44 3.50 110 8.75 A3 44 3.50 154 12.25 Baa1 40 3.18 194 15.43 Baa 1 0.08 195 15.51 Baa2 68 5.41 263 20.92 Baa3 59 4.69 322 25.62 Speculative grade Ba1 26 2.07 348 27.68 Ba 6 0.48 354 28.16 Ba2 34 2.70 388 30.87 Ba3 54 4.30 442 35.16 B1 112 8.91 554 44.07 B 15 1.19 569 45.27 B2 301 23.95 870 69.21 B3 323 25.70 1193 94.91 Caa1 24 1.91 1217 96.82 Caa 26 2.07 1243 98.89 Caa2 9 0.72 1252 99.60 Caa3 0 0.00 1252 99.60 Ca 1 0.08 1253 99.68 Provisional 4 0.32 1257 100 Panel B Standard Mean Median Deviation N Total assets ($ millions) 1318.05 411.92 3510.40 819 Growth 1.72 1.40 1.13 697 Leverage (%) 31.23 27.72 22.09 819 Income (% of total assets) 3.85 6.10 23.11 727 36
    • 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. Performance and Leverage Patterns Around Initial Credit Ratings Income Cash Flow Leverage Year Observations Mean Median Mean Median Mean Median 2.65 -3 607 0.778 2.012 2.785 7 4.415 1.324 2.04 -2 650 0.926 2.059 1.912 7 4.871 2.149 1.78 -1 727 0.946 1.723 1.362 0 5.555 2.655 1.14 23.17 1 911 -2.239 0.331 0.024 6 5 19.447 0.00 25.10 2 947 -1.389 -0.246 0.208 0 5 19.723 0.00 24.99 3 927 -1.482 -0.202 0.645 0 1 18.545 37
    • 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 Long-term Observations Mean Median Year Mean Median 594 0.546 0.194 -3 -1.516 -0.562 641 1.204 0.350 -2 -1.506 -0.750 715 1.449 0.300 -1 -1.671 -0.779 784 1.305 0.354 0 -1.754 -0.494 887 0.994 0.231 1 -2.249 -1.170 923 0.472 -0.295 2 -2.028 -0.971 913 -0.110 -0.090 3 -1.247 -0.681 Panel B: Constant Sample Abnormal Accruals Current Long-term Observations Mean Median Year Mean Median 510 0.610 -0.046 -3 -1.205 -0.510 510 0.884 0.351 -2 -1.405 -0.875 510 0.918 0.097 -1 -1.372 -0.751 510 1.663 0.427 0 -1.836 -0.587 510 0.910 0.340 1 -2.208 -1.051 510 0.454 -0.229 2 -1.494 -0.550 510 0.036 -0.010 3 -1.204 -0.490 38
    • 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. Abnormal Current Accruals Full sample Constant sample (389 firms) Observations Mean Median Quarter Mean Median 529 2.844 1.315 -6 2.358 1.009 538 3.592 1.306 -5 3.319 1.263 573 3.919 1.575 -4 3.511 1.662 589 4.061 1.302 -3 3.839 1.318 603 4.160 1.639 -2 3.808 1.263 624 4.281 1.618 -1 4.061 1.623 625 5.214 1.752 0 5.199 1.805 668 4.622 1.559 1 4.797 1.480 694 4.380 1.300 2 4.658 1.507 715 3.096 1.129 3 3.561 1.277 731 2.060 0.735 4 2.815 1.068 794 2.161 0.715 5 2.354 0.723 812 2.509 0.592 6 2.700 0.719 39
    • 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 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 2 Coefficients 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. 40
    • 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 Regression 2 Coefficients 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 accruals Abnormal 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. 41
    • 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 Regression 2 Coefficients 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 accruals Abnormal 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. 42
    • 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 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 Ratings Mean 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) 43
    • 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-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 2 Coefficients 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. 44