Earnings Management Earnings Management Surrounding Seasoned Bond Offerings: Do Managers Seek to Mislead Rating Agencies and the Bond Market?
Gary L. Caton* Montana State University College of Business P.O. Box 173040 Bozeman, MT 59717-3040 Phone: (406) 994-6190 Email:
[email protected]
Chiraphol N. Chiyachantana Singapore Management University 50 Stamford Road Lee Kong Chian School of Business Singapore 178899 Phone: +(65) 6828-0776 Email:
[email protected]
Chua Choong Tze Singapore Management University 50 Stamford Road Lee Kong Chian School of Business Singapore 178899 Phone: +(65) 6828-0745 Email:
[email protected] Jeremy Goh Singapore Management University 50 Stamford Road Lee Kong Chian School of Business Singapore 178899 Phone: +(65) 6828-0739 Email:
[email protected] Preliminary and incomplete, all comments welcome. JEL Classification: G340 *Corresponding author
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Earnings Management Surrounding Seasoned Bond Offerings: Do Managers Seek to Mislead Rating Agencies and the Bond Market? Abstract We study firms’ earnings management (EM) surrounding seasoned bond offerings, using discretionary current accruals. We find that issuers tend to window dress performance prior to an offering. In order for firms’ efforts to mislead rating agencies, and thereby the market, assigned rating must be too high, which implies increased subsequent downgrades for aggressive earnings managers. However, we find a lower proportion of subsequent downgrades for firms with the most aggressive EM activity. Moreover, regression results indicate that aggressive EM efforts are associated with lower initial ratings. We conclude that rating agencies see through efforts to window dress earnings and may penalize such firms with a lower initial rating.
Earnings Management Earnings Management Surrounding Seasoned Bond Offerings: Do Managers Seek to Mislead Rating Agencies and the Bond Market? I.
Introduction We study the earnings management (EM) efforts of 1,255 firms surrounding seasoned
bond offerings (SBO), using discretionary current accruals (DCA).1 Our results indicate significant issuer efforts to manage earnings upwards in the year of the offering, and cessation of those efforts after the offering. When we partition the sample into two groups, based on whether the new bond’s rating is above or below the investment grade boundary, we find significant EM efforts by firms on both sides, although such efforts are larger and begin earlier when firms are rated speculative grade. After the offering, EM efforts are discontinued, on average, by firms in both groups. We further partition the sample by individual rating and find that the most highly rated firms do not significantly manage their earnings around their bond offerings, that firms with bonds rated single-A and triple-B manage their earnings upward in the year of the offering only, and that firms rated double-B and single-B manage their earnings upward each year from year -3 through the offering year. There is no tendency for firms in any of the groups to manage earnings in the post-offering period. Post-offering EM is significantly lower than offering year EM efforts for firms rated single-A, triple-B, and single-B. These findings are consistent with the conclusion that all but the highest rated issuers attempt to window dress their firm’s performance in the period prior to an offering, in order to obtain higher credit ratings and consequent lower funding costs.
1
We use the word seasoned to indicate a firm that already has rated debt outstanding at the time of the current
offering, even though technically it is an initial offering of the new bond.
2
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The presence of EM, however, is not proof that rating agencies and the bond market is actually misled. For the agencies to be deceived by EM activity, such activity must lead to inappropriately inflated ratings for the bonds of firms that aggressively manage their earnings. We test whether a firm’s issue date rating is inappropriately inflated by examining rating downgrades after the bond offering. Without a subsequent downgrade, the original rating, and the associated offering yield, is implicitly confirmed. We collect all rating revisions for sample bonds for up to three years after their offering dates and partition the subsample of firms subject to a rating revision into terciles based on whether the firm expended relatively heavy, medium or light efforts to manage their earnings in the offering year. Under the null hypothesis that upgrades and downgrades are equally likely across EM terciles, we test the proportion of upgrades to downgrades. For the full sample, we find that aggressive earnings managers suffer proportionally fewer downgrades compared with the least aggressive firms. This finding persists when we partition the sample based on ratings levels, although the difference in the proportion of downgrades reaches the level of statistical significance only for those firms with single-A and triple-B ratings. This result is the statistically significant opposite of what we would expect if the rating agencies were misled by the EM efforts of issuing firms. In fact, our evidence is consistent with the conclusion that rating agencies penalize heavy earnings managers with relatively lower initial bond ratings. Regression analysis supports this conclusion showing a statistically significant relation between heavy EM activity and lower issue date bond rating. In Section II, we develop our hypotheses regarding EM and SBO. We explain the methodology for testing our hypotheses in Section III. Section IV contains a discussion of our sample selection and provides a brief description of our sample. We present and discuss the results outlined above in Section V. Section VI concludes our study.
Earnings Management II.
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Hypothesis Development According to Federal Reserve data, U.S. non-financial businesses issued $881.4 billion of
debt in 2006. Assuming this debt was in the form of coupon bonds, issuers of debt in 2006 paid $88.14 million in interest payments per basis point in offered yield. More specifically, suppose B-rated bonds make up 40% of the total in 2006, which is their proportion in our sample (see Table 1 below), or $350 billion. Caouette, Altman, and Narayanan (1998) report the average Brated firm pays about 150 basis points more than does the average double-B-rated firm. Therefore, B-rated firms would have saved over $5 billion of interest payments each year until maturity if they could have earned a double-B rating instead. How could a firm earn a higher rating and thereby lower its cost of debt? To answer this question we need to know something about bond ratings and how issuers can influence them. According to Kliger and Sarig (2000), essentially all corporate debt issued by large companies is rated. Ederington, Yawitz, and Roberts (1987) find that even after controlling for publicly available accounting variables and issue specific characteristics, bond ratings and yields are significantly related; firms with lower ratings face higher costs of debt. Ederington and Goh (1998) compare the timeliness of rating revisions to earnings forecast revisions and find that while rating downgrades tend to follow declines in actual and forecast earnings, the reverse is also true. Both actual and forecast earnings tend to fall after downgrades. These findings imply that credit ratings provide information beyond that offered by the public data they are, in part, based upon. It also implies that managers who wish to increase their firm’s value must balance the benefits of their decisions, including operational and reporting decisions, against the potential effect of those decisions on their firm’s credit rating, and thereby its cost of capital. For example,
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managers contemplating an increase in their firm’s operational risk should compare the expected benefits of such a move against the expected cost associated with a lower credit rating. In this paper we take the firm’s operating decisions as given and focus on a firm’s reporting decisions. Reported net income is important for a firm’s managers, shareholders, and creditors. They are a fundamental variable in forecasting future earnings, free cash flows, and equity valuations (see for example, Weston, Mitchell, and Mulherin (2004)). In fact, equity value can be very sensitive to reported earnings. For example, Crum (2007) reports that Agilent Technologies share price sank over 11% when their reported earnings missed the market’s expected earnings figure by just one penny. Controlling for earnings forecast errors, Bartov, Givoly, and Hayn (2002) find that firms who meet or beat earnings expectations earn abnormal stock returns over the quarter in which earnings are reported. Interestingly, this outperformance is true even when it is achieved through either expectations management or EM, which indicates a market premium for firms that meet or beat earnings expectations regardless of how it is achieved. As for a firm’s creditors, reported earnings are important for at least two closely related reasons. First, earnings are a proxy for cash available to make interest and principal payments. Second, and more importantly for our analysis, reported earnings are an important factor in determining a bond’s rating. Using logistic regression to explain a firm’s credit rating, Ashbaugh-Skaife, Collins, and LaFond (2006) find two ways in which reported earnings are related to the rating: through a firm’s return on assets, and through its interest coverage ratio, both of which are comprised in part of net income. Ashbaugh-Skaife, et al. (2006) report that both of these ratios are positively and significantly related to a firm’s credit rating. Ederington,
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et al. (1987) find net income to be an important factor in explaining bond ratings through the interest coverage ratio, as well as the deviation from the average coverage ratio. Finally, Standard & Poor’s (2005) itself lists cash flow adequacy as one of five financial risk factors they consider when rating a firm’s credit risk. How can managers affect reported net income? Under Generally Accepted Accounting Principles (GAAP), managers have some discretionary control over the level of reported earnings. For instance, depreciation can be accelerated or decelerated, goods sold but not yet delivered can be recognized or not recognized, and working capital accrual accounts can be increased or decreased. Burgstahler and Eames (2006) find evidence to suggest a two-pronged approach to managing the firm’s reported earnings: expectations management coupled with EM. Under this approach, firms manage market expectations leading up to earnings reports in order to help the market set an earnings-per-share target the firm believes is achievable. Then managers manipulate discretionary accounts in order to hit the targeted number. Stein (1989) develops a theoretical model that explains why even well-intentioned managers are obliged to myopically inflate reported net income. In Stein’s model, market participants expect all managers to inflate reported earnings, and therefore all earnings reports are discounted for an average level of EM. In order to account for this expected discount, all managers are forced to manipulate their reported earnings upward. On the other hand, DeGeorge, Patel, and Zeckhauser (1999) argue that managers are not always motivated to manipulate earnings, positing that there are specific threshold values of reported earnings that trigger managers to manipulate earnings. Rather than the absolute level of reported earnings, it is the level of earnings relative to some benchmark value that is important in determining management motivation and willingness to manage earnings. DeGeorge, et al. (1999) examine
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three different potential thresholds: (1) to prevent negative earnings per share, (2) to show earnings growth over last period, and (3) to show earnings per share that matches or beats forecast earnings, and find all three to be important in shaping managerial behavior. In addition to these thresholds, there may be non-earnings related events that trigger mangers’ earnings manipulation behavior. For example, Erickson and Wang (1999) find that acquiring firms in a stock-for-stock merger tend to manipulate their reported earnings immediately before the bid in an effort to drive their premerger stock prices up, and thereby reduce the cost of the transaction. Teoh, Welch, and Wong (1998a) examine EM behavior surrounding initial public offerings of equity and find that, on average, issuers manipulate upward their reported earnings. Interestingly, they also find that post-issue abnormal returns and reported net income tend to be lower for aggressive manipulators. Teoh, et al. (1998b) find similar results for seasoned equity stock offerings. Another important triggering event for EM may be a bond offering. When a company offers a bond to the market for the first time, an initial bond offering (IBO), rating agencies will conduct a thorough examination of the firm’s business, including its competitive position, financial characteristics, operational results, and future prospects in order to provide an initial bond rating that accurately reflects the firm’s creditworthiness at the offering date (see Standard & Poor’s (2005)). Using comprehensive proprietary data from Moody’s Investors Service, Demirtas, Ghosh, Rodgers, and Sokobin (2006) examine earnings manipulation surrounding IBO. They report significant EM, and estimate that aggressive manipulation of current accruals provides such firms with ratings two steps higher than expected, on average. Of course, these
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firms do not manage their earnings to achieve a higher bond rating, per se, but in order to obtain the lower offering yields that accompany higher ratings. Although rating agencies typically provide surveillance of the firm’s condition after the initial bond is issued, they may not conduct another in-depth analysis of the firm’s rating until a subsequent triggering event. Intuitively, an important trigger for a follow-up analysis may be a firm’s decision to offer additional debt to the market in a SBO. Firms making a SBO have incentive to make their best case for a higher bond rating and a consequent lower offering yield. We posit that some firms make this best case, in part, through EM. In null form, Hypothesis 1 is as follows: Hypothesis 1: EM activities are insignificant surrounding SBO. It is possible that firms manipulate reported earnings surrounding SBO, but that the manipulation is unrelated to the offerings. For example, Teoh, et al. (1998b) find statistically significant manipulation of DCA surrounding seasoned equity offerings, but the manipulative behavior begins three years before the offerings and continues for two years after the offerings. This long period brings into question whether the EM was truly related to the offering.2 If manipulation ceases immediately after the offering, then it is more likely to be related to the offering. We posit that firms will curtail EM efforts once the offering is made. In null form, Hypothesis 2 is as follows: Hypothesis 2: EM activities do not change after the SBO.
2
Teoh, et al. (1998b) argue that EM must continue after the offering in order to preclude potential lawsuits. In fact,
Ducharme, Malatesta, and Sefcik (2004) find that earnings management behavior was especially high for issuing firms subject to post-issue lawsuits.
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Some firms may be more highly motivated to manage their earnings than others. We take two approaches in predicting these differences, one based on bond ratings levels and the other based on predictions of the options pricing model. Caouette, et al. (1998, p. 75) report bond yield spreads for rated corporate debt over 30-year Treasury bonds for each year from 1980 through 1997. The average yield spread over the 18-year period increased from 62 basis points for triple-A rated bonds to 434 basis points for double-B rated bonds. The increase in yield spreads is nonlinear, rising at an increasing rate as the bond rating level declines, and becomes markedly higher as the rating crosses the investment/speculative grade boundary.
For example,
the average cost of dropping from a single-A rating to a triple-B rating is just 59 basis points over the period. However, a drop across the boundary from triple-B to double-B costs the firm an average of 112 basis points. We posit that as the cost of a lower bond rating increases, firms will have higher motivation to manage earnings in an attempt to earn a higher bond rating. In null form, Hypotheses 3 and 4 are as follows: Hypothesis 3: EM activities surrounding SBO are insignificantly different between firms with investment grade and speculative grade bonds. Hypothesis 4: EM activities surrounding SBO are insignificantly different between firms with different levels of bond rating. Our second method of predicting which companies may attempt to manage their earnings is based on predictions obtained using an adaptation of the models of Black and Scholes (1973) and Merton (1974), both of which develop pricing models for common stock and corporate debt using an options pricing framework. Let firm value (V) be written as the sum of the values of equity (E) and risky debt (D) : V = E + D. Solving for equity value, we get:
Earnings Management (1)
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E=V–D The value of risky corporate debt can be written as the difference between the value of an
identical but default-free bond, DRf, and the value of a default put written by bondholders to stockholders, as follows: (2)
D = DRf – Default Put(V/k, σ, T, r)
where, DRf = value of a default-free bond, Default Put(·) = put option giving stockholders the right to put the firm to bondholders in the event of default, the value of which is a function of the following five variables, V = value of the firm, k = face value of debt, which is also the strike price of the put option, σ = firm value volatility, T = time to bond maturity, r = risk-free rate. If V is greater than k at maturity, equity holders will pay bondholders k and maintain ownership of the firm. But if V is less than k, equity holders will put the firm to the bondholders who receive less than their promised payment. Combining (1) and (2), we find: (3)
E = V – DRf + Default Put (V/k, T, r, σ) In the context of our study, EM activities surrounding a bond issue may be a firm’s
attempt to transfer value from buyers of the new debt to current stockholders by making the debt appear more valuable than it truly is. If so, the intent is to deceive buyers of the debt into overpaying for the bonds. The default-free bond price cannot be manipulated through EM.
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Similarly, the variables T, k, and r within the put function are not manageable through earnings manipulation. EM activities can, however, affect the market’s perceived values of both V and σ. We examine the dynamics of (3) by differentiating it with respect to the two variables subject to manipulation by managers. The first derivative with respect to the perceived value of the underlying asset, V, is the following: ∂E ∂ (Default Put(V/k, σ, T, r)) =1+ ∂V ∂V
(4)
This equation defines equity delta, which is the sensitivity of E to changes in perceived V, to be equal to one plus put option delta. We assume that firms with more to gain from changes in perceived V, such as those with higher equity delta, will have greater incentive to conduct EM activity than would firms with lower equity delta. Equation (4) indicates that such firms are those with higher put option delta, which is the sensitivity of the put option value to changes in perceived firm value. Put options have higher delta the further they are out of the money, and are further out of the money when V is large relative to k. Therefore, EM activity will have a greater effect on perceived bond prices the greater is V relative to k, and we expect to see greater efforts to manage earnings for sample companies with greater relative V. In null form, Hypothesis 5 is as follows: Hypothesis 5: EM activities do not vary with the ratio of V to k. The second variable subject to manipulation through EM is σ. One of the primary objectives of EM is earnings smoothing, which would tend to dampen volatility. The first derivative of (3) with respect to σ is represented by the following: (5)
∂ E ∂ (Default Put(V/k, σ , T, r)) = ∂σ ∂σ
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This equation defines equity vega, which is the sensitivity of equity value to changes in volatility, as put option vega. We assume that firms with more to gain from changes in perceived volatility, for example those with higher equity vega, will have greater incentive to conduct EM activity than firms with lower equity vega. Equation (5) indicates that such firms are those with higher put option vega, which is the sensitivity of put option value to changes in volatility. Put options have higher vega the higher the volatility of the underlying asset. Therefore, EM activity will have a greater effect on perceived V the greater the σ. We expect to see greater efforts to manage earnings for sample companies with greater volatility. In null form, Hypothesis 6 is as follows: Hypothesis 6: EM activities do not vary with σ. In summary, the true economic value of the default put option, and the bond, are determined by the underlying true parameters of the option, which include V/k and σ. While EM efforts can do nothing about the true value of the put option, such efforts may be able to manipulate its perceived value and thereby influence the bond’s issue price. If the firm can manage its earnings in a way such that potential lenders believe the firm is more valuable, less volatile, or both, than it really is, then the offered yield may be lower than it otherwise should be and equity holders are able to transfer value from bondholders to themselves. As the title implies, we are examining whether firms mislead rating agencies, and consequently the bond market when offering seasoned debt. Even if firms manage their earnings surrounding SBO, rating agencies may recognize those efforts, and assign a fair rating after adjusting for the accounting gimmick. In null form, Hypothesis 7 is as follows: Hypothesis 7: Ratings assigned to firms issuing SBO are not affected by the EM activities of issuers.
Earnings Management
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Methodology GAAP provides firms with flexibility in managing their accrual accounts. This discretion is
intended to allow firms to report their performance in a way that best reflects economic reality. However, this flexibility can also be used to mask the true economic condition of the company through, for example, EM efforts intended to artificially inflate reported earnings. Firms may manage their earnings for a variety of reasons. For example, they may simply wish to smooth out volatility in their true earnings stream, which is a primary intended use of accounting flexibility. There are, however, less benign reasons to manage earnings. A firm may wish to dress down its reported earnings, when earnings are expected to be negative, in order to take the proverbial bath. Taking a bath would increase the firm’s flexibility to dress up earnings in the future (see Mosebach and Simko (2005)). Conversely, a firm may wish to dress up its reported earnings prior to offering a new stock or bond issue to the market in hopes of obtaining higher prices for their offerings. As noted earlier, specific techniques used to artificially change reported earnings include accelerating or decelerating recognition of depreciation expense in order to change reported costs; recognition of goods sold but not yet delivered can be brought forward in time or slid into the future in order to change reported sales; inventory accrual can be converted to first in/first out or last in/last out in order to change reported costs; et cetera. Reported net income is an accrual figure intended to reflect economic reality without regard to the timing of the underlying cash flows, and is comprised of actual cash flows and changes in total accruals. Given its cash flow component, only a portion of net income is subject to discretion through manipulation of accruals. Changes in total accruals are composed of two components: changes in current accruals (CA) and changes in long-term accruals. The CA
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component represents changes in working capital accounts that support operations such as inventory, receivables, and payables. It is manipulation of CA that is usually associated with EM (see Teoh et al. (1998a, 1998b)). The long-term accruals component represents changes in longterm asset and liability accounts such as depreciation expense and deferred taxes. Some changes in total CA are necessary due to changing industry and economic conditions. Other changes in CA are not dictated by business conditions, but rather are made in order to artificially change reported earnings. Following Teoh, et al. (1998a, 1998b), we use an empirical model that separates nondiscretionary changes in CA resulting from changes in business conditions from discretionary changes in CA resulting from efforts to manipulate earnings.3 Essentially, we separate all firms into industry groups based on 2-digit SIC codes. After excluding sample firms, we regress each firm’s change in CA on its own change in sales for each event year (-3 through +3) by industry, which produces an industry-specific estimated CA coefficient. The estimated equation defines the industry normal or expected change in CA each year relative to sales. A sample firm’s expected change in CA in a given year is computed by plugging a firm’s actual sales for a given year into its industry-specific estimated equation. A firm whose expected change in CA equals its actual change in CA in a given year is not managing its earnings. But a firm whose actual change in CA is different from those predicted by the estimated equation is manipulating its accrual accounts for some reason other than business conditions.
3
For additional details of this modified Jones (1991) model see Teoh, et al. (1998a, 1998b).
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As discussed earlier, while a finding of significant manipulation of DCA is consistent with attempts to mislead the market, it is not conclusive. We conduct two tests for whether EM efforts actually mislead the bond market: one based on an analysis of bond rating revisions after the offering and another based on regression analysis. For the former, we classify sample firms into equal terciles based on whether their EM efforts surrounding a SBO are relatively heavy, relatively light, or intermediate between the two extremes. Each tercile is assumed to be drawn from independent binomial distributions. We collect ratings changes for sample firms from the Fixed Income Securities Database (FISD), and calculate the proportion of downgrades to the sum of downgrades and upgrades. If sample firms are able to deceive the rating agencies when the SBO are issued, we expect to see higer rates of bond-rating downgrades for heavy users of EM. The resulting test statistic is distributed normally, and is computed as follows: (6)
pi − p j
z − statistic =
p(1 − p)(
1 1 + ) Ni N j
where, pi,j = proportion of downgrades to total rating revisions in terciles i and j, p = proportion of downgrades to total rating revisions in the combined terciles, Ni,j = the total number of rating revisions in terciles i and j.
IV.
Sample Selection and Description Our initial sample consists of all 11,474 companies identified by the FISD as having
offered a new bond to the market between January 1, 1995 and December 31, 2005. We are interested in studying EM efforts surrounding the seasoned public offerings of dollardenominated bonds by U.S. industrial firms. Therefore, we delete from the sample any
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observations related to private debt placements, offerings that represent initial public offerings of bonds (the first bond offered by a firm), non-U.S. bond offerings, offerings by financial firms, offerings that do not obtain a Moody’s or Standard & Poor’s rating, and offerings that receive below a single-B rating. In addition, we limit our sample to those firms with sufficient data on Compustat to compute our measure of EM. Finally, we follow the EM efforts of sample companies for three years after the bond offering, implying that our sample period must be shortened to 2003 in order to allow for a full three years of data availability after the offering. Applying these criteria to our initial sample produces a final sample of 1,255 firms that make a SBO. Table 1 presents descriptive statistics regarding the SBO. Panel A of Table 1 shows the distribution of SBO and DCA by year. The frequency of bond offerings is surprisingly uniform across time. The number of offerings declined in 2000 to less than half the average number of annual offerings, but that was a year in which the economy experienced a modest recession. The DCA are not distributed uniformly in time. The 114 sample firms that offered a bond in 1996 manipulated their accruals by an amount more than three times greater than the next largest year, while average EM was negative in 2000. Rating downgrades outnumbered upgrades in every sample year, and by 300 to 160 for the full sample.4 Panel B of Table 1 presents the sample of bonds based on the level of credit rating. Only 6% of the bonds in our sample were offered with a double-A or triple-A rating, whereas over 40% were assigned a single-B rating. Due to the small number of firms receiving the two highest ratings, we combine triple-A and double-A rated firms into one group (there are only 17 triple-A
4
We exclude ratings revisions for triple-A rated firms since they can only be revised downward.
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rated bonds). About 46% of the bonds were investment grade and 54% were speculative grade. DCA were insignificantly positive for bonds in the highest rated category. For bonds in the four lower rating categories, the level of current accruals was relatively uniform. Average DCA were slightly lower for firms with investment grade bonds than for speculative grade bonds. Rating downgrades outnumbered upgrades in every rating category. Insert Table 1 about Here Table 2 provides descriptive statistics for our sample of bond issuers. In panel A, note that sample companies are large in terms of both total assets and market capitalization of equity. The mean (median) total assets are nearly $10 billion ($1.39 billion), while mean (median) market capitalization is over $14 billion ($2.40 billion). Median book-to-market, return on assets, and leverage ratio are .42, 13%, and 23.45%, respectively. Panel B indicates the industry groups of sample firms as reported by FISD. Firms in the broadly defined manufacturing industry make up over 45% of the sample, while there are only three firms from the agriculture industry. Insert Table 2 about Here
V.
Results
A.
Earnings Management Efforts Panel A of Table 3 contains EM activities by firms in the years surrounding their SBO.
In the first column, we report median abnormal DCA for the full sample. Sample firms begin manipulating their DCA upwards in an attempt to dress up their earnings two years prior to offering a seasoned bond to the market and those efforts appear to double in the year of the offering. Although some sample firms appear to use DCA in the years leading up to the bond
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offering, we focus on EM activity in year 0.5 The significantly positive median DCA indicates a rejection of null Hypothesis 1 for the full sample. Sample firms appear to engage in EM behavior in the year of the SBO. Moreover, after the offering, EM efforts tend to be reversed, although the reported negative current accruals is insignificantly different from zero. Nevertheless, DCA remain insignificantly different from zero for at least three years after the offering indicating a curtailment in EM activity subsequent to the offering. In panel B of Table 3 we test whether the DCA in the year of the SBO, year 0, are different from those in subsequent years. A change in these efforts after the offering is consistent with a rejection of null Hypothesis 2, and is consistent with a cessation of EM efforts after the bond is issued. The table presents the change in DCA from year 0 to years +1, +2, and +3 after the offering. For the full sample, results presented in the first column indicate that EM efforts in the offering year are significantly greater than those in all three subsequent years. These results compel us to reject Hypothesis 2 for the full sample, and are consistent with the notion that managers manipulate earnings in order to dress up their earnings when approaching a SBO and end EM efforts once the bond is outstanding. The other two columns in Table 3 partition the sample based on whether the initial rating assigned the offered bond is investment or speculative grade. When the initial rating is investment grade, firms significantly manage their earnings upwards only in year 0, the year of the offering. In contrast, firms with speculative grade initial ratings significantly manage their earnings upwards all three years prior to the offering. Focusing on the year of the offering, a 5
EM efforts cannot be continued indefinitely and must eventually be discontinued or even reversed leading firms to
use these techniques on a short-term basis. In addition, firms are likely to focus their window dressing efforts in the period immediately before the offering. For these reasons we focus on year 0 earnings management activities.
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median test indicates that EM efforts by firms with investment grade ratings are not significantly different from efforts by firms with speculative grade ratings. Both subsets of firms tend to manipulate their current accruals in order to manage earnings upwards prior to the offering, which requires us to reject Hypothesis 1 for both groups. However, null Hypothesis 3 cannot be rejected – EM does not appear to differ across the investment/speculative grade divide. Insert Table 3 about Here In Table 4 we partition the firms into groups depending on their individual credit ratings.6 Note in Panel A that firms in the group with the highest credit rating do not appear to significantly manage their earnings upwards in the year of the bond offering. However, year 0 is the only year this group’s median DCA are positive and there is evidence that they manage their earnings downwards in years -3, -2, and +3. This may indicate conservative accounting decisions that contribute to these firms’ high credit ratings. Firms issuing bonds with single-A or triple-B initial ratings significantly manage their earnings upwards in the year of the offering. Single-A rated firms show statistically significant reversal in median earnings manipulation efforts in the year after the offering and three years after, In contrast, triple-B rated firms tend to discontinue their efforts to manage their earnings after their offerings, but show no statistically significant reversal of DCA. The results for firms rated double-B and single-B indicate statistically significant median EM in years -3, -2 and -1. While in year 0 both groups manage their earnings upwards, only the efforts by single-B rated firms are statistically significant in the year of the bond offering. After the bond offering, both groups appear to discontinue EM efforts without reversal. In summary, in the year of the bond offering the median DCA are positive for firms in 6
As noted earlier, we combine firms with bonds rated triple-A (17) and double-A (60) due to small sample sizes in
each rating group.
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all rating groups indicating efforts to manage earnings upwards. While these efforts are statistically insignificant for the highest rated group and those rated double-B, there is no statistically significant difference between credit rating groups. Therefore, Hypothesis 4 cannot be rejected; there appears to be no significant difference in EM efforts across bond rating groups in the year of the offering. Panel B of Table 4 presents the change in DCA from year 0 to years +1, +2, and +3 after the offering. As above, a significant reduction in current accruals after the offering is consistent with a rejection of null Hypothesis 2. According to the results, all subsamples experience a reduction in EM efforts after the year of the bond offering. The change in those efforts for the subsample with the highest credit rating and for those rated double-B, however, are not statistically significant. This should come as no surprise since firms in neither group tended to significantly manage their earnings in year 0. For single-A, triple-B, and single-B rated firms, the decline in EM activities is statistically significant. Thus, Hypothesis 2 can be rejected only for these firms, while it cannot be rejected for the group including triple- and double-A rated firms, and those rated double-B. Insert Table 4 about Here In order to test Hypotheses 5 and 6, we need to estimate the parameters V/k and σ of the options pricing model. Recall that V is the market value of the firm’s assets, k is the face value of the firm’s debt, and σ is the volatility of the firm’s asset value. We estimate V as a sample firm’s book value of assets, minus its book value of equity, plus its market value of equity. We assume the parameter k is constant and estimate it as the firm’s book value of debt. We estimate the parameter σ as the standard deviation of a sample firm’s stock returns estimated over the 250
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trading days prior to the offering Of course, this estimate is of equity volatility, which we adjust in the following way to better estimate asset volatility. (6)
σ Assets = (w 2σ 2E + (1 − w) 2 σ 2D + 2w(1 − w)cov(E, D))
where, w = capital structure weight on equity; σi = volatility of either equity value (E) or debt value (D); cov(E,D) = covariance between equity value and debt value. We assume σD and cov(E,D) equal zero, which implies that σAssets = w σE. In order to increase the intuition of our discussion, we transform the ratio V/k into the ratio E/V, both of which are positively related to the option’s out of the moneyness. Table 5 presents the results of ordinary least squares regressions of DCA on E/V and σassets . In Columns 1 and 2 note that the estimated coefficients are both positive, indicating that increased EM efforts are associated with more out of the money default put options and higher asset volatility. However, with p-values around .25, neither of these estimated coefficients is significant at traditional levels. In Column 3, we conduct a multiple regression that includes both estimated parameters and each firm’s bond rating. The bond rating parameter is an integer that takes the value of 1, 2, 3, 4, or 5 when the rating is triple-A/double-A, single-A, triple-B, double-B, and single-B, respectively. We include the bond rating as a regressor in order to control for the evidence in Table 4, which indicates that EM efforts vary according to rating level. Results for this third regression model indicate that after controlling for differential impact across ratings levels, firms whose default put options are further out of the money tend to use higher levels of DCA. These results compel us to reject Hypothesis 5, but not Hypothesis 6, and are consistent with the notion that firms better able to benefit from EM are more likely to make the effort.
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22
Insert Table 5 about Here B.
Mislead the Agencies? The evidence we have presented thus far shows that firms making a SBO tend to manage
their earnings upwards in the year of the offering, perhaps in an effort to obtain lower offering yields by earning higher ratings on their new bond issues.7 We take two complimentary approaches in testing whether these EM activities mislead the agencies and the market: one based on an analysis of subsequent rating changes and another based on cross-sectional regressions of issue-date rating on DCA and other issue/issuer characteristics shown to be reliable indicators of default risk. A necessary condition for an effort to earn a higher rating to have been successful is an inflated initial rating. Moreover, if the rating is indeed inflated, then as EM efforts are curtailed, as we show they are in Tables 3 and 4, the rating may by downgraded. Without a subsequent downgrade, we can infer that the issue-date rating, and the consequent offering yield, was largely correct at issue.8 For our first test of Hypothesis 7, we posit that bonds of aggressive earnings managers will tend to be downgraded at a relatively higher rate after the offering than those of firms with lighter EM efforts. We use a proportional test in order to examine Hypothesis 7. Using FISD, we collect ratings revisions within the first three years of the offering for each sample firm, which results in
7
Although we show differences in significance across rating levels, failure to reject Hypotheses 3 and 4 implies that
we can consider the sample as one with respect to DCA. 8
Standard & Poor’s (2005) discusses their desire for stable ratings across different economic conditions, which some
argue can lead to sticky ratings (see Demirtas, et al. (2006)). Curtailment of EM activity after a bond issue is not a case of stability across economic conditions. It is a case of systematic reductions in reported earnings after the issue. Furthermore, sticky ratings would tend to bias against finding significant differences in relative downgrade rates.
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a total of 460 ratings changes after throwing out ratings changes of triple-A rated firms. These 460 firms are ranked in order of their DCA, that is, from heaviest efforts to lightest efforts to manage earnings. Using this ranking, we group firms into equal-size terciles of heavy, medium, and light earnings managers. Of the 460 ratings changes, 300 (65%) are downgrades. The first box in Table 6 presents the z-statistics for the full sample of the binomial test of proportions we discuss in Section III above. Note the z-statistics of ±2.72 in the lower left and upper right-hand corners of the box, respectively. These values are statistically significant and indicate that relatively heavy earnings managers tend to be downgraded less frequently than relatively more conservative firms who make less of an effort to manage their earnings. The bottom row of Table 6 presents the number of downgrades as a proportion of the total rating changes. Note that the downgrade proportion increases monotonically as we move from most to least aggressive earnings managers. This finding for the full sample is evidence for rejection of Hypothesis 7, but in the opposite direction from expected. The most aggressive earnings manipulators are downgraded less frequently than the least aggressive manipulators in the three years after making a SBO, which indicates less rating inflation for aggressive EM users than for their more conservative issuers. The second and third boxes in Table 6 show that firms with both investment and speculative grade bonds contribute to this counterintuitive result. Although only the former is statistically significant with a z-statistic of ±2.54, we find no statistical difference in downgrade proportions across the investment/speculative grade boundary. Note again the monotonic increase in the proportion of downgrades from most to lest aggressive earnings managers for both subsamples. Insert Table 6 about Here
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We partition the sample into individual bond rating categories and in Table 7 present the z-statistics for the binomial test of proportions for each individual category. The bottom row indicates the proportion of downgrades to total rating changes across EM terciles. Note that for every bond rating, aggressive earnings managers have a lower proportion of downgrades than do relatively light earnings managers. Although the difference is statistically significant for just single-A and triple-B rated bonds, there is no statistical difference between the downgrade proportions for the five ratings categories. Insert Table 7 about Here Our second test of whether EM activities allow firms to mislead rating agencies and the market when issuing seasoned debt begins with a replication of and then extends the primary regression analysis of Demirtas et al (2006). Using a sample of firms that issue debt for the first time, an IBO, Demirtas et al (2006) use cross-sectional regression to show a significant positive relation between DCA and the initial bond rating, implying that aggressive EM activity pays off by earning the firm a higher bond rating. Following Demirtas et al (2006), we convert alphabased credit ratings from triple-A/double-A to single-B into a numerical scale from one to five such that an increase in numeric rating implies an increase in default risk. We multiply these numeric ratings by -1 so that an increasing numeric rating is associated with increasing creditworthiness. We then use cross-sectional regression to find the relation between the numeric bond rating, which is the dependent variable, and DCA and a set of control variables, which are the independent variables. Positive estimated coefficients indicate that increases in the independent variables are associated with higher bond ratings. Model 1 in Table 8 uses a sample of IBO firms in order to replicate Demirtas et al (2006, see Table 7) who report a positive and significant relation between DCA and initial bond
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25
ratings.9 For our sample of IBO, we confirm the Demirtas et al findings. The more aggressive the EM efforts to window dress earnings, the higher the bond’s rating tends to be. In other words, EM efforts pay off for firms that make an IBO. This result is inconsistent with our findings above. However, these findings were developed with a sample consisting of firms issuing SBO. Model 2 in Table 8 indicates that the relation between DCA and assigned bond rating reverses when the sample changes from IBO to SBO. Increasing EM leading up to a SBO is associated with a significantly lower assigned bond rating. This finding holds in a regression model with nine other statistically significant regressor variables, is consistent with conclusions drawn from our analysis of bond rating changes, and forces us to reject Hypothesis 7, but again in the wrong direction. Recall that Hypothesis 7 is the heart of our study. If bond issuers are able to mislead rating agencies through earnings manipulation, bond ratings should be higher for the most aggressive earnings managers and these firms should suffer greater rates of subsequent downgrades as their EM efforts are curtailed. We find the opposite to be true. Bond ratings of the least aggressive earnings managers suffer relatively greater downgrades after their offerings. Our regression results for the SBO sample indicate that aggressive EM activity is associated with
lower issue date bond ratings. Taken together, our results are inconsistent with the notion that EM efforts mislead the rating agencies and the bond market. In fact, rating agencies appear to see through efforts to mislead them through EM, and actually assign lower ratings to heavy EM users.
9
For this replication of Demirtas et al. (2006), we follow the same sample selection procedures and methodologies discussed above except we exclude SBO and include IBO.
Earnings Management
VI.
26
Conclusion Our results indicate significant efforts by issuers to manage earnings upwards prior to the
offering. This finding is true whether the new bond’s rating is above or below the investment grade boundary. Moreover, post-offering EM efforts are significantly less than those in the offering year for firms on both sides of the boundary. When we partition the sample by individual rating, we find that all firms manage their earnings upwards in the offering year, but that such efforts are statistically significant only for firms with bonds rated single-A, triple-B, and single-B. The most highly rated firms and double-B rated firms do not significantly manage their earnings in the year of the bond offering. While consistent with the notion that sample firms attempt to window dress their firms’ performance prior to an offering, these results do not prove the market is misled. In order for the market to be misled, aggressive EM activity must lead to an inappropriately inflated initial bond rating relative to the ratings of less aggressive firms. We examine rating downgrades after the bond offering, arguing that without a subsequent downgrade, the original rating, and offering yield, is implicitly confirmed. We test the proportion of upgrades to downgrades across terciles formed based on the aggressiveness of the EM efforts under the hypothesis that more aggressive firms should suffer relatively more downgrades as rating agencies discover these firms’ true earnings when EM efforts are curtailed. For the full sample, we find significantly fewer downgrades for firms with relatively heavy EM efforts, which is the opposite of what we would expect if rating agencies were duped. This finding persists when we partition the sample based on whether the initial rating was investment or speculative grade, and when subgroups are formed based on individual ratings levels. Regression analysis indicates that heavy EM activity
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27
is associated with lower bond ratings, after controlling for other determinants of creditworthiness. Our evidence is consistent with the conclusion that rating agencies penalize heavy EM efforts with lower initial bond ratings. Apparently, while some firms attempt to mislead the market through EM efforts in the year of a seasoned bond offering, the market is not misled.
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References Ashbaugh-Skaife, H.; D.W. Collings; and R. LaFond. “The Effects of Corporate Governance on Firms’ Credit Ratings.” Journal of Accounting and Economics, 42 (2006), 203-243. Bartov E.; D. Givoly; and C. Hayn. “The Rewards to Meeting or Beating Earnings Expectations.”
Journal of Accounting and Economics 33(2002), 173-204. Bebchuk, L. A., and O. Bar-Gill. “Misreporting Corporate Performance.” Working Paper, Harvard University (2003) Black, F., and M. Scholes. “The Pricing of Options and Corporate Liabilities.” Journal of
Political Economy 81 (1973), 637-659. Burgstahler, D. C., and M. J. Eames. “Earnings Management to Avoid Losses and Small Decreases: Are Analysts’ Fooled?” Contemporary Accounting Research 20 (2006), 253294. Caouette J. B.; E. I. Altman; and P. Narayanan. Managing Credit Risk. New York: John Wiley and Sons. (1998). Crum R. “Agilent Shares Hit on Weak Report, Outlook.” Market Watch. August 15, 2007. Demirtas, K. O.; A. Ghosh; K. J. Rodgers; and J. Sokobin. “Initial Credit Ratings and Earnings Management.” Working Paper, City University of New York (2006). DeFond, M. L., and J. Jiambalvo. “Debt Covenant Violation and Manipulation of Accruals.”
Journal of Accounting and Economics, 17 (1994), 145-176. DeGeorge, F.; J. Patel; and R. Zeckhauser. “Earnings Management to Exceed Thresholds.”
Journal of Business,72 (1999), 1-33. DuCharme, L. L.; P. H. Malatesta; and S. E. Sefcik. “Earnings Management, Stock Issues, and Shareholder Lawsuits.” Journal of Financial Economics, 71 (2004), 27-49.
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Ederington, L. H., and J. C. Goh, “Bond Rating Agencies and Stock Analysts: Who Knows What When?” Journal of Financial and Quantitative Analysis, 33 (1998), 569-585. Ederington, L. H.; J. B. Yawitz; and B. E. Roberts. “The Informational Content of Bond Ratings.” Journal of Financial Research, 10 (1987), 211-226. Erickson, M., and S. Wang. “Earnings Management by Acquiring Firms in Stock for Stock Mergers.” Journal of Accounting and Economics, 27 (1999), 149-176. Jones, J. J. “Earnings Management During Import Relief Investigations.” Journal of Accounting
Research, 29 (1991), 193-228. Healy, P. H., and J. M. Wahlen. “A Review of the Earnings Management Literature and Its Implications for Standard Setting.” Accounting Horizons, 13 (1999), 365-383. Huang, C. F., and R. H. Litzenberger. Foundations for Financial Economics. New York: Elsevier Science Publishing, Co. (1988). Kaplan, R. S., and G. Urwitz. “Statistical Models of Bond Ratings: A Methodological Inquiry.”
Journal of Business, 52 (1979), 231-261. Kisgen, D. J. “Credit Ratings and Capital Structure.” Journal of Finance, 61 (2006), 1035-1072. Kliger, D., and O. Sarig “The Information Value of Bond Ratings” Journal of Finance, 55 (2000), 2879-2902. Merton, R. C. “On the Pricing of Corporate Debt: The Risk Structure of Interest Rates.” Journal
of Finance 29 (1974), 449-470. Mosebach, M., and P. J. Simko. ”Earnings Management and the Emergence of Profitability.” Working Paper, University of Virginia (2005). Sengupta, P. “Corporate Disclosure Quality and the Cost of Debt.” The Accounting Review, 73 (1998), 459-474.
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Shivakumar, L. “Do Firms Mislead Investors by Overstating Earnings Before Seasoned Equity Offerings?” Journal of Accounting and Economics, 29 (2000), 339-371. Standard & Poor’s, 2005 Corporate Ratings Criteria 2006, Standard & Poor’s Inc., http://www2.standardandpoors.com/spf/pdf/fixedincome/corporateratings_2006.pdf Stein, J. C. “Efficient Capital Markets, Inefficient Firms: A Model of Myopic Corporate Behavior.” The Quarterly Journal of Economics, 104 (1989), 655-669. Teoh, S. H.; I. Welch; and T. J. Wong. “Earnings Management and the Underperformance of Seasoned Equity Offerings.” Journal of Financial Economics, 50 (1998), 63-99. Teoh, S. H.; I. Welch; and T. J. Wong. “Earnings Management and the Long-Run Market Performance of Initial Public Offerings.” Journal of Finance, 53 (1998), 1935-1974. Weston; Mitchell; and Mulherin. Takeovers, Restructuring, and Corporate Govenance. New Jersey: Pearson Prentice Hall. (2004). White, H., “A Heteroscedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroscedasticity.” Econometrica 48 (1980), 817-838.
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TABLE 1
Bond Offering Statistics This table reports descriptive statistics regarding our sample of SBO, which consists of all firms covered by the FISD that made a SBO between January 1, 1995 and December 31, 2003. We delete from the sample any observation related to private debt placements, foreign bond offerings, offerings by financial firms, offerings that do not obtain a Moody’s or Standard & Poor’s rating, offerings that receive below a B rating, and firms for which sufficient financial data is not available on Compustat. DCA is a measure of EM in the given year and are computed using the Teoh, et al. (1998a) method. Up (down) revisions are the number of bond upgrades (downgrades) in the three years after the bond offering.
Panel A. SBO by Year Bond Issue Offering Year Frequency
Percent of Sample
DCA
Up Revisions
Down Revisions
1995 1996 1997 1998 1999 2000 2001 2002 2003
105 114 171 175 138 70 175 133 174
8.4% 9.1% 13.6% 13.9% 11.0% 5.6% 13.9% 10.6% 13.9%
0.08% 3.22%*** 0.25% 0.53% 0.76%* -0.21% 0.95%*** 0.29% 0.49%**
22 31 17 13 15 8 21 18 15
44 33 38 47 29 14 33 28 34
Total
1,255
100%
0.64%***
160
300
Up Revisions
Down Revisions
Panel B. SBO by Rating S&P Bond Rating Obs.
Percent of Sample
DCA
AAA & AA A
77 224
6.1% 17.9%
0.14% 0.92%***
25
110
BBB BB
274 171
21.8% 13.6%
0.58%*** 0.58%
66 21
82 37
B
509
40.6%
0.70%***
48
71
Investment Speculative
575 680
46% 54%
0.58%*** 0.72%***
91 69
192 108
Total
1,255
100%
0.64%***
160
300
*** Significant below the 1% level ** Significant below the 5% level * Significant below the 10% level
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TABLE 2
Sample Statistics This table presents firm and industry statistics regarding our sample of SBO, and consists of all firms covered by the FISD that made a SBO between January 1, 1995 and December 31, 2003. We delete from the sample any observation related to private debt placements, foreign bond offerings, offerings by financial firms, offerings that do not obtain a Moody’s or Standard & Poor’s rating, offerings that receive below a B rating, and firms for which sufficient financial data is not available on Compustat.
Panel A. Firm-Level Statistics Variable Total Assets Market Capitalization Book-to-Market Equity Return on Assets Leverage Ratio
Mean $ 9,828.07 $13,899.79 0.44845615 16.49% 2.61
Median $1,388.11 $2,382.59 0.422149 12.87% 1.55
75th percentile 25th percentile $6,522.39 $394.21 $9,571.26 $742.72 0.6767376 0.243713 17.87% 8.98% 2.37 1.12
Panel B. Sample Firm Industries Description
Frequency
Percent
Agriculture
3
0.2%
Construction Manufacturing Mining Other Retail Trade Services Transport, Comm Electric, Gas Wholesale Trade Total
39 567 84 20 115 171 206 50 1,255
*** Significant below the 1% level ** Significant below the 5% level * Significant below the 10% level
3.1% 45.2% 6.7% 1.6% 9.2% 13.6% 16.4% 4.0% 100.0%
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TABLE 3
Median Abnormal Changes in Discretionary Current Accruals by Event Year This table presents the median abnormal DCA by event year. Relative Year 0 is the year in which sample firms made an SBO to the market. We use the Teoh, et al. (1998) method to compute annual changes in a sample firm’s DCA. Positive and significant DCA indicates significant efforts to manage a firm’s earnings upwards. DCA is estimated by (1) regressing industry changes in current accruals on changes in sales to find the industry norm for the relation between the two, (2) using the estimated parameters to compute the expected change in current accruals for sample firms, and (3) subtracting these expected accruals from sample firms’ actual changes in accruals. The terms Investment Grade and Speculative Grade refer to the level of the issue date rating assigned to the SBO by rating agencies.
Panel A. Discretionary Current Accruals by Relative Year Relative Year
Full Sample
-3
-0.01%
-0.57%***
1.03%***
-2 -1 0 +1 +2 +3
0.30%*** 0.31%*** 0.64%*** -0.11% 0.03% -0.10%
0.22% 0.11% 0.59%*** -0.30% -0.10% -0.33%
1.13%*** 0.65%*** 0.72%*** 0.08% 0.09% 0.12%
0.89%*** 0.69%*** 0.91%***
0.63%*** 0.63%** 0.60%*
Panel B. Changes in Discretionary Current Accruals Change in DCA from year 0 to year: +1 0.75%*** +2 0.61%*** +3 0.74%*** *** Significant below the 1% level ** Significant below the 5% level * Significant below the 10% level
Investment Grade
Speculative Grade
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TABLE 4
Median Abnormal Changes in Discretionary Current Accruals by Rating Class and Event Year This table presents the median abnormal DCA by event year after partitioning the sample based on the assigned bond rating. Relative Year 0 is the year in which sample firms made an SBO to the market. We use the Teoh, et al. (1998) method to compute annual changes in a sample firm’s DCA. DCA is a measure for EM activity. Positive and significant DCA indicates significant efforts to manage a firm’s earnings upwards. DCA is estimated by (1) regressing industry changes in current accruals on changes in sales to find the industry norm for the relation between the two, (2) using the estimated parameters to compute the expected change in current accruals for sample firms, and (3) subtracting these expected accruals from sample firms’ actual changes in accruals.
Panel A. Discretionary Current Accruals by Relative Year Year AAA or AA A BBB -3 -0.73%* -0.71%*** -0.40% -2 -0.87%** -0.12% 0.01% -1 -0.14% 0.35% 0.01% 0 0.14% 0.92%*** 0.58%*** 1 -0.28% -0.57%* 0.02% 2 -0.56% -0.01% -0.11% 3 -1.42%* -0.57%* 0.34%
BB 0.87%** 1.71%*** 0.75%* 0.58% 0.24% 0.08% 0.25%
B 1.13%*** 1.10%*** 0.66%*** 0.76%*** 0.05% 0.19% 0.11%
Panel B. Changes in Discretionary Current Accruals Change in DCA from year 0 to year: +1 0.43% 1.49%*** 0.56%** +2 0.70% 0.92%** 0.70%** +3 1.57% 1.48%*** 0.24%
0.33% 0.50% 0.33%
0.71%*** 0.57%* 0.65%*
Obs
77
*** Significant below the 1% level ** Significant below the 5% level * Significant below the 10% level
224
274
171
509
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TABLE 5
Regression of Discretionary Current Accruals on Equity Ratio and Asset Volatility This table presents results of an ordinary least squares regression of DCA on the variables Equity ratio, Asset volatility, and Bond rating. We use the Teoh, et al. (1998a) method to compute annual changes in a sample firm’s DCA. DCA is estimated by (1) regressing industry changes in current accruals on changes in sales to find the industry norm for the relation between the two, (2) using the estimated parameters to compute the expected change in current accruals for sample firms, and (3) subtracting these expected accruals from sample firms’ actual changes in accruals. Equity ratio is the ratio of market value of equity to market value of assets. Asset volatility is the product of the equity ratio and stock return volatility. Bond rating is an integer value that indicates the sample bond’s rating. Triple-A/double-A, single-A, triple-B, double-B, and single-B are each assigned the value of 1, 2, 3, 4, and 5 (p-values are in parentheses).
Model Intercept
Equity ratio
1 0.003 (0.856) 0.286 (.243)
2 0.010 (0.416)
0.687 (.274)
Asset volatility Bond rating Adj-R2 *** Significant below the 1% level, ** Significant below the 5% level * Significant below the 10% level
0.00
0.00
3 -0.120*** (0.000) 0.093** (0.012) 0.087 (0.914) 0.023*** (0.000) 0.02
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TABLE 6 Comparison of Ratings Changes Subsequent to Bond Offerings Across Earnings Management Terciles This table presents z-statistics for a two sample test of proportions. We classify individual firms into terciles based on whether their EM efforts are relatively heavy, medium, or light surrounding a seasoned bond offering. Each tercile is assumed to be drawn from independent binomial distributions. We collect ratings changes for sample firms, and calculate the proportion of downgrades to total ratings changes. The null hypothesis for the binomial test is that the proportion of downgrades is equal across EM terciles. Our sample consists of firms that made a SBO between January 1, 1995 and December 31, 2003. We use the Teoh, et al. (1998) method to compute annual changes in a sample firm’s DCA. The z-statistic is computed as (p1-p2)/SE where p1 and p2 are the proportions of downgrades to total rating revisions in sample 1, and 2, respectively, and SE is the standard error and is defined as the square root of (p(1p)(1/N1+1/N2)) where p is the proportion of downgrades in the combined sample and N1 and N2 are the respective sample sizes. Full Sample
Investment Grade
Speculative Grade
Z-Statistic
Z-Statistic
Z-Statistic
DCA Tercile
Heavy
Medium
Heavy
-
-1.40
Medium
1.40
-
-1.39
2.72***
1.39
-
149
177
Downgrade Proportion .60 *** Significant below the 1% level ** Significant below the 5% level * Significant below the 10% level
.67
Light Total Revisions
Light
H
M
-2.72***
-
-1.07
1.07
L -2.54**
H
M
L
-
-1.01
-1.35
-
-1.53
1.01
-
-0.37
2.54**
1.53
-
1.35
0.37
-
173
100
113
109
49
64
64
.74
.63
.70
.79
.53
.63
.66
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TABLE 7 Comparison of Ratings Changes Subsequent to Bond Offerings Across Earnings Management Terciles This table presents z-statistics for a test of proportions for two samples. We classify individual firms into terciles based on whether their EM efforts are relatively heavy, medium, or light surrounding a SBO. Each tercile is assumed to be drawn from independent binomial distributions. We collect ratings changes for sample firms, and calculate the proportion of upgrades to total ratings changes. The null hypothesis for the binomial test is that the proportion of upgrades is equal across EM terciles. Our sample consists of firms that made a SBO between January 1, 1995 and December 31, 2003. Relative Year 0 is the year in which sample firms made an SBO to the market. We use the Teoh, et al. (1998a) method to compute annual changes in a sample firm’s DCA. The z-statistic is computed as (p1p2)/SE where p1 and p2 are the proportions of downgrades to total rating revisions in sample 1, and 2, respectively, and standard error (SE) and is defined as the square root of (p(1-p)(1/N1+1/N2)) where p is the proportion of downgrades in the combined sample and N1 and N2 are the respective sample sizes.
AAA or AA
A
BBB
BB
B
Z-Statistic
Z-Statistic
Z-Statistic
Z-Statistic
Z-Statistic
DCA Tercile
H
M
L
H
M
L
H
M
L
H
M
L
H
M
L
Heavy
-
0.73
-0.77
-
-0.98
-1.90*
-
-0.90
-1.93**
-
-1.65*
-1.08
-
-0.19
-0.85
Medium
-0.73
-
-1.19
0.98
-
-0.97
0.90
-
-1.06
1.65*
-
0.66
0.19
-
-0.75
Light
0.77
1.19
-
1.90*
0.97
-
1.93**
1.06
-
1.08
-0.66
-
0.85
0.75
-
Total Revisions
16
14
9
36
48
51
48
51
49
20
17
21
29
47
43
1.00
.72
.81
.88
.46
.55
.65
.5
.76
.67
.55
.57
.65
Downgrade Proportion .94 .86 *** Significant below the 1% level ** Significant below the 5% level * Significant below the 10% level
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TABLE 8
Regression of Bond Rating on Discretionary Current Accruals and Control Variables This table reports parameter estimates from cross-sectional regressions of credit rating on DCA and a set of control variables. The credit rating is the issue date rating transformed into an integer value so that a higher integer value means higher credit quality. We use the Teoh, et al. (1998a) method to compute annual changes in a sample firm’s DCA. DCA is estimated by (1) regressing industry changes in current accruals on changes in sales to find the industry norm for the relation between the two, (2) using the estimated parameters to compute the expected change in current accruals for sample firms, and (3) subtracting these expected accruals from sample firms’ actual changes in accruals. Cash flow is operating cash flow scaled by assets. Leverage is the sum of short and long term debt scaled by assets. Growth is the sum of the market value of equity and book value of debt scaled by assets. Capital expenditures is the ratio of capital expenditures to assets. Issuer size is the logarithm of total assets. Issue size is the logarithm of the face value of debt. Sales is the logarithm of sales. R&D is the ratio of research and development expense to assets. Years to maturity is the logarithm of the number of years the SBO has to maturity. Seniority is a dummy variable that equals one if the bond is senior. Model 1 uses a sample of Initial Bond Offerings (IBO) in order to make comparisons with Demirtas et al (2006). Model 2 uses our primary sample of SBO (t-statistics are in parentheses and are corrected for heteroscedasticity following White (1980)).
Model Intercept
DCA Cash flow Leverage Growth Capital expenditure Issuer size Issue size Sales R&D Years to maturity Seniority Adj-R2 *** Significant below the 1% level, ** Significant below the 5% level * Significant below the 10% level
Model 1 -4.913*** (-4.31) 0.046* (1.81) 0.658** (2.19) -0.456*** (-3.44) -0.001 (-1.09) -0.249 (-1.01) 0.035 (0.48) -0.191* (-1.76) 0.209*** (3.16) 3.634*** (2.94) 0.116 (1.15) 1.196*** (10.22) 0.518
Model 2 -5.349*** (-7.05) -0.313** (-2.50) 1.108*** (4.17) -0.273*** (-3.24) 0.049* (1.80) 0.289** (2.41) 0.353*** (6.53) -0.380*** (-5.58) 0.239*** (5.42) 6.767*** (5.53) 0.072 (1.13) 0.829*** (13.67) 0.659
