Seasoned Equity Offerings: Quality of Accounting Information and Expected Flotation Costs

Transcription

1 Seasoned Equity Offerings: Quality of Accounting Information and Expected Flotation Costs Gemma Lee Culverhouse College of Business University of Alabama Phone: and Ronald W. Masulis Owen Graduate School of Management Vanderbilt University Phone: October 30, 2006 We thank Paul Chaney, Debra Jeter, Craig Lewis, Michelle Lowry and Veronika Krepely Pool and workshop participants at Auckland University and Pennsylvania State University and conference participants at the 2006 FMA Annual Meeting for their helpful comments and suggestions. We want to especially thank Junsoo Lee for his invaluable advice about several important econometric issues.

2 Seasoned Equity Offerings: Quality of Accounting Information and Expected Flotation Costs Abstract Equity offering flotation costs represent a significant loss of capital to issuing firms and these costs appear to be related to information asymmetry between issuers and outside investors. We use the quality of accounting information as a proxy for asymmetric information, less reliable accounting information makes it more difficult for investors to evaluate a firm s true performance. By increasing market uncertainty about firm performance, poor accounting information quality lowers demand for a firm s equity, thereby raising both the expected cost and risk associated with underwriting a seasoned equity offering (SEO). Using a large sample of SEO filings over the period, we examine the relation between expected flotation costs and accounting information quality. We first show that poor accounting information quality is associated with larger offerings. We then confirm that poor accounting information quality is associated with larger expected flotation costs measured by (1) larger underwriting fees as a percent of the offering size, (2) larger negative SEO announcement effects, and (3) a higher probability of SEO withdrawals. These results are robust to adjustments for potential sample selection bias. To measure quality of accounting information, we follow the approach developed by Dechow and Dechev (2002) and use the standard deviation of the estimation errors from an earnings accruals model to assess the reliability of accounting information. We also develop an improvement on this model based on a firm fixed effects model. 2

3 I. Introduction There is considerable evidence that equity flotation costs are economically significant. There is also evidence that a large part of expected flotation costs can be explained by asymmetry of information between issuers and outside investors. However, this information asymmetry is neither directly observable, nor is there a generally agreed upon proxy. This is one reason that the determinants of flotation costs continue to be poorly understood. We propose a new measure of information asymmetry taken from the accounting literature and examine whether this variable has a significant impact on expected flotation costs. While many studies have examined how information asymmetry affects expected flotation costs, they have use proxies such as stock return volatility, dispersion in analyst forecasts and debt ratings as measures of information asymmetry. However, none of these variables has a strong theoretical claim to being a clear or complete measure of information asymmetry. To bring new light to this question, we measure information asymmetry by the quality of the issuer s accounting information. Accounting information quality clearly affects investor uncertainty about past firm performance and as a result should affect investor demand for its stock. As investor uncertainty rises and demand of equity fall, equity underwriting costs are likely to rise. Several earlier studies have examined the relation between accounting information quality and equity cost of capital (Francis, Lafond, Olsson, and Shipper, 2004, 2005), but none have looked at its relationship to flotation costs. We examine the effects of accounting information quality on the offer size and expected flotation costs of seasoned equity offerings (SEOs). For this purpose, we focus on three major components of expected flotation costs, namely underwriting fees, offering announcement effects and the probability of issue withdrawal. Our sample is comprised of SEO filings over the period by U.S. issuers using a firm commitment underwriting contract. In a firm commitment underwriting contract, an investment bank guarantees to purchase of the entire equity offering at a fixed price, bearing full responsibility for reselling the shares to the public once the contract is signed, usually on the evening before the public offering date. By signing this contract, underwriters face significant risks associated with unexpected reductions in investor demand for the offering. When firms with poor accounting information quality announce SEOs, the decision can increase investor uncertainty about the value of issuers common stock and lower investor demand for the equity issues. In response to a higher level of investor uncertainty, underwriters have several choices: they can reduce the size of the security offering; they can increase their underwriting fees; or they can withdraw from the underwriting assignment. 3

4 By reducing the offering size, underwriters can lower their risk exposure and the potential distribution costs. Thus, for the same underwriting fees, the investment bank s compensation is raised in terms of the percentage of offering s gross proceeds paid in fees, which is called the underwriter s gross spread. Alternatively, holding offer size fixed, investment banks must charge issuers higher underwriting fees to be adequately compensated for their greater underwriting risk and expected distribution costs. This enables an investment banks to offset any increase in expected underwriting losses and distributional expenses. In either case, the underwriter s gross spread rises with poorer accounting quality. We test for a negative relation between underwriter gross spreads in SEO issues and accounting information quality using several recently developed measures of accounting information quality as explained below. Another potential cost of an SEO is an offer cancellation. The inability to raise external capital is one of the greatest costs that a company can face if it delays valuable investment opportunities or forces the company to turn to more costly sources of external capital. An SEO issuer also loses the registration fees, accounting expenses and management time devoted to the offering process when an issue is withdrawn. Thus, we view the probability that an issue is withdrawn from registration as an additional component of the expected flotation costs of an SEO. Since poor issuer accounting quality raises investor uncertainty about the value of an issue and concern about adverse selection, it can reduce demand for the issue and increase the likelihood of offer withdrawal. Faced with greater investor uncertainty about issuer stock valuation and lower issue demand, an investment bank could choose not to sign the final underwriting agreement, dramatically raising the probability of issue withdrawal. Thus, accounting information quality is likely to be negatively related to the frequency of SEO withdrawals. The equity offering announcement effect is another component of expected flotation cost since an SEO can only occur after it is announced and numerous studies have documented that SEOs are associated with significantly negative announcement effects, which average between 2% and 3% for U.S. industrial firms. 1 So a rational issuer must expect to sell its new stock at a price 2% to 3% below its current stock price. 2,3 For this reason, the quality of issuers 1 See for example Asquith and Mullins (1986), Masulis and Korwar (1986), Mikkelson and Partch (1986), Bhagat and Hess (1986) and Eckbo and Masulis (1992). 2 It could be further argued that if a firm raises new capital representing as much as 10% of its outstanding equity, a 2% downward revaluation of the existing shares also implies that 20% of the gross proceeds of the issue is absorbed by the negative price reaction and the firm s equity capital base rises by only 80% of the SEO s gross proceeds. However, since their negative information is eventually released to the public, the only effect of the SEO announcement is to accelerate the release of this negative information, so this added equity price drop should not be treated as a further flotation costs. 3 See Eckbo, Masulis and Norli (2005) for a further discussion. 4

5 accounting information has yet another implication. Poor quality accounting information can obscure a firm s financial health and its performance, which increases the information asymmetry between issuers and outside investors. On initially hearing news of an equity offering, investors are likely to downgrade their valuation of a firm with poor quality accounting information to take into account the severe agency problems and increased adverse selection risk that investing in such a firm entails. So we expect issuers with poor accounting quality to be associated with more negative announcement returns relative to issuers with better accounting quality. Measuring the quality of accounting information is complicated by the fact that applicable accounting standards and the transparency of accounting information can vary considerably across companies and industries. In the accounting literature, the quality of accounting information is often measured by accruals quality. Until recently, accruals quality was primarily measured in terms of discretionary accruals using the Jones (1991) or the modified Jones model (Dechow, Sloan and Sweeney, 1995). The rationale for this focus on discretionary accruals is that managers can exploit their discretion over accounting decisions to enhance discretionary accruals. However, even in the absence of intentional earnings management, accounting information is likely to be influenced by uncertainty about a firm s fundamental economic environment, and its industry and firm specific characteristics. Thus, we follow the more recent financial accounting literature by not distinguishing between intentional earnings management and unintentional estimation error from models of earnings quality, since both imply poor accounting information quality. 4 Following Dechow and Dechev (hereafter DD, 2002), we measure earnings quality as the standard error of a model mapping yearly current accruals into operating cash flows in the prior, current and future years, where larger estimation errors imply poorer quality accounting information. This model was modified by McNichols (2002) to control for changes in sales revenue and property plant and equipment and is called the modified DD model (hereafter MDD), which we use in our later analysis. We also propose a new measure of accruals quality which adjusts the MDD model for firm fixed effects to capture unobserved firm characteristics (hereafter FDD). This new accruals quality measure has the advantage of capturing unobservable firm characteristics which are time invariant such as internal accounting policies and cash flow characteristics. This new accruals quality measure also more directly adjusts for a major source of heteroskedasticity in the MDD model. Thus, the standard errors from the 4 Francis, Lafond, Olsson and Shipper (2004, 2005). 5

6 adjusted MDD model should be lower and their cross sectional variability should better reflect differences in firm accounting information quality. Poor accruals quality creates more uncertainty about a firm s true performance to outside investors and thus, increases the asymmetric information between issuer managers and outside investors, regardless of whether it is intentionally created through earnings management or unintentionally produced. By increasing the information asymmetry between managers and outside investors, poor accruals quality is likely to make investors more reluctant to invest in SEOs of these firms and thus, to make underwriting SEOs of these issuers more risky and costly. Therefore, we predict that issuers of otherwise identical SEOs, except for poor accruals quality, should be associated with larger expected flotation costs. Two previous studies (Francis, Lafond, Olsson and Shipper, 2004, 2005) examine the relation between accruals quality and the cost of equity capital, and show that poor accruals quality is related to a higher equity cost of capital. To measure equity cost of capital, Francis et al. (2004) employ an ex ante expected returns measure following Brav, Lehavy and Michaely (2005). 5 However, this measure is strongly influenced by analysts four-year-target price forecasts, and forecasts of next period s dividend, and dividend growth. In addition, several studies (Payne, Robb, and Payne, 2000; Burgstahler and Eames, 2006) uncover evidence of earnings management by managers seeking to meet analysts earnings forecasts. If firms manage their accounting information so as to raise analysts target prices for their stocks (defined in footnote 5), then the positive relation between firm cost of capital and accounting information quality may be an artifact of a positive relation between firm accounting information quality and the target price forecast, rather than reflecting a positive relation with a firm s cost of capital. In the second study by Francis, Lafond, Olsson and Schipper (2005), earnings-price ratio is employed as a proxy of firm s equity cost of capital. Moreover, firms earnings are likely to be susceptible to earnings management when there are strong incentives to change stock prices around important firm specific event dates, such as management bonus plan ending dates or stock option expiration dates (Healy, 1985; Sloan, 1993; Gaver et al., 1995; Holthausen et al.,1995; Balsam, 1998; Guidry et al., 1999; Aboody and Kaznik, 2000), mergers and acquisitions (Erickson and Wang, 1999), management buyouts (DeAngelo, 1988; Perry and Williams, 1994), and new equity offerings (Teoh, Welch and Wong, 1998a, 1998b; Teoh, Wong 5 The cost of equity equation for Francis et al. (2004) as follows: 4 4 (1 + CofC) (1 + g) DIV TP CofC g 4 ( 1+ CofC) = +, where CofC is the estimate of the ex ante cost of equity. P P P is a stock price nine days prior to the date of the Value Line report. TP, DIV, and g are Value Line analyst s four-year-out target price, next period dividend, and dividend growth respectively. 6

7 and Rao, 1998). For these reasons, we can draw misleading influence of relation between quality of accounting information and equity cost of capital. In addition, a firm s cost of capital can be influenced by multiple risk factors, but existing asset pricing models can not explain a large proportion of these cross sectional variations in realized stock returns. To overcome these concerns, in our study, we measure the effects of poor accruals quality on equity flotation costs rather than firm s yearly equity cost of capital. Studying changes in outstanding shares allows researchers to directly evaluate the link between accruals quality and a firm s cost of issuing new shares, which is a major component of its equity cost of capital, namely its expected flotation costs. Earnings are one of the most frequently cited measures of firm performance. It is not surprising that there is substantial interest in whether earnings are manipulated to dress up firm performance so as to raise investor interest in a stock. Many existing studies examine the opportunistic uses of accounting information around various types of corporate events and one corporate decision that has received substantial interest is a SEO. Evidence of earnings management in order to raise gross proceeds or offer prices is reported in a number of recent studies (DuCharme, Malatesta and Sefcik, 2004; Kim and Park, 2005; Ragan, 1998; Shivakumar, 2000; Teoh, Welch and Wong., 1998a, 1998b, 2002). These studies employ discretionary accruals as a proxy of earnings management, and presume managers are intent on manipulating or managing accounting information. However, other researchers in financial accounting have recently questioned whether the existing accounting evidence on earnings accruals can reliably distinguish between earnings management and a changing economic environment, which is also captured by the MDD model. This is one major reason for our preference of the MDD model, which focuses on the absolute value of the deviations in the relation between a firm s operating accruals and its prior, current and future cash flows. To preview our results, we find that the empirical evidence strongly supports the hypothesis that poor accruals quality is associated with larger expected flotation costs. Using a sample of 1,291 SEOs by U.S. firms completed between 1991 and 2004, we find that issuers who manage earnings more aggressively can raise more equity capital. However, the tradeoff is that poor accruals quality is associated with (1) larger underwriting fees, (2) a more negative market reaction to equity offer announcements, and (3) a higher probability of issue withdrawal. These results are robust to taking into account the joint determination of offer size and controlling for potential sample selection bias The rest of this paper is organized as follows. In section II, we introduce and discuss the accruals quality measure. Data sources and sample characteristics are discussed in section III. In 7

8 Section IV, we examine how accruals quality can be related to a firm s ability to raise equity capital. In the next three sections (V, VI, and VII), we investigate the relationship between accruals quality and three major components of equity flotation costs - underwriting fees, announcement returns, and issue withdrawal probability. This is followed by the sensitivity analyses of the result in section VIII. Finally, IX presents the conclusions and highlights the contributions of this study. II. Measure of Accruals Quality Reported earnings decompose into two main parts - cash flow from operations and accounting adjustments called accruals. While accounting earnings are purported by the accounting standards board to be a superior (to cash flow) measure of firms economic fundamentals, the accruals component of earnings is subject to managerial discretion, estimation errors, and the allocation of cash flow into other periods. For this reason, earnings quality is often interpreted as synonymous with accruals quality. Dechow and Dichev (DD) (2002) proposes a new measure of accruals quality based on the idea that accruals map into cash flow realizations in contemporaneous and adjacent periods. The intuition behind this measure is the timing of cash flow recognition. In other words, the timing of the firm s economic accomplishment and sacrifice often differs from the timing of the related cash flows. For this reason, managers can benefit when they use accruals to adjust cash flow timing at the cost of correcting accrual components from future accruals and earnings. 6 Therefore, DD suggests that estimation errors in accruals and their subsequent corrections are likely to reduce the beneficial role of accruals, thus the quality of accruals is decreasing in the magnitude of accrual estimation errors. DD also argues that analyzing current accruals can be more accurate than estimating discretionary accruals given the controversies as to how nondiscretionary accruals are to be estimated. 7 Wysocki (2005) is a good example of the types of criticism that has been leveled against standard discretionary accrual models. The DD model for estimating accruals quality is specified as: CA φ + v (1) t = c + 1CFOt 1 + φ2cfot + φ jcfot + 1 where = changes from year t to year t-1, t = year, CA = total current accruals = current 6 For example, recording a receivable accelerates the recognition of a future cash flow in earnings, and matches the timing of the accounting recognition with the timing of the economic benefits from the sale. However, if net proceeds from a receivable are less than the original estimate, then a subsequent entry records both the cash collected and the correction of the estimation error. 7 Ecker et al. (2005) extend the Dechow and Dichev (2002) model after substituting total accruals for current accruals. They show that current accruals serve as a reliable instrument for total accruals in estimating accounting information quality. Thus, this evidence indicates that using current accruals, rather than total accruals in the MDD model is not a serious concern. t 8

9 assets (Compustat item 4) - current liabilities (Compustat item 5) + cash (Compustat item 1) + debt in current liabilities (Compustat item 34), CFO = cash flow from operation = net income before extraordinary items (Compustat item 18) total accruals, and total accruals = current accruals depreciation and amortization expense (Compustat item 14). All variables are scaled by the average of total assets (Compustat item 6) between year t-1 and year t. In this study, we adopt the MDD model as proposed by McNichols (2002), as our first proxy of accruals quality. In this modified model, changes in sales revenue and property, plant, and equipment (PPE) are added to equation (1), since these components are important in forming expectations about current accruals, beyond their direct effects on operating cash flows. She shows that adding these two variables to equation (1) significantly increases its explanatory power in cross-sectional regressions, thus reducing measurement error. The MDD regression equation is as follows: CA φ PPE + v (2) t = c + 1CFOt 1 + φ2cfot + φ3cfot+ 1 + φ4 Salest + φ5 where Sales = total revenue (Compustat item 12), and PPE = property, plant, and equipment (Compustat item 7). The estimation of MDD model follows two steps. First, we estimate equation (2) for each of the Fama and French (1997) 48 industry groups with at least 20 firms over the year t-4 through t. Then we calculate the standard deviation of firm j residuals of v j, t through v j,t-4. Therefore, larger standard deviations of residuals, which reflect a greater unexplained portion from the estimation, indicate poorer accruals quality. For robustness, we also estimate equation (2) with a firm s book-to-market ratio included as an additional regressor, following Larcker and Richardson (2004). 8 tables. The main results are qualitatively similar to the results reported in the Although the MDD accruals model has become a popular approach for estimating accruals quality in financial accounting studies, this model is subject to several concerns. First, consider the case of two firms, where one has consistently small residuals through time, while the other has consistently large residuals through time. When we estimate their accruals quality employing the MDD model, the standard deviation of the residuals for these two firms may be small, implying that these two firms have relatively good accruals quality. However, these two firms may be treated separately in terms of evaluating their accruals quality, but MDD model has a limited ability to distinguish these cases. Second, although including the change in sales revenue and PPE significantly increases the explanatory power of the cross-section DD t t 8 Larcker and Richardson (2004) argue that there is a positive relation between accruals and growing firms, proxied by the book to market ratio. 9

10 regression, there may be some other firm characteristics that also affect a firm s accruals. We proposed to further modify the MDD model by augmenting it with firm fixed effects, which we call the FDD model. In other words, we estimate the following model to obtain our second proxy of accruals quality. CA φ PPE + v (3) t = c j + 1CFOt 1 + φ2cfot + φ3cfot+ 1 + φ4 Salest + φ5 where, j= 1, 2,.1,291 and t = 1992, 1993, In estimating the equation (3) panel regressions, the firm j specific intercepts, c j, address the prior concerns about the limitations of MDD model. Thus, this second measure of accruals quality has several potential advantages over the MDD model. First, the firm fixed effect coefficient, t t c j, is likely to capture time invariant firm characteristics, so that it is likely to detect and distinguish between firms, where one has consistently large residuals and another has consistently low residuals. Second, firm fixed effects can also mitigate omitted variable problems by capturing unobservable firm characteristics that are time-invariant, such as unobservable accounting policies, cash flow characteristics, etc., which can inflate the estimated accruals quality measure in the MDD model. Third, when we estimate (3) with heteroskedasticity robust standard error, we control for not only heteroskedasticity, but also arbitrary serial correlation in the error terms. 9 Since accounting data tends to exhibit higher autocorrelation through time, our FDD model is a potentially helpful way to obtain robust standard error. 10 Overall, when we estimate accruals quality based on the FDD model, the null hypothesis of all c j = 0 is rejected with F statistics of 3.4 (p-value = 0.000). Thus, we confirm the usefulness of including a firm fixed effect in estimating accruals quality. Table I reports descriptive statistics (percentiles, mean, median, and standard deviations) for our two proxies of accruals quality. Accruals quality in the MDD model is observed to be larger than it is in the FDD model. 11 This is consistent with the firm fixed effect term, capturing unobserved time-invariant firm characteristics. Thus, the unexplained portion of the current accruals regression is reduced under a firm fixed effects specification. c j, 9 We do not need to use a HAC (heteroskedasticity autocorrelation covariance) estimator in a fixed effect panel regression when the sample period is small. The robust standard errors are valid in the presence of unknown heteroskedasticity or serial correlation when estimation time is small relatively to number of cross sectional observations. More detailed discussions are provided in Arellano (1987) and Wooldridge (2002). 10 Since our accruals quality measure relies on the error terms, which are not affected by heteroskedasticity or serial correlation, this last advantage may not be as important in this study. 11 The contemporaneous correlation between these two proxies is approximately 52%. 10

11 Turning to a practical matter, the DD approach has an important limitation when applied in a general empirical framework. In order to estimate the DD model, we need at least 8 years of consecutive financial accounting data, implying that companies have survived for at least 6 years prior to their SEO announcements. 12 Therefore, we systematically exclude younger firms (recently listed) and firms delisted over this sample period, which may have different characteristics that could also affect equity flotation costs. We cannot ignore the possibility that the results are spuriously driven by a nonrandom selection criterion. To address this concern, we employ the Heckman (1979) selection model to test (and, if necessary, to correct) for any significant selection bias in Section VIII. III. Data and Sample Description The SEO sample consists of 1,291 completed offers and 77 withdrawn offer filings by U.S. issuers for the 1992 to 2004 period, and is obtained from the Securities Data Company (SDC) New Issue database. The sample criteria requires SEOs to be common stock by U.S. issuers, listed on NYSE, NASDAQ, or AMEX, and excludes: (1) SEOs lacking CRSP daily stock returns and prices for the SEO announcement period and the prior 90 trading days, (2) firms lacking COMPUSTAT annual financial statement data for the 6 years prior to the SEO filing date, the offer year and the following year, which are needed to estimate firm accruals quality, (3) financial and utility issuers, (4) completed SEOs with offer prices less than $5 and withdrawn SEOs with filing range midpoints less than $5, (5) spin-offs, (6) reverse LBOs, (7) closed-end fund, unit investment trusts, REITs and limited partnerships, (8) rights and standby issues, (9) simultaneous offers or combined offers of several classes of securities such as unit offers of stock and warrants and (10) simultaneous international offers. Table II presents descriptive statistics of our sample of SEOs by year, issuer frequency, primary and secondary offering classifications, and industry. Panel A in Table II highlights that our sample includes a number of hot (1992, 1993, 1996, and ) and cold (1994, 1995 and ) equity offering periods. Panel B of Table 1 shows that over 36% of the SEO sample involves issuers making more than one SEO. Panel C describes the frequency of pure primary, combined primary and secondary, and pure secondary offerings. We can see that combined primary and secondary offers (28.7%) pure secondary offerings (11.4%) are fairly 12 When we estimate equation (2) at time t, we have to include CFO at time t-1 and t+1. In addition, CFO is defined as the difference between net income and total accruals, which is obtained by subtracting depreciation from current accruals. In other words, CFO at time t-1 has to include accounting components at time t-2. Therefore, estimating equation (2) at time t has to include accounting information at t+1 and estimating equation at time t-4 has to include accounting information at t-6. Therefore, estimating the MDD model requires a total of 8 years of accounting information. 11

12 common. Panel D presents SEO frequencies by industry and reveals that companies in chemical products, computer hardware and software, electrical equipment and electric, gas and sanitary service industries account for approximately 40% of the SEO sample. In sum, SEOs exhibit strong clustering by offer year and industries as well as by issuers. IV. Accruals Quality and Proceeds There are many studies which document evidence of earnings management around SEOs (e.g. Teoh, Welch and Wong, 1998b; Ragan, 1998; Shivakumar, 2000). These studies all show that equity issuers relative to non-issuers exhibit significantly higher levels of earnings management. In these studies earnings management is estimated from discretionary accruals using the Jones model (1991) or the modified Jones model (McNichols, 2002). These studies also report negative relations between earnings management and subsequent earnings and stock price performance of these equity issuers. However, none of these studies directly examine whether earnings management or poor accruals quality adversely or favorably affects an issuer s ability to raise equity capital. Therefore, before we examine relations between accruals quality and SEO flotation costs, we first consider how accruals quality can be related to a firm s ability to raise equity capital. We consider two alternative hypotheses about the relation between accruals quality measures and SEO net proceeds. First, poor accruals quality increases the asymmetric information between managers and shareholders. Following the arguments of Myers and Majluf (1984) and Krasker (1986), given this information asymmetry, manager-shareholders are more likely to time equity offerings when their stock prices are overvalued and to increase equity offering size to further benefit from this mispricing. Managers in these firms can also have strong incentives to manage earnings prior to SEOs, so as to further increase their stock prices and the equity capital they raise, if investors in these particular firms are on average mislead. These asymmetric information arguments yield several reasons for a positive relation between poor accruals quality and net proceeds. On the other hand, if poor accruals quality adversely affects investor perceptions of the quality of an issuer s accounting information, then investment banks anticipating greater market skepticism about the offering could be more reticent to underwrite such as SEO or at a minimum demand a reduction in the offering size. Thus, investor concerns about adverse selection risk could create a negative relation between poor accruals quality and net proceeds. The empirical evidence on these two hypotheses is reported in Table III. The dependent variable in these OLS regressions is the log of net proceeds. In the first two columns, we 12

13 measure of accruals quality using the MDD model and in the last two columns, we measure accruals quality by the residual standard deviations in the FDD model, where a larger value indicates poorer accruals quality. AQ1 and AQ5 are indicator variables of issuers in the smallest and largest quintiles of accruals quality, which allows for a non-linear relation between accruals quality and log of net proceeds. As for control variables, we include the log of total assets, the leverage ratio, Tobin s q, underwriter rank, percent of secondary shares, stock return volatility during the period (-90, -11) prior to the issue date, share turnover during the period (-90, -11) prior to the issue date, and capital expenditure scaled by total assets. 13 In addition, we also include a NYSE stock exchange indicator which takes value 1 if the issuer s stock is listed on NYSE and is 0 otherwise, a Rule 415 shelf indicator, which take value 1 if the issue is registered as a shelf offering, as well as a credit rating indicator variable which takes value 1 if an SEO issuer has a debt rating and is 0 otherwise. All regressions include year and industry fixed effects based on Fama and French (1997) industry classifications. All tests of coefficient significance use White heteroskedasticity robust standard errors with adjustment for firm clustering. Consistent with the argument that earnings management exacerbates the managershareholder conflict of interest, poor accruals quality is significantly associated with increases in net proceeds for both measures of earnings quality. When we compare issuers with the best accruals quality represented by AQ1 and the worst accruals quality represented by AQ5, we find that issuers who manage earnings more aggressively (poor accruals quality) are likely to raise more equity capital, while issuers who manage earnings more conservatively (high accruals quality) are apt to raise less equity capital, after controlling for other determinants of offer size. Thus, issuers using earnings management appear to be able to mislead investors as to the true value of their stocks. V. Accruals Quality and Investment Banking Fees 13 The leverage ratio is defined by book value of short and long term debt (Compustat item 9 + Compustat item 34) over total assets (Compustat item 6) measured at the most recent pre-offer fiscal year-end. We define Tobin s q as the ratio of the market value of total assets (Compustat item 6 Compustat item 60+ Compustat item 25 * Compustat item 199) over the book value of total assets (Compustat item 6). Underwriter rank is based on the Carter and Manaster reputation measure. Stock return volatility is measured as standard deviation of daily returns over the event window bounded by trading days (-90,-11). Share turnover is measured by the ratio of average daily value of issuer shares traded during the period (- 90,-11) prior to the SEO public offering date divided by the pre-seo total shares outstanding. Credit rating is based on Compustat item 280. Capital expenditure is defined by Compustat item

14 A U.S. issuer almost always uses a firm commitment method to sell an SEO paying an underwriter fee to commitment to buy the SEO at a fixed price. Thus when the security is issued, the issuer only receives the net proceeds or the gross proceeds (offer price x new shares issued) minus the underwriter fee as new capital. This fee is generally represented as a discount from the offer price, and is called the gross spread. As a direct flotation cost, an issuer pays the gross spreads, whose main components consist of the management fee, underwriting fee, and selling concession. Panel A in Table IV shows the magnitude of these fees. To graphically illustrate the pattern of gross spreads by offer size, we plot gross spreads versus offer size in Figure I. This graph shows a negative and linear relation between gross spreads and the log of net proceeds, suggesting an economy of scale effect in underwriting. In addition, there is a considerable dispersion in the spreads paid on different deals, unlike the well documented concentration of IPO spreads at 7%. Moreover, there is a tendency to set gross spreads at integers, which accounts for 22% of the sample. In this section, we investigate the impact of accruals quality on a major component of flotation cost, namely underwriter gross spreads. We conjecture that poor accruals quality in a firm leads to high information risk, since it raises investor uncertainty as well as asymmetric information, which SEO announcements could exacerbate. These properties are likely to reduce investor demand for the stock and increase stock price volatility. Thus, investment bankers will face higher valuation risk and expected selling expenses when underwriting the seasoned equity offerings of these firms, which should cause underwriters to charge them higher gross spreads as compensation. To investigate this hypothesis, we first examine average gross spreads by accruals quality quintiles in Panel B and C of Table VI. As accruals quality diminishes (from smallest to largest quintile), gross spreads increase monotonically for both proxies of accruals quality. This pattern is observed across all the quintiles, implying that underwriter gross spreads have positive relations with each of the two accruals quality measures. While these univariate results support the hypothesis that poor accruals quality is associated with higher investment banking fees, this evidence can be misleading if there are confounding effects between accruals quality and gross spreads. In addition, there may be other issue characteristics that could differ across the samples and also affect gross spreads, thus leading to a similar effect. As a consequence, we undertake a multivariate analysis in the next section where we control for a number of other issue characteristics that prior studies have found to affect equity offering flotation costs, including: Offer size (log of net proceeds): Many studies find that underwriting fees per dollar of gross proceeds exhibit an economy of scale effect (Smith, 1977). Therefore, we expect a negative 14

15 relation between underwriter gross spreads and offer size measured by the log of net proceeds. Percent of secondary shares: Issuers of SEOs with secondary offers are likely to be older firms as well as firms with larger book value of assets, larger sales, higher cash flow margins and higher ratios of tangible assets (Brav and Gompers, 2003; Dor, 2003). For this reason, higher proportions of secondary shares imply that the issuers are subject to a reduced asymmetric information problem. Thus, we expect a negative relation between the percent of secondary shares and underwriter gross spreads. Underwriter rank (based on the Carter and Manaster reputation ranking): In terms of an underwriter prestige effect, Puri (1999) argues that the underwriting market is oligopolistic, so more reputable underwriters can charge higher underwriting fees as a form of rent seeking. On the other hand, prestigious underwriters possess superior information about issuers, implying lower expected due diligence costs. As a result, they could afford to charge lower underwriting fees in a competitive underwriting market (Li and Masulis, 2005). To control for these two possible underwriter effects on gross spreads, we include underwriter reputation ranking in the multivariate analysis. Firm size (log of total assets): Larger companies are more likely to be followed by stock analysts, business news services, institutional investors and other market participants. Thus, more information is likely to be available about these issues, which allows outside investors to better evaluate these security issuers. Therefore, information asymmetry between issuers and outside investors is likely to be relatively small, implying that the benefits of underwriter due diligence investigations are reduced. Therefore, we expect a negative relationship between firm size and gross spreads. Leverage ratio: A firm s growth opportunities can be viewed as a valuable set of call options. In more levered firms, managers seeking to maximize shareholder wealth have greater incentives to undertake risky, high expected return projects since they can transfer a greater portion of the additional risk to debt holders. Higher leverage ratios are also associated with higher expected bankruptcy costs, which raise underwriter risk and expected losses. Therefore, both these effects imply a positive relation between leverage ratios and gross spreads. Stock return volatility: Firms experiencing higher volatility in their stock returns tend to face more uncertainty and risk exposure. This should again raise the expected value of the implicit put option written by the underwriter to the issuer. Thus, we expect a positive relation between stock return volatility and gross spreads. 15

16 Tobin s q: Firms with greater growth opportunities and better performing firms are likely to have more profitable investment opportunities. Choe, Masulis, and Nanda (1993) show that firms face lower adverse selection costs when they have more profitable investment opportunities. Higher growth firms are also likely to return to the capital market more frequently and therefore represent more attractive customers for investment banks, which may offer these clients lower prices for their underwriting services. Therefore, investment banks are expected to charge lower gross spreads for better performing and higher growth firms. Share turnover: Butler, Grullon and Weston (2005) documents that higher liquidity is associated with lower issuing costs. Therefore, we include share turnover as a stock liquidity measure and expect a negative relation between share turnover and gross spreads, given that stock greater liquidity should make the SEO easier to place. Credit rating: Liu and Malatesta (2006) document that firms with credit ratings are associated with lower gross spreads. They argue that credit ratings are likely to reduce information asymmetry between managers and outside shareholders, especially around the time of equity offerings. Therefore, we expect a negative relation between the existence of credit rating and gross spreads. Rule 415 shelf: Under shelf registration rules, an issuer can choose to make an offering any time within a two year window from among a large list of potential underwriters. This has several effects. First, it increases competition among underwriters, potentially lowering underwriting fees. Second, it nearly eliminates any opportunity for an underwriter to conduct a thorough due-diligence investigation of the issuer, increasing adverse selection risk. Autore, Kumar and Shome (2005) report that after adjusting for selection bias, shelf registered SEOs have lower underwriting fees and other expenses and more positive announcement returns. In addition to these control variables, we also include year and industry fixed effects to capture time variation in equity market conditions between hot and cold periods and industry clustering. Since residuals from cross sectional regressions typically exhibit serious heteroskedasticity and our sample also contains multiple offerings by the same issuers, we use White heteroskedasticity robust standard errors with adjustment for issuer clustering. The specification of the empirical model is as follows: Gross spread (%) = i accruals quality + log(net proceeds) + log(total assets) 1 percent of i i 5 secondary shares credit rating + α Rule 415 shelf + i + leverage ratio + Tobin's q + underwriter ranking 11 i 2 i 6 8 i i + return volatility + shares turnover 3 9 i i i (4) 16

17 Before we estimate equation (4), we need to take into account of the fact that a firm s SEO net proceeds is endogenously determined. In addition, we see from Table III that our two measures of accruals quality are also significantly associated with firm s proceeds. For this reason, if we estimate equation (4) in OLS framework, our estimates can be biased and inconsistent due to endogeniety problems. Therefore, we estimate gross spread regressions employing 2SLS procedure. In other word, we estimate the log of expected offer size (net proceeds) in our first stage regression using NYSE exchange indicator variable and capital expenditures as instrumental variables, then replace the fitted value of the log of net proceeds obtained from the first stage in our second stage of OLS regressions. 14 Panel A of Table V presents the estimates from this 2SLS analysis as a way to investigate how accruals quality affects investment banking fees. The dependent variable is the percent of gross spreads. In the first two columns, the measure of accruals quality is based on regular MDD model and in the last two columns, the measure of accruals quality is based on the FDD firm fixed effect model. AQ1 and AQ5 are indicator variables for issuers in the best and worst quintiles of accruals quality to allow for a non-linear relation between accruals quality and gross spread. Consistent with the previous quintile results, the accruals quality variable is significantly and positively related to gross spreads. When we proxy for accruals quality with indicators for the lowest quintile, AQ1, and highest quintile, AQ5, of accruals quality, the worst accruals quality quintile, AQ5, is associated with significantly larger gross spreads. In contrast, the best accruals quality quintile, AQ1, is significantly associated with smaller gross spreads. From Figure I, we see that gross spreads tend to be concentrated at integers. This may reflect conventional underwriter pricing practices, which are often mentioned in determining offer prices. For example, Lee, Lochhead, Ritter, and Zaho (1996) also report a tendency SEO offer prices to be rounded down to the nearest eighth or integer value. Mola and Loughran (2004) find that SEO offer prices are clustered at integers and do not tend to occur at odd eighths. Corwin (2003) observes that underwriters of Nasdaq stocks tend to be priced at the prior trading day s closing bid quote, rather than the closing transaction price. This pattern suggests that underwriters may round gross spreads up or down to the nearest integer. To accommodate this possibility, we also estimate our model of gross spreads with an ordered logit regression. The empirical model is written as Gross spread * i = 0 + α1 α accruals quality + other control variables + µ + ε where we assign an ordered dependent variable as flows: 14 Our instrumental variables of NYSE indicator and capital expenditure are not significantly associated with gross spreads in the 2 nd stage regressions, thus we satisfy the identification condition for estimating 2SLS regressions. i i (5) 17

18 0 1 2 gross spread = if if if if if if gross spread 2.5 gross spread 4.5 gross spread 5.5 gross spread 6.5 gross spread * < gross spread * * * * < 3.5 < 4.5 < 5.5 < 6.5 Since our structural form is an ordered logit model, which is a non-linear model, 2SLS requires certain conditions to hold. If a linear reduced form model for the log of net proceeds exists, then we can include the residuals ( µ ) obtained from the first stage regression of Table III in our second stage ordered logit regression. This is an application of the Hausman test for endogeniety and a significant coefficient on µ implies that there is endogeniety between gross spreads and the log of net proceeds. 15 Panel B of Table III repeats this analysis based on Ordered Logit estimation to take account of the very limited number of observed gross spread values. Consistent with 2SLS estimation results, poor accruals quality has a significant effect on increasing underwriting fees. In terms of the control variables, the log of net proceeds after controlling for endogeniety is negatively related to gross spreads, consistent with an economy of scale effect. Gross spreads are significantly lowered by firm size, measured by the log of total assets, higher expected growth rate of assets, measured by Tobin s q, and share liquidity, measured by share turnover. In addition, riskier stocks, measured by pre-offer daily stock return volatility are associated with higher gross spreads. These results imply that investment banks charge higher underwriting fees to higher risk issuers and lower fees to profitable, high growth firms. Prestigious underwriters charge a lower fee, supporting the argument by Li and Masulis (2005). The coefficient for the percent of secondary shares and the Rule 415 shelf indicator are significantly negative, indicating that reducing the total primary shares offered and increasing competition among underwriters appears to lower underwriter risk or selling expense. Overall, the evidence in this section confirms the hypothesis that poor accruals quality is associated with larger equity flotation cost component represented by investment banking fees. * VI. Accruals Quality and Announcement Returns Many studies estimate SEO announcement effects by U.S. industrial issuers and document an average negative 2% to 3% announcement return. If a firm raises new capital representing as 15 More detailed discussions of this issue are found in Rivers and Vyong (1988), Vella and Verbeek (1999), and Gallet, Hoover, and Lee (2006). 18

19 much as 10% of its outstanding equity, a 2% downward revaluation of the existing shares implies that 20% of the gross proceeds are offset and the market value of the firm s equity capital is reduced by an equal amount. Therefore, this negative announcement returns can be interpreted as another component of equity flotation cost. In addition, since the SEO must follow its public announcement, rational issuers must anticipate that they will sell their SEO at 2% below the current stock price due to this negative announcement effect. The SEO announcement evidence is largely consistent with either an adverse selection or an agency model framework. Assuming capital market efficiency and managers maximize existing shareholder wealth, the adverse selection models of Myer and Majluf (1984) and Krasker (198) predict that managers issue equity only if the current stock price is high enough and that more stock is issued as overpricing rises. Rational investors will take this decision rule into account, and interpret an equity issue announcement as conveying management s opinion that the stock is not undervalued. As a consequence, the stock price falls and falls more for larger offerings. In the alternative agency model framework, managers often pursue their own objectives. In this context, managers often propose equity issues as a means of achieving empire-building at shareholder expense. Consistent with this explanation, Jung, Kim and Stulz (1996) find that firms without valuable investment opportunities have more negative announcement returns than firms with substantial growth opportunities, approximated by high market-to-book ratios. Choe, Masulis and Nanda (1993) also document that offer announcement effects are less negative in expansionary periods since these periods are characterized by the existence of more promising investment opportunities, and are subject to less moral hazard risk. Applying the above evidence to addressing the key question in this study, we conclude that poor accounting information prevents investors from evaluating a firm s true financial health and it increases asymmetric information between issuers and outside investors. Since the measure of accruals quality in this study is based on estimation errors in accruals, it is affected by both manager discretion in manipulating or managing earnings and unintentional errors related to unexpected economic factors. Issues with larger estimation errors are subject to more agency problems and are presumed to be riskier. For this reason, we hypothesize that issue announcement returns for the firms with poorer accruals quality are associated with more negative returns than those of firms with better accruals quality. Table VI reports the median and mean values of cumulative abnormal returns (CAR) surrounding initial announcements of SEOs, based on continuous compounding. We searched for the Factiva database to find the initial SEO announcement dates. In approximately 20% of 19

20 the SEO sample, announcements could not be found, in which case we use the original filing date taken from the SDC New Issues database as the announcement date. Abnormal returns (AR) for individual firms are calculated from market adjusted returns and mean adjusted returns. The market adjusted returns are calculated from a one factor market model, where the market return is proxied by the CRSP value weighted index and the parameters are estimated by OLS using the stock s daily returns over trading days -160 to -10 prior to the SEO announcement. The means adjusted returns are adjusted for the stock s prior average daily return calculated from trading days -160 to -10 prior to the SEO announcement. We calculate cross-sectional average CARs over various standard time intervals, where CAR (t1, t2) represent the crosssectional average CAR over the period from t1 to t2. Consistent with previous studies, the average CARs shown in Table VI exhibit negative values ranging between -2% and 3%, depending on the length of the event window. These negative announcement returns are between two and ten times larger for issuers in the low accruals quality group AQ5 than for high accruals quality group AQ1. This result supports the hypothesis that the SEO announcement returns of issuers with poor accruals quality are more negative than those of issuers with better accruals quality. Next, we evaluate the cross sectional relationship between the SEO announcement effect and our accruals quality measure. As in our prior analysis, we include an array of other control variables capturing various issue characteristics such as log of filing gross proceeds, underwriter ranking, log of total assets, leverage ratio, standard deviation of daily pre-offer returns, percent of secondary shares, share turnover, credit rating, and Tobin s q. In order to capture industry and business cycle effects, we also include industry and year fixed effects. Since the residuals from cross sectional regressions typically exhibit serious heteroskedasticity and our sample contains multiple offerings by the same issuers, we use White heteroskedasticity robust standard errors with adjustment for issuer clustering. Table VII presents 2SLS estimates of accruals quality on SEO announcement returns, where the first stage of net proceeds regression has the form of Table III and the second stage dependent variable is a 2 day CAR. In the first two columns, we measure accruals quality with the regular MDD model and in the last two columns, we measure accruals quality with the modified FDD model. AQ1 and AQ5 are indicator variables for the best and worst quintiles of accruals quality issuers. We observe that accruals quality shown in columns 1 and 3 has a significant negative coefficient, indicating that SEO announcement returns become more negative as accruals quality deteriorates. Thus, our multivariate analysis also supports the hypothesis that poor accruals quality is associated with a more negative market reaction to the 20