Investment in audit services and the external financing needs of the firm.

Transcription

1 Investment in audit services and the external financing needs of the firm. Bartley R. Danielsen College of Management, North Carolina State University, Raleigh, NC David M. Harrison Department of Finance, Texas Tech University, Lubbock, TX Bonnie F. Van Ness University of Mississippi, Oxford, MS Richard S. Warr* College of Management, North Carolina State University, Raleigh, NC This draft: June 2010 * Contact author.

2 Investment in audit services and the external financing needs of the firm Abstract We examine the effect of audit fees on security market transparency and in particular the case for firms that are frequent issuers of seasoned equity. We find that equity issuers invest more heavily in audit services but benefit from greater stock market liquidity as a result. Furthermore, we find that more liquid firms have lower ex ante costs of capital and higher (less negative) SEO announcement returns. Our findings support the hypotheses that firms can improve their transparency by investing in audit services and that such investment has real economic benefits. Keywords: audit fees, bid-ask spreads, seasoned equity offerings, SEO.

3 1. Introduction Myers and Majluf (1984) first recognized the adverse selection problem facing firms issuing equity securities to the public. Subsequently, researchers have documented ways in which firms might mitigate this adverse selection problem. For example, researchers have found that when IPOs and SEOs are audited by a big name auditor, the negative price reaction around a seasoned equity offering announcement is reduced (see for example, Beatty (1989)). In recent years, following the consolidation of the audit industry, the ability of a firm to credibly signal quality via the purchase of audit services from a high reputation provider has diminished because the number of auditors is limited. However, an alternative approach exists for firms seeking to purchase auditor certification, namely, the over-investment in audit services. Because auditors gain unique insights into the firm, the magnitude of the audit fees may contain important information about the transparency of the firm. Furthermore, by purchasing more audit services, the firm may be able to provide greater clarity to the market. In essence, the firm may be going above and beyond the basic audit required by law in an effort to improve the firm s transparency. In this paper, we hypothesize that after controlling for known audit fee cost determinants, firms purchasing higher quality (more expensive) audits will enjoy greater security market transparency. For firms that are active security issues, the benefit of greater transparency will be more pronounced for two reasons. First, greater transparency and lower adverse selection costs should result in a less negative price reaction to an equity offering announcement. Second, a more transparent firm should enjoy a lower cost of external equity capital, to the extent that liquidity is a risk priced by investors. 1

4 We therefore expect firms that are planning to issue equity securities will benefit from over-investing in audit services if such actions materially enhance the transparency of the firm. The goal of this research is therefore to test the hypothesis that active equity issuers will invest more heavily in audit services that make the firm more transparent to outside investors. This research ties together three distinct areas of the accounting and finance literature. The first strand of literature uses publicly available data to predict auditor fees and has been developed in academic accounting journals. As one would expect, these models typically focus on the role of financial accounting measures as fee determinants. In addition to incorporating publicly available information into fees, auditors are also concerned with risks that are not presented in the financial statements. In a sense, auditors are corporate insiders who observe private information. They may also be better informed about the relative quality of public information. Because the auditor bears the risk for a failed audit and also has private information about the firm, the fees paid to the auditor may provide important signals to the market about firm transparency. Raw audit fees however, are correlated with firm size as well as many other accounting attributes, and therefore instead of using raw fees we develop a measure of abnormal audit fees. Abnormal audit fees are the residual from a regression of raw fees on known fee determinants. We hypothesize that abnormal fees represent the over investment in audit services. The second strand of literature has demonstrated the certification role of auditors in the IPO and secondary offering markets. These papers observe that equity issuers face an adverse selection problem when issuing equity. For example, Myers and Majluf (1984) 2

5 note that a firm is more likely to issue equity when the market overvalues the firm s assets. Thus, investors are likely to suspect a lemon s problem for all equity issuances. Early research suggests that employing one of the big name auditors prior to an equity issuance mitigates this lemons problem so that both IPO underpricing and the share price declines associated with seasoned equity offerings are attenuated. Unfortunately, during the 1990 s it became hard to test the effect of audit quality and audit fees on equity offerings because Big Five firms came to dominate the audit market for equity issuers. Evidence from seasoned equity issuances is also confounded by the agency problem of how management will actually deploy any money raised. The final area of literature examines the use of the bid-ask spread and its various derivatives as proxies for the security market transparency of the firm. Prior research has shown that greater uncertainty and opacity about firm value is directly correlated with the magnitude of the bid-ask spread. As such, discretionary firm actions designed to reduce the opacity of the firm s operating position and characteristics may well result in observable changes in the trading characteristics of the organization. Our paper makes several empirical predictions. First, firms that actively engage in seasoned equity offerings will tend to pay larger audit fees after controlling for the known determinants of audit fees. Second, greater investment in audit fees will result in greater security market transparency. Finally, greater market transparency achieved through investment audit services will result in lower ex-ante costs of capital and higher (less negative) returns surrounding SEO announcements. Large audit fees may simply reflect the fact that opaque firms require greater auditing effort, and thus, we might reasonably expect that opaque firms will have both high 3

6 audit fees and wide bid-ask spreads (spreads are a well established market proxy for transparency). In this case fees and spreads would be positively correlated. On the other hand, if audit spending helps to improve transparency, fees and spreads should be negatively correlated (i.e. a narrower spread indicates greater transparency). Danielsen, Van Ness and Warr (2007) conduct such a test, and conclude that for the set of S&P 1500 firms in the year 2000, audit fees are positively correlated with spreads. This result is consistent with the idea that opacity is reflected in audit fees and spreads jointly. However, it would be premature to conclude that auditors do not lend transparency to security markets. An alternative interpretation of their results could be that the joint impact of inherent opacity on fees and spreads simply swamps the transparency benefits provided by robust audits. Furthermore, it is notable that their study was conducted pre- Sarbanes Oxley. With this in mind, it seems natural to ask the following question: If higher cost audits can improve transparency, which firms are most likely to benefit from this improvement and will be willing to bear the higher cost? Drawing on insight from the seasoned equity issuance literature, it seems that firms who need to frequently tap the seasoned equity markets are likely to place a high value on auditor certification. This conjecture is supported by recent work by Danielsen, Harrison, Van Ness and Warr (2008) who find that the relation between fees and spreads is negative for Real Estate Investment Trusts (REITs). A possible explanation for the negative relation between fees and spreads for REITs is that REITs are forced by regulation and the tax code (which effectively serve to limit profit retention) to be frequent security issuers in the capital markets. Thus, perhaps for REITs, being transparent to the equity markets is valuable if it lowers their cost 4

7 of equity capital. In this study we are therefore asking whether the equity issuance explanation for REITs is also manifested in the larger population of stocks that regularly seek external equity financing. As a preview of our results, we find that SEO firms do indeed have higher abnormal audit fees (which are defined as the excess fee over the fee predicted by known determinants). Furthermore, we find that firms that have higher abnormal audit fees also had greater security market transparency. Combined, these results are consistent with firms purchasing greater audit services, and presumably higher quality audits, in order to improve transparency in the securities markets. We also find some evidence of the economic benefit of higher audit fees. Specifically, we find that firms that pay higher fees experience larger (less negative) announcement returns for SEOs. We also find that the higher audit fee firms enjoy lower implied costs of capital. Our results support the idea that firms can invest more heavily in audit services to purchase greater security market transparency and such transparency has economic benefits. The paper proceeds as follows: Section 2 presents the literature review and develops our key testable hypotheses. Section 3 presents the data. Section 4 presents the main analysis, while section 5 concludes. 2. Literature review and hypothesis development. Our research derives from the academic literature in the fields of both finance and accounting. From finance we use market based measures of transparency as well as the insights developed in the seasoned equity issuance literature. From accounting we rely on the extensive literature on the determinants of audit fees. 5

8 2.1. Financial Market Transparency The link between asymmetric information and the firm s financial market liquidity was originally proposed by authors such as Demsetz (1968) and Bagehot (1971), who demonstrate a relationship between a firm s bid-ask spread and the trading properties of the underlying securities. Kyle (1985) and Glosten and Milgrom (1985) demonstrate that asymmetric information can produce trading frictions in non-transparent security markets that include both informed and uninformed traders. Glosten and Milgrom show that spreads are a function of the informed trader s private information and the level of the informed trader s information directly impacts her level of trading. Amihud and Nelson (1986, 2000) show that greater information asymmetry will result in higher costs of capital for externally generated funds. The important conclusion from these early works is that bid-ask spreads reflect, at least in part, the magnitude of information asymmetries faced by financial market participants. In this study we use the firm s quoted and effective bid-ask spreads as our measures of the firm s financial market liquidity Accounting Function Disclosure Metrics Beginning February 5, 2001, the SEC required public disclosure of both audit and non-audit fees in firms proxy statements in order to give investors insight into the relationship between a company and its auditor [SEC (2000)]. This regulation occurred prior to the Enron scandal (breaking in 2001), and the passage of the Sarbanes-Oxley Act in At the time of passage, the SEC had become concerned about the nature of auditor fees and the transparency of financial information and markets. In particular the SEC was concerned with the non-audit fees paid to the auditor for consulting services. 6

9 As audits generated relatively low margins for the auditing firms, the SEC focused on high-margin non-audit fees which might compromise auditor independence. Since the passage of Sarbanes-Oxley, auditors are not allowed to provide consulting services to clients in addition to their audit services, therefore there is good reason to believe that audit fees are no longer likely to be loss leaders that are priced low to garner other more lucrative services. Publicly traded firms are required to retain third-party auditors to assist them in providing financial statements that disclose the firm s operating and financial position. Although the engagement of an outside auditor is required by regulation, the development of independent audit activities predates these regulatory requirements. As further evidence of the importance of independent financial certification, banks frequently require nonpublic companies to have their financial statements audited before extend them credit. Thus, in addition to meeting regulatory requirements, outside audits are an important component in the provision of capital in financial markets. A well defined and extensive literature has developed within the accounting discipline describing the determinants of the audit fees paid by individual organizations. 1 These studies conclude that firm size, profitability, and the complexity of the organization s activities are important determinants. A related strand of the literature builds upon this foundation, and suggests there should also be a positive relationship between the level of audit fees paid by a firm and the quality of the audit received. 2 To the extent audit quality is correlated with audit fees, firms which anticipate accessing capital 1 See, for example, Simunic (1980), Low, Tan, and Koh (1990), Chan, Ezzamel, and Gwilliam (1993), Brinn, Peel, and Roberts (1994), O Keefe, Simunic, and Stein (1994), Pong and Whittington (1994), Whisenant, Sankaraguruswamy, and Raghunandan (2003), and McMeeking, Peasnell, and Pope (2006). 2 Support for this contention may be found in Arnett and Danos (1979), Simunic (1980), Shockley and Holt (1983), Lennox (1990), and Bar-Yosef and Sarath (2005). 7

10 markets should be particularly willing to invest in premium audit services in an effort to add credibility and certification to the firm s financial disclosures because the enhanced transparency can be expected to decrease the firm s cost of raising external capital. For example, Beatty (1989) and Firth and Liau-Tan (1998) argue that the audit s certification value is particularly important for firms at the IPO stage. Firms at the IPO stage that contract for premium-priced audits appear to enjoy higher offering prices for their stock. Rauterkus and Song (2005) demonstrate that audit quality is also important for firms at the SEO stage. After the Enron scandal broke in 2001, other clients of Enron s auditor Arthur Anderson -- found raising capital via the SEO market to be more expensive Hypotheses This research is designed to further our understanding of the relationship between the auditor and the auditee, and shed light on the hereto unrecognized benefits that the auditee might gain by investing more heavily in audit services. Specifically, we argue the auditor is a pseudo-insider of the firm who has knowledge about the firm's operations that are not known to the market. Thus, our research question is particularly important given the perception that auditor opinion may well be compromised (or bought) by the payment of excessive fees. Specifically, we hypothesize that firms paying more for audit services are not necessarily trying to bribe the auditor, but may merely require greater diligence on the part of the auditor (which of course costs money). Thus, these well audited firms are able to provide a credible signal of transparency to the market. From the firm's point of view, this information quality signal is very beneficial if greater auditing improves the firm's stock market liquidity and reduces the firm's cost of equity capital. 8

11 Our research should be of interest to researchers in finance and accounting, and also to regulators who might seek to limit the scope of auditor activities. Furthermore, our research should be of interest to investors as it will provide further insight into whether a high audit fee should be viewed negatively (the firm is opaque and hard to value) or positively (the firm is transparent and easy to value). The hypotheses formally stated, together with brief motivations are presented below: H 1 : Firms that are active security issuers will invest more heavily in audit services. For firms that must raise capital in the security markets, greater transparency should reduce the cost of external capital and create value overall. H 2 : Greater investment in audit services will result in the firm effectively purchasing that auditor s reputational capital. There will be a negative correlation between audit fees and security market measures of opacity. Greater investment in audit services will result in more detailed accounting disclosures. In effect, the firm will go above the minimum required audit level, and in doing so become more transparent to outside investors. H 3 : Firms that are more transparent will have lower costs of capital. SEO firms will have larger (more positive, less negative) returns around equity offering announcements. Transparency alone is not value creating. To create value, the transparency must have tangible benefits. 3. Data and Method In February 2001, the SEC began requiring firms to explicitly disclose in their proxy statements the fees they paid to their auditors. For this study, we obtain audit fee 9

12 information from the AuditAnalytics database for the years for all US publicly traded firms that operate in non-regulated industries. 3 To identify firms that issue stock, we collect details of seasoned equity offerings from Thomson s SDC issues data base. We augment the dataset with market microstructure characteristics derived from the NYSE Trades and Quotes Database (TAQ) and accounting information and stock data from Compustat and CRSP. To align our data we impose a structure be applied to the timing of the various variables. First, to align the firms in the data set by calendar year, we drop all observations with non-december year ends. This constraint allows us to reasonably estimate when the firm s proxy statement (that contains audit fee information) will be made public. For most firms, the proxy statement is released before the annual meeting, and usually 3-5 months after the fiscal year end. We then measure the microstructure properties of the firm in the month of August. The use of August is not arbitrary. By using August we are assured that market participants are aware of the audit fee data for the preceding fiscal year. Using a month earlier increases the risk that the audit fee data was not yet publicly known, because the proxy was not released. Using a later month risks the audit fee data becoming staler. For the stock issuance data, we examine all issuances made during the fiscal year in the year following the audit year. The following timeline will illustrate these time periods. < Year of audit >< Year of equity issuance > December Spring August year-end (Proxy released) (Liquidity Measured) The following sections detail the construction of the relevant variables used in the 3 We start our data collection in the post Sarbanes Oxley period to provide insights that are more relevant to current market conditions. 10

13 analysis Abnormal Audit Fees We are seeking to measure the amount of audit services that a firm purchases in excess of the amount that would be needed to audit the firm based on the firm s observed complexity and financial structure. We hypothesize that a portion of these excess fees are being used to render the firm more transparent to outside investors. In order to estimate abnormal fees we must first build a model of normal fees. Fortunately, the accounting literature provides direction in this regard. To compute abnormal audit fees (i.e. audit fees above those predicted by the firm's financial characteristics) we use the approach of Whisenant, Sankaraguruswamy, and Raghunandan (2003) who estimate abnormal audit fees as the residual from the following equation: ln[audit Fee] = β 0 + β 1 ln[assets] + β 2 ln[employees] + β 3 Leverage + β 4 Liquidity + β 5 Invrec + β 6 ROA + β 7 Initial + β 8 Foreign_ops + β 9 Loss + β 10 Sales_growth + β 11 Qualified_opinion + β 12 Employee_plans + β 13 Book-to-market + β 14 Disc_ops + ε (1) where the expected determinants of audit fees are: Assets = Total Book Value of Assets, Employees = Number of Full-Time Equivalent Employees, Leverage = Total Debt / Total Assets, Liquidity = Total Current Assets / Total Current Liabilities, Invrec = [Inventories + Accounts Receivable] / Total Book Value of Assets, ROA = Net Income / Total Book Value of Assets, Initial = 1 if the auditor is in the first or second year of audit engagement with the 11

14 firm, 0 otherwise, Foreign_ops = 1 if the firm reports foreign operations, 0 otherwise, Loss = 1 if the firm reported negative net income in either of the previous two fiscal years, 0 otherwise, Sales_growth = Growth rate in total revenues over the previous fiscal year, Qualified_opinion= 1 if the firm received a qualified opinion in either the current or previous fiscal year, 0 otherwise, Employee_plans = 1 if the company has an employee pension plan, 0 otherwise, Book-to-market = the firm s book to market ratio as of the end of the fiscal year, Disc_ops = 1 if the company reported extraordinary items or discontinued operations during the fiscal year, 0 otherwise, We estimate this regression annually for all firms in our data set using Fama- Macbeth annual regressions. We report the average coefficients of these regressions and the appropriately adjust standard errors. The results from estimating equation 1 are presented in table 1. The regression coefficients are as expected. Variables that are associated with greater firm complexity tend to have positive coefficients. For example, firms that had foreign operations had greater audit fees Microstructure Measures of Opacity In assessing the transparency of a firm s financial statements to the marketplace, we rely upon a pair of traditional market microstructure liquidity metrics; the quoted bidask spread and the effective bid-ask spread. Both of these metrics have long been recognized within the literature as being inversely related to the liquidity of the market for the underlying security, as well as being related to the informational opacity of the 12

15 operating and financial characteristics of the underlying firm. In calculating these microstructure measures, we employ data from the NYSE Trades and Quotes (TAQ) database. The spread variables are: Percentage Spread it = (Ask Price it Bid Price it )/Average Quoted Price it. (2) To measure trading costs when transactions occur at prices inside the posted bid and ask quotes, we use the Effective Spread it, which is defined as: Effective Spread it = 2D it (Trade Price it Average Quoted Price it ) (3) where Trade Price it is the transaction price for security i at time t, and D it is a binary variable which equals +1 for customer buy orders and -1 for customer sell orders. We estimate these variables on a daily basis for each stock during the month of August for each year of the sample. We then average the daily observations to generate monthly observations for each stock for each year. We also compute daily price volatility, number of trades and trade size for each stock in a similar manner. These variables are used as controls in our regression analysis that follows Stock Issuance Data To identify firms issuing seasoned equity to the public we use Thomson s SDC data set. We collect all stock issuances by year and aggregate the proceeds to a single sum for the year. We scale this variable by the firm s market value of equity. We also create a dummy variable that indicates whether or not a given stock conducted an SEO during the 13

16 year. 4. Results and Analysis 4.1. Hypothesis 1: Audit fees for SEO firms. The first step in our analysis is to examine whether audit fees are correlated with SEO issuances as described in the first hypothesis. We use the abnormal audit fees estimated from equation 1 (reported in table 1) as the right hand side variable and then compare these to the SEO issuance variables. The basic regression specification is as follows: Abnormal Audit Fee = β 0 + β 1 DIDSEO + β 2 proceedpct + ε (4) Where DIDSEO is the dummy variable that equals 1 if the firm did an SEO in the year following the audit and proceedpct is the total SEO proceeds divided by the firm s market value of equity at the beginning of the year. The results of the regression are reported in table 2. The coefficient on proceedpct is positive and significant indicating that firms that engage in SEOs are more likely to have larger abnormal audit fees. The second regression in the table just looks at the SEO firms only and again the proceedpct variable is significant and positive Hypothesis 2: Audit fees and stock market transparency Our next test examines the relation between the magnitude of the abnormal audit fee and the firm s stock market liquidity. In these tests we use the two spread variables percentage quoted spread and percentage effective spread as the dependent variables. In the regressions which follow we control for various known spread determinants such as 14

17 price, volatility and trading volume. Our variables of interest are the abnormal audit fee and the SEO dummy. The specific regression specification is as follows. ln(spread) = = β 0 + β 1 number of trades + β 2 trade size + β3 stock price + β 4 standard deviation of quote + β 5 abnormal audit fee + β 6 proceedpct + β 7 exchange dummy + β 8 ln[mve] + ε (5) In this model, the dependent variable is one of the spread measures (percentage bidasked spread or percentage effective spread). The independent variables include controls for firm size and various stock trading characteristics that are well documented as impacting spreads. Finally abnormal audit fees are the residuals from the estimation of equation 1. Proceedpct is the amount of equity issued as a percentage of the firm s market value. Under our second hypothesis we would expect a negative sign on the coefficient β 5 indicating that investment in greater audit fees is correlated with a more transparent firm. A negative coefficient on β6 would be consistent with SEO firms being more transparent. Table 3 reports the results of these regressions. The first regression uses the percentage quoted spread as the dependent variable. The second regression uses the percentage effective spread as the dependent variable. In both regressions the coefficient on the abnormal audit fee variable is negative and significant indicating that firms investing more heavily in audit services are likely to be rewarded with enhanced financial market transparency. Furthermore the proceedpct variable is negative and significant indicating that stock issuing firms are also more transparent. 15

18 4.3. Hypothesis 3. Economic effects of greater audit services. In this section, we examine the effect of stock market liquidity on the announcement effects of seasoned equity offerings. We compute cumulative abnormal returns around the SEO announcement window using a market model. The summary statistics for the cumulative abnormal returns are presented in table 4. The mean abnormal return is negative 1.5%. The median return is about negative 1%. When we examine CARs for the lowest and highest spread quartiles we observe that for the small spread firms the average CAR is only -0.57%, while for the large spread firms, the CAR is %. A t-test of these means reveals that they are significantly different at the 4% level. In table 5 we examine the effect of stock market liquidity on the announcement CAR. The regression is as follows: CAR = β 0 + β 1 proceedpct + β 1 ln(mve) + β 1 ln(spread measure) + ε (6) In both regressions, the spread measure is negative and significant indicating that firms with smaller spreads have larger (less negative, more positive) announcement returns. This result is consistent with a clear benefit of liquidity. Greater transparency reduces the asymmetric information surrounding stock issuance. To the extent that firms can obtain greater transparency through investments in greater audit services, such investments will generate positive economic benefits. The other potential benefit for the SEO firm of investing more heavily in audit services was hypothesized to be a lower ex-ante cost of capital. To test this hypothesis, we estimate the ex-ante costs of capital for the firms in our sample on an annual basis, using 16

19 the approach presented in Lee, Myers, and Swaminathan (1999) and Gebhardt, Lee, and Swaminathan (2000). This technique employs the residual income valuation model which has a long history in the accounting literature and is sometimes referred to as the Edwards- Bell-Ohlson model. 4 The model itself equates the value of the firm s stock price to the current book value plus the present value of residual income. Residual income can be thought of as abnormal earnings, i.e. earnings above the required dollar return on capital, and is closely related to Economic Value Added (EVA). For our purposes, the residual income model is appealing as a valuation model because it can be implemented using forward-looking analyst earnings forecasts. The basic setup of the model is as follows: P t = Bt + = B + t i= 1 i= 1 E E t t [ NI r B ] t+ i (1 + r ) e t+ i 1 i e [( ROE r ) B ] t+ i e i e (1 + r ) t+ i 1 (7) where: P t =stock price at time t B t =book value at time t E t [.] = expectation based on information available at time t NI t+i = Net Income for period t+i r e =cost of equity ROE t+i =after tax return on book equity for period t+i The residual income model uses abnormal earnings, rather than raw earnings. As with economic value added, the model presumes that abnormal earnings cannot persist 4 For an example of early use of the residual income model, see Feltham and Ohlson (1995). Lee, Myers, and Swaminathan (1999) use the residual income model to estimate the intrinsic value of the Dow 30 stocks. 17

20 indefinitely. It is presumed that firms with positive abnormal earnings, i.e. NI > r e B, will see their competitive advantage erode through time. Conversely, firms with negative abnormal earnings are presumed to see performance improvements as management (or new management) takes corrective actions. Practical estimation of equation 7 requires that we generate known inputs for all variables except r e, the implied cost of equity capital, which we estimate by iteration. Since equation 7 is an infinite series, we use explicit earnings forecasts for the first 2 years and treat the third year forecast as a terminal perpetuity. Our basic implementation of the model is as follows: P ( ROE r ) B ( ROE r ) B ( ROE r ) B 1 e 0 2 e 1 3 e 2 0 = B (8) (1 + re ) (1 + re ) re (1 + re ) We compute ROE using analyst earnings forecasts from I/B/E/S. Where the third year forecast is unavailable, we apply the forecasted long term growth rate to the second year forecast. When there is no long term growth rate, we use the second year growth rate to forecast the third year forecast. Although I/B/E/S publishes forecasts throughout the year, we use the forecast made mid-year as this is around the time that we estimate the spread variables. Where this forecast is unavailable, we use the latest preceding forecast available. Because the forecasts are not a whole year prior, we adjust the time to present value accordingly. Book equity is forecasted for each year using the clean surplus relation: B t = B 1 + NI DIV. (9) t t t where DIV t is the forecasted dividend for year t. Price, the left hand side variable, 18

21 is the CRSP stock price at the end of June in the year of the forecast, and should be close to the time that the forecast was made. The implied cost of capital, r e, is found through solving equation 8 by iteration. Using the forgoing method, we estimate the cost of capital for each firm in each year. The implied forward-looking cost of equity capital averaged 8.7% across the sample observations. This estimate seems reasonable, but we also note that the accuracy of the point estimate is of little relative importance. Instead, the cross-sectional rank ordering of each firm s cost of capital is what matters in this study. We assume that a reasonable mean value signifies a reasonable cross-sectional rank-ordering. In table 6 we compare the implied ex-ante cost of capital with the spread measures and the abnormal audit fee measure. Again we use both spread metrics. Two results of note arise. First, firms with wider spreads tend to have greater implied costs of capital. This result is to be expected. More informationally opaque firms are harder to value and presumably trade at a discount to more transparent firms. The second result is that firms that invest more heavily in abnormal audit fees have lower costs of capital. This result is consistent with these firms reducing information asymmetries through greater financial reporting and thus garnering a higher stock price (and thus lower implied cost of capital). This effect on the cost of capital is the second hypothesized economic benefit that the firm gains from investing more heavily in audit services. 5. Conclusion In this paper we test the hypotheses that firms are able to obtain greater security market transparency through the purchase of abnormal audit services. Such services are considered to be those greater than would be predicted by the financial operating condition 19

22 of the firm alone. Firms that engage in seasoned equity offerings are hypothesized to engage in this behavior because they have the most to gain from increasing their security market transparency. Our results find support for the hypothesis that SEO firms invest more heavily in audit services. Furthermore, our results show that greater investment in audit services is associated with greater security market transparency as measured by the firm s bid ask spread. From the SEO firm s view, there are two potential ways in which greater transparency may translate into an economic benefit for the firm. First, greater transparency may result in larger (less negative) cumulative abnormal returns on the day of the announcement. Second, by being more transparent, the firm may enjoy a lower cost of capital. Both of these are direct economic benefits to the issuing firm. We find firms that are more transparent (using spreads as a proxy) enjoy less negative abnormal returns when they announce their SEO. Furthermore, we find that firms investing more heavily in audit services enjoy lower costs of capital. In sum, our findings support the role of audit services as a means for firms to credibly signal greater transparency to the security markets, while the benefit of such a signal is greater for firms that frequently access the security markets. 20

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25 Table 1. Estimation of Abnormal Audit Fees. The residuals from the regression reported in this table are used as the estimates of abnormal audit fees. Data descriptions are in the text. The reported coefficients and standard errors are estimated using Fama-Macbeth annual regressions. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Constant 9.350*** (0.081) Ln(Assets) 0.303*** (0.026) Ln(MVE) 0.142*** (0.023) Ln(Employees) 0.272*** (0.010) Leverage (0.081) Liquidity (0.000) Inventory and Receivables * (0.169) Return on Assets ** (0.017) Initial Engagement ** (0.038) Foreign Operations 0.402*** (0.017) Loss 0.329*** (0.031) Sales Growth (0.003) Qualified Opinion 0.151* (0.065) Employee Retirement Plans (0.034) Book to Market (0.057) Earnings from Extraordinary Operations (0.064) Observations R-squared

26 Table 2. Regressions of abnormal audit fees on SEO activity. The dependent variable is the abnormal audit fee estimated in table 1. Robust standard errors clustered at the firm level are in parentheses. Unreported year fixed effects are also included. *** p<0.01, ** p<0.05, * p<0.1 All Firms SEO Firms only Constant (0.016) (0.036) DIDSEO 0.048* -- (0.025) Proceedpct 0.140*** 0.128*** (0.047) (0.048) Observations 11,479 1,388 R-squared

27 Table 3. Regressions of abnormal audit fees on spreads. The dependent variable is either percent quoted or percent effective spread. Robust standard errors clustered at the firm level are in parentheses. Unreported year fixed effects are also included. *** p<0.01, ** p<0.05, * p<0.1 Percent Quoted Spread Percent Effective Spread Constant 1.487*** 0.816*** (0.110) (0.113) Ln(Trades) 5.167*** 6.582*** (0.338) (0.391) Ln(Trade Size) *** *** (5.877) (6.618) Average Price 0.002*** 0.002*** (0.001) (0.000) Ln(Std Quote Midpoint) (0.001) (0.001) Ln(Abnormal Audit Fees) ** *** (0.013) (0.013) Proceedpct *** *** (0.038) (0.039) Exchange Code 0.506*** 0.440*** (0.021) (0.020) Ln(MVE) *** *** (0.008) (0.008) Observations 11,477 11,477 R-squared

28 Table 4. Cumulative announcement returns around SEO announcements. CARs are measured using a single factor market model. For firms that have more than one SEO during the year, the CARs are averaged. Small percentage spread firms are those firms with percentage spreads in the lower quartile. Large percentage spread firms are those firms with percentage spreads in the upper quartile. Mean Median Std Dev. All Firms CAR (-1 to +1) % % 6.328% Small Percentage Quoted Spread Firms CAR (-1 to +1) % % 4.179% Large Percentage Quoted Spread Firms CAR (-1 to +1) % % 7.346% t-test statistic of equal means for small and large spread firms: p =

29 Table 5. Regressions of spreads on cumulative abnormal returns for SEO announcements. The dependent variable is the three-day CAR (t=-1 to 1) surrounding the SEO announcement date. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Constant (0.019) (0.017) Proceedpct (0.006) (0.005) Ln(MVE) (0.002) (0.002) Ln(Percent Sprd) * -- (0.003) Ln(Percent Effective Sprd) ** (0.003) Observations 1,273 1,273 R-squared

30 Table 6. Regressions of spreads and audit fees on the implied cost of capital The dependent variable is the ex-ante implied cost of capital estimated from the Residual Income Model. Year fixed effects are included. Standard errors clustered at the firm level are in parentheses *** p<0.01, ** p<0.05, * p<0.1. Cost of Equity Cost of Equity Constant 0.058** 0.093*** (0.025) (0.024) Ln(MVE) *** *** (0.004) (0.004) Ln(Abnormal Audit Fees) *** *** (0.005) (0.005) Ln(Percent Spread) 0.024*** (0.004) Ln(Effective Spread) 0.031** (0.004) Market to Book 0.013*** 0.013*** (0.002) (0.002) Ln(Audit fee) 0.037*** 0.035*** (0.004) (0.004) Observations R-squared