Capital Market Consequences of Individual Audit Partners

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1 Capital Market Consequences of Individual Audit Partners Daniel Aobdia Kellogg School of Management Northwestern University Chan-Jane Lin College of Management National Taiwan University Reining Petacchi Sloan School of Management Massachusetts Institute of Technology June, 2014 We thank Hsiao-Lun Lin for kindly providing the data on auditor changes. We thank Michael Ettredge (the editor), two anonymous referees, Michelle Hanlon, John Hughes, Robert Magee, Joseph Piotroski, Brett Trueman, Rodrigo Verdi, Beverly Walther, Joseph Weber, the senior staff and Board members of the PCAOB, and workshop participants at UCLA for helpful comments on earlier versions of this paper. We gratefully acknowledge financial support of the Kellogg School of Management and MIT.

2 Capital Market Consequences of Individual Audit Partners Abstract This paper examines whether the identity of the individual audit partners provides informational value to capital market participants beyond the value provided by the identity of the audit firms. Using data from Taiwan where firms are mandated to disclose the names of the engagement partners, we find a positive association between the partner s quality and the client firm s earnings response coefficient. We also find a positive market reaction when a firm replaces a lower quality partner with a higher quality one. Moreover, we find evidence that firms audited by higher quality partners experience smaller IPO underpricing and are able to obtain better debt contract terms. Overall, these results suggest that the quality of engagement partners matters to capital market investors.

3 1. Introduction This paper investigates whether the identity of the engagement partners provides informational value to capital market participants. Prior research has documented, theoretically and empirically, that hiring a higher quality audit firm or practice office leads to positive capital markets consequences. 1 For example, Titman and Trueman (1986) and Datar, Feltham and Hughes (1991) show that hiring a higher quality audit firm provides a positive signal to uninformed investors about the underlying firm value. However, little is known at a more granular level, perhaps because in the U.S. detail data on the personnel implementing the audit are unavailable. Francis (2011) suggests audits are of higher quality when undertaken by competent people ; however, the fact remains that we know very little about the people who conduct audits. The purpose of this paper is to fill this gap by assessing the informational role of engagement partners. Ex-ante, given that audit firms use standardized audit processes and have large reputation capital, it is unclear whether individual partners provide additional informational value to the capital market participants beyond the identity of the audit firms they work for. To assess the informational value of engagement partners, we investigate the following questions: Do the markets respond positively when a firm switches from a lower quality partner to a higher quality partner? Do the markets perceive earnings to be more informative when higher quality partners conduct the audits? Do investors reward companies for using higher quality engagement partners? In particular, do companies who hire higher quality partners experience less IPO underpricing and receive better debt contract terms? The answers to these questions present new 1 For theoretical models see for example, Titman and Trueman (1986), Datar, Feltham and Hughes (1991), and Dye (1993). For empirical work, see for example, Teoh and Wong (1993), Francis, Maydew, and Sparks (1999), Reynolds and Francis (2001), Francis and Ke (2006), and Francis and Yu (2009). 1

4 insights into the economic consequences of engagement partners and provide a market-based assessment on the value investors place on these partners. Our paper contributes to the literature in several dimensions. First, we are the first to investigate and provide large sample evidence that the names of the engagement partners provide informational value to capital market participants beyond the value provided by the identity of the audit firms. In particular, the study responds to the call by DeFond and Francis (2005), who suggest using settings in countries such as Australia and Taiwan where the names of the engagement partners are required to be disclosed in audit reports to study auditor behavior and audit quality at the individual engagement partner level. The paper is also timely in that recent regulatory changes around the world have begun to require disclosure of the names of the engagement partners. For example, in 2006 the European Union adopted the Eighth Company Law Directive, which requires the engagement partner to sign the audit report (Directive 2006/43/EC, Article 28). In 2011, the Public Company Accounting Oversight Board (PCAOB) proposed to require public accounting firms to identify the names of the engagement partners on the audit reports. 2 We conduct our analyses using the setting in Taiwan, where individual partners are required to sign the audit reports. Given that the identity of the partners is disclosed, audit partners develop a track record over time, which is observable to investors. If investors value the quality of audit partners, we would expect a positive association between the quality of audit partners and various capital market outcomes. To measure engagement partner audit quality, we examine client firms unsigned discretionary accruals, a measure well established and extensively used in the auditing literature (e.g. Lim and Tan, 2008; Francis and Yu, 2009; 2 A related paper by Carcello and Li (2013) uses the setting in the United Kingdom and finds that requiring the engagement partner to sign the audit report has a positive effect on audit quality. 2

5 DeFond and Zhang, 2014). 3 DeFond and Zhang (2014) suggest that an accrual-based measure that reflects financial reporting quality is conceptually well suited for measuring audit quality, where high audit quality is defined as greater assurance that the financial statements faithfully reflect the firm s underlying economics, conditioned on its financial reporting system and innate characteristics. We then employ the methodology developed by Bertrand and Schoar (2003) to quantify the quality of each engagement partner conditional on the quality of the audit firm and the firm s innate reporting characteristics. 4 To avoid using forward-looking data, we use the sample period from 1995 to 2005 to estimate partner quality and from 2006 to 2010 to run the capital market analyses. In our estimation sample, we find that engagement partners have incremental effects on their clients accrual quality that cannot be explained by characteristics of the firm and the audit firm. This result is consistent with Gul et al. (2013), who find that audit quality varies statistically and economically across individual auditors. 5 We further validate the accuracy of our measure using a series of out-of-sample tests. The testing sample spans between 2006 and First, we regress the unsigned discretionary accruals on the estimated engagement partner quality. Consistent with our expectation, we find that the coefficient on the variable is negative and significant, suggesting that clients of high quality partners tend to have smaller abnormal 3 The unsigned discretionary accruals are the absolute value of the discretionary accruals. We use these two terms interchangeably throughout the paper. 4 Other common proxies for audit quality require us to estimate the model in a nonlinear form (i.e., logit or probit), which is not appropriate under the Bertrand and Schoar (2003) methodology. In addition, most other proxies are narrower in scope in that they only capture extreme audit failures (DeFond and Zhang, 2014). This makes them unsuitable to precisely estimate the quality of each engagement partner. However, we validate our measure of partner quality using out of sample tests and we show that high quality partners tend to have higher audit quality proxied by other non-accrual based measures (see Section 4 for details). 5 As noted by Bertrand and Schoar (2003), this type of analysis does not establish causality. In particular, it is possible that firms with higher accrual quality tend to self-select high quality engagement partners. However, the tenor of our results does not depend on the causal inferences between firm quality and partner quality, because the choice of a specific partner provides a signal about the quality of the firm to the capital markets (Titman and Trueman, 1986). Therefore, the disclosure of the partner name itself has informational value to the capital market participants. 3

6 accruals. Second, we use the likelihood of client meeting earnings benchmarks and restating its financial statements as two alternative measures of audit quality (Defond and Zhang, 2014). We find that clients audited by higher quality partners are also less likely to meet benchmark earnings targets and restate their financial statements. The focus of the paper is to assess whether engagement partners provide informational value to capital market participants beyond the value provided by the audit firms. Therefore, we condition all our capital market analyses on the quality of audit firms. Using the testing period from 2006 to 2010, we find a positive association between earnings response coefficients (ERCs) and individual partner s quality. This result suggests that investors perceive earnings to be more informative when higher quality partners perform the audit. We also find that the markets react positively when the firm switches from a lower quality partner to a higher quality partner. Specifically, we find that replacing a partner with one having quality one-quartile higher is associated with a positive abnormal return of 2% over window (-10, +10). Finally, the IPO literature shows that when a firm sells its shares for the first time, the firm value is imperfectly known to the investors and hiring a good auditor can serve as a positive signal to the market (e.g., Titman & Trueman, 1986; Beatty, 1989). We find consistent results at the engagement partner level. Specifically, we find that firms audited by higher quality partners have a lower underwriting discount when they go public. Our results in the equity markets extend to the debt markets. We find that high quality engagement partners can reduce the information asymmetry between borrowers and banks and firms who employ a better quality partner enjoy better contract terms. Specifically, we find that firms audited by higher quality partners pay lower interest rates, have greater access to credit, and are less likely to be required to post collateral. 4

7 Overall, the findings in the paper indicate that disclosure of the names of the engagement partners provides informational value to both equity and debt market investors. Importantly, this effect is incremental to the informational value of audit firms. Thus, our findings extend our understanding of the capital market consequences of hiring a more reputable auditor and confirm that hiring a high quality partner, when disclosed, can act as another signal that enhances firm value. Recently, the PCAOB is considering mandating the disclosure of the names of the engagement partners and the Board argues that disclosing such information helps financial statement users to evaluate the extent of an engagement partner s experience on a particular type of audit and, to a degree, his or her track record. Such information could be useful to investors making investment decisions (PCAOB, 2009). Our findings support this assertion. The remainder of this paper is organized as follows. Section 2 develops the hypotheses. Section 3 describes the sample and Section 4 estimates individual audit partner s quality. Section 5 assesses the equity market consequences of individual audit partners and Section 6 assesses the debt market consequences. Section 7 conducts robustness tests and Section 8 concludes. 2. Hypothesis Development 2.1 Engagement partner quality and capital market consequences Prior research on audit quality has largely focused the analyses at the audit firm or branch office level. For example, DeAngelo (1981) argues that large audit firms have more incentives to supply a higher level of audit quality because they have more to lose in an audit failure. Consistent with this argument, Palmrose (1988) finds that non Big N audit firms are more likely to be sued than Big N audit firms and Francis, Maydew, and Sparks (1999) find that firms employing Big N audit firms tend to have lower amounts of discretionary accruals. Reynolds and Francis (2001) argue that audit-client relationship and auditor incentives had better be analyzed 5

8 at the office level because practice offices represent an important decision making unit where auditors interact with clients and issue audit reports. Following on this line, Francis and Yu (2009) find that larger offices tend to supply higher quality audits. Recent research has begun to push the unit of the analyses further down, investigating whether engagement team personnel affect audit quality. For example, using data from Taiwan, Chen, Lin, and Lin (2008) investigate the relation between audit partner tenure and client earnings quality. Using data from China, Chen, Sun, and Wu (2010) study the impact of client importance on individual auditors propensity to issue modified audit opinions. However, to date studies conducting analyses at the individual level remain scarce. DeFond and Francis (2005) call for more research on auditor behavior and audit quality at the individual engagement partner level. Francis (2011) also suggests audits are of higher quality when undertaken by competent people ; however, the fact remains that we know very little about the people who conduct audits. Responding to this call, a recent paper by Gul, Wu, and Yang (2013) uses individual auditor data from China and finds that the effects of individual auditors on audit quality are both economically and statistically significant. In this paper, we extend Gul et al. (2013) and examine the economic consequences of hiring a high quality engagement partner. In particular, we are interested in the informational effects of engagement partners on the capital markets incremental to the effects of audit firms. A high quality engagement partner can provide informational value to capital market participants through two channels. First, hiring a high quality partner can act as a positive signal to uninformed investors about the underlying firm value. Titman and Trueman (1986) show that a higher quality auditor is able to supply more precise information about the firm s value, 6

9 thereby entrepreneurs with more favorable information about their firms will choose higher quality auditors. Recognizing this behavior, investors are able to infer the entrepreneur s private information from his choice of auditor. This signaling story is in similar spirit with Dye (1993), who argues that the informational value of an audit varies based on perceived audit quality. Second, if a high quality partner can produce more accurate information about the firm (Titman and Trueman, 1986), hiring a high quality partner reduces the information asymmetry between the firm and its investors. Since engagement partners can provide informational value to market participants through both the signaling and information accuracy channels, we cannot draw causal inferences from our empirical analyses. Instead, our objective is to assess whether capital market participants care about the quality of engagement partners. We examine the following four potential effects of engagement partners on the capital markets: the extent to which new earnings is capitalized into the stock price, the market s reaction to the announcement of a partner change, IPO underpricing, and debt contracting. Regarding the first, prior studies commonly use the extent to which new earnings information is capitalized into the stock price as a measure for investors perception of earnings quality. They document that this valuation effect is associated with various audit firm characteristics, such as size (Teoh and Wong, 1993), tenure (Ghosh and Moon, 2005), and whether the firm provides non-audit services (Francis and Ke, 2006). If the quality of the engagement partners matters to equity market participants, we would expect the markets valuation of earnings to be higher when higher quality engagement partners conduct the audits. On the other hand, given the relatively large size of audit teams working on a given account and the use of fairly standardized processes across clients, it is possible that the identity of the audit firm is the only parameter capital market participants focus on. Our first hypothesis stated in alternative form is: 7

10 H1: Earnings are capitalized in the stock price to a larger extent when higher quality engagement partners conduct the audits. If the markets perceive earnings to be more informative when audited by higher quality partners, we would expect the markets to react positively when a firm switches from a low quality partner to a high quality partner. This prediction is consistent with prior research s finding that the market reaction to an auditor change depends on auditor characteristics. For example, Eichenseher et al. (1989) find a positive market reaction when a firm switches from a non Big Eight audit firm to a Big Eight audit firm. Knechel et al. (2007) further show that the market reaction to an auditor switch depends on whether the successor auditor is an industry specialist. Our second hypothesis stated in alternative form is: H2: Stock markets react positively to the announcement of a partner change when the firm switches to a higher quality engagement partner. Studies on IPOs argue that when a firm offers shares for the first time, the quality of the auditor chosen provides a signal about the firm s true value to uninformed investors (Titman and Trueman, 1986; Datar, Feltham and Hughes, 1991). Balvers, McDonald, and Miller (1988) argue that to preserve their reputation capital, investment bankers prefer high quality auditors to participate in the underwriting coalition. They further show that high quality auditors can reduce the level of IPO underpricing. Motivated by theoretical studies of ex-ante uncertainty and underpricing of the IPO (Rock, 1986; Beatty and Ritter, 1986), Beatty (1989) finds similar evidence that firms hiring more reputable accounting firms exhibit smaller IPO underpricing. If high quality engagement partners are able to mitigate the informational uncertainty associated with a new equity issue, we would expect firms hiring higher quality partners to experience a lower level of IPO underpricing. Our third hypothesis stated in alternative form is: H3: Firms audited by higher quality engagement partners are associated with a lower level of IPO underpricing. 8

11 Quality audits are also valuable to debt market participants. Financial statements are commonly used in debt contracts and quality audits reduce creditors monitoring costs (Watts & Zimmerman, 1986). Under the threat of competition, creditors will be forced to pass along these cost reductions to borrowers in the forms of lower interest rates or better contract terms. Prior research find supporting evidence for this argument. For example, Blackwell, Noland, and Winters (1998) find that private companies whose financial statements are audited pay lower interest rates on their bank loans than those whose financial statements are not audited. Minnis (2011) further finds that lenders place more weight on audited financial information in setting the loan rates. Mansi, Maxwell, and Miller (2004) provide corresponding evidence in the public debt markets, documenting that companies employing better quality auditors enjoy lower cost of debt. If high quality partners enhance the credibility of the financial statements and hence, reduce creditors monitoring costs, we would expect firms hiring higher quality partners to obtain better debt contract terms. Our fourth hypothesis stated in alternative form is: H4: Firms audited by higher quality engagement partners are associated with more favorable debt contract terms. 2.2 Accrual based measure of engagement partner quality We infer a partner s quality from her clients earnings properties. Specifically, we consider a partner to be higher quality if her clients on average exhibit a lower level of discretionary accruals. We choose to rely on an accrual-based measure of partner quality for the following reasons. First, accrual quality directly maps into the concept of audit quality and is one of the most common proxies for audit quality in the literature. 6 In a recent review paper, DeFond 6 Studies that use accrual quality to proxy for audit quality include, but not limited to, Reynolds and Francis (2001), Balsam et al. (2003), Krishnan (2003), Myers etl al. (2003), Chen et al. (2008), Lim and Tan (2008), Chi et al. (2009), Francis and Yu (2009), and Carcello and Li (2013). 9

12 and Zhang (2014) suggest that audit quality extends well beyond the simple detection and reporting of GAAP violations In particular, we expect high quality auditors to consider not only whether the client s accounting choices are in technical compliance with GAAP, but also how faithfully the financial statements reflect the firm s underlying economics. Given that the goal of various accrual models in the literature is to capture the extent to which the financial reporting reflects the underlying economic condition of the firm, accrual quality fits well into the concept of audit quality. In contrast, other common measures of audit quality, such as audit opinions and client restatement history, are narrower in scope in that they only reflect whether the auditor detects and reports the breach of GAAP (by issuing an unclean opinion or requiring a restatement). Second, previous studies have established a relationship between accrual quality and various capital market consequences that we investigate in the paper. For example, Hanlon et al. (2008) argue that when accruals are used opportunistically, they introduce noise into earnings and lower[ing] the informativeness of reported earnings. Dechow et al. (2010) show that accrual quality is positively associated with ERC. Several studies document that firms use accruals to manipulate earnings before an IPO (e.g., Aharony et al., 1993; Friedlan, 1994; Teoh et al., 1998a, b; DuCharme et al., 2001). Boulton et al. (2011) take one step further and provide evidence that earnings quality reduces IPO underpricing around the world. Focusing on debt markets, Francis et al. (2005) provide evidence on the impact of accrual quality on the interest cost of debt. Bharath, et al. (2008) further show that accrual quality not only affects interest costs, but also affects other debt contract terms, such as maturity and collateral requirements. Overall, these studies provide evidence that accrual quality is a reasonable proxy for managerial opportunistic behavior and market participants care about accrual quality. To the extent that high quality 10

13 partners can enhance client accrual quality, we would expect them to elicit positive capital market consequences. From an empirical standpoint, accrual quality is the most suitable proxy for our study because it is continuous in nature. Other proxies for audit quality used in the literature, such as whether the firm incurs a small profit or files a restatement, require an estimation of nonlinear models (probit or logit). As we discussed below, we follow the methodology in Bertrand and Schoar (2003) to quantify the quality of each engagement partner and this methodology requires us to include a large set of fixed effects. 7 Including a large set of fixed effects in nonlinear models is problematic because it makes the maximum likelihood estimators inconsistent (Greene, 2004). To address the possibility that accrual quality may capture audit quality with measurement error, in the robustness tests we use regulatory sanction history of engagement partners as an alternative proxy for partner quality (see Section 7). We also perform a series of out of sample tests to validate our accrual-based measure of audit quality (see Section 4). 3. Sample Selection The regulations in Taiwan require the financial reports of public companies to be certified by two audit partners. 8 One audit partner is the lead partner, who is in charge of planning and implementing the audit engagement. The other audit partner is the review partner, who is usually not actively involved in the audit and only reviews the final audit report. 9 Public companies also must disclose the names of the partners and the audit firms. These distinctive 7 We need to include about 1,000 fixed effect dummies in our model. 8 Public companies are those listed on the Taiwan Stock Exchange or the GreTai Securities Market (i.e., over the counter market). Before November 2001, private companies whose capital level exceeds a certain threshold were also required to file audited financial statements. However, most private companies cease to file audited financial statements after the rule was lifted in 2001 (Chi, Myers, Omer, & Xie, 2011). 9 Private conversations with the Big 4 audit firms in Taiwan suggest that the main reason for this arrangement is to reduce audit cost due to the high competition in the audit market in Taiwan. The average audit fee for each audit engagement is only about $100,000 U.S. dollars, a quarter of the fee charged in China and Hong Kong. 11

14 features of the Taiwanese audit market allow us to track the audit partners and audit firms across companies over time and to study the effects of individual audit partners on the capital markets, conditional on the effects of audit firms. Our main data source is the Taiwan Economic Journal (TEJ) database. TEJ covers all public companies in Taiwan and collects data on the financial statements, the restatement history, the names of the signing partners and the accounting firm who implement the audit, the regulatory sanction history of the partners, the dates of the quarterly earnings announcements, and the company s stock price and IPO date. TEJ also collects data on loan borrowings and corporate credit ratings. We supplement the TEJ data by manually collecting the announcement dates of audit partner changes and the names of the new and old partners and the audit firms from the Market Observation Post System (MOPS). All the public companies in Taiwan are required to announce their material events on the MOPS. From the MOPS, we also collect announcements related to changes in executives, dividends payouts, capital raising, and restatements to control for potential confounding events in our market reaction to partner change test. 10 Our sample period is from 1995 to In April 2003, Taiwan Stock Exchange (TSE) and GreTai Securities Market (GTSM), the two major stock exchanges in Taiwan, adopted a set of rules that require listed companies to rotate their audit partners every five years. The rules became fully effective in Using data prior to the mandatory rotation rules, Chen et al. (2008) find that the partner with the longer continuous tenure with the client tends to be the lead 10 There are no announcements related to mergers, acquisitions, and restructuring activities on the dates of partner change in our sample. 11 Although these rules are often referred to as audit partner mandatory rotation rules (e.g., Chi et al., 2009), the rotation requirement is not strictly mandatory. Companies who do not comply with these rules are subject to an investigation by the exchanges and the exchanges can refer the noncompliant cases to the Financial Supervisory Commission, the regulator for CPAs in Taiwan. The Commission will then send a written notice to the noncompliant company and the audit firm to require the change of the audit partner. However, no fine or penalty will be imposed. 12

15 partner in the audit engagement who has a greater influence on the reporting quality. This practice changed around 2005 when it became customary that the lead partner signs the audit report first. 12 We follow Chen et al. (2008) and define the lead engagement partner as the partner who has the longest continuous tenure with the client for the period of For the period of , we define the lead engagement partner as the first partner signing the audit report (see Section 7 for additional validation of the measure). To avoid using forward-looking information in our capital market tests, we use data from 1995 to 2005 to estimate the engagement partner quality and data from 2006 to 2010 to conduct the capital market analyses. This research design is similar to Yang (2012), who studies the capital market consequences of managers disclosure styles. The number of observations in each capital market test varies based on data requirement. Table 1 Panel A reports the industry distributions for sample firms who have non-missing data for estimating engagement partner quality during and for all public firms during the same sample period. Industry classification is based on the TSE industry codes. The sample contains firms in every economic sector and does not show any particular industry clustering. The industry distributions between the sample firms and all public firms are fairly comparable. 4. Estimation of Individual Audit Partner Quality 4.1 Empirical model We begin our analysis by measuring the quality of each audit partner using her clients discretionary accruals. Since managers can manage earnings upward or downward depending on 12 Private conversations with the Big 4 audit firms confirm that the lead partner tends to be the one that has the longer tenure with the client prior to the adoption of the partner rotation rules and the practice changes around the adoption of the new rules (2004/2005). Now it is common practice that the lead partner signs the audit report first. We find that in more than 80% of the cases in our sample, the first partner signing the report is also the one with the longest continuous tenure with the client. However, this proportion goes down after 2005, thus confirming a change in the definition of the lead partner around this time. 13

16 their objectives, and accruals-based earnings management reverses over time (Dechow, Hutton, Kim and Sloan, 2012), we use the absolute value of the discretionary accruals to proxy for audit quality. To quantify each audit partner s quality, we follow the methodology developed by Bertrand & Schoar (2003) 13 and estimate the following model: ABSDA it = βx it + Σα t Year t + Σγ m Audit Partner m + Σθ j Audit Firm j + Σδ i Client i + ε it (1) where ABSDA is the absolute value of the discretionary accruals measured using the crosssectional modified Jones model (Dechow, Sloan, & Sweeney, 1995). DeFond and Zhang (2014) suggest that the Jones discretionary accrual model is the most frequently used measure of client financial reporting quality in the auditing literature. As a robustness test, we also estimate accrual quality based on the Dechow and Dichev (2002) model and obtain similar results (see Section 7.1). X is a vector of time-varying characteristics at the client, audit firm, and audit partner levels that prior studies find to affect accrual quality. We discuss the predictions on these control variables in Appendix A.1 and list detailed variable definitions in Appendix B. In all our analyses, we winsorize continuous variables at the top and bottom 1 percentiles to reduce the effect of outliers. Discretionary accrual (DA) is the residual from the following regression model: TA t /ASSET t-1 = β 1 (1/ASSET t-1 ) + β 2 (ΔSALES t ΔAR t )/ASSET t-1 + β 3 PPE t /ASSET t-1 + β 4 ROA t-1 + ε t (2) where TA is total accruals measured as earnings before extraordinary items minus net cash flow from operations, ΔSALES is change in net sales, ΔAR is change in net accounts receivable, PPE is net property, plant, and equipment, and ROA is the rate of return on assets. We include ROA 13 The fixed effect methodology developed by Bertrand and Schoar (2003) has been widely used in accounting. See for example, Dyreng, Hanlon, and Maydew (2010), Bamber, Jiang, and Wang (2010), Yang (2012), and Gul, Wu, and Yang (2013). 14

17 in the model, because Kothari, Leone, and Wasley (2005) suggest that controlling for firm performance increases the power of the Jones model. We deflate both the dependent and independent variables by lagged total assets and estimate equation (2) by industry-year. Our measure of audit quality ABSDA is the absolute value of DA. 14 Equation (1) includes an indicator variable for each year (year fixed effects), client (firm fixed effects), audit firm (audit firm fixed effects), and audit partner (audit partner fixed effects). 15 The client fixed effects control for all time invariant firm characteristics that may affect accrual quality. The audit firm fixed effects control for the effects of the audit firm on accrual quality. We focus on the audit partner effects, whose estimates are our proxy for engagement partner quality. In all of our subsequent analyses we include client fixed effect estimates, so that our inferences on partner audit quality are conditional on the firm s financial reporting quality. This design is consistent with DeFond and Zhang (2014), who argue that audit quality should be conditional on the client s financial reporting system and innate characteristics. Since our interest is in assessing the extent to which individual partners provide informational 14 Following Kothari et al. (2005), our estimation of the modified Jones model is based on cross-sectional regressions and it slightly differs from the one used in Dechow et al. (1995), which is based on time-series regressions. In particular, Dechow et al. (1995) assumes that sales are not managed in the estimation period and that the entire change in accounts receivables in the event period are managed. Kothari et al. (2005) argue This approach is likely to generate a large estimated discretionary accrual whenever a firm experiences extreme growth in the test period compared to the estimation period. Following Kothari et al. (2005), we cross-sectionally estimate the modified Jones model and assume that all changes in accounts receivable arise from earnings management. 15 Francis and Yu (2009) find that larger branch offices of audit firms provide higher audit quality in the U.S. We do not include practice office fixed effects in equation (1) for the following reasons. First, Taiwan is a small island and there are limited branch offices besides the headquarters. These branch offices are usually small and only have limited clients. Gul et al. (2013) indicate that branch offices conduct less than 5% of the audits in China, and we expect the percentage to be even lower in Taiwan. Because the headquarters conduct the majority of the audits, our data source TEJ does not identify whether a particular audit was carried out by a branch office. (We are also unaware of any data source provides such information.) Moreover, given that branch offices only have few small clients, it is unclear whether we are able to identify the partner fixed effects for the partners working in the branch offices (the partners need to work for at least 2 clients for at least 3 years, and each client needs to have publicly available financial data for the fixed effect estimation). Finally, since the clients of branch offices tend to be small, local firms that are not traded, it is unlikely that they will be in our capital market tests. However, we recognize that a small number of partners may be working in the branch offices instead of the headquarters. To the extent that these partners quality is positively correlated with the branch office quality, our results may be picking up some branch office effect. 15

18 value to the capital markets beyond the value provided by the audit firms, we also include audit firm fixed effects in all our capital market analyses. We estimate equation (1) using the sample period from Because of the inclusion of firm fixed effects, the individual audit partner fixed effects can only be identified on firms that have been audited by more than one partner over the estimation period. Following prior literature (e.g., Bertrand and Schoar, 2003), we further require each partner to work for at least 2 such clients and have an average work experience at each client for at least 3 years. 16 We then apply the estimated fixed effect coefficients (δ i, θ j, and γ m ) using the sample period from for the capital market analyses. We multiply the estimated fixed effects coefficients by -1, so higher fixed effects coefficients indicate higher audit quality. To reduce measurement error and facilitate comparison of the partner, audit firm, and client effects, we rank the transformed fixed effects coefficients into quartiles and denote them as QPartnerFE, QAuditFirmFE, and QFirmFE. Table 1 Panel B presents descriptive statistics for our sample. The number of audit firms each year is about 14 with small variation across years. An audit firm on average has about 38 unique clients per year and 10 lead partners per year. Each lead partner on average has about 4 unique clients per year. 4.2 Estimation results Table 2 Panel A presents the summary statistics on the variables in equation (1) for the full sample period from ABSDA has a mean value of 0.06 and a median value of 0.041, which are of similar magnitudes to the ones documented in Chen et al. (2008). The mean 16 These requirements increase the precision of the partner effect estimates, because we ensure that each estimate is based on at least six client firm-year observations. Stricter restrictions (e.g., each partner works for at least 2 clients for at least 5 years) enhance precision, but also reduce sample sizes, which is problematic for the IPO and partner change market reaction analyses. Both tests have small sample sizes to begin with. However, stricter restrictions generally enhance our results on the ERC and debt contracting tests, both of which have large enough sample sizes. 16

19 value of LOSS is 0.073, showing that the vast majority of firms report positive earnings during the sample period. An average firm finances a quarter of its assets from debt (LEVERAGE), has an asset turnover ratio (TURNOVER) of 0.9, a book to market ratio (BTM) of close to 1, and is about 24 years old (AGE). The average cash flow from operations (CFO) is about 10% of the firm s total assets, with a standard deviation (STDCFO) of about We find that on average 59.3% of the firm-years have experienced a business model shock (BMS), which suggests that the occurrence of a business model shock is fairly common. This is consistent with Owens et al. (2013), who document that 55% of their sample years have experienced business model shocks. The average tenure for the audit firm (TENURE_AF) is about 8 years and for the engagement partner (TENURE_EP) is about 5 years. On average an audit firm has an industry market share (IND_MKTSHARE_AF) of about 21% and concentrates 7% of its client portfolio within the same industry (IND_PORTFOLIO_AF). This pattern is reverse for engagement partners. On average an engagement partner has an industry market share (IND_MKTSHARE_EP) of about only 5%, but concentrates 20% of her client portfolio within the same industry (IND_PORTFOLIO_EP). On average, each client only accounts for 4% of an audit firm s portfolio (CI_AF). In contrast, each client accounts for about 26% of a partner s portfolio (CI_EP). We also find that the Big N audit 84% of our sample firms (BigN). Table 2 Panel B presents results on equation (1). The signs of the control variables are generally consistent with prior studies. 17 We find that the engagement partner effects are jointly significant (F-statistic of 1.19, p-value < 0.01). To examine the extent to which partner effects improve the model s explanatory power, we follow previous studies (e.g., Collins et al., 1997; 17 Some of the control variables are not significant. This is not surprising because we include firm fixed effects in the model to control for firm heterogeneity. By including firm fixed effects, we are only exploiting variation within the same firm over time. For variables that do not vary much over time (such as size, age, tenure), they will have large standard errors, reducing their statistical significance. 17

20 Gul et al., 2013) and calculate the incremental R 2 and the relative percentage increase in R 2 attributable to the engagement partners. 2 where R Full R 2 2 = R Full %R 2 2 = (R Full 2 - R no EP R no EP ) / R no EP is the adjusted R 2 2 of the full model including all fixed effects and R no EP is the adjusted R 2 of the model excluding partner fixed effects. Untabulated analysis shows that including engagement partner effects increases the adjusted R 2 of the model by 0.89%, with a percentage increase of 3.38%. This increase in explanatory power is large compared to the audit firm fixed effects. We find that including audit firm fixed effects only increases the adjusted R 2 of the model by 0.35%, with a percentage increase of 1.32%. Therefore, the incremental explanatory power provided by individual audit partners is more than twice as large as the incremental explanatory power provided by the audit firm. The finding that individual partner has a larger impact on audit quality than the audit firm is consistent with Gul et al. (2013). 4.3 Out of sample validation of partner fixed effect estimates We perform a series of out of sample tests to validate our partner fixed effect estimates. First, we regress ABSDA on the estimated partner effects, audit firm effects, client effects, and the control variables using the sample period from We expect partners and audit firms with higher (transformed) fixed effect estimates to be associated with higher accrual quality (i.e., lower ABSDA). Table 3 column [1] reports the results. Consistent with the expectation, we find that the fixed effect estimates are negatively associated with ABSDA. The coefficient on QPartnerFE is (t-statistic of ) and the coefficient on QAuditFirmFE is (t-statistic of ). These negative associations confirm that our fixed effect estimates capture the individual partner and audit firm effects on audit quality. 18

21 We use two additional measures of audit quality that have been widely used in prior literature to further validate our fixed effect estimates. 18 Our first measure is the presence of a small profit. Prior studies document that firms have incentives to avoid losses and the presence of a small profit above zero is evidence of upward earnings management (e.g., Hayn, 1995; Burgstahler and Dichev, 1997). We define a firm as reporting a small profit (SP = 1) if its quarterly earnings before interests and taxes deflated by total assets is between 0 and 1 percent. We regress SP on the fixed effect estimates and the same set of the control variables using a logit model. Table 3 column [2] reports the results. We find that both QPartnerFE and QAuditFirmFE are negatively associated with SP. The results suggest that when the engagement partner and audit firm are of higher quality, the client is less likely to manage earnings upward to report small positive earnings. Our second measure of audit quality is whether the client firm later restates its financial statements. As mentioned in DeFond and Zhang (2014), restatements are very direct [ ] measures of audit quality because they indicated that the auditor erroneously issued an unqualified opinion on materially misstated financial statements. We expect that firms audited by higher quality auditors are less likely to issue restatements. We define RESTMT as an indicator variable equal to one if the current year annual report is restated later on. We regress RESTMT on the fixed effect estimates using a logit model. Table 3 column [3] reports the results. We find that the coefficients on QPartnerFE and QAuditFirmFE are both negative. However, only the coefficient on QPartnerFE is statistically significant See discussion in DeFond and Zhang (2014) for studies that have applied these proxies for audit quality. 19 DeFond and Zhang (2014) suggest that modified audit opinion is another common proxy for audit quality. They suggest that in the U.S. going concerns (GCs) are the only modified audit opinions accepted in public company filings with the SEC. However, in other foreign jurisdictions modified audit opinions (MAO) are usually used as an alternative to GCs. Several studies using settings in China have used MAO to proxy for audit quality (e.g., DeFond et al., 2000; Wang et al., 2008; Chan and Wu, 2011; Gul et al., 2013). We do not find that the likelihood of issuing a MAO is associated with either QPartnerFE or QAuditFirmFE. The lack of results is likely due to the specific setting 19

22 Overall, the results in Tables 2 and 3 suggest that our fixed effect estimates capture engagement partner quality. We find that individual partners have large explanatory power on clients reporting quality. Using out of sample tests, we further show that high quality partners clients are less likely to engage in earnings management and restate their financial statements. 5. Equity Market Consequences 5.1 Earnings response coefficients To examine the market s perceptions of audit quality, we compare the ERCs between firms audited by high quality partners and firms audited by low quality partners. The model takes the form: CAR= α + β 1 E + β 2 E + β 3 E*QPartnerFE + β 4 E*QPartnerFE + β 5 E*QAuditFirmFE + β 6 E*QAuditFirmFE + β 7 E*QFirmFE + β 8 E*QFirmFE + β 9 QPartnerFE + β 10 QAuditFirmFE + β 11 QFirmFE j=1 β 12+2 j-1 E*X j + j=1 β 13+2 j-1 E*X j + j=1 β 31+j X j + ε (3) where CAR is the market adjusted daily cumulative abnormal returns for the 16 month period ending four months after the fiscal year-end. E is income from continuing operations and ΔE is changes in income from continuing operations, both deflated by lagged total assets. Prior studies show that including both the level and the change in earnings reduces the measurement error in unexpected earnings and increases the explanatory power of the ERC model (Easton and Harris, 1991; Ali and Zarowin, 1992). We measure CAR over a 16-month window because in Taiwan public companies are required to release their audited annual reports within 4 months after the fiscal year end. This design ensures that market participants are aware of the attributes of the in Taiwan, where MAO includes more types of unqualified audit opinion with explanatory notes. In contrast to China, where the probability of issuing a MAO is around 15% (see descriptive statistics in Wang et al., 2008 and Chan and Wu, 2011), almost 80% of our firm-years from receive a MAO. This result suggests that receiving a MAO during this period is a fairly common event in Taiwan and does not necessarily distinguish the quality of auditors. 20

23 engagement partner and also reduces the potential downward bias in ERCs due to prices leading earnings (Collins and Kothari, 1989; Ghosh and Moon, 2005). 20 We estimate equation (3) in our testing sample period from 2006 to The sum of the coefficients on earnings and changes in earnings (β 1 + β 2 ) is the earnings response coefficient, or ERC. A higher ERC suggests that capital markets perceive earnings to be higher quality. Our variable of interest is the sum of the coefficients on E*QPartnerFE and ΔE*QPartnerFE (β 3 + β 4 ). Under the hypothesis that the markets perceive financial reports audited by high quality partners to be higher quality, we would expect β 3 + β 4 to be positive. We also include audit firm fixed effect estimates (QAuditFirmFE) and client fixed effect estimates (QFirmFE) and interact these variables with E and ΔE to control for audit firm- and client-specific reporting quality. X is a vector of standard control variables. We interact these control variables with E and ΔE to control for their effects on the ERC. We discuss these control variables in Appendix A.2. Table 4 Panel A reports the descriptive statistics. We have 3,306 firm-years for this analysis. CAR has a median value of 5.6%. 21 The median E is around 5% and the median ΔE is close to zero. The average quartile ranks for firm fixed effects, audit firm fixed effects, and partner fixed effects are around Equation (3) is an association study method that uses a long window to examine whether earnings determination process captures in a meaningful and timely fashion the valuation relevant event (Collins and Kothari, 1989). An alternative design is an event study method that uses a short window to examine whether earnings announcements convey information about future cash flows (Collins and Kothari, 1989). We obtain consistent results using an event study method. Specifically, we regress 3 day CAR around earnings announcements on earnings surprise, the three fixed effect estimates, the interaction of these variables, and a set of control variables. We find that the coefficient on the interaction term between earnings surprise and QPartnerFE is positive and significant. We prefer to use a long window design because it ensures that market participants are aware of the identity of the engagement partner. 21 The average CAR is 18.9%. We find that the high average CAR is largely driven by years 2006 and For 2006, the average CAR is 30% (median 17%) and for 2009 it is 49% (median 32%). For the rest of the years (2007, 2008, 2010), the average CARs are around 1% to 6%. To ensure that our results are not sensitive to a specific year, we include year fixed effects in our model specifications (see Table 4, Panel B). 21

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