Capital Market Consequences of Individual Audit Partners

Daniel Aobdia d-aobdia@kellogg.northwestern.edu

Kellogg School of Management Northwestern University

Chan-Jane Lin cjlin@ntu.edu.tw

College of Management National Taiwan University

Reining Petacchi rnchen@mit.edu

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.

 

 

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.

 

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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).

 

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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.

 

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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 2010. 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.

 

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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.

 

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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

 

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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,

 

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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:

 

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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.

 

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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).

 

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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

 

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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.

 

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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 2010. 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 2004.11 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.  

 

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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 1995-2005. For the period

of 2006-2010, 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 1995-2005 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.

 

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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:

ABSDAit = βXit + ΣαtYeart + ΣγmAudit Partnerm + ΣθjAudit Firmj + ΣδiClienti + εit (1)

where ABSDA is the absolute value of the discretionary accruals measured using the cross-

sectional 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:

TAt/ASSETt-1 = β1(1/ASSETt-1) + β2(ΔSALESt – ΔARt)/ASSETt-1 + β3PPEt/ASSETt-1 + β4 ROAt-1 + εt (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).

 

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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.

 

16

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 1995-2005. 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 (δi, θj, and γm) using the sample period from

2006-2010 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 1995-2010. 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.  

 

17

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 0.085. 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.

 

18

Gul et al., 2013) and calculate the incremental R2 and the relative percentage increase in R2

attributable to the engagement partners.

△R2  =  RFull2  -  Rno EP2

%R2 = (RFull2  - Rno EP2 ) / Rno EP

2

where RFull2 is the adjusted R2 of the full model including all fixed effects and Rno EP2 is the

adjusted R2 of the model excluding partner fixed effects. Untabulated analysis shows that

including engagement partner effects increases the adjusted R2 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 R2

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 2006-2010. 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 -0.006 (t-statistic of -3.391) and the coefficient on QAuditFirmFE

is -0.005 (t-statistic of -2.109). These negative associations confirm that our fixed effect

estimates capture the individual partner and audit firm effects on audit quality.

 

19

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.19

                                                                                                                         18 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

 

20

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  +  β1E  +  β2∆E  +  β3E*QPartnerFE  +  β4∆E*QPartnerFE   +  β5E*QAuditFirmFE  +  β6∆E*QAuditFirmFE  +  β7E*QFirmFE   +  β8∆E*QFirmFE  +  β9QPartnerFE  +  β10QAuditFirmFE  +  β11QFirmFE   + β12+2 j-1 E*Xj

10j=1 + β13+2 j-1 ∆E*Xj+ β31+jXj

10j=1

10j=1 +  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 2006-2010 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.

 

21

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 2010. The sum of the

coefficients on earnings and changes in earnings (β1 + β2) 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 (β3 + β4).

Under the hypothesis that the markets perceive financial reports audited by high quality partners

to be higher quality, we would expect β3 + β4 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 2.5.

                                                                                                                         20 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 2009. 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).

 

22

Table 4 Panel B reports the regression results of equation (3). For brevity, we suppress

the results on the stand-alone interaction terms and on the control variables and report them in

Appendix C for interested readers. In column [1], we report a baseline model without fixed effect

estimates. We find that the ERC is 5.13.22 In column [2], we add the three fixed effect estimates.

In column [3], we further include year and industry dummies and interact them with E and ΔE to

control for unobservable time and industry effects on ERC (Lim and Tan, 2008; Balsam,

Krishnan, and Yang, 2003). Regardless of the specifications, we find that earnings of firms

audited by high quality partners are valued higher than earnings of firms audited by low quality

partners. The sum of the coefficients on E*QPartnerFE and ΔE*QPartnerFE are all positive and

significant at 5% level or better across specifications. We also find that firms audited by higher

quality audit firms and firms with higher reporting quality also tend to have larger ERCs

(E×QAuditFirmFE + ΔE×QAuditFirmFE and E×QFirmFE +ΔE×QFirmFE are both positive).

To evaluate the economic significance of the impact of a high quality partner on the

firm’s ERC, we compare the sum of the coefficients on E×QPartnerFE and ΔE×QPartnerFE

(column [2]) with the baseline model (column [1]). To facilitate comparison, we first use the

baseline model to compute the ERC for an average firm that is profitable and also audited by a

Big N audit firm. We focus on firms that are profitable and audited by Big N audit firms, because

the majority of the firms in the sample are of this type (see Table 4 Panel A). We find that the

                                                                                                                         22 Theoretically, the magnitude of the ERCs can be as large as 20 (Kothari, 2001). See discussion in Kothari (2001) on why empirically estimated ERCs are relatively small.

 

23

ERC for this type of firms is 3.53.23 Therefore, a one-quartile increase in partner quality

increases ERC by about 17%.24

Overall, the evidence indicates that when firms hire higher quality engagement partners

to conduct the audit, investors perceive the financial reports to be higher quality. If investors

perceive high quality partners to be valuable, we would expect the markets to react positively

when a firm switches from a low quality audit partner to a high quality audit partner. We

investigate this possibility in the next section.

5.2 Market reaction to announcement of partner change

To examine whether the market reacts positively when a firm switches to a higher quality

partner, we conduct an event study and collect data on the announcement dates of partner

changes from 2006-2010. In Taiwan, public companies are required to make a public

announcement within 2 days when the board decides to change the audit firm or audit partner.

The announcement includes information on the old partners and audit firms, the new partners

and audit firms, and the reasons for the partner change.25

                                                                                                                         23 We demean all the continuous variables in the baseline model to compute the ERC for an average firm. After demeaning, the sum of the coefficients on E + ΔE (=3.89) is the ERC for a firm with the average values of the continuous variables. We then compute the baseline ERC as E + ΔE + E*BigN + ΔE*BigN = 3.89 – 0.361 = 3.53. This is the ERC for an average firm that is also profitable and audited by a Big N audit firm. 24 0.613/3.53 = 17%. We think this magnitude is reasonable comparing to prior studies. For example, Ghosh and Moon (2005) state that “investors pay a premium of 2 percent … for earnings as the audit engagement increases by an additional year.” Given that the average quartile for auditor tenure is about 5 years in their sample, their result suggests that a one-quartile increase in audit tenure increases ERC by 10%. Note that Ghosh and Moon (2005) evaluate the economic significance of the results by assuming all the control variables equal to zero. We do not think this comparison is as meaningful because we do not have any firms in the sample that have 0 total assets or 0 book-to-market ratios. However, adopting their approach, our results suggest that a one-quartile increase in partner quality increases ERC by 13.6% (= 0.613/4.511). 25 The Taiwan Security Exchange Act, article 36-2 requires public companies to disclose any material events within two days from the date of occurrence. The Taiwan Stock Exchange Corporation Procedures for Verification and Disclosure of Material Information of Companies with Listed Securities further defines any change in audit partners and audit firms as a material event. GreTai Securities Market adopts the same rules for firms traded in the OTC markets in GreTai Securities Market Procedures for Verification and Disclosure of Material Information of Companies with GTSM Listed Securities. Companies are not required to file a specific filing for partner and audit firm changes, but they need to make an announcement at the MOPS. The announcement typically includes the

 

24

We restrict our sample to firms changing the lead partners, as lead partners are the ones

most likely to influence audit quality. We exclude all the announcements made outside the 2-day

window from the board meeting, as we are unable to identify the date the market learns the news.

Such late announcements happen when the firm does not comply with the regulatory requirement.

Our empirical model takes the form:

CAR = α + β1ChgQPartnerFE + β2ChgQAuditFirmFE + β3DChgAuditFirm + δX + ε (4)

where CAR is the cumulative abnormal returns around the announcement window. The Taiwan

stock markets impose a 7% single day price limit (up and down 7%), except for the first 5

trading days of an IPO. Prior research shows that imposing a price limit may lead to less efficient

price discovery (Kim and Rhee, 1997). Therefore, we examine both a short-term 3-day CAR

window (-1, +1) and a long-term 21-day CAR window (-10, +10). ChgQPartnerFE is the change

in QPartnerFE, where a positive value indicates hiring a higher quality audit partner. 26

Oftentimes when a firm changes the partner, it also changes the audit firm. To ensure our results

are not driven by the change in audit firm quality, we control for the change in audit firms.

DChgAuditFirm is an indicator variable equal to 1 if the firm changes the accounting firm, and 0

otherwise. ChgQAuditFirmFE is the change in QAuditFirmFE for firms hiring a new audit firm,

and 0 for firms retaining the same audit firm. X is a vector of control variables, which we discuss

in detail in Appendix A.3.

Table 5 Panel A reports the descriptive statistics. We have 117 observations for this

analysis. The average 3-day CAR and 21-day CAR are about 0.4% and 0.7%, respectively. The

                                                                                                                                                                                                                                                                                                                                                                                                       reasons for the partner and audit firm change; however, the descriptions of the reasons are usually very vague. Oftentimes companies will just use reasons such as “operational needs” or “audit firm personnel relocation.” 26 When the company replaces both the lead partner and the review partner at the same time, ChgQPartnerFE equals the sum of the changes in QPartnerFE for both audit partners. We obtain similar results if we only consider the change in QPartnerFE for the lead partner.

 

25

median value of ChgQPartnerFE is zero, suggesting that the majority of the new hire partners are

of similar quality as the old partners. 21% of the firms change their audit firms when they replace

their lead audit partner and about half of them are traded on the OTC market. The median values

of other potential confounding events (earnings announcements and announcements related to

executive turnover, dividend payouts, capital raising, and restatements) are zero, with mean

values all below 10%. This suggests that other events not directly related to the change of

partners are unlikely to confound our results.

Table 5 Panel B reports the results of equation (4). Column [1] reports the result where

CAR (-1, +1) is the dependent variable and column [2] reports the result where CAR (-10, +10)

is the dependent variable. We find that the market reacts positively to the announcement of the

partner change when the new partner is of higher quality than the old partner. The magnitude of

the coefficient on ChgQPartnerFE suggests that a 1-quartile increase in the partner quality

increases the 21-day CAR by 2.0% (t-statistic of 2.523). The coefficient remains positive but

insignificant for the 3-day window test.

Overall, the results in Table 5 provide corroborating evidence to our ERC analysis and

suggest that changes in partner quality alter the capital markets’ beliefs about the firm’s financial

reporting quality. The market reacts positively when the firm switches to a higher quality partner,

and importantly, this effect is conditional on the change in audit firm quality. Therefore, our

results suggest that the equity market views individual audit partner as a relevant factor in

determining the quality of corporate financial reporting.27

5.3 Initial public offerings underpricing

                                                                                                                         27 Although we have included an extensive set of concurrent announcements to control for potential confounding events, as in any event study, we cannot completely rule out the possibility that some other events we do not control for are driving the results. Therefore, we caution the interpretation of the results.

 

26

In this section, we explore whether partner quality affects IPO underpricing. Our model

takes the form:

UP = α + β1QPartnerFE + β2QAuditFirmFE + δX + ε (5) UP is the level of underpricing and is computed as the 1-day buy and hold return of the stock

(price at the end of the first trading day deflated by offering price minus one). Since the IPO

markets in developing countries may be less efficient28, we also estimate UP as the buy and hold

return over the first 3 and 20 trading days. X is a vector of control variables that have been

shown to influence IPO underpricing and we discuss these control variables in detail in

Appendix A.4. We also include underwriter fixed effects to control for the underwriter prestige

and reputation. We test equation (5) using IPO data from 2006 to 2010. Under the hypothesis

that hiring a higher quality audit partner serves as a positive signal and leads to a reduction in the

underpricing discount, we expect β1 to be negative.29

Table 6 Panel A, reports the descriptive statistics. We have 146 observations for this

analysis. The median 1-day, 3-day and 20-day returns are high at around 35%, suggesting that

most IPOs in our sample period are heavily underpriced. The large underpricing in the Taiwan

IPO market is consistent with prior research (Loughran et al., 1994; Peng and Wang, 2007; Lu,

Kao, and Chen, 2012).30 The median age of the companies is 11 years and the median BTM is

                                                                                                                         28 For example, Loughran, Ritter, and Rydqvist (1994) Table 1 shows that the IPO underpricing for developing countries such as Brazil, Korea, and Taiwan are above 45%, while for developed countries such as Canada, France, and Netherlands are below 10%. 29 In contrast to other specifications, we cannot include QFirmFE as an explanatory variable because this variable cannot be estimated during the estimation period, which occurred prior to the firms’ IPOs. 30 Our underpricing descriptives are higher than Peng and Wang (2007) and Lu et al. (2012). Both of them show an underpricing mean value of about 20%. This difference is likely due to different sample periods used. Both Peng and Wang (2007) and Lu et al. (2012) use a sample period before 2005, while we use a sample period from 2006-2010. Taiwan stock exchanges imposed a 7% price limit and this limit was lifted in March 2005 for the first 5 trading days of an IPO. Moreover, we find a higher proportion of IPO firms in growth industries (e.g., semiconductor, optoelectronic) during 2006-2010. In contrast, we find a higher proportion of firms in mature industries (e.g., construction industry) during 1985-2005. Using the data from 1985–2005, we find that the median 3-day return is 16.8% and 20-day return is 14.4%. These figures are comparable to Peng and Wang (2007) and Lu et al. (2012).

 

27

below 0.50. These results suggest that IPO firms during the period of 2006-2010 were relatively

young and with high growth opportunities.

Table 6 Panel B presents the results of equation (5). Column [1] shows that IPO firms

who employ higher quality engagement partners experience less underpricing. The coefficient on

QPartnerFE is -0.077 (t-statistic of -2.552), suggesting that a 1-quartile increase in partner

quality is associated with a 7.7% reduction in the first day returns. In column [2] we include

underwriter dummies to control for the effect of underwriter reputation on IPO underpricing. We

find that the coefficient on QPartnerFE remains significant and has the same magnitude as the

coefficient in column [1]. In columns [3] to [6] we repeat the analysis using 3-day and 20-day

returns to measure underpricing and find consistent results. In particular, a 1-quartile increase in

partner quality is associated with an approximately 7% reduction in the first 3-day returns and an

approximately 10% reduction in the first 20-day returns.

Overall Table 6 shows a negative association between engagement partner quality and

IPO discounts. This result is consistent with the interpretation that when the value of the firm is

imperfectly known, hiring a high quality engagement partner serves as a positive signal to the

investors.

6. Debt Market Consequences

To examine whether partner quality matters to the debt market participants, we

investigate whether firms audited by higher quality partners enjoy more favorable terms in their

debt contracts. For this analysis, we focus on the private debt market because the corporate bond

market is still not well developed in Taiwan. Only government enterprises and some large

corporations raise capital in the bond markets. Moreover, the market becomes largely inactive

 

28

after 2005 and once imposing additional data requirements we can only obtain limited bond

issuances (about 75 observations) for our testing period of 2006-2010.31

We use the following regression model to investigate the impact of audit partner quality

on debt contracting.

Contract Term = α + β1QPartnerFE + β2QAuditFirmFE + β3QFirmFE + δX + ε (6)

We examine both price and non-price contract terms. The price term is the interest rate charged

on the loan (INTEREST RATE). 32 The non-price terms are the borrowing amount

(Log(AMOUNT)) and whether the loan requires collateral (SECURED). We use the borrowing

amount to examine whether partner quality affects firms’ credit access and the security

requirement to examine whether partner quality affects contract stringency. 33 When the

dependent variables are INTEREST RATE and Log(AMOUNT), we estimate equation (6) using

an OLS model and when the dependent variable is SECURED, we estimate the equation using a

logit model. X is a vector of control variables that have been found in prior literature to influence

debt contracting. We discuss these variables in detail in Appendix A.5.

We estimate equation (6) over the testing sample period from 2006 to 2010. To ensure

that lenders observe the attributes of the engagement partner, we match the debt contract with the

                                                                                                                         31 The Taiwanese bond market has become inactive since 2005 for the following reasons. First, a few large investors, with better resources and information processing capabilities, have driven small retail investors away. In addition, only limited hedging vehicles are available to hedge interest rate risk. The trading activities in interest rate swaps, options, and futures have also declined after 2005. Therefore, it has become difficult to hedge interest rate risk using interest rate derivatives. Finally, Taiwan government imposes a high tax rate (20%) on bond investments. All these factors have led to low demand in the bond market. 32 All the interest rates in our sample are fixed rates. We exclude loans with variable rates because in Taiwan companies do not disclose the specific spread charged on the loan, instead they just disclose a range of the rates. The range tends to be wide and imprecise. The majority (71% in our sample) of the loans initiated from 2006-2010 are also fixed rate loans. This feature is distinct from the private debt market in U.S. Our results remain robust (albeit slightly weaker) when including variable rate loans and assuming that the interest rate is the average of the range of the rates disclosed. 33 In Taiwan companies are not required to publicly file their debt contracts, so detailed information on debt contract terms (e.g., covenant restrictions) is unavailable. However, the financial statements list each loan’s collateral requirement. Therefore, we use collateral requirement to proxy for debt contract stringency.

 

29

most recent annual report. This is the report that is most likely to be used in the loan application

process. Since contract terms vary across facilities of a given loan deal, we conduct our analysis

at the facility level. Under the hypothesis that higher quality partners lower information

asymmetry between the borrower and the banks, we would expect β1 to be negative when the

dependent variable is either INTEREST RATE or SECURED and positive when the dependent

variable is Log(AMOUNT) (i.e., lower interest rates, less likelihood of requiring security, and

larger credit access for firms audited by higher quality partners).

Table 7 Panel A provides descriptive statistics on the variables. We have 9,079 facilities

representing 6,976 loan deals and 1,513 firm-years for this analysis. The average quartiles of the

partner, audit firm, and client fixed effects estimates are around 2.5. The average interest rate

charged on the loan is 2.52%, the average borrowing amount is about 49 million NT dollars, and

about 43% of the loans require collateral. The average lending relationship (LENGTHREL) is

about 7 years and unlike the debt market in the U.S., most of the loans in Taiwan are not

syndicated.

Table 7 Panel B reports the results of the loan pricing analysis. In column [1] we only

include year fixed effects and in column [2] we add industry fixed effects to control for

unobserved industry heterogeneity. In both specifications, we find that QPartnerFE is negatively

associated with the loan rate, suggesting that firms can lower their borrowing costs by hiring

high quality audit partners. A one-quartile increase in partner quality is associated with an

approximate 6 basis point reduction in borrowing rate. Panel C reports the results on borrowing

amount. We find a positive association between audit partner quality and borrowing amount

regardless of whether we include industry fixed effects. In particular, a one-quartile increase in

audit partner quality is associated with an approximate 14% to 19% increase in borrowing

 

30

amount. This result suggests that by hiring a high quality audit partner, firms can have larger

credit access to the private debt market. Finally, Panel D reports the results on security

requirement. We find evidence that engagement partner quality is negatively associated with

collateral requirement. The coefficient on QPartnerFE is negative and significant across both

specifications. Overall, these results suggest that being audited by higher quality audit partners

helps the company to obtain more favorable debt contract terms. We also find that audit firm

quality and the company’s financial reporting quality are generally positively associated with

more favorable debt contract terms.

Overall, Table 7 provides evidence that engaging a high quality audit partner is

associated with a reduction in borrowing costs, an increase in credit access, and less stringent

contract terms. Importantly, this effect is conditional on the effect of audit firm quality.

Therefore, our evidence suggests that individual partner can enhance the credibility of financial

statements and reduce information asymmetry between the borrower and the creditor beyond the

reputation effect of the audit firm.

7. Additional Analyses and Robustness Tests

7.1 Audit quality measure based on the Dechow and Dichev (2002) model

Although the most frequently used measure of financial reporting quality in auditing is

based on the Jones (1991) discretionary accruals model (DeFond and Zhang, 2014), to ensure

that our results are not sensitive to this specific model choice, we rerun our analyses using

accrual quality measured under the Dechow and Dichev (2002) (hereafter DD) model. We

augment the DD model following McNichols (2002) and Francis et al. (2005).

TCAt = ϕ0 + ϕ1CFOt-1 + ϕ2CFOt + ϕ3CFOt+1 + ϕ4ΔRevt + ϕ5PPEt + υt (7)

 

31

where TCA is total current accruals (= ΔCurrent Assets – ΔCurrent Liabilities – ΔCash +

ΔCurrent Portion of the Long-term Debt), CFO is cash flow from operations, ΔRev is change in

revenues, and PPE is the net property, plant, and equipment. All variables are scaled by average

total assets.

The absolute value of the residual from equation (7) is our alternative measure of client

accrual quality.34 We then rerun equation (1) using this alternative measure as the dependent

variable to estimate QPartnerFE, QAuditFirmFE, and QFirmFE. Note, though, that, as detailed in

Dechow et al. (2010), the DD model only focuses on short-term working capital accruals.

Consequently, the model is unable to measure the importance of the role the auditor plays in

detecting potential distortions induced by long-term accruals. This could create additional noise

in the estimation of individual engagement partners’ quality if quality can be measured more

precisely when assessing the impact of the audit partner on both short-term and long-term

accruals. In addition, in contrast to the Jones model that focuses on capturing managerial

manipulation of accruals, the DD model focuses on capturing estimation error in accruals,

regardless of managerial choices (Dechow and Dichev, 2002; Dechow, Ge and Schrand, 2010).

In other words, a firm can have low accrual quality based on the DD model because of its

business fundamentals. If a firm has low accrual quality due to its innate characteristics, auditors

should have no impact on such accrual quality. Therefore, we think the accrual quality measured

under the Jones model is more suitable for our study than that measured under the DD model.

Table 8 reports the results on the capital market analyses using accrual quality measured

under the DD model. Panel A reports the results on ERC, Panel B on market reactions to partner

                                                                                                                         34 Both Dechow et al. (2010) and Dechow and Dichev (2002) suggest that the absolute value of the residual from the model is an appropriate measure of accrual quality. They also propose using the standard deviation of the residuals. We choose to use the absolute value, because we only have 10 years of data and running the standard deviation greatly reduces the sample size.

 

32

changes, Panel C on IPO underpricing, Panel D on loan pricing, Panel E on credit access, and

Panel F on loan collateral requirement. To save space, we suppress the results on the control

variables. Full results are available upon request. Overall, the results are consistent with our

earlier findings. In particular, we find a positive association between QPartnerFE and the firm’s

ERCs from returns-earnings regressions in Panel A. We also find a negative association between

QPartnerFE and IPO underpricing in Panel C when using 20-day returns. We also find a positive

association between QPartnerFE and borrowing amounts, and a negative relationship between

QPartnerFE and collateral requirements. However, we do not find results on market reactions to

the announcement of a partner change (Panel B) and any association between QPartnerFE and

private debt interest rate (Panel D). The slightly weaker results could be due to lowered precision

of the partner fixed effects estimates given that the DD model can only measure the impact of the

partner on current accruals.

7.2 Determination of the lead partner

We conduct additional robustness tests that we summarize here without tabulation. To

validate that it is the lead partner that affects audit quality and that our procedure for identifying

the lead partner has sufficient precision, we run two additional analyses. First, we add review

partner fixed effects and their control variables in equation (1). We do not find an increase in the

adjusted R2 of the model, despite the inclusion of many more fixed effects for the review

partners.35 This result suggests that the review partner has little influence on audit quality in

Taiwan and is consistent with Chi and Chin (2011), who conclude that review partners alone are

not associated with higher audit quality.

                                                                                                                         35 The adjusted R2 actually slightly decreases from 27.2% to 27%.

 

33

Second, we rerun all our capital markets tests on the review partner instead of the lead

partner. If review partners only rubber-stamp the audit reports we would not expect market

participants to respond to the quality of the review partner.36 Consistent with the expectation, we

find no association between the estimated review partner quality and capital markets effects,

except for the debt analysis, where in some specifications we observe a weak association

between review partner quality and debt pricing and amount. The lack of results on review

partners provide support that it is the lead partner that affects audit quality and elicits capital

market consequences.

7.3 Results on signed accruals

To explore whether the market participants value partner quality differently based on

whether the partner constrains income increasing accruals or income decreasing accruals, we

rerun equation (1) separately with positive DA and negative DA as the dependent variables. This

research design is consistent with prior auditing studies that investigate whether certain auditor

attributes (such as tenure, the size of the local offices) are related to income increasing or income

decreasing earnings management (e.g., Myers et al., 2003; Francis and Yu, 2009).37 The

disadvantage of this design for our study is that it greatly reduces our sample size due to the

identification restriction we need to impose on the fixed effect estimates (see discussion in

Section 4). This is less of a concern for our ERC and debt analyses, both of which still have

reasonably large sample sizes (observations range from 1,555 to 5,859). However, this is an issue

for our IPO and partner change analyses. Both of these tests have small sample sizes to begin                                                                                                                          36 We thank the referee for making this suggestion. 37 We do not use DA as the dependent variable without partitioning the variable into positive and negative values, because accrual based earnings management reverses over time (Dechow et al. 2012). When running a panel dataset, this reversal behavior cancels out positive accruals against negative accruals. Since the fixed effect estimates capture the average DA, we may incorrectly conclude that a partner fixed effect estimate close to zero indicates no earnings management, which in fact has large income increasing accruals canceled out by large income decreasing accruals due to reversal.

 

34

with. For example, for the partner change analysis, our sample size reduces to only 32 (46)

observations when we estimate the partner fixed effects using positive (negative) DA. Given that

we have more than 10 control variables in these tests, running a regression on such small sample

sizes reduces statistical power and precision. Therefore, we focus on the ERC and debt analyses

for the signed accrual tests.

For the ERC analyses, our results hold using partner fixed effect estimates measured

under both positive DA and negative DA. The result suggests that investors perceive earnings to

be more informative when the engagement partner constrains both income-increasing and

income-decreasing accruals. This result is consistent with the general findings in the literature

that higher audit quality curbs both upward and downward earnings management (e.g., Myers et

al., 2003; Francis and Yu, 2009) and that when accruals are used opportunistically, they lower

the informativeness of earnings (Hanlon et al., 2008). For the debt analysis, our results are mixed.

We find that companies enjoy a greater credit access when they hire partners constraining either

income-increasing accruals or income decreasing accruals. Companies are less likely to be

required to post collateral when they hire partners constraining income-increasing accruals. We

do not find results on loan rates when we separate out income increasing accruals from income

decreasing accruals. Overall, these results suggest that lenders are more likely to care about the

magnitude of the accruals than the sign of the accruals and are consistent with Bharath, Sunder

and Sunder (2008) who find that “both public and private debt lenders appear to factor in the

magnitude of operating accruals rather than the sign of the accruals in setting debt contract terms.”

7.4 An alternative proxy for partner quality

Another concern with our results is that our accrual based measure for partner quality

may suffer from measurement error. We do not view this as a particular issue for our study,

 

35

because we have several capital market tests (ERC, IPO, partner change, and various debt contract

terms). To make wrong inferences persistently, the measurement error has to correlate with our

variable of interest in all these tests. We think this is unlikely. In addition, as DeFond and Zhang

(2014) point out measurement error in audit quality is likely to bias against finding significant

associations. Nonetheless, we address this issue by using partners’ regulatory sanction history as

an alternative measure for partner quality.

A regulatory sanction case is usually outright fraud or involves a serious accounting

irregularity. Sanctioned partners are typically warned, suspended, or have their license revoked.

These regulatory sanction actions are public information. Therefore, a sanctioned partner is

deemed to be perceived as low quality by capital market participants. Although free from

measurement error, a sanction is uncommon, which greatly reduces variation among partners.38

The low variation increases standard errors, which bias against us finding results.

Our data source TEJ started collecting the sanction data in 1998. From 1998-2005 there

are 77 sanction cases involving 60 audit partners that have non-missing data for the capital

market analyses. We observe a significantly positive market reaction of 2.9% over (-10, +10)

when a company replaces a partner with a history of regulatory sanction with a partner without

such a history.39 Moreover, we find that firms audited by partners with a regulatory sanction

history tend to have less favorable loan contracts. Specifically, their loan rate is 25.3 basis points

higher than firms that audited by partners without a sanction history. These results are consistent

with our findings that the quality of engagement partners matters to capital market participants.40

                                                                                                                         38 In other words, regulatory sanction has low Type I error but high Type II error. 39 Similar to Table 5, we do not find market reactions in the short window (-1, +1). 40 We do not find partner sanction history associated with ERCs and IPO underpricing. The lack of results is likely due to the low frequency of sanctioned partners in these samples. For example, only 3.7% of the firm-years in the ERC sample use a partner with a sanction history. The low variation in partner quality greatly reduces our ability to find results.

 

36

8. Conclusion

This study responds to the call for more academic research on individual auditors

(DeFond and Francis, 2005; Francis, 2011). We use a setting where public firms are required to

disclose the names of the engagement partners to investigate the economic consequences of

hiring a high quality partner. We find that disclosure of the names of engagement partners

provides informational value to the capital market participants. In particular, investors perceive

the financial reporting to be more credible when audited by higher quality engagement partners.

The market also reacts positively when a firm switches from a low quality partner to a high

quality partner. Moreover, hiring a high quality partner serves as a positive signal, which helps

IPO firms reduce the level of underpricing. We also find that firms hiring higher quality

engagement partners can lower their cost of debt, have greater credit access, and is less likely to

be required to post collateral.

Our paper contributes to the literature by providing evidence that investors value quality

individual auditors. To our knowledge, this study is the first to provide such evidence. Our paper

is also of interest to policymakers in that recent regulatory changes have begun to require the

identification of the names of engagement partners. For example, in 2006 the European Union

issued the Eighth Company Law Directive, which requires member states to adopt a requirement

mandating the statutory auditors to sign the audit reports (Directive 2006/43/EC, Article 28). In

2011, the PCAOB proposed to require the public accounting firms to disclose the name of the

engagement partner in the audit report (PCAOB 2011). One potential benefits of requiring

engagement partners to sign the audit reports is that “…it would increase transparency about who

is responsible for performing the audit, which could provide useful information to

investors …(PCAOB 2009).” Our paper provides evidence supporting this argument.

 

37

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44

Appendix A

A.1. Control variables in equation (1)

We include a set of firm specific characteristics that prior studies have shown to affect

accrual quality. DeFond and Jiambalvo (1994) find that companies with more debt have more

incentives to use accruals to manipulate earnings to avoid covenant violations. We include the

company’s leverage ratio (LEVERAGE) and expect it to be positively related to ABSDA.

Dechow et al. (2010) suggest that weak performance provides incentives to engage in earnings

management. We control for whether the firm reports negative earnings in the year (LOSS) and

the size of its cash flow from operations (CFO). We expect LOSS to be positively associated

with ABSDA and CFO to be negatively associated with ABSDA. We also control for the

standard deviation of cash flow from operations (STDCFO), because Hribar and Nichols (2007)

report a positive association between cash flow volatility and unsigned abnormal accruals. Myers

et al. (2003) find that older, larger clients tend to have smaller discretionary accruals. We include

the firm’s age (AGE) and size (SIZE) and expect both the variables to be negatively associated

with ABSDA. Lim and Tan (2008) and Gul et al. (2013) find that clients’ book to market and

asset turnover ratios are associated with discretionary accruals. We include the firm’s book-to-

market ratio (BTM) and asset turnover ratio (TURNOVER) and expect both variables to be

negatively associated with ABSDA. Owens et al. (2013) show that business model shocks tend

to result in large unsigned abnormal accruals. Following Owens et al. (2013), we include a return

based proxy for business model shocks (BMS) and expect the variable to be positively associated

with ABSDA.

45

We also include an extensive set of audit firm (denoted by _AF) and engagement partner

(denoted by _EP) characteristics. Myers et al. (2003) and Chen et al. (2008) document a positive

association between accrual quality and auditor tenure. We include tenure (TENURE) and expect

the variable to be negatively associated with ABSDA. Prior research finds that auditors with

more industry knowledge are more likely to detect errors in the financial statements and their

clients in general have higher reporting quality. (e.g., Bedard and Biggs, 1991; Krishnan, 2003).

We use market share in an industry (IND_MKTSHARE) and portfolio concentration in the

industry (IND_PORTFOLIO) to capture industry expertise.41

Chen et al. (2010) suggest that

client importance may give auditor incentives to compromise audit quality. We control for the

relative importance of a client in the portfolio (CI) and expect the variable to be positively

associated with ABSDA. Finally, we control for the size (SIZE) of the audit firm and the

partner.42

Francis et al. (1999) find that large audit firms can constrain potentially opportunistic

reporting of accruals. However, Chen et al. (2010) find that large partners tend to have lower

audit quality. Therefore, we expect SIZE_AF to be negatively associated with ABSDA, but

SIZE_EP to be positively associated with ABSDA.

A.2. Control variables in equation (3)

Kothari (2001) suggests that “it has become an industry standard now in the capital

markets literature to control for the effects of persistence, risk, and growth and focus on the

incremental effect of a treatment variable … on earnings response coefficients.” We include

41

We do not use audit fees to compute our industry specialist measures because before 2002 public companies in

Taiwan are not required to disclose audit fees. After 2002, public companies are required to disclose audit fees only

when certain criteria are met. In the U.S. since most audit fee details were not available until 2000 and Compustat does not provide audit fee information for all listed firms, prior research also tends to rely on client sales, assets, or

the number of clients to construct industry expertise measures (see for example, Reynolds and Francis, 2001;

Krishnan, 2003; Balsam, Krishnan, and Yang, 2003; Dunn and Mayhew, 2004; Lim & Tan, 2008). 42 We measure audit firm and partner sizes using the sum of client assets. Taking the log transformation of the

variable yields similar results.

46

earnings persistence (PERSIST) and volatility (VOLATY) and expect PERSIST to have a

positive effect on ERC and VOLATY to have a negative effect on ERC. We include systematic

risk (BETA), default risk (LEVERAGE), and book to market ratio (BTM) and expect all these

variables to have a negative effect on ERC. Hayn (1995) find that reported losses are perceived

by investors as temporary and have lower information content than profits. We control for

whether the firm reports a loss in the year (LOSS) and expect the variable to reduce ERC. Teoh

and Wong (1993) find that the ERCs of big audit firm clients are higher than the ERCs of small

audit firm clients. We control for whether the audit firm is a Big N audit firm (BigN) and expect

the variable to increase ERC. We also control for the audit firm tenure (TENURE_AF), because

Ghosh and Moon (2005) find that audit firms with longer tenure enhance ERC. Finally, we

include firm size (SIZE) and age (AGE) to control for the overall information environment of the

firm.

A.3. Control variables in equation (4)

We include a set of firm characteristics that are likely to affect market reactions to an

event. Kluger and Shields (1989) find that financially distressed firms may switch auditors

because they cannot successfully suppress the unfavorable information with the incumbent

auditor. Schwartz and Menon (1985) find consistent evidence that distressed firms are more

likely to change auditors and suggest that there is “a definite need to control for the presence of

financial distress in studies on auditor switching.” We control for the firm’s leverage ratio

(LEVERAGE) and expect the variable to be negatively associated with CAR. We also include

the firm’s book to market ratio (BTM), size (SIZE), whether the firm reports a loss in the prior

quarter (LOSS), and whether the firm is traded on over the counter market (OTC) to control for

the general information environment of the firm.

47

In addition to the firm characteristics, we include a comprehensive set of hand-collected

control variables for potential confounding events. Market participants may react negatively to a

restatement announcement if they view the announcement as a signal of low reporting quality.

We control for concurrent restatement announcements (RestatementAnn) and expect the variable

to be negatively associated to CAR. Aharony and Swary (1980) argue that managers may use

dividend announcements to convey private information about the prospects of the firm. They

also document positive abnormal returns around dividend announcements. We control for

concurrent dividend payout announcements (DivAnn) and expect the variable to be positively

associated with CAR. In addition, we control for whether the partner change announcement

coincides with an earnings announcement (EA) and announcements related to changes in

executives (ChgExec) and capital raising (CapitalRaising).43

However, we do not have specific

predictions on these variables.

A.4. Control variables in equation (5)

We include an extensive set of firm specific characteristic. Previous studies (e.g., Beatty,

1989; Leone et al., 2007; Peng and Wang, 2007) find that older, larger firms and firms with more

transparent financial reporting have lower uncertainty before an IPO and experience smaller IPO

underpricing. We control for IPO firm’s age (AGE), size (SIZE), and accounting transparency

(ABSDA) and expect AGE and SIZE to be negatively associated with IPO underpricing and

ABSDA to be positively associated with IPO underpricing. In addition, we control for the risk of

the firm using whether the firm reports a loss (LOSS) immediately before an IPO and its

leverage ratio (LEVERAGE). High-risk firms have higher uncertainty about future prospects of

43 We also confirm that no firms in the sample announce mergers, acquisitions, or restructuring activities during the

event window.

48

the firm and therefore, are likely to have a higher level of IPO underpricing. We expect the

coefficients on LOSS and LEVERAGE to be positive. We control for the firm’s growth

opportunities (BTM). Investors have higher expectations about growth firms and therefore, these

firms are more likely to experience positive returns when they go public. Leone et al. (2007) also

document a positive association between growth and IPO underpricing. We expect BTM to have

a negative coefficient. We control for the exchange the firm listed (OTC), because Peng and

Wang (2007) find that firms go public on the over the counter markets are more likely to be

underpriced in Taiwan. We control for whether the firm is in the electronics industry (ELEC),

because electronics industry is the core economic driver in Taiwan and prior studies (e.g.,

Loughran and Ritter, 2004; Leone et al., 2007) also find that firms in high tech industry are more

likely to be underpriced at an IPO. We expect a positive coefficient on ELEC.

In addition to firm characteristics, we also control for issue specific characteristics. We

control for offer size (IPO SIZE), because Peng and Wang (2007) find that the offer size is

negatively related to IPO underpricing in Taiwan. Brennan and Franks (1997) suggest that IPO

firms use underpricing to create oversubscription, which allows the managers to retain the

control of the firm and reduce outside monitoring. We control for the probability of successful

purchase in the random drawing in the case of oversubscription (SUCCESS) and expect the

variable to be negatively associated with IPO underpricing. We also control for whether the firm

selects the auction flotation method in an IPO (FM), because Derrien and Womack (2003) find

that auction IPO procedure is associated with less IPO underpricing. Leland and Pyle (1977)

derive a model in which the entrepreneur can use ownership retention to signal her private

information about the future prospects of the firm. Building on Leland and Pyle (1977), Grinblatt

and Hwang (1989) further show that the entrepreneur can signal the true value of the firm by

49

offering the shares at a discount and by retaining some of the shares. Therefore, there is a

positive relation between IPO underpricing and ownership retention. We control for the

proportion of shares sold at the IPO over shares outstanding (FLOAT) and expect a negative

coefficient on the variable.

Finally, we control for the general market conditions around the IPO time. Derrien and

Womack (2003) find that recent market returns and market volatility are positively related to IPO

underpricing. We include the market returns and standard deviation of the market returns before

an IPO (MKTRET48 and STDMKTRET48) and concurrent market returns at the time of the IPO

(MKTRET). We expect all the variables to be positive.

A.5. Control variables in equation (6)

Prior studies (e.g., Bharath et al., 2008; Graham et al., 2008) find a positive association

between debt contract stringency and the borrower’s default risk, cash flow volatility, and

leverage ratio. They also find a negative association between debt contract stringency and the

borrower’s profitability, current ratio, size, asset tangibility, and growth opportunities. We use

the standard deviation of cash flow from operations (STDCFO) to capture cash flow volatility.

We use the Ohlson’s O-score (OHLSON) and whether the firm is non-investment grade (JUNK)

to proxy for default risk. Since not all firms are rated, we also include an indicator variable (NO

RATING) to control for the availability of a credit rating.44

We control for borrower’s

profitability (EBITDA), current ratio (CURRENT), size (SIZE), book to market ratio (BTM),

and the amount of fixed assets (TANGIBILITY).

44 The credit ratings classification is from TEJ Taiwan Corporate Credit Risk Index. TEJ assigns a credit rating to 98%

of all public companies, regardless whether the company is active in the bond market. Only companies in financial

and media industries do not receive a rating. The rating ranges from 1 to 9 with large numbers indicating worse

ratings. Ratings at 6 or higher are considered to be non-investment grade.

50

In addition, we control for the lending relationship with the bank (LENGTHREL),

because Petersen and Rajan (1994) find evidence that building a relationship with the creditor

grants the borrower more favorable contract terms in debt pricing and credit access. We control

for whether the loan is syndicated (SYNDICATION). Although we do not have a prediction on

the relationship between loan rate and syndication, syndicated loans tend to have larger

borrowing amounts and are more likely to require security. Following Bharath et al. (2008) and

Graham et al. (2008), we include the maturity of the loan (Log(MATURITY), because they find

that the variable is positively related to collateral requirement, loan rate, and borrowing amount.

They also find that loan rate is negatively related to borrowing amount and positively related to

security requirement. We also predict a positive relationship between security and borrowing

amounts, because larger loans are riskier and banks are more likely to require collateral

protection. Finally, we control for the interest rate at the macro level using the interbank

overnight borrowing rate in Taiwan (TIBOR) in the loan rate regression. We expect the variable

to be positively associated with the loan rate.

51

Appendix B: Variable Definitions

ABSDA = Absolute value of discretionary accruals, where discretionary accruals are measured

cross-sectionally based on the modified Jones model

AGE = Age of the firm since the firm was founded

AMOUNT = Loan borrowing amount (in thousands of NT dollars)

BETA = Market model regression coefficient estimated using the past 1 year daily returns

BIG N =

Indicator variable equal to 1 if the engagement partner works for one of the 5

largest accounting firms (Arthur Andersen, Ernst & Young, Deloitte & Touche,

KPMG, and Pricewaterhouse Coopers). The Big 5 becomes Big 4 in 2003 when the

Arthur Andersen Taiwanese operations merged with Deloitte

BTM = Shareholder's equity divided by market capitalization

BMS = Indicator variable equal to 1 if the firm’s maximum monthly abnormal return

during the year is greater than 20% and 0 otherwise

CapitalRaising = Indicator variable equal to 1 if the firm announces to raise capital and 0 otherwise

CAR =

Market adjusted daily cumulative abnormal returns (For the ERC analysis, the

variable is measured for the 16 month period ending four months after the fiscal

year-end.)

ChgExec = Indicator variable equal to 1 if the firm announces a change in executives and 0

otherwise

ChgQAuditFirmFE = The change in QAuditFirmFE for firms hiring a new audit firm, and 0 for firms

retaining the same audit firm

ChgQPartnerFE = The total of the changes in QPartnerFE for the switching partners

CFO = Cash flow from operations divided by total assets

CI_AF = Client importance at the audit firm level, measured as the client’s total assets

divided by SIZE_AF

CI_EP = Client importance at the engagement partner level, measured as the client’s total

assets divided by SIZE_EP

CURRENT = Current assets divided by current liabilities

E = Income from continuing operations deflated by the lagged total assets

∆E = The difference between income from continuing operations for the current year and

that of last year deflated by the lagged total assets

EA = Indicator variable equal to 1 if the partner change announcement coincides with an

earnings announcement and 0 otherwise

EBITDA = Earnings before interest and taxes plus depreciation deflated by total assets

ELEC = Indicator variable equal to 1 if an IPO firm is in the electronic industry and 0

otherwise

DChgAuditFirm = Indicator variable equal to 1 if the firm changes its audit firm and 0 otherwise

DivAnn = Indicator variable equal to 1 if the firm announces a change in dividends and 0

otherwise

FLOAT = The proportion of shares sold at the IPO over shares outstanding

FM = Indicator variable equal to 1 if the firm selects the auction flotation method in an

IPO and 0 otherwise

52

IND_MKTSHARE_AF = Audit firm industry market share

IND_MKTSHARE_EP = Engagement partner industry market share

IND_PORTFOLIO_AF = Audit firm portfolio concentration in a particular industry

IND_PORTFOLIO_EP = Engagement partner portfolio concentration in a particular industry

INTEREST RATE = The interest rate charged on the loan

IPO SIZE = Natural logarithm of the IPO amount raised

JUNK = Indicator variable equal to 1 if the company is not investment grade and 0

otherwise

LENGTHREL = Number of days since a given bank started lending to a given borrower

LEVERAGE = Total debt divided by the sum of total debt and shareholder's equity. Total debt is

the sum of current portion of long-term debt and long-term debt

LOSS = Indicator variable equal to 1 if earnings before extraordinary items are smaller than

0, and 0 otherwise

MATURITY = The maturity of the debt (in days)

MKTRET = Concurrent market return at the time of an IPO

MKTRET48 = Market index return for the period of 48 trading days before an IPO

NO RATING = Indicator variable equal to 1 if the company does not have a credit rating, and 0

otherwise

OHLSON = The Ohlson O-score constructed based on Ohlson (1980)

OTC = Indicator variable equal to 1 if the firm is traded in the over the counter market and

0 otherwise

QPartnerFE = Quartile rank of the negative partner fixed effects estimated based on equation (1)

QAuditFirmFE = Quartile rank of the negative audit firm fixed effects estimated based on equation

(1)

QFirmFE = Quartile rank of the negative client firm fixed effects estimated based on equation

(1)

PERSIST = First-order autocorrelation of income from continuing operations per share for the

past 16 quarters

RESTMT = Indicator variable equal to 1 if the current year annual report is restated later on and

0 otherwise

RestatementAnn = Indicator variable equal to 1 if the firm announces a restatement and 0 otherwise

SANCTION = Indicator variable equal to 1 if the partner experiences regulatory sanctions between

1995-2005 and 0 otherwise

SECURED = Indicator variable equal to 1 if the loan requires collateral and 0 otherwise

SIZE = Natural logarithm of the total assets (in millions of NT dollars)

SIZE_AF = Size of the audit firm, measured as the sum of the total client assets audited by the

audit firm (in trillions of NT dollars)

SIZE_EP = Size of the engagement partner, measured as the sum of the total client assets

audited by the engagement partner (in trillions of NT dollars)

SP = Indicator variable equal to 1 if quarterly earnings before interests and taxes deflated

by total assets is between 0% and 1% and 0 otherwise

STDCFO = Standard deviation of cash flow from operations deflated by the lagged total assets

over the current and prior four years

53

STDMKTRET48 = The standard deviation of the market index return for the period of 48 trading days

before an IPO

SUCCESS = The probability of successful purchase in the random drawing in the case

of oversubscription

SYNDICATION = Indicator variable equal to 1 when the loan is syndicated and 0 otherwise

TANGIBILITY = Net PP&E divided by total assets

TENURE_AF = The number of continuous years the audit firm has worked for the client

TENURE_EP = The number of continuous years the engagement partner has worked for the client

TIBOR = Taiwan interbank overnight rate

TURNOVER = Net sales revenues divided by total assets

UP = The level of IPO underpricing, defined as either the first 1 day, 3 day, or 20 day

buy and hold return of the stock

VOLATY = Standard deviation of income from continuing operations per share for the past 16

quarters

54

Appendix C: Full Results for the ERC Analysis

Dependent variable = CAR

Coefficient Predicted sign [1] [2] [3]

E β1 1.684* 3.449*** -0.222

[1.805] [3.807] [-0.135]

∆E β2 3.446*** 1.062 2.480**

[3.167] [1.208] [2.100]

β1 + β2 + 5.130 4.511 2.258

[21.10]*** [18.54]*** [2.65]

E × QPartnerFE β3 -0.251 -0.063

[-1.241] [-0.329]

∆E × QPartner FE β4 0.864*** 0.923***

[3.610] [3.950]

β3 + β4 + 0.613 0.860

[5.90]** [12.18]***

E × QAuditFirmFE β5 -0.500** -0.452**

[-2.190] [-2.180]

∆E × QAuditFirmFE β6 0.979*** 0.931***

[3.511] [3.399]

β5 + β6 + 0.479 0.479

[2.90]* [3.25]*

E × QFirmFE β7 -0.083 0.070

[-0.667] [0.672]

∆E × QFirmFE β8 0.341** 0.309*

[1.978] [1.857]

β7 + β8 + 0.258 0.379

[2.90]* [5.89]**

QPartnerFE β9 -0.018 -0.016

[-1.091] [-1.071]

QAuditFirmFE β10 -0.007 0.017

[-0.366] [0.962]

QFirmFE β11 -0.014 -0.022**

[-1.277] [-2.212]

Control variables

E × BigN (β12) / ∆E × BigN (β13) β12 + β13 + -0.361 0.214 0.140

[0.42] [0.19] [0.08]

E × TENURE_AF (β14) / ∆E × TENURE_AF (β15) β14 + β15 + 0.038 0.023 0.017

[1.50] [0.91] [0.48]

E × LEVERAGE (β16) / ∆E × LEVERAGE (β17) β16 + β17 - 0.908 1.620 1.342

[0.95] [6.01]** [3.73]*

E × SIZE (β18) / ∆E × SIZE (β19) β18 + β19 +/- -0.185 -0.393 -0.355

[2.31] [21.82]*** [13.03]***

E × BTM (β20) / ∆E × BTM (β21) β20 + β21 - -0.896 -1.332 -1.919

[19.59]*** [19.75]*** [30.48]***

E × AGE (β22) / ∆E × AGE (β23) β22 + β23 +/- -0.004 0.014 0.036

[0.05] [0.72] [3.31]*

E x PERSIST (β24) / ∆E x PERSIST (β25) β24 + β25 + 2.048 0.903 0.306

55

[7.34]*** [2.00] [0.20]

E x VOLATY (β26) / ∆E x VOLATY (β27) β26 + β27 - -0.048 0.029 0.020

[0.12] [0.10] [0.05]

E x BETA (β28) / ∆E x BETA (β29) β28 + β29 - 0.038 0.095 -0.201

[0.00] [0.07] [0.29]

E x LOSS (β30) / ∆E x LOSS (β31) β30 + β31 - -2.447 -1.418 -1.375

[14.67]*** [11.55]*** [9.02]***

BigN β32 0.070** 0.034 0.040

[2.348] [1.088] [1.193]

TENURE_AF β33 -0.001 -0.001 -0.002

[-0.646] [-0.788] [-1.019]

LEVERAGE β34 0.078 0.047 0.053

[1.144] [0.794] [0.892]

SIZE β35 0.002 0.006 0.002

[0.154] [0.683] [0.327]

BTM β36 -0.172*** -0.152*** -0.177***

[-8.982] [-9.004] [-7.570]

AGE β37 0.001 0.001 -0.001

[0.828] [1.106] [-0.825]

PERSIST β38 0.123** 0.148*** 0.144***

[2.042] [3.052] [3.161]

VOLATY β39 -0.109*** -0.078*** -0.075***

[-5.200] [-3.885] [-3.895]

BETA β40 -0.128*** -0.127*** 0.009

[-2.836] [-3.344] [0.221]

LOSS β41 0.060 0.070 0.045

[1.310] [1.636] [1.132]

Constant α 0.352*** 0.352***

[4.164] [3.927]

Year FE / Year FE x E / Year FE x ∆E NO NO YES

Industry FE / Industry FE x E / Industry FE x ∆E NO NO YES

Observations 3,306 3,306 3,306

Adjusted R-square 0.190 0.207 0.335

56

Table 1: Sample Description

This table provides information on the sample composition during the estimation period of 1995-2005. Panel A provides a

comparison of the industry distribution between the sample firms and the whole universe of the public firms. Panel B

provides descriptive statistics for audit firms and individual partners.

Panel A: Industry distribution

TSE Industry Code Industry Name Sample Firms (%) All Listed Firms (%)

01 Cement 1.1 0.6

02 Food 3.3 2.0

03 Plastic 3.5 2.4

04 Textile & Fiber 7.2 4.7

05 Electrical Engineering & Machinery 5.2 5.4

06 Appliance & Cable 2.0 1.3

08 Glass & Ceramics 0.6 0.4

09 Papermaking 1.1 0.6

10 Steel & Iron 4.5 3.4

11 Rubber 1.5 0.9

12 Auto 0.6 0.4

14 Construction 6.8 5.2

15 Sea Transport 2.9 2.1

16 Tourism 1.4 1.1

18 Wholesale & Retailing 2.0 1.5

19, 20 Miscellaneous & Other 7.0 5.7

21 Chemical 3.9 3.3

22 Biotechnology & Medical Care 2.0 3.7

23 Oil, Gas & Electricity 1.4 1.1

24 Semiconductor 7.3 8.9

25 Computer & Peripheral Equipment 7.2 7.7

26 Optoelectronic 4.5 6.9

27 Communications & Internet 4.0 5.4

28 Electronic Components 10.2 13.7

29 Electronic Products Distribution 2.4 3.2

30 Information Service 2.4 3.1

31 Electronic - Other 4.0 5.4

Total 100 100

Panel B: Sample descriptive statistics for audit firms and individual partners

Variable Mean Stdev p25 p50 p75

Number of audit firms per year 14.364 2.335 12 16 16

Number of clients per audit firm-year 37.722 56.858 2 11 46

Number of lead partners per audit firm-year 10.044 13.195 1 4 13

Number of clients per lead partner-year 3.758 2.781 2 3 5

57

Table 2: Estimation of Individual Engagement Partner Quality

This table presents the estimation of engagement partner quality using the methodology in Bertrand and Schoar (2003).

Panel A provides descriptive statistics for the full sample from 1995 to 2010. Panel B reports the estimation results, where

the estimation period is from 1995 to 2005. The summary statistics on QPartnerFE, QAuditFirmFE, and QFirmFE in

Panel A are for the validation period from 2006 - 2010. All variables are defined in Appendix B. Reported in brackets are

t -statistics calculated based on White heteroskedastic consistent standard errors and adjusted for clustering by company.

***, **, and * represent 1, 5, and 10% level of significance, respectively, based on one-tailed tests for variables that we

predict an expected difference and two-tailed tests for variables that we do not predict an expected difference.

Panel A: Descriptive statistics

Variable N Mean Stdev p25 p50 p75

ABSDA 10,210 0.060 0.065 0.018 0.041 0.078

LOSS 10,210 0.073 0.260 0 0 0

LEVERAGE 10,210 0.259 0.191 0.094 0.250 0.396

TURNOVER 10,210 0.894 0.616 0.503 0.763 1.110

SIZE 10,210 8.422 1.354 7.460 8.246 9.170

BTM 10,210 0.982 0.933 0.497 0.773 1.203

AGE 10,210 23.990 11.623 15 23 32

CFO 10,210 0.092 0.118 0.032 0.076 0.137

STDCFO 10,210 0.085 0.094 0.037 0.059 0.099

BMS 10,210 0.593 0.491 0 1 1

TENURE_AF 10,210 8.376 5.123 4 8 12

IND_MKTSHARE_AF (%) 10,210 21.293 16.134 8.504 17.647 31.664

IND_PORTFOLIO_AF (%) 10,210 6.813 11.647 1.399 3.421 7.313

SIZE_AF 10,210 2.964 2.356 1.080 2.632 4.666

CI_AF 10,210 0.036 0.123 0.001 0.002 0.011

TENURE_EP 10,210 4.940 3.819 2 4 7

IND_MKTSHARE_EP (%) 10,210 4.584 7.590 0.599 1.589 4.642

IND_PORTFOLIO_EP (%) 10,210 20.222 24.323 2.856 9.721 28.688

SIZE_EP 10,210 0.091 0.129 0.018 0.041 0.109

CI_EP 10,210 0.258 0.294 0.041 0.128 0.370

Big N 10,210 0.836 0.370 1 1 1

QPartnerFE 3,384 2.158 1.021 1 2 3

QAuditFirmFE 3,384 2.682 0.861 2 2 4

QFirmFE 3,384 2.505 1.123 1 3 4

58

Panel B: OLS estimation of engagement partner quality

Predicted sign Dependent variable = ABSDA

LOSS + 0.003

[0.648]

LEVERAGE + 0.026***

[2.347]

TURNOVER - -0.004

[-0.594]

SIZE - -0.004

[-0.960]

BTM - -0.005***

[-2.511]

AGE - 0.001

[0.884]

CFO - 0.020

[0.689]

STDCFO + 0.098***

[3.837]

BMS + 0.002

[1.165]

TENURE_AF - 0.0004

[0.737]

IND_MKTSHARE_AF - 0.0001

[1.080]

IND_PORTFOLIO_AF - -0.001*

[-1.588]

SIZE_AF - -0.007**

[-1.791]

CI_AF + 0.068**

[1.983]

TENURE_EP - -0.0004

[-0.905]

IND_MKTSHARE_EP - 0.0002

[0.885]

IND_PORTFOLIO_EP - 0.0001*

[1.304]

SIZE_EP + 0.024*

[1.544]

CI_EP + 0.007

[0.962]

Year FE YES

Engagement Partner FE YES

Audit Firm FE YES

Firm FE YES

Observations 6,826

Adjusted R-Square 0.272

Joint significance of engagement partner FE: F-statistic = 1.19, p-value < 0.01

59

Table 3: Validation of Engagement Partner Fixed Effect Estimates

This table validates the engagement partner fixed effect estimates as a measure of partner quality. The table reports results on three out-of-sample tests using the

sample period from 2006 to 2010. All variables are defined in Appendix B. Reported in brackets are t- or z-statistics calculated based on White heteroskedastic

consistent standard errors and adjusted for clustering by company. ***, **, and * represent 1, 5, and 10% level of significance, respectively, based on one-tailed

tests for variables that we predict an expected difference and two-tailed tests for variables that we do not predict an expected difference.

Dependent variable = ABSDA Dependent variable = SP Dependent variable = RESTMT

Predicted sign [1] Predicted sign [2] Predicted sign [4]

QPartnerFE - -0.006*** - -0.128** - -0.264**

[-3.391] [-1.725] [-1.679]

QAuditFirmFE - -0.005** - -0.181** - -0.265

[-2.109] [-1.932] [-1.194]

QFirmFE - -0.005*** - -0.058 - -0.206**

[-3.603] [-1.081] [-1.777]

Controls YES YES YES

Quarter FE NO YES NO

Year FE YES YES YES

Observations 3,384 13,548 3,384

Adjusted R-Square / Pseudo R-Square 0.207 0.194 0.113

Regression Type OLS Logit Logit

60

Table 4: ERC Analysis

This table uses an ERC analysis to examine whether investors perceive earnings audited by higher quality partners to be

more informative. Panel A reports descriptive statistics and Panel B reports the regression results. The sample period is

from 2006-2010. All variables are defined in Appendix B. For brevity, the results on the stand-alone interaction terms and on the control variables are reported in Appendix C. Reported in brackets are F-statistics for testing sum of the

coefficients and t–statistics for the rest of the variables. The t– and F-statistics are calculated based on White

heteroskedastic consistent standard errors and adjusted for clustering by firm. ***, **, and * represent 1, 5, and 10% level of significance, respectively, based on one-tailed tests for variables that we predict an expected difference and two-tailed

tests for variables that we do not predict an expected difference.***, **, and * represent 1, 5, and 10% level of

significance (two-tailed), respectively

Panel A: Descriptive statistics

Variable N Mean Stdev p25 p50 p75

CAR 3,306 0.189 0.570 -0.192 0.056 0.403

E 3,306 0.052 0.096 0.006 0.049 0.103

∆E 3,306 0.013 0.083 -0.027 0.006 0.043

QPartnerFE 3,306 2.168 1.021 1 2 3

QAuditFirmFE 3,306 2.678 0.860 2 2 4

QFirmFE 3,306 2.518 1.122 2 3 4

Big N 3,306 0.912 0.283 1 1 1

TENURE_AF 3,306 12.632 6.198 8 12 17

LEVERAGE 3,306 0.227 0.188 0.052 0.208 0.363

SIZE 3,306 8.514 1.458 7.474 8.318 9.309

BTM 3,306 0.944 0.727 0.507 0.765 1.161

AGE 3,306 25.639 11.852 16 24 34

PERSIST 3,306 0.299 0.238 0.097 0.302 0.500

VOLATY 3,306 1.110 1.029 0.552 0.859 1.366

BETA 3,306 0.872 0.325 0.649 0.908 1.098

LOSS 3,306 0.203 0.402 0 0 0

61

Panel B: ERC regression results

Dependent variable = CAR

Coefficient Predicted sign [1] [2] [3]

E / ∆E β1 + β2 + 5.130 4.511 2.258

[21.10]*** [18.54]*** [2.65]

E × QPartnerFE / ∆E × QPartner FE β3 + β4 + 0.613 0.860

[5.90]** [12.18]***

E × QAuditFirmFE / ∆E × QAuditFirmFE β5 + β6 + 0.479 0.479

[2.90]* [3.25]*

E × QFirmFE / ∆E × QFirmFE β7 + β8 + 0.258 0.379

[2.90]* [5.89]**

E β1 1.684* 3.449*** -0.222

[1.805] [3.807] [-0.135]

∆E β2 3.446*** 1.062 2.480**

[3.167] [1.208] [2.100]

QPartnerFE β9 -0.018 -0.016

[-1.091] [-1.071]

QAuditFirmFE β10 -0.007 0.017

[-0.366] [0.962]

QFirmFE β11 -0.014 -0.022**

[-1.277] [-2.212]

Constant α 0.352*** 0.352***

[4.164] [3.927]

Controls / Controls × E / Controls × ∆E YES YES YES

Year FE / Year FE × E / Year FE × ∆E NO NO YES

Industry FE / Industry FE × E / Industry FE × ∆E NO NO YES

Observations 3,306 3,306 3,306

Adjusted R-square 0.190 0.207 0.335

62

Table 5: Market Reactions to Partner Changes

This table examines the market reaction to the announcement of a partner change. Panel A reports descriptive statistics for

the variables used in the analysis. Panel B reports the regression results. The sample period is from 2006 to 2010. All

variables are defined in Appendix B. Reported in brackets are t-statistics calculated based on White heteroskedastic

consistent standard errors and adjusted for clustering by quarter. ***, **, and * represent 1, 5, and 10% level of

significance, respectively, based on one-tailed tests for variables that we predict an expected difference and two-tailed

tests for variables that we do not predict an expected difference.

Panel A: Descriptive statistics

Variable N Mean Stdev p25 p50 p75

CAR (-1, +1) 117 0.004 0.055 -0.024 -0.001 0.028

CAR (-10, +10) 117 0.007 0.116 -0.066 -0.005 0.073

ChgQPartnerFE 117 -0.09 1.58 -1 0 1

ChgQAuditFirmFE 117 0.06 0.65 0 0 0

DChgAuditFirm 117 0.21 0.41 0 0 0

OTC 117 0.49 0.50 0 0 1

EA 117 0.05 0.22 0 0 0

ChgExec 117 0.07 0.25 0 0 0

DivAnn 117 0.10 0.30 0 0 0

CapitalRaising 117 0.03 0.16 0 0 0

RestatementAnn 117 0.01 0.09 0 0 0

LOSS 117 0.18 0.39 0 0 0

LEVERAGE 117 0.37 0.17 0.23 0.37 0.49

BTM 117 0.75 0.45 0.43 0.67 0.93

SIZE 117 14.92 1.43 13.98 14.65 15.47

63

Panel B: Regression results on the market reactions to partner changes

Predicted sign Dependent variable = CAR (-1, +1) Dependent variable = CAR (-10, +10)

ChgQPartnerFE + 0.004 0.020**

[1.027] [2.523]

ChgQAuditFirmFE + -0.009 0.034

[-0.762] [1.145]

DChgAuditFirm +/- -0.000 -0.051*

[-0.021] [-1.712]

OTC +/- 0.015 0.014

[1.354] [0.542]

EA +/- 0.004 -0.081***

[0.157] [-3.032]

ChgExec +/- 0.020 0.083***

[1.024] [2.738]

DivAnn + 0.032*** 0.000

[2.589] [0.005]

CapitalRaising +/- 0.008 0.048

[0.134] [0.768]

RestatementAnn - 0.014 0.041

[0.568] [0.858]

LOSS +/- 0.014 0.015

[0.667] [0.333]

LEVERAGE - -0.013 0.007

[-0.401] [0.119]

BTM +/- 0.025* 0.022

[1.959] [0.662]

SIZE +/- 0.006* 0.002

[1.876] [0.228]

Year Fixed Effects YES YES

Observations 117 117

R-squared 0.120 0.164

64

Table 6: IPO Underpricing Analysis

This table examines the association between the quality of the engagement partner and IPO underpricing. Panel A reports

descriptive statistics for the variables used in the analysis. Panel B reports the regression results. The sample period is

from 2006 to 2010. All variables are defined in Appendix B. Reported in brackets are t-statistics calculated based on

White heteroskedastic consistent standard errors and adjusted for clustering by quarter. ***, **, and * represent 1, 5, and

10% level of significance, respectively, based on one-tailed tests for variables that we predict an expected difference and

two-tailed tests for variables that we do not predict an expected difference.

Panel A: Descriptive statistics

Variable N Mean Stdev p25 p50 p75

UP 1-DAY 146 0.56 0.51 0.19 0.37 0.82

MKTRET 1-DAY 146 0.00 0.01 -0.01 0.00 0.01

UP 3-DAYS 146 0.52 0.47 0.18 0.35 0.82

MKTRET 3-DAYS 146 0.00 0.03 -0.02 0.00 0.01

UP 20-DAYS 146 0.59 0.67 0.15 0.36 0.87

MKTRET 20-DAYS 146 0.01 0.07 -0.04 0.01 0.05

LOSS 146 0.01 0.08 0 0 0

LEVERAGE 146 0.17 0.17 0 0 0

ELEC 146 0.86 0.35 1 1 1

AGE 146 13.86 9.33 7 11 18

ABSDA 146 0.08 0.09 0.02 0.05 0.11

FM 146 0.01 0.08 0 0 0

OTC 146 0.66 0.47 0 1 1

SIZE 146 14.40 1.03 13.69 14.23 14.95

SUCCESS 146 3.81 10.47 0.84 1.76 2.90

MKTRET48 146 0.04 0.11 -0.04 0.05 0.09

STDMKTRET48 146 1.35 0.50 0.94 1.32 1.67

QPartnerFE 146 2.31 0.92 2 2 3

QAuditFirmFE 146 2.51 0.79 2 2 3

IPOSIZE 146 12.44 1.06 11.57 12.40 13.09

FLOAT 146 0.10 0.02 0.09 0.10 0.11

BTM 146 0.50 0.31 0.26 0.43 0.73

65

Panel B: Regression results on the relation between IPO underpricing and engagement partner quality

Predicted sign Dependent variable

= UP-1 DAY

Dependent variable

= UP-3 DAYS

Dependent variable

= UP-20 DAYS

[1] [2] [3] [4] [5] [6]

QPartnerFE - -0.077*** -0.077** -0.074*** -0.069** -0.115** -0.104**

[-2.552] [-2.137] [-2.694] [-1.969] [-2.130] [-2.276]

QAuditFirmFE - -0.027 -0.026 -0.053* -0.053 -0.060 -0.051

[-0.596] [-0.456] [-1.660] [-1.264] [-0.848] [-0.697]

LOSS + -0.208 -0.041 -0.254 -0.178 0.004 0.060

[-0.832] [-0.150] [-1.035] [-0.633] [0.013] [0.177]

LEVERAGE + 0.037 -0.021 0.164 0.087 -0.354 -0.484

[0.114] [-0.052] [0.534] [0.235] [-0.956] [-0.902]

ELEC + -0.040 -0.006 0.018 0.062 -0.019 0.083

[-0.392] [-0.053] [0.178] [0.518] [-0.138] [0.459]

AGE - -0.008** -0.004 -0.007* -0.004 -0.012* -0.007

[-1.854] [-1.004] [-1.398] [-0.770] [-1.453] [-0.948]

ABSDA + 0.704 0.878* 0.397 0.444 0.513 0.430

[1.201] [1.354] [0.778] [0.700] [0.753] [0.537]

FM - 1.781*** 1.849*** 1.295*** 1.382*** 1.451*** 1.643***

[4.716] [4.749] [4.380] [4.324] [2.883] [2.993]

OTC + -0.064 -0.112 -0.045 -0.121 -0.082 -0.174

[-0.434] [-0.645] [-0.373] [-0.782] [-0.534] [-0.987]

SIZE - -0.148 -0.110 -0.094 -0.049 -0.119 -0.049

[-1.010] [-0.700] [-0.657] [-0.319] [-0.470] [-0.197]

SUCCESS - -0.005* -0.007** -0.004* -0.005* -0.003 -0.004

[-1.414] [-1.805] [-1.403] [-1.551] [-0.831] [-1.089]

MKTRET48 + 1.362*** 1.544*** 1.184*** 1.397*** 1.504*** 1.702***

[2.795] [3.268] [3.003] [3.615] [3.662] [5.029]

STDMKTRET48 + -0.262* -0.197 -0.203 -0.117 -0.287 -0.165

[-1.334] [-0.960] [-1.229] [-0.664] [-1.223] [-0.762]

MKTRET + 6.124** 7.356*** 2.270* 2.869** 2.099* 2.193**

[2.466] [3.404] [1.364] [1.799] [1.726] [2.175]

IPOSIZE - 0.008 -0.012 -0.021 -0.055 -0.005 -0.064

[0.058] [-0.079] [-0.161] [-0.400] [-0.018] [-0.247]

FLOAT - -2.301 -2.418 -0.679 -1.174 -1.580 -1.848

[-1.024] [-0.961] [-0.342] [-0.487] [-0.376] [-0.439]

BTM - 0.094 -0.024 -0.049 -0.201 0.141 -0.122

[0.335] [-0.080] [-0.172] [-0.664] [0.320] [-0.243]

Year FE YES YES YES YES YES YES

Underwriter FE NO YES NO YES NO YES

Observations 146 146 146 146 146 146

Adjusted R-Square 0.249 0.223 0.233 0.201 0.158 0.134

66

Table 7: Debt Contract Analysis

This table investigates whether firms audited by higher quality partners can obtain better loan contract terms. Panel A

reports descriptive statistics for the variables used in the analysis. Panel B reports the regression results on loan pricing.

Panel C reports the regression results on credit access. Panel D reports the regression results on security requirement. The

sample period is from 2006 to 2010. All variables are defined in Appendix B. Reported in brackets are t- or z-statistics

statistics calculated based on White heteroskedastic consistent standard errors and adjusted for clustering by firm-year.

***, **, and * represent 1, 5, and 10% level of significance, respectively, based on one-tailed tests for variables that we

predict an expected difference and two-tailed tests for variables that we do not predict an expected difference.

Panel A: Descriptive statistics

Variable N Mean Stdev p25 p50 p75

QPartnerFE 9,079 2.28 0.96 2 2 3

QAuditFirmFE 9,079 2.53 0.74 2 2 3

QFirmFE 9,079 2.48 1.12 1 2 3

INTEREST RATE 9,079 2.52 1.30 1.72 2.43 2.97

TIBOR 9,079 1.17 0.81 0.23 1.59 2.00

Log(AMOUNT) 9,079 10.80 1.59 9.90 10.82 11.76

SECURED 9,079 0.43 0.50 0 0 1

LENGTHREL 9,079 2,582 2,262 583 2,008 4,270

SYNDICATION 9,079 0.07 0.25 0 0 0

Log(MATURITY) 9,079 5.35 1.30 4.50 5.20 6.31

OHLSON 9,079 -4.76 1.07 -5.56 -4.74 -3.96

EBITDA 9,079 0.10 0.06 0.06 0.09 0.13

STDCFO 9,079 0.07 0.07 0.03 0.05 0.08

CURRENT 9,079 1.51 0.73 1.10 1.36 1.77

SIZE 9,079 15.85 1.52 14.72 15.59 16.88

BTM 9,079 1.16 0.73 0.64 0.98 1.50

TANGIBILITY 9,079 0.34 0.17 0.22 0.34 0.46

LEVERAGE 9,079 0.36 0.16 0.25 0.38 0.46

JUNK 9,079 0.63 0.48 0 1 1

NO RATING 9,079 0.00 0.03 0 0 0

67

Panel B: Regression results on the relation between loan pricing and engagement partner quality

Predicted sign Dependent variable = INTEREST RATE

[1] [2]

QPartnerFE - -0.065** -0.058*

[-1.824] [-1.641]

QAuditFirmFE - -0.008 -0.022

[-0.169] [-0.485]

QFirmFE - -0.042* -0.018

[-1.618] [-0.681]

TIBOR + 0.158*** 0.140***

[2.825] [2.680]

LENGTHREL - -0.000*** -0.000***

[-3.508] [-2.716]

SYNDICATION +/- -0.098 0.092

[-1.122] [0.915]

Log(MATURITY) + 0.129*** 0.122***

[6.797] [6.553]

Log(AMOUNT) - -0.173*** -0.176***

[-7.330] [-8.171]

SECURED + 0.203*** 0.181***

[3.423] [3.236]

OHLSON + 0.202*** 0.200***

[4.147] [4.346]

EBITDA - 0.677* 0.400

[1.398] [0.845]

STDCFO + 0.541* 0.459

[1.301] [0.986]

CURRENT - -0.051 -0.062*

[-0.986] [-1.603]

SIZE - 0.098*** 0.081***

[2.804] [2.542]

BTM + 0.140*** 0.185***

[2.555] [3.551]

TANGIBILITY - -0.232* 0.045

[-1.308] [0.248]

LEVERAGE + -0.421* -0.500**

[-1.359] [-1.659]

JUNK + 0.148* 0.092

[1.629] [1.051]

NO RATING +/- -0.398 -0.581**

[-1.229] [-2.120]

Year FE YES YES

Industry FE NO YES

Observations 9,079 9,079

Adjusted R-Square 0.416 0.437

68

Panel C: Regression results on the relation between credit access and engagement partner quality

Predicted sign Dependent variable = Log(AMOUNT)

[1] [2]

QPartnerFE + 0.139*** 0.192***

[3.142] [4.228]

QAuditFirmFE + 0.144*** 0.168***

[2.981] [3.540]

QFirmFE + 0.044* 0.099***

[1.517] [3.253]

LENGTHREL + 0.000 0.000

[0.030] [0.203]

SYNDICATION + -0.177 0.275*

[-1.100] [1.324]

Log(MATURITY) + 0.178*** 0.153***

[9.674] [8.742]

SECURED + 0.120** 0.079*

[2.060] [1.504]

OHLSON - -0.160*** -0.192***

[-2.709] [-3.498]

EBITDA + 1.488*** 0.952**

[2.842] [1.941]

STDCFO - 1.155*** 1.158***

[2.413] [2.477]

CURRENT + -0.075** -0.105***

[-1.651] [-2.372]

SIZE + 0.632*** 0.570***

[18.343] [18.598]

BTM - -0.257*** -0.205***

[-3.759] [-3.246]

TANGIBILITY + -0.422** -0.169

[-2.145] [-0.730]

LEVERAGE - 0.121 0.171

[0.412] [0.599]

JUNK - 0.112 -0.048

[1.247] [-0.584]

NO RATING +/- 0.526 0.462

[1.617] [0.985]

Year FE YES YES

Industry FE NO YES

Observations 9,079 9,079

Adjusted R-Square 0.427 0.449

69

Panel D: Regression results on the relation between security requirement and engagement partner quality

Predicted sign Dependent variable = SECURED

[1] [2]

QPartnerFE - -0.191*** -0.167**

[-2.361] [-2.060]

QAuditFirmFE - -0.169** -0.169**

[-1.925] [-1.927]

QFirmFE - -0.199*** -0.146***

[-3.097] [-2.363]

LENGTHREL +/- 0.000*** 0.000***

[5.971] [5.819]

SYNDICATION + 0.152 0.872***

[0.318] [2.685]

Log(MATURITY) + 0.263*** 0.261***

[5.931] [6.583]

Log(AMOUNT) + 0.077** 0.049*

[1.894] [1.305]

OHLSON + -0.175* -0.105

[-1.510] [-0.885]

EBITDA - -0.867 -0.399

[-0.878] [-0.385]

STDCFO + 0.649 0.173

[0.767] [0.210]

CURRENT - -0.057 -0.127

[-0.507] [-1.264]

SIZE - -0.394*** -0.444***

[-5.645] [-6.506]

BTM + 0.282*** 0.355***

[2.965] [3.944]

TANGIBILITY - -0.303 -0.822**

[-0.771] [-1.867]

LEVERAGE + 1.598*** 1.198**

[2.659] [1.942]

JUNK + 0.485*** 0.330**

[2.716] [2.031]

NO RATING + 1.699*** 1.231**

[4.924] [1.738]

Year FE YES YES

Industry FE NO YES

Observations 9,079 9,079

Adjusted R-Square 0.102 0.145

70

Table 8: Assessing Capital Market Consequences Using the DD Model to Measure Accrual Quality

This table presents results on the capital market consequences of hiring a high quality audit partner, where partner quality

is measured based on the Dechow and Dichev (2002) accrual model. Panel A reports the results on the ERC analysis,

Panel B on market reactions to partner changes, Panel C on IPO analysis, Panel D on loan pricing analysis, Panel E on

credit access analysis, and Panel F on collateral requirement analysis. The sample period is from 2006 to 2010. All

variables are defined in Appendix B. For testing the sum of the coefficients in the ERC analysis, reported in brackets are

F-statistics. For the rest analyses, reported in brackets are t- or z-statistics calculated based on White heteroskedastic

consistent standard errors and adjusted for clustering by firm, quarter, or firm-year, depending on the specification (see

Tables 4-7 for clustering levels for each analysis). ***, **, and * represent 1, 5, and 10% level of significance,

respectively, based on one-tailed tests for variables that we predict an expected difference and two-tailed tests for

variables that we do not predict an expected difference.

Panel A: ERC analysis

Dependent variable = CAR

Coefficient Predicted sign [1] [2]

E / ∆E β1 + β2 + 3.504*** 2.789**

[13.11] [5.41]

E × QPartnerFE / ∆E × QPartner FE β3 + β4 + 0.514*** 0.337*

[7.73] [3.63]

E × QAuditFirmFE / ∆E × QAuditFirmFE β5 + β6 + 0.707*** 0.641***

[10.65] [8.50]

E × QFirmFE / ∆E × QFirmFE β7 + β8 + 0.213 0.389**

[1.97] [6.34]

E β1 2.836*** 1.832

[3.909] [1.625]

∆E β2 0.668 0.957

[0.741] [0.876]

QPartnerFE β9 -0.020 -0.012

[-1.508] [-0.892]

QAuditFirmFE β10 -0.017 -0.027

[-1.029] [-1.588]

QFirmFE β11 0.003 0.006

[0.352] [0.610]

Constant α 0.311***

[3.649]

Controls / Controls × E / Controls × ∆E YES YES

Year FE / Year FE × E / Year FE × ∆E NO YES

Industry FE / Industry FE × E / Industry FE × ∆E NO YES

Observations 3,256 3,256

Adjusted R-square 0.192 0.326

71

Panel B: Market reactions to partner changes

Predicted sign Dependent variable = CAR (-1, +1) Dependent variable = CAR (-10, +10)

[1] [2]

ChgQPartnerFE + -0.006 -0.005

[-1.291] [-0.552]

ChgQAuditFirmFE + 0.008 -0.006

[0.789] [-0.207]

DChgAuditFirm +/- 0.003 -0.044*

[0.180] [-1.550]

Controls

YES YES

Year Fixed Effects

YES YES

Observations

117 117

R-squared

0.130 0.132

Panel C: IPO analysis

Predicted sign Dependent variable

= UP-1 DAY

Dependent variable

= UP-3 DAYS

Dependent variable

= UP-20 DAYS

[1] [2] [3]

QPartnerFE - -0.005 -0.028 -0.107*

[-0.067] [-0.480] [-1.515]

QAuditFirmFE - -0.059 -0.049 -0.166**

[-0.773] [-0.846] [-2.057]

Controls YES YES YES

Year FE YES YES YES

Underwriter FE YES YES YES

Observations 145 145 145

Adjusted R-Square 0.213 0.189 0.137

Panel D: Loan pricing analysis

Predicted sign Dependent variable = INTEREST RATE

[1] [2]

QPartnerFE - -0.035 -0.011

[-0.963] [-0.296]

QAuditFirmFE - -0.056 -0.030

[-1.080] [-0.563]

QFirmFE - -0.053** -0.042*

[-1.717] [-1.378]

Controls YES YES

Year FE YES YES

Industry FE NO YES

Observations 9,019 9,019

Adjusted R-Square 0.417 0.438

72

Panel E: Credit access analysis

Predicted sign Dependent variable = Log(AMOUNT)

[1] [2]

QPartnerFE + 0.067** 0.049

[1.751] [1.267]

QAuditFirmFE + 0.002 -0.005

[0.051] [-0.115]

QFirmFE + 0.060** 0.072**

[1.715] [2.085]

Controls YES YES

Year FE YES YES

Industry FE NO YES

Observations 9,019 9,019

Adjusted R-Square 0.426 0.446

Panel F: Collateral requirement analysis

Predicted sign Dependent variable = SECURED

[1] [2]

QPartnerFE - -0.110* -0.152**

[-1.523] [-1.894]

QAuditFirmFE - 0.014 -0.007

[0.163] [-0.070]

QFirmFE - -0.165*** -0.179***

[-2.709] [-2.782]

Controls YES YES

Year FE YES YES

Industry FE NO YES

Observations 9,019 9,019

Adjusted R-Square 0.102 0.148