Does Auditor Reputation Determine Post-IPO Stock Returns and Operating

Performance?

Sudip Datta*

Department of Finance

Mike Ilitch School of Business

Wayne State University

5201 Cass Avenue

Detroit, MI 48202

sdatta@wayne.edu

Mark Gruskin

Department of Finance

Penn State University Lehigh Valley

2809 Saucon Valley Road

Center Valley, PA 18034

Mai Iskandar-Datta

Department of Finance

Mike Ilitch School of Business

Wayne State University

5201 Cass Avenue

Detroit, MI 48202

We are grateful to Sudarshan Jayaraman and S. P. Kothari for very detailed and insightful

comments on earlier drafts. We also acknowledge valuable comments from the participants at the

2016 American Accounting Association's annual meeting.

* Corresponding author

Does Auditor Reputation Determine Post-IPO Stock Returns and Operating

Performance?

Abstract

We establish the auditor certification effect for IPOs by documenting that high-ranked auditors

play an important (positive) role in determining long-run post-IPO stock returns. Further, this

certification effect is robust and persists longer than the previously established underwriter

certification effect. Issues with low-ranked underwriters benefit more from high quality

auditors. We document a substitution effect on the IPO returns between auditor and underwriter

rank. It is also more pronounced for IPO firms with high growth opportunities and high R&D

intensity. Differentiating between the certification effect of high-ranked auditors and the effect

of their superior monitoring/disciplining ability, we also find support for the latter as these firms

contemporaneously experience superior post-IPO operating performance. Stock liquidity also

improves significantly in the post-IPO years, supporting the notion that reputable auditors have

superior information production ability that reduces information asymmetry. Overall, we find

support for the certification effect, superior monitoring ability, and superior information

production ability associated with high-quality auditors.

JEL classification: M49; G32

Keywords: Auditor reputation; Long-run post-IPO returns; Information asymmetry; Growth

opportunities; Research and development intensity; Operating performance

1

Does Auditor Reputation Determine Post-IPO Stock Returns and Operating

Performance?

I. INTRODUCTION

Financial intermediaries, such as underwriters and auditors, provide crucial external

certification that help investors evaluate the true firm value in the face of information

asymmetry. Initial public offerings (IPOs) provide one of the most compelling settings to

examine the certification effects associated with the quality of the financial intermediaries. While

the underwriter certification effect for IPOs has been well established in the literature (see e.g.,

Carter, Dark and Singh, 1998; Carter and Manaster, 1990; Logue, 1973), the role of auditor

quality on the long-run performance of IPO firms remains largely unexamined. Further, the

current literature is silent on the interaction between auditor quality and underwriter reputation

on IPO performance.

With respect to auditor quality signaling information to investors on the value of the

initial public offering (IPO) firm, theoretical research has posited that high quality auditors are

valuable to firms going public (Titman and Trueman, 1986). Due to limited information on these

firms prior to the offering, the higher audit quality associated with reputable auditors can certify

the quality and the true value of the IPO firm, which should lead to lower IPO underpricing and

relatively higher (or less negative) post-IPO returns.1 The certification effect due to superior

audit quality and, unique to auditors, superior post-IPO monitoring and information production is

expected to lead to superior long-run stock returns. In this study we examine the influence of

1 Underpricing is the difference between a stock’s first day closing price and the offer price. Prior studies have

documented that IPOs typically experience a significant increase in the stock price during the first-day of trading

relative to the original offer price. Underpricing measures this phenomenon as the first-day closing price minus the

original offer price as a percentage of the original offer price.

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auditor reputation (or audit quality) on long-run post-IPO stock returns. Further, to gain insight

into the real or operational factors underlying post-IPO stock returns, we also examine the post-

IPO operating performance.

The credibility and reputation of the auditor become even more important and valuable to

investors in the case of IPOs as compared to already public firms because of the information

asymmetry associated with new issues. Therefore, the benefits to investors from auditor

reputation should be more readily detected for IPO companies than for companies with a

publicly available track record (see Menon and Williams, 1991). Also, auditors convey important

information to investors not just in the short-run (around the IPO) but also in the long-run due to

their continued monitoring of the firm after it goes public. In addition to the certification effect,

the post-IPO monitoring by auditors, which is not available for underwriters, makes it interesting

to examine the effect of auditor quality on long-term performance of IPO firms. Given this

backdrop, IPOs provide a unique setting to examine the effect of auditor certification and

monitoring on post-IPO stock price performance. Complementing the underwriter certification

literature, this study provides evidence of the interaction of auditor reputation and underwriter

certification effects on IPO pricing and post-IPO performance.

Prestigious audit firms with brand name reputations have been shown to produce higher

quality audits and have higher perceived audit quality than less reputable auditors (e.g. Becker,

DeFond, Jiambalvo, and Subramanyam, 1998; Dopuch and Simunic, 1982; Francis, Maydew,

and Sparks, 1999; Jensen and Meckling, 1976). Prior research on already public firms has

documented that higher perceived audit quality is associated with higher financial reporting

quality (Jensen and Meckling, 1976; Dopuch and Simunic, 1982), higher earnings quality

(Francis, LaFond, Olsson, and Schipper, 2004), enhanced accounting transparency (Mansi,

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Maxwell, and Miller, 2004; Pittman and Fortin, 2004), and lesser earnings management (e.g.,

Becker, DeFond, Jiambalvo, and Subramanyam, 1998; Francis, Maydew, and Sparks, 1999).

Also, reputable auditors have been found to reduce the cost of equity (Khurana and Raman,

2004) and debt capital (Mansi, Maxwell, and Miller, 2004; Pittman and Fortin 2004), and

increase initial public offering proceeds (Willenborg, 1999).

Related to IPOs, most prior research examines only the effect of auditor reputation on

initial-day IPO returns. Arguably however, first-day returns do not reflect the intrinsic value of

the firm (Levis, 1993; Michaely and Shaw, 1995; Ritter, 1991). Michaely and Shaw (1995) is the

only study that examines the long-run effect of auditor ranking on initial public offerings and

finds insignificant results. They report that the auditor reputation has no effect on long-run post-

IPO performance, beyond the underwriter certification effect.2

The prevailing evidence in the literature shows that the choice of the audit firm affects

the underpricing at the offering; more specifically, more reputable audit firms are found to be

associated with less underpricing (Balvers, McDonald, and Miller, 1988; Beatty, 1989; also see

Knechel, Krishnan, Pevzner, Shefchik, and Velury (2012) for a review of the literature). This

finding has been attributed to the idea that the reputation of the auditor provides information

about the firm's true value and serves to reduce uncertainty about future cash flows of the newly

traded firm (Balvers, McDonald, and Miller; 1988; Simunic and Stein, 1987; Datar et al., 1991;

and Titman and Trueman. 1986;), thereby leading to less underpricing.

The underwriter certification effect on IPOs (Carter, Dark and Singh, 1998; Carter and

Manaster, 1990; Logue, 1973) is based on the notion that lower quality firms will have greater

degree of underpricing at the IPO. Besides underwriter certification, theoretical research has

2 It is important to note that Michaely and Shaw (1995) do not rely on a control sample and their excess returns are

obtained by subtracting geometrically calculated CRSP value-weighted return from the two-year geometric return

for the stock for the same period.

4

posited that for firms going public high quality auditors are also at least as prestigious

underwriters (Titman and Trueman, 1986). The higher audit quality associated with reputable

auditors certifies the value of the IPO, which is expected to lead to lower IPO underpricing and

higher (less negative) post-IPO returns.

We specifically address the following unanswered questions: What is the role of auditor

quality on long-run IPO performance? Does auditor reputation have any incremental effect on

IPO performance, beyond the investment banker certification effect? What is the interaction

effect between auditor quality and underwriter rank on IPO returns? In other words, are these

two certification effects partial substitutes? Does the effect of auditor quality on post-IPO returns

persist longer as compared to the underwriter certification effect? Does auditor certification

effect vary with the degree of information asymmetry of an IPO? In other words, do growth

opportunities and research and development (R&D) expenditures enhance the role of auditor

quality on post-IPO returns? What are the possible underlying channels which determine the

effect of high quality auditors on post-IPO stock returns, such as superior operating performance

due to better ongoing monitoring and/or increased stock liquidity due to enhanced transparency

and risk reduction?

The paper proceeds as follow: In section 2 we develop the hypotheses. Section 3 details

the sample formation process, data sources and research methodology. Our empirical findings

are presented in section 4. Section 5 concludes.

II. HYPOTHESIS DEVELOPMENT

Auditors serve as certifiers of the quality of financial information disclosed at the time of

the offering and subsequent to the IPO. High audit quality increases the reliability of financial

5

reports, and therefore, provides more transparency to investors about the financial health and

prospects of the firm. It is also argued that reputable auditors will provide more accurate and

useful information about the entrepreneur’s private information to investors (Datar, Feltham, and

Hughes, 1991), leading investors to assess a higher value on the firm. Datar et al. also argue that

the value of an audit is increasing in audit quality and the firm-specific risk faced by the

entrepreneur and is a non-decreasing function of the entrepreneur’s expectations about the future

value of the firm. Using a theoretical framework, DeAngelo (1981) shows that larger auditors

have less incentive to behave opportunistically, and thus, are able to provide a higher perceived

audit quality. Hence, the amount of mispricing of IPOs associated with high-quality auditors will

be smaller leading to superior post-IPO stock return performance.

We therefore reason that the post-IPO long-run stock price performance for firms using

prestigious auditors will be superior (or less negative and less severe) to that of firms using lower

quality auditors. We call this the auditor certification effect. Hence, we propose hypothesis H1:

H1 (Auditor certification effect): The post-IPO long-run performance for firms

employing prestigious auditors will be superior to that of their counterparts

associated with non-prestigious auditors.

It is important to recognize that while the relationship between an IPO firm and its

auditor is an ongoing one as it continues beyond the IPO, the relationship with the underwriter is

typically associated with the IPO and is expected to be episodic. Therefore, we argue that as time

passes from the IPO, the underwriter certification effect will wane but the auditor quality effect

on the post-IPO stock return performance will persist due to the fact that the auditor-firm

relationship is typically longer lasting and regular. Hence, we propose hypothesis H2:

H2 (Persistence of auditor quality effect): The effect of prestigious auditors on long-

run post-IPO stock returns will persist for a longer period beyond the IPO, while

6

the underwriter certification effect is expected to weaken with the passage of time

after the IPO.

Both auditors and underwriters serve in their own way as certifiers of the financial

health of the issuer. The underwriter certification effect on IPOs is well established. If there

is an auditor certification effect on IPO returns then the effect of high auditor quality

should be more pronounced for issuers associated with low underwriter rank because the

IPO is now more reliant on the auditor quality. On the other hand, if the underwriter rank is

high, then we would expect a dampened effect of auditor quality on IPO returns. Put

differently, we expect some degree of substitution between the underwriter certification

effect and auditor quality effect. Hence, we propose the following hypothesis:

H3 (Substitution effect of auditor quality and underwriter rank): The effect of

prestigious auditors on long-run post-IPO stock returns will be more pronounced

for issuers associated with low quality underwriters.

Prior literature argues that greater information asymmetry introduces potential for

opportunistic behavior by management. Firms associated with high research and development

intensity and with greater growth opportunities exhibit greater information disparity between

investors and the firm (Myers, 1977). Greater information asymmetry and the attendant agency

costs increase the relative importance of the monitoring function and the expertise of the auditors

(DeFond, 1992; Francis and Wilson, 1988). Francis, Maydew and Sparks (1999) argue that firms

with greater likelihood for opportunistic behavior are more in need of prestigious auditors to

provide assurance to investors that reported earnings are credible.

Investment opportunities are comprised of growth options and assets-in-place (Myers,

1977). Firms with relatively greater proportion of growth options require greater managerial

7

discretion, face greater information disparity between managers and investors, and are thus

harder to value due to greater information asymmetry and uncertainty. Similar arguments apply

to firms with higher R&D intensity. Such firms require greater judgment from auditors whose

discernment of firms’ expenditure and detection of risk reduce agency costs (Smith and Warner,

1979; Godfrey and Hamilton, 2005). Prior studies, which focus on the choice of auditors, find

that prestigious auditors reduce information uncertainty at equity issues (Feltham, Hughes and

Simunic, 1991; Slovin, Sushka and Hudson, 1990).

Given the above reasoning, we argue that auditor quality and credibility are expected to be

more valuable to certain IPO firms than others. Hence, we posit that firms with higher growth

prospects that retain highly ranked auditors will exhibit superior post-IPO performance than

similar firms associated with lower ranked auditors. Similarly, firms with high R&D intensity are

expected to exhibit better performance if they retain highly ranked auditors. With this backdrop,

we propose the following two hypotheses:

H4 (Growth opportunities and effect of auditor quality): Firms with higher growth

opportunities that retain reputable auditors will exhibit superior performance

relative to comparable firms associated with less reputable auditors.

H5 (R&D intensity and effect of auditor quality): Firms with higher research and

development intensity that retain reputable auditors will exhibit superior

performance relative to comparable firms associated with less reputable auditors.

The superior monitoring (and disciplining) of firms and their managers associated with high

quality auditors should manifest in superior post-IPO operating performance of these firms

relative to those audited by low-ranked auditors. This is one channel that may underlie, and

explain, the auditor certification effect of superior post-IPO stock returns for such firms. Based

on this line of reasoning, we propose the following hypothesis.

8

H6 (Real effects channel): Firms with prestigious auditors will be better monitored and

disciplined resulting in superior post-IPO operating performance compared to

their counterparts with low-ranked auditors.

This channel will also help us show that the auditor certification effect at the time of the

offering (H1) could be augmented by the superior monitoring effect ascribed to high quality

auditors which would manifest in superior post-IPO operating performance (H6).

Finally, another way by which auditor quality can influence post-IPO stock returns is

through the stock liquidity channel. If high ranked auditors through their superior information

production ability can enhance firm transparency thereby, reducing the information asymmetry

associated with IPO firms, we expect the liquidity of the stock to increase. Based on this

reasoning, we propose the following hypothesis:

H7 (Liquidity channel): High quality auditors with their superior information

production ability will be able to improve liquidity of their client IPO firms, due to

enhanced transparency and reduction in information asymmetry.

Eckbo and Norli (2005) show that stock liquidity (turnover) is associated with long-run IPO

performance. Further, researchers have argued that increase in liquidity is a risk-reducing factor

for IPO stocks (see e.g. Brennan and Subrahmanyam, 1996). Hence, it can be argued that the

increase in liquidity due to association with high quality auditors is expected to lower the

expected return of IPO stocks due to risk reduction. Hence, this liquidity channel (H7) introduces

a tension, as it contrasts with the positive expected relation between high quality auditor and

stock return performance underlying H5.

9

III. SAMPLE FORMATION PROCESS AND RESEARCH METHODOLOGY

Sample Formation Process and Data Sources

To create our sample, we start with Jay Ritter’s website which lists all IPOs based on the

selection criteria in Loughran and Ritter (2004).3 We access multiple sources to obtain

information about the new issue, such as the proceeds from the offering and whether the shares

are primary shares or secondary shares. Information on the offering and the underwriter is

obtained from the SDC New Issues Database. Underwriter rankings and firm age are extracted

from Jay Ritter’s website. Standard and Poor’s Compustat database is the source of firm

fundamentals and the auditor rank, while stock returns are obtained from the Center for Research

in Security Prices (CRSP) monthly stock files. Our final sample spans initial public offerings

made from 1986 to 2006, and ends in 2006 to ensure that we have 60 months of stock returns

post-offering. The final sample is composed of 4,190 issues.

Given that not all IPOs represent a first IPO, we distinguish between reverse leveraged

buyouts (RLBOs) and first-IPOs (FIPOs). We then determine whether an IPO is one of three

types of RLBOs (public-to-private-to-public, division-to-private-to-public, and private-to-

private-to-public) by employing the Securities Data Company (SDC) Mergers and Acquisitions

(M&A) database to identify leveraged buyouts with a future RLBO. Next, we use the SDC New

Issues database to obtain information about the offering and identify the RLBO date. The SDC

M&A database stopped tracking future RLBOs after 1998. To fill this data void, we hand

collected transactions for the period 1999-2006 by matching IPOs with a prior leveraged buyout.

We further supplement the data with the RLBOs in Cao’s (2011) sample. The sample includes

3,666 First IPOs and 524 RLBOs (204 Public RLBOs, 175 Private RLBOs, and 145 Division

3 http://bear.warrington.ufl.edu/ritter/ipodata.htm

10

RLBOs).

Research Methodology

Computing Benchmark-Adjusted Buy-and-Hold Stock Returns

In our univariate analysis, in addition to computing raw returns, we compute long-run

buy-and-hold returns following Barber and Lyon’s (1997) methodology defined as buy-and-

hold return of sample firms (in our case IPO firms) less buy-and-hold return of corresponding

control sample over the same time-window (t = 1 to ).

1

,

1

, )(11t

ti

t

tii RERBHR (1)

We calculate two different benchmark-adjusted buy-and-hold stock returns over different

post-IPO time horizons: 12-months, 24-months, 36-months, and 60-months. Our first measure is

the industry-adjusted returns based on median returns by year for the 49 Fama and French

(1997) industry groupings.

The second measure is the control-firm adjusted returns that take into account the fact

that new public offerings generally differ in attributes from the population of firms at large, and

thus, it is important to control for such differences. To do so, we create a sample of control firms

using the propensity score methodology that minimizes the difference between our sample firms

and control firms on multiple dimensions.

Propensity Score Methodology to Select Control Firms

We select control firms based on the propensity scores calculated at the offering in order

to compute control-adjusted returns (Villalonga, 2004).4 The propensity score matching

4 Lawrence, Minutti-Meza, and Zhang (2011) use propensity score control firms in their study of audit quality

proxies and Big 4 versus non-Big 4 accounting firms.

11

technique utilizes information from the pool of firms with similar salient characteristics.5 This

matching procedure also circumvents any effect of sample selection bias on our results (Imbens

and Wooldridge, 2009).

We estimate a logistic model using our sample firms at the year of the offering and control

firms with Compustat data for the same year. The dependent variable, IPO Dummy, assumes a

value of one for IPOs, and 0 otherwise. Guided by past research, we choose explanatory

variables employed in equation (2).

IPO Dummy = f(Assets, ROA, Tobin’s Q, Div/TA, R&D/Sales, Capex/Sales, Turnover,

Year Dummies, FF49 Dummies) (2)

The variables are defined as follows. Assets is the book value of total assets in real 2006

dollars; ROA is earnings before interest divided by total assets; Tobin’s Q is calculated as total

assets less common equity plus market value of equity divided by total assets, Div/TA is defined

as dividends divided by total assets, R&D/Sales is research and development expenses scaled by

sales, Capex/Sales is capital expenditures relative to sales, Turnover is measured as common

shares traded divided by common equity shares outstanding, and FF49 Dummies represent

industry dummies based on the Fama and French’s (1997) 49 industry groupings. The appendix

details how we construct and define all the variables used in the study.

Next, we group our sample firms by propensity score quintiles. Control firms with a

predicted IPO probability below the lowest quintile or above the highest quintile are dropped.

We then assign the control firms (without replacement) to the IPO quintiles based on the

smallest absolute difference of propensity score with our sample firms. The robustness of our

assignment process is verified using difference in means and medians at the IPO.

5 The propensity score approach allows multiple firm characteristics to be distilled down to a single score and

enables us to avoid some of the issues documented by Brav, Geczy, and Gompers (2000) in their study of equity

issuances.

12

Addressing Concerns with Buy-and-Hold Returns

To address concerns that test statistics obtained from the Barber and Lyon (1997)

approach are influenced by high skewness and kurtosis, we follow Cowan and Sergeant (2001),

who show that both concerns are ameliorated by winsorizing at the third standard deviation. A

second concern with buy-and-hold returns is that test statistics may be overstated due to cross-

sectional correlation (Fama, 1998; Lyon, Barber, and Tsai, 1999; Brav, 2000). To deal with this

issue, we compute propensity score adjusted abnormal returns again following Cowan and

Sergeant (2001) who recommend computing unpaired p-values.

Another solution to the cross-sectional dependence is to employ a calendar time

methodology using Carhart’s (1997) four-factor model. To do this, we compute mean calendar

month returns for the IPO firms, the controls, and the difference between the two. Then we

measure abnormal returns using Brav and Gomper’s (1997) calendar time approach in

conjunction with Carhart’s (1997) model as follows:

rt = f (αt, RMRFt, SMBt, HMLt, MOMt) (3)

The intercept of this model indicates abnormal returns. The dependent variable, r, is the

mean monthly return less the risk-free rate, RMRF is the market risk premium, SMB (Small

minus Big) represents firm size difference, HML (High minus Low), is the monthly difference

in returns between high and low book-to-market stocks and MOM is momentum. We avoid

survivorship bias by computing the long-run stock returns for the post-IPO horizon period or

until delisting, whichever comes first.6

6 Our analysis using Carhart’s (1997) model uses calendar time returns adjusted with propensity score selected

control firms. In contrast to Brav, Geczy, and Gomers (2000) our approach control’s for both firm specifics

attributes and market performance factors.

13

Sample Description

Table 1 reports salient characteristics of all IPOs, first IPOs (FIPOs), and reverse

leveraged buyouts (RLBOs). Specifically, we report statistics on firm size (Assets), the size of

the gross proceeds from the offering (Proceeds) in 2006 dollars, return on assets (ROA), Tobin’s

Q, firm leverage (Leverage), the age of the firm in years (Age), the daily stock return standard

deviation (RetStdDev) computed for the period from offer date +6 through offer date +260, and

beta. It is clear that RLBO firms differ significantly from FIPO firms on many dimensions.

FIPO firms are smaller in terms of assets, younger in term of years since inception, have smaller

proceeds, are less profitable (ROA), hold less debt, and are also more risky. We note that all

prior IPO studies are based on commingled samples of first IPOs and RLBOs. In this study we

recognize that these two types of IPO transactions are fundamentally different.

Table 2 cross-tabulates auditor and underwriter rankings partitioned by low and high

ranking for all IPOs, FIPOs, and RLBOs. We segment the IPO market into these categories

recognizing that they are fundamentally different. Specifically, we expect that first IPOs (firms

going public for the first time) will be associated with a higher degree of information

asymmetry at the time of going public as compared to RLBOs, which thereby determines the

importance of the auditor certification of the offering. The chi-squared test statistics of the

differences between FIPOs and RLBOs are highly significant and confirms our expectations.

Therefore, in our analyses we generally focus on FIPOs.

To define underwriter reputation, we adopt the modified Carter-Manaster (CM) system

for our analysis. Carter, Dark, and Singh’s (1998) study shows that the modified CM system

provides the strongest relationship between underwriter reputation and stock returns. These

rankings are based on the listing position of underwriter names in the “tombstone”

14

announcements of stock offerings. In our analysis of long-run stock returns we define an auditor

to belong in the highly reputable group if they are one of the Big N in the year of the offering.7

Table 2 shows that 47.9 percent of our IPO firms have high-ranked auditor and low-ranked

underwriter, while 41.86 percent of the sample have both high-ranked auditor and high-ranked

underwriter. Only 1.41 percent of the IPOs have high-ranked underwriter and low-ranked

auditor, while in 8.83 percent of cases we find both the underwriter and the auditor are low-

ranked. A similar pattern is revealed for the FIPO subsample and for all RLBO firms going

public.

IV. EMPIRICAL RESULTS

Univariate Analysis of Auditor Quality on Post-IPO Long-run Stock Returns

Table 3 reports the long-run post-IPO stock price performance following all IPOs. We

report the mean and median buy-and-hold stock returns computed using raw unadjusted returns

(Raw Returns), industry-adjusted returns (Industry-adjusted), and control firm adjusted

(Control-adjusted) returns. While previous IPO studies, such as Loughran and Ritter (1995) and

Carter, Dark and Singh (1998), report results based on three-year post-IPO returns, we examine

various post-IPO time horizons ranging from 12 months to 60 months. Test statistics are

computed using firm level paired means and medians as well as unpaired group differences

between IPOs and the benchmark groups.

The mean (and median) industry-adjusted cumulative long-run stock returns for the

group of firms with less reputable auditors are consistently negative and statistically significant

for all horizons. For example, the mean industry-adjusted return for the low auditor group is -

7 A limited number of observations change auditor class at the longer stock return horizons. To ensure our results

are not biased by these transactions we replicate our analysis by dropping these firms that change auditor class. All

our conclusions continue to hold when these observations are excluded.

15

6.09 percent, -19.66 percent, and -27.72 percent for the 12, 36, and 60-month periods

respectively. Thus, the abnormal return gets progressively worse for the group of firms

associated with a less reputable auditor. When we restrict our sample to just FIPOs (see Table

4), our industry-adjusted results are qualitatively similar.

For firms with low-ranked auditors, as shown in Table 3, control-adjusted mean and

median returns are negative and significant for 36 and 60 months. When the time horizon is 12

months, only the median returns are significantly negative. When we restrict the sample to

FIPOs in Table 4, our findings using control-adjusted returns are even stronger as mean and

median returns are negative and significant for all durations, providing additional evidence of

the need to disentangle these very different types of transactions.

The high-ranked auditor group in Table 3, in contrast, has the following corresponding

mean industry-adjusted returns for the three time horizons: 4.61 percent, 9.89 percent, and 15.62

percent. We document similar pattern of results when we restrict our sample to FIPOs in Table

4. Statistical tests of the differences in mean (and medians) returns between the low auditor and

high auditor groups are highly significant in Tables 3 and 4 irrespective of whether the returns

are raw, industry-adjusted, or control firm-adjusted for all three time horizon. Figure 1

graphically depicts these results. Hence, our univariate results on the effect of auditor quality on

post-IPO returns are robust as they hold for both samples: all IPOs and FIPOs.

The main conclusions that can be drawn from Tables 3 and 4 are that the difference

between the low and high auditor groups is statistically significant for (a) all time horizons --

12, 36 and 60 months, (b) for all three measures of returns, (c) for both mean and median

returns, and (d) for all IPOs as well as for FIPOs. These findings support our hypothesis H1.

16

Robustness Check using the Carhart Model

To ensure that our findings are not affected by cross-sectional correlation, we measure

abnormal returns using Brav and Gomper’s (1997) calendar time approach in conjunction with

Carhart’s (1997) model as described earlier in equation (3). Using this approach based on

calendar time returns, abnormal returns are measured with the sign and significance of the

intercept, α. The results for the control-adjusted long-run returns over 12, 36 and 60-month

intervals are reported in Table 5. Panel A reports results for all IPOs, while Panel B documents

the findings for first IPOs. As the intercepts represent a monthly return, we report compounded

returns in parentheses to provide comparability with the findings in Tables 3 and 4. The

intercepts for all three horizons are consistently positive and highly significant. Overall, the

results using four-factor abnormal returns reinforce our preceding results, supporting H1, that

IPO firms that engage a reputable auditor outperform the control firms for up to 60 months after

the offering.

Univariate Analysis of Auditor Quality and Underwriter Rank on Post-IPO Returns

In Table 6 we examine for all IPOs the control firm-adjusted buy-and-hold returns over

12-months, 36-months, and 60-month time horizons by taking into consideration both the

underwriter and the auditor rankings. This examination allows us to test hypothesis H1 by

measuring the degree to which the auditor’s quality benefits the offering firm over and beyond

the benefits from underwriter certification. In the first panel of the table we focus on offerings

with less prestigious underwriters, and examine the differential impact of high-ranked auditors

on control-adjusted returns compared to less prestigious auditors for all three time horizons. In

the second panel, we repeat the analysis for offerings associated with prestigious (or high

ranked) underwriters.

17

In the first panel of Table 6 where the offerings are associated with low-ranked

underwriters, the difference in control firm-adjusted long-run stock returns between high and

low ranked auditors is highly significant. In the second panel where we examine offerings

associated with high-ranked underwriters, the auditor certification effect is evident for the

difference in median returns over the 60-month post-offer period. In other words, consistent

with hypothesis 3, these results show that the differential effect of auditor quality is more

pronounced when a low-ranked underwriter is involved in the offering because the auditor

certification is solely influencing the post-offer stock returns. Since our findings in Table 6 for

high-ranked underwriters may result from the differences between FIPOs and RLBOs, as

documented earlier, in Table 7 we repeat the analysis in Table 6 only for FIPOs. As expected,

the results are stronger in terms of the effect of the auditor quality on post-offer stock returns in

both panels: (a) low-ranked underwriters, and (b) high-ranked underwriters. As in Table 6,

differences in mean and median returns for firms with a low-ranked underwriter are highly

significant for all time horizons. Turning to the high-ranked underwriter subgroup (second panel

of Table 7), the effect of auditor certification as captured by the difference in mean and median

buy-and-hold returns for high-ranked auditor versus low-ranked auditor is evident for the 36-

months and 60-month time horizons. These results are consistent with H3.

Overall, the univariate analyses presented in Tables 3, 4, 5, 6, and 7 document a

significant auditor certification effect on post-IPO stock returns, using three different measures

of buy-and-hold returns, over various time horizons, 12, 36, and 60 months. To be comparable

to Michaely and Shaw (1995) we also estimate 24-month post-IPO returns and find that our

results are robust to this time horizon. We find that the results are stronger for FIPOs relative to

all IPOs. Controlling for the underwriter certification effect by categorizing the sample into

18

IPOs associated with high-ranked underwriter and low-ranked underwriter, our univariate

results show that auditor certification effect is strong and persists over a long time horizon.

Examination of the control-adjusted returns over lengthening horizons also enables us to

test hypothesis H2. If as time horizon from the IPO lengthens, the benefits from underwriter

certification weaken relative to the benefits from continued certification by prestigious auditors,

then the long-run stock returns of offering firms that employ prestigious auditors will continue

to be superior in the longer-run. In support of H2, our univariate analysis of FIPOs shows that

the auditor certification effect remains significant (and is larger particularly for mean returns)

and persists over longer time horizon following the IPO compared to the underwriter

certification effect.

Disentangling the auditor certification effect from the underwriter certification effect, we

document for the first time that there is a significant auditor certification effect on long-run

post-IPO stock returns, which is in addition to the previously documented underwriter

certification effect. Because we find that there is very little variation in auditor quality for

RLBOs, we focus on FIPOs to conduct our multivariate analysis in the following section.

However, in unreported results, we find that our conclusions based on first IPOs are robust to

using all IPOs (including RLBOs).

Multivariate Analysis

Disentangling Auditor Reputation and Underwriter Certification Effects

To examine the independent effects of auditor quality and underwriter rank on long-run

post-IPO stock returns (BHRi) we first use the Carter, Dark and Singh (1998) regression model

and incorporate auditor rank and underwriter rank as separate focus variables.

19

BHRi = αi + β1*High Auditori + β2*High UWi + β3*Log(Proceeds)i + β4*Log(Age)i

+ β5*Secondaryi + β6*RetStdDevi (4)

High Auditor capturing auditor quality is a binary variable set to one if the auditor is one

of the Big N audit firms, otherwise it assumes a value of zero, while underwriter rank is

captured by the dummy variable High UW, which is set to one if the underwriter ranking is at

least nine, and otherwise it takes a value of zero. We estimate four models based on raw, value-

weighted index-adjusted, industry-adjusted, and median control firm-adjusted 36-month buy-

and-hold post-IPO returns as the dependent variable. We follow Carter, Dark, and Singh by

including the control variables Log (Proceeds) defined as the log of issue proceeds, Log (Age)

defined as log of firm age at the time of the offer, Secondary defined as the percentage of

secondary shares in the offering, and RetStdDev defined as the daily return standard deviation

calculated from six days preceding the offering date to 260 days following the offer.

Table 8 presents the regression estimates for four models. Following Loughran and

Ritter (1995) and Carter, Dark and Singh (1998), we focus on 36-month post-IPO returns for

these regressions. The multivariate results corroborate our univariate findings. In all four

models both our focus variables, High Auditor and High UW, are positive and highly

significant. This is the first study to document that after controlling for underwriter quality there

is a significant effect of auditor quality on long-run post-IPO stock returns. A study by

Michaely and Shaw (1995) failed to find a significant auditor reputation effect on long-run

stock excess return after controlling for underwriter prestige. They used a sample of IPOs (like

all previous IPO studies, without separating the RLBOs from the FIPOs) for the period 1984-

20

1988 and focused on stock excess returns over corresponding CRSP value-weighted return for a

two-year post-IPO window.

In contrast, our sample spans a longer time period, 1986-2006, and we apply various, and

more refined, estimates of buy-and-hold returns over different post-IPO time windows. Our

results are uniformly consistent and document that auditor quality effect is highly robust and

significant across all measures of buy-and-hold returns, after controlling for underwriter quality

and other relevant control variables suggested in earlier studies.

Effects of Auditor Quality and Underwriter Rank on Post-IPO Returns: Using Controls

To examine the effects of auditor reputation and underwriter rank on post-IPO stock

returns we use the following general model specification where the dependent variable is the

unadjusted buy-and-hold return:

BHRi = αi + β1*FIPOi + β2*High Auditor Groupi x FIPOi + β3*High UW Groupi x FIPOi

+ β4*High Qi + β5*High R&Di + β6*Propensity Scorei

+ β7*High Auditor Groupi x Low UW Groupi x FIPOi

+ β8*High Auditor Groupi x FIPOi x High Qi + β9*Secondaryi

+ β10*RetStdDevi + β11*High Auditor Groupi x FIPOi x High R&Di

+ β12*Market Capi + β13*High Auditor Groupi + β13*High UW Groupi (5)

The variables High Auditor Group (High UW Group) are set to one for all IPOs and

their matching controls where the offering firm is associated with a high-ranked auditor

(underwriter). In one specification we include a dummy set to one if a firm is not associated

with a high-ranked underwriter (Low UW Group). Our first proxy for high growth opportunities

is Tobin’s Q (High Q), and is set to one if the firm's Tobin's Q falls in the top quartile; our

second proxy (High R&D) is set to one if the firm falls in the top quartile for this measure.

These are the focus variables.

21

Our other control variables include Propensity Score, which is the predicted probability

of a first-IPO from equation (1), Market Cap defined as the market capitalization four months

after the offering, Secondary and RetStdDev are as defined earlier.

We employ the double differences methodology to examine the effects of auditor

reputation and underwriter rank on post-IPO stock returns.8 This methodology was developed

to measure differences between “treatment” and “control” groups (see Imbens and Wooldridge,

2009). In our case, FIPOs are the “treatment” group. This methodology is ideal for comparing

high-ranked auditors relative to their matched controls in contrast to low-ranked auditors and

their controls. In our analysis the second difference is whether a “treatment” firm is associated

with a Big N auditor. The approach has the benefit of including all the control firms while

controlling for any differences between FIPOs and their matching controls by including the

propensity score.

Table 9, Panel A presents regressions controlling for auditor quality, underwriter

quality, whether a firm is a FIPO, firm characteristics that have been identified previously to

influence IPO returns, and propensity score matched control firms. In addition to 36 and 60

months buy-and-hold returns, we also include 24-month returns for comparability with

Michaely and Shaw (1995). Estimates in Models 1-5 reinforce our univariate findings and the

multivariate analysis presented in Table 8. As expected, FIPO is significantly negative in all

five models in Table 9, Panel A supporting the argument that the long-run stock returns of first-

IPOs underperform benchmarks.

Estimates in Models 1-3 show that the positive effect of auditor quality on post-IPO

stock returns, High Auditor Group X FIPO, is highly significant and robust, after controlling for

underwriter quality and relevant firm characteristics captured by the vector of control variables

8 Butler and Cornaggia (2011) use this double differences methodology in a different context.

22

in these regression models. These findings again reinforce support for hypothesis H1. It is

noteworthy that the interaction term High UW Group X FIPO is significantly positive only in

Model 2 for the 36-month post-IPO window but not in Models 1 and 3, indicating a lack of

robustness of the underwriter certification effect. Notably, Carter, Dark and Singh (1998), only

use the 36-month window to draw their conclusions regarding the effect of underwriter

certification effect on IPO returns. As in Carter, Dark and Singh (1998), the control variable

RetStdDev measuring the risk of the firm is negative and highly significant, and Secondary is

positive and significant in all models.9

Supporting hypothesis H2, we find that the effect of auditor quality on post-IPO buy-

and-hold returns remains positive and highly significant across all time horizons up to 60-

months (5 years) after going public. In contrast, the effect of underwriter rank on post-IPO

returns after controlling for auditor certification is weak and only significant for the 36-months

(3 years) window and is insignificant over the 60-months post-IPO period.

The interaction term High Auditor Group X FIPO X Low UW Group is included in

Models 4 and 5 to test the substitution effect of high auditor quality on post-IPO returns of

issuers associated with low-ranked underwriters. Consistent with hypothesis H3, we find that

this interaction term is positive and highly significant. The results reinforce our conclusions on

underwriter reputation as the interaction term High UW Group X FIPO becomes significant in

Models 4 and 5 once we exclude the High Auditor Group X FIPO variable.

In Table 9, Panel B we test our hypothesis H4, in Model 4 (36-months) and Model 5

(60-months) by including the term High Q and the triple interaction term High Auditor Group X

9 The propensity score methodology is intended to minimize differences between IPOs and their controls. However,

we include the propensity score in these models to ensure our results are not biased by the minor differences that do

exist.

23

FIPO X High Q. As expected, High Q is negative and highly significant in both models

supporting the argument that firms with high growth opportunities are associated with more

information asymmetry and hence have a negative effect on post-IPO returns. The interaction

term High Auditor X FIPO X High Q captures high growth opportunity firms undertaking a

FIPO and are associated with a high quality auditor. As hypothesized, these firms benefit

significantly more from being associated with a high quality auditor than their counterparts and

matched controls. This benefit is over and above the significant positive effect of high quality

auditors that we document for all FIPOs. The coefficients of this interaction term are highly

significant in both models with 37.27 (p-value = 0.00) and 41.28 (p-value = 0.00) respectively.

Hence, we find support for hypothesis H4.

To test hypothesis H5 related to R&D intensity, in Models 8 (36-months) and 9 (60-

months) we include R&D/sales and an interaction term, High Auditor Group X FIPO X High

R&D. We argue that firms with high research and development intensity are associated with

more information opacity. Therefore, IPO firms with high degrees of R&D will benefit even

more from high auditor quality. The coefficients of R&D/sales in Models 6 and 7 are, as

expected, negative and highly significant.

Moreover, in support of our hypothesis H5, the interaction term High Auditor Group X

FIPO X High R&D is positive and highly significant in both Models 8 and 9 with coefficients of

15.43 (p-value = 0.01) and 28.80 (p-value = 0.00) respectively. The findings in Table 9, Panel B

underscore the importance of the monitoring function and credibility of auditors and

complements the literature on the relevance of auditor reputation to firms that face asymmetric

information (DeFond, 1992; Godfrey and Hamilton, 2005).10

10 We investigate whether our findings are an artifact of differential first-day underpricing for firms associated with

high-ranked and low-ranked auditors. Using a multivariate analysis of underpricing for first IPOs we find that

24

Effect of Auditor Quality on Post-IPO Operating Performance

To investigate the underlying mechanism behind the sustained superior post-IPO stock

return performance by offerings associated with high quality auditors documented in this study,

we examine the effect of auditor quality on two salient operating performance metrics: (a)

return on assets (ROA), and (b) free-cash flow scaled by total assets (FCF/TA), over the five-

year post-IPO period. This analysis allows us to differentiate the auditor certification effect at

the time of the IPO (hypothesis H1) from the longer-term ongoing monitoring effect expected

from a high quality auditor following the IPO, manifesting in superior operating performance

(hypothesis H6).

In Table 10 we present regressions explaining industry-adjusted ROA (Panel A) and

control firm adjusted ROA (Panel B) for each of the post-IPO years. Results in Panel A show

that the High Auditor dummy is highly significant for each of the five post-IPO years in

explaining the industry-adjusted ROA. Panel B presents regressions of FIPOs and control firms

on unadjusted ROA for each of the five years. The focus variable, High Auditor Group X FIPO,

is highly significant for each year indicating that after including appropriate controls, high

quality auditors have a significant positive impact on the ROA in the post-IPO years.

Clearly, these results support hypothesis H6 and the notion that high-ranked auditors

through their superior auditing function have a significant beneficial monitoring and

disciplining effect on the firm, on an ongoing basis, resulting in better operating performance.

These results indicate that there is a "real channel" in terms of significantly better operating

offerings associated with low-ranked auditors are more underpriced, and therefore have first-day returns that are

significantly greater (10.3 percent) than that for offerings of firms with high-ranked auditors, irrespective of whether

returns are unadjusted or adjusted using the value-weighted index. Hence, we rule out the initial-day returns driving

our post-IPO long-run stock returns.

25

performance underlying our finding of superior post-IPO stock returns of firms associated with

high-ranked auditors.

As an alternative operating performance metric and for robustness check, in Table 11 we

replicate the analysis of Table 10 explaining the effect of high quality auditor on the free cash

flow scaled by total assets of the firm (FCF/TA) in each of the five post-IPO years. Consistent

with our findings for ROA, this operating performance measure also reveals that high-ranked

auditors play a significant beneficial role in determining the free cash flow of these firms in the

post-IPO years. The results show that the focus variables High Auditor in Panel A and High

Auditor Group X FIPO, in Panel B are highly significant in each of the years indicating that

after appropriate controls, reputable auditors have a significant positive effect on the free cash

flow that can be ascribed to their superior monitoring and disciplining abilities.

Taken together, the findings in Tables 10 and 11 indicate that high quality auditors play

an important role of monitoring the firm on an ongoing basis (hypothesis H6), beyond the initial

certification effect at the time of the IPO (hypothesis H1). Figure 2 graphically depicts the effect

of high-quality auditors on operating performance measured by return on assets (ROA) and

free-cash flow scaled by total assets (FCF/TA) for the five-year post-IPO period.

Effect of Auditor Quality on Post-IPO Stock Liquidity

Next, we explore the liquidity channel (hypothesis H7) related to the role of auditor

quality on the post-IPO liquidity of the stock. To measure liquidity we follow Rouwenhorst

(1999) and use stock turnover defined as common shares traded divided by common shares

outstanding.

26

We use two approaches in our analysis of yearly operating performance and stock

liquidity. In our first specification the dependent variable is adjusted by the industry median.

The independent variable is set to one if a firm is associated with a Big N auditor (High

Auditor) in the year of the observation. The second model employs control firms and the double

differences methodology of Butler and Cornaggia (2011), where we examine the effects of

auditor reputation on the unadjusted dependent variable. The focus variable is the interaction

term between FIPO and High Auditor Group.11 In Table 12, Panel A we present yearly

regression estimates. Except for year 1, the focus variable, High Auditor, is highly significant in

explaining industry-adjusted liquidity for each of the subsequent four years. In Panel B, we

report regression estimates where the dependent variable is unadjusted stock liquidity for FIPOs

and control firms. In support of hypothesis H7 we find that our focus interaction variable, High

Auditor Group X FIPO is significantly positive in years IPO+3, IPO+4 and IPO+5. In years

IPO+1 and IPO+2, the results may be confounded by the issuance itself or any price support

activity by the investment banker or share lock-up provisions.

These results suggest that reputable auditors enhance the transparency and thereby,

reduce the information asymmetry of these firms. This is yet another channel by which high

quality auditors benefit their client IPO firms. These results in combination with our results in

Table 10, 11 and 12 indicate that even though high auditor quality enhances liquidity, the

positive effect of auditor quality on stock returns based on superior monitoring and disciplining

(H6) more than offsets any negative effect on stock returns due to improved liquidity (H7).

Figure 3 reports a graph of the effect of high-quality auditors on stock liquidity (Turnover) for

the five-year post-IPO period.

11 We replicate our results including a dummy for an offering by a high-ranked underwriter and find that it is not a

consistent determinant of operating performance and stock liquidity.

27

V. CONCLUSIONS

This study documents that auditor reputation plays an important role in determining

post-IPO stock returns and operating performance. We also show that the auditor reputation

effect on post-IPO stock returns is robust and persists longer than the underwriter certification

effect. Our results are even stronger for first IPOs (firms going public for the first time)

suggesting that these firms benefit even more from the auditor reputation effect because of

greater information asymmetry associated with these firms. Further, our analysis disentangles

the auditor reputation effect from the underwriter certification effect by establishing that the

auditor reputation effect is in addition to the well-documented underwriter certification effect.

Our findings also reveal that the effect of auditor quality is further enhanced in the absence of a

high-ranked underwriter and vice versa. Overall, our results provide empirical support for the

theoretical models, such as that of Titman and Trueman (1986), based on auditor quality

signaling information to investors on the value of the IPO firm.

Further, our analysis reveals that firms associated with high information asymmetry,

specifically, those with (a) high growth opportunities and (b) high R&D intensity benefit even

more from hiring high quality auditors as far as post-IPO stock return performance is concerned

compared to their matched counterparts. These results are robust to various stock return metrics.

We also investigate alternative, but non-mutually exclusive, underlying channels

influencing the long-run superior post-IPO stock return performance of firms associated with a

reputable auditor. First, we find that the operating performance of these firms, measured by

return on assets (ROA) and free-cash flow to total assets (FCF/TA), are significantly higher for

firms that are associated with high-ranked auditors. These results indicate that there is a "real

channel" underlying the superior post-IPO stock return performance of such firms. The superior

28

operating performance is consistent with the notion that high-ranked auditors are associated with

more effective ongoing monitoring and disciplining of these firms. This finding also enables us

to distinguish between the certification effect (which occurs only at the time of the IPO) from the

long-run ongoing monitoring function provided by high quality auditors.

Second, we explore another possible channel of influence on stock returns by examining

the effect of high quality auditors on stock liquidity. High quality auditors are expected to

improve transparency and reduce information asymmetry of their client IPO firms. We find

strong evidence that reputable auditors significantly increase stock liquidity of IPO firms over

the five-year post-IPO period. Past research suggests that increased liquidity will lead to lower

expected stock returns. However, the other channels of reputable auditor influence on post-IPO

stock returns, such as the auditor certification effect and the positive effect on operating

performance ("real channel") consistent with superior monitoring by high-ranked auditors, more

than offset any potential negative effect on stock returns due to increased liquidity.

Based on the findings in this study, future research can examine the cost and benefit of

hiring a high quality auditor where the cost of higher audit fee is balanced against the benefits

generated in terms of superior post-offer stock returns, operating performance and stock

liquidity, taking into account the characteristics of the issuing firms, such as their growth

opportunities and R&D intensity.

27

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31

Appendix

Construction of Compustat Firm Fundamental Variables

Assets Book value of total assets (item #6) in real 2006 dollars

Auditor Formerly part of auditor/auditor’s Opinion (item #199) and now is part of the

audit table

Capex/Sales Capital expenditures (item #128) divided by sales (item #12)

Cash/TA Cash (item #1) divided by total assets (item #6)

FCF/TA Earnings before interest, taxes and depreciation (item #13) less interest expense

(item #15) taxes (item #16) less dividends (items #9+#21) over assets (item #6)

Div/TA Dividends (items #19 + #21) divided by total assets (item #6)

FF49 Dummies Industry dummies based on Fama and French’s (1997) 49 industries using the

historical SIC code (item #324) if available otherwise the current SIC code

Leverage Long-term debt (items #9 + #44) divided by total assets (item #6)

Market Cap Fiscal closing share price (item #199) times common shares outstanding (item

#25)

ROA Earnings before interest (items #172 + #15) divided by total assets (item #6)

R&D/Sales Research and development expenses (item #46) divided by sales (item #12)

Tobin’s Q Total Assets (item #6) less common equity (item #60) plus market value of

equity (items #199 * #25) divided by total assets (item #6)

Turnover Common shares traded (item #28) divided by equity shares outstanding (item

#25)

IPO/RLBO Variable Definitions

Age Firm age at the time of the offering

High Auditor Dummy set to one if the auditor is one of the Big 8 through Big 4 depending on

time period

High UW Dummy set to one if the underwriter ranking is 9.0

Proceeds The proceeds from the offering in real 2006 dollars

Propensity The predicted probability of an IPO/RLBO at the time of the offering

Secondary Secondary shares in the offering as a fraction of total shares offered

Stock Return Variable Definitions

Beta Firm beta based on stock returns from offer date + 6 through offer date + 260

BHR Geometric monthly stock returns over 12, 24, 36, and 60 months

HML Fama and French’s (1993) high minus low book-to-market portfolio

MOM Carhart’s (1997) momentum variable

rt Monthly average calendar-time return

RMRF Market risk premium

SMB Fama and French’s (1993) small minus big firm portfolio

32

Figure 1 Median buy-and-hold abnormal returns by auditor ranking

Offering

Cont

rol-A

djus

ted Hi

gh

Cont

rol-A

djus

ted

Low

Indu

stry

-Adjus

ted Hi

gh

Indu

stry

-Adj

uste

d Lo

w

Raw H

igh

Raw Low

60 M

onth

36 M

onth

12 M

onth

60 M

onth

36 M

onth

12 M

onth

60 M

onth

36 M

onth

12 M

onth

60 M

onth

36 M

onth

12 M

onth

60 M

onth

36 M

onth

12 M

onth

60 M

onth

36 M

onth

12 M

onth

0

-10

-20

-30

-40

-50

-60

-70

Ab

no

rma

l R

etu

rn

Notes:

This figure plots the median buy-and-hold returns at 12, 36, and 60-month intervals for FIPOs by

auditor ranking. Unadjusted raw returns are reported for low-ranked (Raw Low) and high-ranked

auditors (Raw High). Industry-adjusted (Industry-adjusted Low and Industry-adjusted High) and

control-adjusted (Control-adjusted Low and Control-adjusted High) abnormal returns are also

reported by auditor ranking. Auditors are categorized in the High group if they are part of the Big

8 to Big 4 based on the time period, otherwise they are classified in the Low group. Control-

adjusted returns are calculated using propensity score matching technique. Returns are

winsorized using the Cowan and Sergeant (2001) methodology.

33

Figure 2 Operating performance of IPOs associated with high-ranked auditors

Variable

FCF/TARO

A

IPO+5

IPO+4

IPO+3

IPO+2

IPO+1

IPO+5

IPO+4

IPO+3

IPO+2

IPO+1

14

12

10

8

6

4

2

0

Pe

rce

nt

Notes: This figure plots the results of regression estimates of return on assets (ROA) and free cash flow to total assets (FCF/TA) for first-IPO firms with a high-ranked auditor relative to the associated control firms for the first five years following the IPO. The graph reports coefficients of the interaction between FIPO and High Auditor Group.

34

Figure 3 Liquidity (stock turnover) for IPO firms associated with high-ranked auditors

Variable Turnover

IPO+5IPO+4IPO+3IPO+2IPO+1

500

400

300

200

100

0

-100

-200Sh

are

s T

rad

ed

/ C

om

mo

n S

ha

res O

uts

tan

din

g

Notes: This figure plots the results of regression estimates of stock turnover (Turnover) for first-IPO firms with a high-ranked auditor relative to the associated control firms for the first five years following the IPO. Stock turnover is defined as shares traded in a year divided by common shares outstanding (in thousands). The graph reports coefficients of the interaction between FIPO and High Auditor Group.

35

Table 1

Descriptive statistics for all IPOs, first IPOs, and RLBOs All IPOs First IPO RLBOs First IPO – RLBO

Mean (Median) Mean (Median) Mean (Median) Mean (Median)

Firm Characteristics

Assets ($ millions) 328.00

(79.18)

$240.18

($68.07)

969.95

(359.92)

-729.77a

(-291.85a)

Proceeds ($ millions) 92.46

(45.23)

$77.96

($42.02)

198.62

(102.01)

-120.66a

(-59.99a)

ROA (%) -1.05

(6.15)

-2.97

(5.78)

7.24

(7.68)

-10.21a

(-1.89a)

Tobin’s Q 3.29

(2.36)

3.46

(3.00)

2.10

(1.67)

1.36a

(1.33a)

Leverage (%) 18.48

(7.05)

16.15

(5.19)

37.18

(36.67)

-21.03a

(-31.48a)

Age (years) 18.24

(9.00)

14.61

(7.00)

37.91

(27.00)

-23.30a

(20.00a)

RetStdDev (%) 4.59

(4.13)

4.74

(4.30)

3.54

(3.14)

1.20a

(1.16a)

Beta 0.97

(0.87)

0.98

(0.88)

0.92

(0.83)

0.06c

(0.05)

Observations 4,190 3,666 524

Notes:

This table summarizes the descriptive statistics for salient variables for firms going public between 1986 and 2006. Means

(medians) are reported for all IPOs, first IPOs and RLBOs. a, b, c represent significance levels at 99%, 95%, and 90%

respectively.

36

Table 2

Distribution of auditor and underwriter rankings for all IPOs, first IPOs, and RLBOs Underwriter Ranking

Auditor Ranking Low High Total

Panel A: All IPOs Obs. Percent Obs. Percent Obs. Percent

Low 370 8.83 59 1.41 429 10.24

High 2,007 47.90 1,754 41.86 3,761 89.76

Total 2,377 56.73 1,813 43.27 4,190 100.00

Panel B: First IPOs

Low 359 9.79 59 1.42 411 11.21

High 1,820 49.65 1,435 39.14 3,255 88.79

Total 2,179 59.44 1,487 40.56 3,666 100.00

Panel C: RLBOs

Low 11 2.10 7 1.34 18 3.44

High 187 35.69 319 60.88 506 96.56

Total 198 37.79 326 62.21 524 100.00

Panel D: First IPOs vs RLBOs

χ2 (Underwriter) 87.56a

χ2 (Auditor) 30.16a

χ2 (Auditor, Underwriter) 101.14a

Notes:

This table reports the distribution for All IPOs, first IPOs, and RLBOs by auditor and underwriter rankings. a denotes significance at the

99% level.

37

Table 3

Raw, industry-adjusted, and control firm-adjusted buy-and-hold returns by auditor ranking for all IPOs 12 Month Buy-and-Hold 36 Month Buy-and-Hold 60 Month Buy-and-Hold

Paired Unpaired Paired Unpaired Paired Unpaired

IPOs Controls Return P-value P-value Return P-value P-value Return P-value P-value

Raw

Low Auditor 429 0 Mean -2.878 0.39 N/A -13.226 0.01 N/A -18.630 0.00 N/A

Median -15.909 0.00 N/A -49.223 0.00 N/A -65.657 0.00 N/A

High Auditor 3,761 0 Mean 6.552 0.00 N/A 16.056 0.00 N/A 27.881 0.00 N/A

Median -6.965 0.19 N/A -25.000 0.00 N/A -29.529 0.00 N/A

High - Low 4,190 0 Mean 9.430 N/A 0.01 29.282 N/A 0.00 46.512 N/A 0.00

Median 8.944 N/A 0.00 24.223 N/A 0.00 36.127 N/A 0.00

Industry-adjusted

Low Auditor 429 429 Mean -6.091 0.06 N/A -19.664 0.00 N/A -27.720 0.00 N/A

Median -9.418 0.05 N/A -20.608 0.00 N/A -30.808 0.00 N/A

High Auditor 3,761 3,761 Mean 4.606 0.00 N/A 9.891 0.00 N/A 15.620 0.00 N/A

Median 0.622 0.00 N/A -2.208 0.00 N/A 1.021 0.00 N/A

High - Low 4,190 4,190 Mean 10.697 N/A 0.00 29.555 N/A 0.00 43.341 N/A 0.00

Median 10.040 N/A 0.00 18.400 N/A 0.00 31.830 N/A 0.00

Control firm-adjusted

Low Auditor 324 5,086 Mean -13.647 0.77 0.00 -37.094 0.00 0.00 -44.688 0.00 0.00

Median -18.601 0.03 0.03 -45.159 0.00 0.00 -52.390 0.00 0.00

High Auditor 2,426 42,924 Mean 0.459 0.00 0.68 -0.426 0.00 0.83 -1.790 0.00 0.52

Median -4.473 0.00 0.00 -11.387 0.00 0.00 -15.912 0.00 0.00

High - Low 2,750 48,010 Mean 14.106 N/A 0.03 36.668 N/A 0.00 42.898 N/A 0.00

Median 14.128 N/A 0.01 33.772 N/A 0.00 36.478 N/A 0.00

Notes:

This table summarizes long-run buy-and-hold stock returns for all IPOs between 1986 and 2006. We report raw returns and returns

benchmarked to the industry and to propensity score control firms. Industries are based on Fama and French’s (1997) groupings.

Buy-and-hold returns are winsorized using the methodology of Cowan and Sergeant (2001). We provide test statistics based on

paired and unpaired differences. Median test statistics are based on the Wilcoxon Signed-Rank test.

38

Table 4

Raw, industry-adjusted, and control firm-adjusted buy-and-hold returns by auditor ranking for first IPOs 12 Month 36 Month 60 Month

Paired Unpaired Paired Unpaired Paired Unpaired

Obs* Return P-value P-value Return P-value P-value Return P-value P-value

Raw

Low Auditor 411 Mean -3.37 0.32 N/A -13.65 0.01 N/A -17.73 0.01 N/A

Median -16.67 0.00 N/A -49.25 0.00 N/A -65.66 0.00 N/A

High Auditor 3,255 Mean 5.25 0.00 N/A 13.61 0.00 N/A 29.20 0.00 N/A

Median -8.64 0.01 N/A -27.27 0.00 N/A -32.96 0.00 N/A

High - Low 3,666 Mean 8.62 N/A 0.02 27.26 N/A 0.00 46.93 N/A 0.00

Median 8.03 N/A 0.01 21.98 N/A 0.00 32.70 N/A 0.00

Industry-adjusted

Low Auditor 411 Mean -6.67 0.04 N/A -20.38 0.00 N/A -26.76 0.00 N/A

Median -9.91 0.03 N/A -22.37 0.00 N/A -31.29 0.00 N/A

High Auditor 3,255 Mean 3.53 0.00 N/A 7.94 0.00 N/A 17.17 0.00 N/A

Median -1.43 0.00 N/A -3.71 0.00 N/A -1.96 0.00 N/A

High - Low 3,666 Mean 10.31 N/A 0.00 28.32 N/A 0.00 43.93 N/A 0.00

Median 8.48 N/A 0.00 18.66 N/A 0.00 29.33 N/A 0.00

Control firm-adjusted

Low Auditor 1,537 Mean -15.76 0.00 0.09 -42.68 0.00 0.00 -52.71 0.00 0.00

Median -18.68 0.00 0.00 -40.01 0.00 0.00 -44.34 0.00 0.00

High Auditor 8,644 Mean -2.05 0.04 0.08 -1.49 0.37 0.06 -0.39 0.86 0.02

Median -4.53 0.00 0.68 -7.05 0.00 0.00 -7.73 0.00 0.00

High - Low 10,181 Mean 13.71 N/A 0.00 41.19 N/A 0.00 52.32 N/A 0.00

Median 14.15 N/A 0.00 33.03 N/A 0.00 36.61 N/A 0.00

Notes:

This table summarizes long-run buy-and-hold stock returns for First IPOs between 1986 and 2006. We report raw returns and returns

benchmarked to the industry and to propensity score control firms. Industries are based on Fama and French’s (1997) groupings.

Buy-and-hold returns are winsorized using the methodology of Cowan and Sergeant (2001). We provide test statistics based on

paired and unpaired differences. Median test statistics are based on the Wilcoxon Signed-Rank test.

39

Table 5

Robustness check of high minus low auditor ranking long-run returns for IPOs

Panel A: All IPOs Propensity Score Control Adjusted

Returns Calendar Time Returns Using a Carhart Model

12 Month 36 Month 60 Month

Coef P-value Coef P-value Coef P-value

Intercept Monthly 1.96a 0.00 1.046a 0.01 0.82a 0.01

Cumulative Intercept (26.19) (45.42) (63.9)

Propensity Score Quintiles Yes Yes Yes

Sample Size 908 1,194 1,267

Panel B: First IPOs Propensity Score Control Adjusted

Returns Calendar Time Returns Using a Carhart Model

12 Month 36 Month 60 Month

Coef P-value Coef P-value Coef P-value

Intercept Monthly 1.70a 0.00 1.01a 0.01 0.66c 0.06

Cumulative Intercept (22.34) (43.72) (48.49)

Propensity Score Quintiles Yes Yes Yes

Sample Size 878 1,151 1,205

Notes:

This table summarizes long-run stock returns for a sample of all IPOs (Panel A) and and first IPOs (Panel B) using calendar-time returns

in a Carhart (1997) model. Calendar-time returns are computed for low and high-ranked auditors. The difference between high

and low calendar-time returns are regressed. Returns are adjusted using propensity score selected control firms. Calendar time

return intercepts are reported for 12, 36, and 60 month horizons. We also report the cumulative value of the monthly intercept.

Propensity quintile dummy variables and Carhart factors are not reported for conciseness. Industries are based on Fama and

French’s (1997) groupings. a, b, and c denote statistical significance at the 1%, 5%, and 10% level respectively.

40

Table 6

Control firm-adjusted long-run stock returns of all IPOs by auditor and underwriter ranking 12 Month Buy-and-Hold 36 Month Buy-and-Hold 60 Month Buy-and-Hold

Paired Unpaired Paired Unpaired Paired Unpaired

IPOs Controls Return P-value P-value Return P-value P-value Return P-value P-value

Low-ranked underwriter

Low Auditor 278 4,089 Mean -14.552 0.70 0.00 -37.490 0.00 0.00 -46.857 0.00 0.00

Median -22.683 0.01 0.00 -46.924 0.00 0.00 -53.328 0.00 0.00

High Auditor 1,284 20,191 Mean -1.325 0.00 0.38 -6.097 0.00 0.03 -6.861 0.00 0.07

Median -7.762 0.00 0.00 -17.320 0.00 0.00 -24.250 0.00 0.00

High - Low 1,562 24,280 Mean 13.227 N/A 0.03 31.393 N/A 0.00 39.996 N/A 0.00

Median 14.921 N/A 0.00 29.604 N/A 0.00 29.078 N/A 0.00

High-ranked underwriter

Low Auditor 46 997 Mean -8.019 0.10 0.32 -32.184 0.00 0.04 -28.950 0.00 0.17

Median -1.505 0.59 0.51 -37.731 0.00 0.01 -37.049 0.00 0.01

High Auditor 1,142 22,733 Mean 2.427 0.00 0.14 5.821 0.00 0.05 3.413 0.00 0.39

Median 0.676 0.00 0.74 -1.754 0.00 0.25 -4.982 0.00 0.01

High - Low 1,188 23,730 Mean 10.446 N/A 0.65 38.005 N/A 0.96 32.362 N/A 0.25

Median 2.181 N/A 0.45 35.977 N/A 0.33 32.066 N/A 0.00

Notes:

This table summarizes long-run buy-and-hold stock returns for all IPOs between 1986 and 2006 using returns benchmarked to propensity score

control firms. Buy-and-hold returns are winsorized using the methodology of Cowan and Sergeant (2001). We provide test statistics based

on unpaired differences. Median test statistics are based on the Wilcoxon Signed-Rank test.

41

Table 7

Control firm-adjusted long-run stock returns of first IPOs by auditor and underwriter ranking 12-Month Buy-and-Hold 36-Month Buy-and-Hold 60-Month Buy-and-Hold

Unpaired Unpaired Unpaired

Obs* Return P-value Return P-value Return P-value

Low-ranked Underwriter

Low-ranked Auditor 1,347 Mean -18.69 0.00 -47.06 0.00 -32.02 0.00

Median -22.55 0.00 -43.04 0.00 -47.69 0.00

High-ranked Auditor 5,012 Mean -10.50 0.00 -14.54 0.00 14.95 0.00

Median -11.54 0.00 -18.01 0.00 -19.26 0.00

High - Low 6,359 Mean 8.19 0.00 32.53 0.00 46.97 0.00

Median 11.01 0.00 25.03 0.00 28.43 0.00

High-ranked Underwriter

Low-ranked Auditor 190 Mean 4.73 0.38 -12.12 0.20 -56.74 0.00

Median 7.18 0.14 -4.81 0.39 -5.46 0.23

High-ranked Auditor 3,632 Mean 9.75 0.00 16.74 0.00 -14.33 0.00

Median 5.92 0.00 8.04 0.00 8.44 0.00

High - Low 3,822 Mean 5.03 0.46 28.85 0.01 42.41 0.00

Median -1.27 0.93 12.85 0.01 13.90 0.01

Notes:

This table summarizes long-run buy-and-hold stock returns for first IPOs between 1986 and 2006 using returns benchmarked to propensity score

matched control firms. Buy-and-hold returns are winsorized using the methodology of Cowan and Sergeant (2001). We provide test

statistics based on unpaired differences. Median test statistics are based on the Wilcoxon Signed-Rank test.

42

Table 8

Regressions of the effects of auditor quality and underwriter certification on stock returns following IPOs

Notes:

This table reports results of OLS regressions explaining 36-month buy-and-hold returns of FIPO firms using a Carter, Dark, and Singh (1998) model. Following

Carter, Dark and Singh, we focus on 36-month buy-and-hold returns. Raw return is the logarithm of (1+ raw return). Adjusted returns are the logarithm

of (10 + adjusted-return). Control firm-adjusted returns use the median return of matching control firms. P-values are reported corresponding to each

coefficient estimate. a, b, c represent significance levels at 99%, 95%, and 90% respectively. Standard errors are computed using White’s (1980)

correction for heteroskedasticity.

Raw VW Index-adjusted Industry-adjusted Control Firm-adjusted

Model 1 Model 2 Model 3 Model 4

Independent

Variables Coeff. P-Value Coeff. P-Value Coeff. P-Value

Coeff. P-Value

Focus Variables

High Auditor 0.03a 0.00 0.02a 0.00 0.03a 0.00 0.02a 0.00

High UW 0.01b 0.02 0.02a 0.00 0.02 0.00 0.02a 0.01

Control Variables

Log (Proceeds) -0.01a 0.00 -0.00 0.75 -0.01a 0.01 0.00 0.86

Log (Age) 0.00 0.90 0.01a 0.00 -0.00 0.84 0.00 0.77

Secondary 0.04a 0.00 0.03a 0.00 0.03a 0.00 0.01 0.55

RetStdDev -1.54a 0.00 -0.85a 0.00 -0.98a 0.00 -0.78a 0.00

Intercept 2.38a 0.00 2.27a 0.00 2.36a 0.00 2.30a 0.00

Observations 3,666 3,666 3,666 2,261

Adjusted R-Squared (%) 9.92 4.46 5.37 2.25

43

Table 9

Regressions of auditor and underwriter certification on post-IPO stock returns for FIPOs and control firms

Panel A: Independent Variables

Predicted

Sign

Model 1

(24 months)

Model 2

(36 months)

Model 3

(60 months)

Model 4

(36 months)

Model 5

(60 months)

Focus Variables

FIPO - -22.14a

(0.00)

-30.09a

(0.00)

-39.53a

(0.00)

-29.69a

(0.00)

-42.45a

(0.00)

High Auditor Group X FIPO + 19.11a

(0.00)

24.97a

(0.00)

36.96a

(0.00)

High UW Group X FIPO + 5.05

(0.21)

9.09c

(0.08)

6.25

(0.38)

32.33a

(0.00)

44.31a

(0.00)

High Auditor Group X FIPO X

Low UW Group

+ 24.30a (0.00)

40.21a (0.00)

Control Variables

Propensity Score -9.41

(0.40)

-2.25

(0.86)

1.47

(0.92)

-0.77

(0.95) 3.95

(0.80) High Auditor Group + 4.42

(0.12)

4.23

(0.20)

2.67

(0.55)

5.15 (0.12)

3.24 (0.46)

High UW Group + -1.86

(0.42)

-2.31

(0.41)

-2.43

(0.48)

-2.41

(0.39)

-2.51

(0.47)

Market Cap + 0.00

(0.85)

0.00

(0.98)

0.00

(0.71)

0.00

(0.98) 0.00

(0.71) RetStdDev - -5.94a

(0.00)

-6.61a

(0.00)

-7.31a

(0.00)

-6.61a

(0.00)

-7.32a

(0.00)

Secondary + 0.254a

(0.01)

0.39a

(0.00)

0.49a

(0.00)

0.39a

(0.00)

0.49a

(0.00)

Intercept 28.19a

(0.00)

34.78a

(0.00)

44.88a

(0.00)

33.97a

(0.00)

44.38a

(0.00)

# of observations 10,016 10,016 10,016 10,016 10,016

44

Table 9 (contd.)

Panel B:

Independent Variables

Predicted Sign Model 6

(36 months)

Model 7

(60 months)

Model 8

(36 months)

Model 9

(60 months)

Focus Variables

FIPO - -27.28a

(0.00)

-36.12a

(0.00)

-29.92a

(0.00)

-39.93a

(0.00)

High Auditor Group X FIPO + 15.05b

(0.03)

24.75a

(0.01)

24.33a

(0.00)

31.92a

(0.00)

High Q - -14.30a

(0.00)

-23.29a

(0.00)

High Auditor Group X FIPO X High Q + 37.27a

(0.00)

41.28a

(0.00)

High R&D - -8.18a

(0.00)

-12.01a

(0.00)

High Auditor Group X FIPO X High R&D + 15.43a

(0.01)

28.80a

(0.00)

Control Variables

Propensity Score 0.13

(0.99)

11.67

(0.45)

3.75

(0.77)

6.07

(0.71)

High Auditor Group + 2.48

(0.45)

0.63

(0.89)

2.60

(0.43)

0.95

(0.83)

High UW Group +

Market Cap + 0.00

(0.88)

0.00

(0.60)

0.00

(0.69)

0.00

(0.90)

RetStdDev - -7.88a

(0.00)

-8.65a

(0.00)

-7.92a

(0.00)

-8.80a

(0.00)

Secondary + 0.39a

(0.00)

0.48a

(0.00)

0.40a

(0.00)

0.50a

(0.00)

Intercept 43.83a

(0.00)

55.78a

(0.00)

42.46a

(0.00)

54.45a

(0.00)

# of observations 9,933 9,933 9,933 9,933

45

Table 9 (contd.)

Notes:

This table reports results double difference regressions explaining stock returns of FIPOs where the dependent variable is the unadjusted long-run buy-and-

hold returns for the specific time window. Panel A reports results of regressions of auditor and underwriter certification. Panel B examines whether

auditor certification is greater for firms with high growth opportunities and/or high R&D. Standard errors are clustered by firm and control for

heteroskedasticity using White’s (1980) correction. P-values are in parentheses below each coefficient estimate. a, b, c represent significance

levels at 99%, 95%, and 90% respectively.

46

Table 10

Yearly regressions explaining post-IPO return on assets (ROA) by auditor rank PANEL A Industry-Adjusted ROA Using All IPOs

IPO+1 IPO+2 IPO+3 IPO+4 IPO+5

Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value

Intercept -10.904 0.00 -15.585 0.00 -18.455 0.00 -17.745 0.00 -19.644 0.00

High Ranked Auditor 8.708 0.00 5.848 0.00 6.895 0.01 7.596 0.02 11.042 0.00

Observations 3,666 3,393 2,985 2,638 2,335

PANEL B Control-Adjusted ROA Using FIPOs

IPO+1 IPO+2 IPO+3 IPO+4 IPO+5

Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value

Intercept -4.819 0.00 -0.870 0.47 -3.524 0.06 -0.216 0.92 2.233 0.25

High Auditor Group x FIPO 7.361 0.00 7.648 0.00 7.272 0.03 9.392 0.03 9.765 0.02

FIPO -1.357 0.55 -6.382 0.01 -7.652 0.01 -10.159 0.02 -10.623 0.01

High Auditor Group 1.904 0.13 0.693 0.59 5.299 0.00 1.441 0.48 -1.062 0.58

Propensity Score -30.488 0.00 -57.843 0.00 -64.781 0.00 -55.836 0.00 -51.364 0.00

Observations 10,181 7,945 6,328 5,318 4,621

Notes:

This table reports results of regressions explaining auditor quality of FIPOs where the dependent variable is the yearly return on assets for up to five years after the

public offering. ROA in Panel A is adjusted by the industry median while Panel B employs unadjusted ROA for FIPOs and control firms. Control firm

regressions include standard errors are clustered by firm and all regressions control for heteroskedasticity using White’s (1980) correction. P-values are in

parentheses below each coefficient estimate. a, b, c represent significance levels at 99%, 95%, and 90% respectively.

47

Table 11

Yearly regressions explaining post-IPO free cash flow to total assets (FCF/TA) by auditor rank PANEL A Industry-Adjusted FCF/TA for All IPOs

IPO+1 IPO+2 IPO+3 IPO+4 IPO+5

Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value

Intercept -10.904 0.00 -15.585 0.00 -14.001 0.00 -14.225 0.00 -14.770 0.00

High Ranked Auditor 8.708 0.00 5.848 0.00 8.284 0.00 8.871 0.00 10.854 0.00

Observations 3,666 3,393 2,985 2,638 2,335

PANEL B Control-Firm Adjusted FCF/TA for FIPOs

IPO+1 IPO+2 IPO+3 IPO+4 IPO+5

Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value

Intercept -3.104 0.01 1.916 0.04 2.737 0.08 4.658 0.00 6.582 0.00

High Auditor Group x FIPO 7.387 0.00 10.230 0.00 7.211 0.01 8.136 0.02 14.087 0.00

FIPO -3.772 0.10 -6.630 0.00 -5.25 0.05 -7.352 0.02 -12.376 0.00

High Auditor Group 2.273 0.06 -0.566 0.58 5.135 0.00 2.804 0.12 -0.481 0.74

Propensity Score -36.309 0.00 -52.602 0.00 -52.412 0.00 -52.927 0.00 -48.832 0.00

Observations 10,181 7,945 6,328 5,318 4,621

Notes:

This table reports results of regressions explaining auditor quality of FIPOs where the dependent variable is the yearly FCF/TA for up to five years after the public

offering. FCF/TA in Panel A is adjusted by the industry median while Panel B employs unadjusted FCF/TA for FIPOs and control firms. Control firm

regressions include standard errors clustered by firm and all regressions control for heteroskedasticity using White’s (1980) correction. P-values are in

parentheses below each coefficient estimate. a, b, c represent significance levels at 99%, 95%, and 90% respectively.

48

Table 12

Yearly regressions explaining post-IPO stock liquidity by auditor rank PANEL A Industry-adjusted stock turnover for all IPOs

IPO+1 IPO+2 IPO+3 IPO+4 IPO+5

Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value

Intercept 400,849.08 0.00 718,204.58 0.00 677,187.28 0.00 589,401.71 0.00 536,442.46 0.00

High Auditor -2,767.33 0.96 177,530.74 0.02 317,966.80 0.00 417,145.11 0.00 565,458.67 0.00

Observations 3,666 3,393 2,985 2,638 2,335

PANEL B Control-firm adjusted stock turnover for FIPOs

IPO+1 IPO+2 IPO+3 IPO+4 IPO+5

Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value

Intercept 765,036.93 0.00 861,061.41 0.00 959,021.82 0.00 1,014,702.68 0.00 1,037,838.97 0.00

High Auditor Group x FIPO -170,182.96 0.03 90,110.91 0.28 329,365.56 0.00 395,880.67 0.00 438,669.85 0.00

FIPO 121,223.25 0.11 186,972.38 0.02 15,077.84 0.86 -98,004.77 0.30 -125,803.32 0.25

High Auditor Group 98,480.84 0.02 60,559.20 0.28 52,355.81 0.39 63,484.70 0.40 82,970.74 0.32

Propensity Score 1,477,800.87 0.00 1,502,430.36 0.00 1,220,685.56 0.00 1,209,144.20 0.00 1,156,339.71 0.00

Observations 10,181 7,945 6,328 5,318 4.621

Notes:

This table reports results of regressions explaining auditor quality of FIPOs where the dependent variable is the yearly stock turnover for up to five years after the

public offering. Stock Turnover in Panel A is adjusted by the industry median while Panel B employs unadjusted Stock Turnover for FIPOs and control

firms. Control firm regressions include standard errors clustered by firm and all regressions control for heteroskedasticity using White’s (1980) correction. P-

values are in parentheses below each coefficient estimate. a, b, c represent significance levels at 99%, 95%, and 90% respectively.