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Third-Party Reaction to Hedge Fund Activism:

Auditor’s Perspective

Huimin (Amy) Chen*

Lally School of Management

Rensselaer Polytechnic Institute

[email protected]

Bill B. Francis



[email protected]

Yinjie (Victor) Shen



[email protected]

Qiang Wu



[email protected]

*Contact Author

mailto:[email protected]




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Abstract

This paper investigates the relationship between the auditors and the firms that are targeted by

hedge fund activists. We provide a unique perspective, that of the auditors, who play an external

monitoring role for the hedge fund activists’ target firms. We hypothesize that auditors react to

hedge fund activists by charging higher audit fees and that the reaction is due to their concerns

about target firms’ business risk. We find that within two years after targeting, audit fees of the

targeted firms increase by 22.1%. This effect is more pronounced when target firms have high

business risk or have financial reporting personnel turnovers. The finding is supported by

additional tests based on propensity-score matching and difference-in-difference tests. Our study

identifies and confirms a strong causal relation between hedge fund activism and audit fees.

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1. Introduction

Hedge fund activists have played an increasingly important role in the capital market over

the last decade. For example, the number of hedge fund activists has almost doubled since 2001

(Brav, Jiang and Kim 2015), and the total assets under management rose about tenfold since 2003

to around $115 billion in 2015 (PwC 2015). The rapid growth of hedge fund activisms have spurred

considerable attention by regulators and academia. According to Security and Exchange

Commission (SEC) Chairman Mary Jo White, hedge fund activists have “undeniably changed the

corporate landscape” (White 2015).

The extant literature has extensively examined whether hedge fund activists create or destroy

value for shareholders. Most literature have shown improvement in performance in terms of stock

performance, operating performance and real effects such as production (e.g. Brav, Jiang, Partnoy,

and Thomas 2008; Brav, Jiang and Kim 2013). However, these studies have concentrated on

shareholders. Only a few papers started to examine the impact of hedge fund activism on other

stakeholders (Brav, Jiang and Kim 2015). While hedge fund activism may benefit shareholders, it

may increase risk to other stakeholders such as bondholders (Klein and Zur 2011) and bank loan

contractors (Li and Xu 2009) as well as extract value from employees (Brav et al. 2015). In this

paper, we investigate a unique perspective, that of auditors, who play important roles as external

monitors (e.g., Jensen and Meckling 1976; Becker et al. 1998; Nelson et al. 2002). Specifically,

we investigate how auditors react to hedge fund activism, using audit fees as the main indicator1.

We choose auditors’ perspective because it may provide unique insight. First, although the

prior literature has shown that hedge fund activists improve target firms’ performance (e.g. Brav

1 We focus on audit fees for two reasons. Firstly, this measure has variation that produces statistical power. Other

measures such as going concern opinions do not have enough variation. Secondly, audit fee models are typically

well-specified with R-squares exceeding 70% to 80%, which lessens concerns about correlated omitted variables.

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et al. 2008), auditors may still view activists’ impact differently since performance is not a major

concern for auditors. Auditors concern more about clients’ risk since the current audit

methodology is risk-oriented. Second, auditors have proprietary information about the target firms,

which may give them a different view, while some stakeholders such as investors may have only

public information.

We hypothesize that auditors view the hedge fund activists’ target firms as having higher

business risk. Unlike shareholders of the target firms, auditors do not benefit from hedge fund

activism. Instead, they are concerned about the heightened business risk in the target firms. In

response to the business risk concern, auditors charge higher audit fees.

We are motivated by the anecdotal evidence which suggests that auditors may have a negative

view against hedge fund activists. A recent report by one CPA firm (PwC, 2016) lays out strategies

for the potential target firms to stay out of the activists. For example, it states in the report that

“Activists go after companies with vulnerabilities, and most companies have some.” “If companies

want to get ahead of an activist threat, they’ll want to understand their potential vulnerabilities,

including how an outside hedge fund activist might see things. They can start with our risk

assessment tool.” From this report, it indicates that auditors view the potential target firms as

vulnerable. We argue that such vulnerability is the business risk that auditors concern about. Once

the firm is targeted, the auditor charges higher fees to compensate their increased business risk.

In implementing our analysis, we follow Brav et al. (2008) in collecting the hedge fund

activists targeting events and extend the dataset to the period of 2003 – 2012. We use 1,351

targeting events and investigate the target firms’ audit fees changes. We obtain 5,329 firm-year

observations and control for variables such as auditors and going-concern opinions as well as

industry fixed effect. We compare the audit fees two years after the hedge fund intervention with

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two years before intervention. According to our baseline OLS regression model, we find that audit

fees increase by 22.1% two years after intervention.

We use a difference-in-differences methodology to further investigate the causal relationship

between hedge fund activisms and audit fees. Using propensity score matching to find paired

treatment and control firms that have similar firm characteristics, we find that there is about 32.2%

increase in audit fees among treatment firms in the years subsequent to hedge fund activists’

targeting events as compared to control firms. The result from the difference-in-differences model

is consistent with the result from the baseline regressions. Our main finding is also robust to

additional control variables and alternative testing windows.

The relationship we identify involves three parties: auditors, hedge fund activists and target

firms. We conduct further tests to explore possible alternative explanations of our finding. First, it

is possible that hedge fund activists influence the managers of the target firms to require more

audit service. Following Brav et al. (2008), we focus on the director and officer changes that have

shown to be the impact of hedge fund activism. We use the changes in directors, changes in

chairman, changes in CEO and whether hedge fund activists successfully change target firms’

governance to test hedge funds’ influence on the firms’ audit fees. We find that there is no

significant difference of audit fees between target firms with and without director and officer

changes, indicating that the increased audit fees are less likely driven by the demand of hedge fund

activisms.

Secondly, it is also possible that target firms demand more audit service as defending against

the hedge fund activists. To test this possibility, we use a subsample that indicates whether the

target firms involve in proxy fights and examine whether such firms demand more audit service

(more audit fees) compared to target firms that do not involve in proxy fights. However, we do not

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find any significant difference between these two subsample, indicating our result is less likely

driven by target firms’ demand for more audit service.

Thirdly, it is possible that regulators such as SEC may pay more attention to and increase

scrutiny in the target firms and causes more audit fees. Using SEC’s Accounting and Auditing

Enforcement Releases (AAER) letters as a proxy for regulators’ attention, we find that AAER has

no significant impact on the increased audit fee, indicating that SEC scrutiny is less likely a factor

which affects audit fees after hedge fund activisms.

The audit fee literature also suggests that audit fees could be driven by audit efforts and/or

audit risk. DeFond and Zhang (2014) summarize four strategies auditors use to counter risk: (1)

reduce risk by increasing effort; (2) price risk by charging a premium; (3) avoid risk through client

retention and acceptance; and (4) attenuate risk through lobbying. The first two strategies are

related to our topic since they lead to higher audit fees. Finally, we examine whether the increased

audit fees after hedge fund intervenes are due to increased efforts of auditors. Following prior

literature, we use the number of days between the date of fiscal year end and the audit report

signature date to measure audit efforts. Our results show that auditors do react to the business risk

by increasing audit effort.

Our paper makes contribution to two streams of research. Firstly, we contribute to the emerging

line of research that studies the impact of hedge fund activism. Most of the prior literature focuses

on shareholders of target firms and pays less attention to the possible influence on other market

participants. Following the seminal paper Brav et al. (2008), vast literature has centered around

the question whether hedge fund activism creates or destroys value for shareholders, while the

impact on other participants is understudied. Our paper extends the literature on third-party

participants, namely auditors, and examines how they are affected by hedge fund activism.

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Secondly, our paper contributes to the audit fee literature. To our knowledge, our paper is the

first study to document the impact of hedge fund activism on audit fees. The prior literature studies

the impact of a broader group of owners. For example, Mitra, Hossain and Deis (2007) studies the

relationship between institutional ownership and audit fees. In our paper, we study a specific group

of investors – hedge fund activists. Hedge fund activists have been viewed quite differently than

other kinds of institutional investors. Contrary to the general view that firms benefit from the

monitoring effect by institutional investors, the views on hedge fund activists are highly

contradictory. Mitra et al. (2007) finds decrease of audit fees and attributes it to the benefits of

institutions’ monitoring. In our paper we find increase of audit fees, which is explained by auditors’

reaction to increased business risk due to hedge fund activisms.

The remainder of the paper is organized as follows. Section 2 provides a literature review and

develops the hypotheses examined in the paper. Section 3 describes our research design, the data

selection process and summary statistics. Section 4 presents the empirical results, including the

baseline regression results, channel tests, propensity score matching, difference-in-differences

results, robustness tests, possible alternative explanations and additional tests on auditors’ reaction.

We provide the conclusion in Section 5.

2. Literature Review and Hypothesis Development

In this section, we first review the hedge fund activism literature, especially the effect on operating

performance and corporate governance. We then review the literature about auditors’ reaction to

business risk. We argue that auditors react to the possible high business risk in the target firms

after hedge fund activists’ intervention by increasing audit fees.

2.1. The Impact of Hedge Fund Activism

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The fundamental question of hedge fund activism is whether it creates or destroys value. The extant

literature has mostly concentrated on the impact of hedge fund activism on existing shareholders,

while a few papers examine other stakeholders. Although many papers consistently find positive

excess returns to shareholders, it’s still understudied whether other stakeholders bear the risk or

are expropriated the wealth.

First of all, it has been shown in most literature that hedge fund activism leads to positive stock

market reaction. The seminal paper by Brav et al. (2008) examines a sample of 1,059 hedge fund

activism events over the period 2001-2006 and shows that the abnormal return from 20 days before

to 20 days after the announcement of activism is significantly positive. They attribute such value

creation to the improvement in corporate governance and operating performance. Following Brav

et al. (2008), a few papers have examined the stock market reaction using different samples

collected or focusing on different types of hedge fund activism (Clifford 2008; Griffin and Xu

2009; Klein et al. 2009;). These papers consistently find significantly positive stock market return

in the short run (less than one year). However, the short-term positive stock market reaction cannot

easily give the conclusion that hedge fund activism is value-creation. To shed light on that question,

it is worth investigating the impact of hedge fund activism on corporates in other aspects. Brav,

Jiang and Kim (2013) find that the productivity of target firms increases after the hedge fund

activism intervention. The work hours and wages of the employees of the target firm decrease

despite of the increase of labor productivity. A different view from Clifford (2008), who also finds

higher stock returns and better operating performance (ROA) in the target firms, attributes the

improvement to the divestiture of under-performing assets.

Secondly, despite of the positive stock price reaction that have been documented in the

literature, researchers are still investigating the alternative explanations for such finding. One

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possibility is that the positive returns to shareholders are simply wealth transferred from other

stakeholders (Brav, Jiang and Kim 2009). Greenwood and Schor (2009) argue that the positive

abnormal short- and long-term returns are driven solely by targets that are acquired later. Uchida

and Xu (2008) indicates that the stock price reaction is more favorable for the target firms with

higher leverage. More importantly, two papers have clearly shown the wealth transfer from

shareholders to other stakeholders. Klein and Zur (2011) finds that the average excess bond return

is −3.9% around the initial hedge fund activists’ targeting, and is an additional −4.5% over the

remaining year. They argue that the hedge fund managers’ actions in the target firms can in fact

result in lower profitability, loss of collateral, or an increase in the firms’ debt ratios. They use

Standard and Poor’s (2006a,b) assessment as an example - both Heinz and Wendy’s in 2006 are

downgraded based partially on the “aggressive” financial policies instituted by Trian Fund

Management. In addition, they find that the unsystematic equity risk increases due to shareholder

activism. Li and Xu (2009) has similar findings on bank loan contracts. They find that hedge fund

target firms pay higher spreads, face more covenant restrictions on their financial and investment

policies, and have shorter loan maturities.

Based on the discussion above, it appears that although shareholders may benefit from hedge

fund activism, other stakeholders may bear the loss. The changes caused by hedge fund activists

in the target firms include higher CEO turnover, changes in board composition, changes in

operating strategies and higher payouts and debt-to-equity ratio. While these changes may benefit

shareholders, they also increase risk in the target firms and other stakeholders.

In this paper, we investigate the impact of hedge fund activism through the lens of external

auditors. As independent external monitors, auditors have different views than shareholders. The

client firms’ performance can not financially benefit auditors. Instead, they are more concerned

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about the client firms’ inherent risk that may lead to their financial or reputational losses. If other

stakeholders bear losses in the hedge fund activists’ target firms, they may sue the firms and blame

auditors since they are often considered to have “deep pockets”. Considering these possible

consequences, auditors are more likely to have a negative view on hedge fund activism rather than

positive. We conjecture that auditors may view the client firms that are targeted by hedge fund

activists as having higher business risk.

2.2.1. Auditors’ Reaction to Client Business Risk

The prior literature on audit fees has well-established theory and extensive findings to explain the

determinants of audit fees. Simunic (1980) provides a theoretical framework for explaining how

auditors’ risk judgment enter into the audit pricing. The auditor’s total expected cost, E(c) includes

two elements: (1) the cost of resources invested in the audit (cq), and (2) expected cost arising

from potential losses due to litigation and/or reputational damage. The audit pricing function is as

follow:

E(c) = cq + E (d|a, q) * E(ø)

where:

E(c) = audit fees

c = per-unit factor cost of audit resources to the auditor, including all opportunity costs and,

therefore, a provision for a normal profit;

q = quantity of resources invested by the auditor in performing the audit;

a = quantity of resources invested by the auditee in operating the internal accounting system;

E(d|a,q) = expected present value of possible future losses that may be attributed to the audited

financial statements, given a and q; and

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E(ø) = expected likelihood the future losses will become the responsibility of the auditor.

Auditors’ professional judgement plays a critical role in assessing E (d|a) * E(ø) and choosing

q and E(c). Conceptually, the component E (d|a, q) * E(ø) represents the audit risk that auditors

undertake. Audit risk is formally defined as “the risk that the auditor may unknowingly fail to

appropriately modify his or her opinion on financial statements that are materially misstated’’

(American Institute of Certified Public Accountants [AICPA] 1983, AU 312.02). In our paper, we

examine one important determinant of audit risk – client business risk, which is the firm’s future

outcome uncertainty in operating performance.

In the prior literature, researchers have examined the relationship between client business risk

and audit fees (e.g. Pratt and Stice 1994; Simunic and Stein 1996; Bell, Landsman and Shackelford

2001; Lyon and Maher 2005). The findings are mostly consistent in that high business risk leads

to high audit fees. Managers of clients with high business risk may have the pressure to

intentionally bias the financial reports –smooth out earnings over the years for a persistent growth

rate or conceal declining performance. Bentley, Omer and Sharp (2013) finds that firms with high

growth rate and heavy investment in research and development activities are charged with higher

audit fees. They argue that these firms are vulnerable to overextending their resources and

increasing their risk of incurring losses. The auditors spend more effort and charge higher fees for

these firms due to their risk-oriented focus and organizational instability.

In our context, we argue that auditors may perceive hedge fund activists as leading to higher

business risk in the target firms and react to such risk by charging higher audit fees. Based on the

discussion above, we propose the two following hypotheses:

HYPOTHESIS 1. Audit fees increase after clients are targeted by hedge fund activists.

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HYPOTHESIS 2.1. The impact of hedge fund activists’ intervention on audit fees is more

pronounced for firms with higher business risk.

We acknowledge that in response to the high risk, auditors can either spend more audit effort

or charge higher risk premium to compensate. Bell, Landsman and Shackelford (2001) finds that

auditors perceive firm-level differences in business risk and obtain compensation through billing

additional hours, not by raising the hourly charge. In Section 4.7 in this paper, we also examine

how auditors react to the target firms’ business risk.

2.2.2. Financial Reporting Resources and Audit Fees

A client firm exposed to high business risk is at greater risk of lacking the resources necessary

for preparing reliable reports. In our context, as the target firms of hedge fund activism experience

sudden improvement in performance, the financial personnel may need to process a vast amount

of transactions. The lacking of resources will be more severe if the firm changes CFO or financial

director. The prior literature has shown that hedge fund activists tend to exert their influence on

the target firms by changing the managers. Brav et al. (2008) finds that during the year after the

announcement of activism, the CEO turnover rate increases by almost 10 percentage points. As

the hedge fund activists demand changes of CEOs, they may also demand changes of CFOs. Mian,

S. (2001) finds that CFO turnover is preceded by a decline in operating return on assets and by

high CEO turnover. Since CFOs have more decision-making power on financial reporting, we

expect the CFO turnovers are the underlying mechanism on the positive association between hedge

fund activism and audit fees. Particularly, Cheng, Huang and Li (2015) finds that CEO changes

are not a significant driver for the improvement of accounting conservatism in hedge fund target

firms. Nevertheless, CFO turnovers are associated with greater increases in conservatism

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following the hedge fund intervention. We expect that the impact of hedge fund intervention on

audit fees is more pronounced when the target firms’ CFOs change following intervention.

Similarly, the financial person on the board of the firm also has the responsibility for the faithful

representation and relevance of the financial report. The changes of such persons may cause the

firm have less resource for financial reporting and lead to higher audit fees.

Therefore, we hypothesize that:

HYPOTHESIS 2.2 The impact of hedge fund activists’ intervention on audit fees is more

pronounced for firms with CFO’s or financial director’s turnover.

3. Research Design and Summary Statistics

3.1. Data and Sample Selection

We use a unique dataset of hedge fund activism to test our research question. Following Brav

et al. (2008), we start our data collection process by gathering the entire Schedule 13D filings

from the SEC’s EDGAR database between year 2003 and 2014. Using the information contained

in item 2 of Schedule 13D, we then exclude filers that are classified as banks, brokerage companies,

regular corporations, foreign institutions, individuals, insurance companies, pension funds and

trusts. After cross checking with the activist hedge funds contained in Brav et al. (2008), we use

Google search to pin down a list of activist hedge funds from the remaining filers. We then exclude

those Schedule 13D filings that are related to risk arbitrage, distress-financing, and M&As, or

those target investment trusts or closed-end funds. The remaining Schedule 13D files filed by the

list of activist hedge funds represent all the activist events where more than 5% of the target

company’s shares are owned by the activist hedge funds. For hedge fund activism events that are

not contained in Schedule 13D (fewer than 5% of the target company’s shares are owned), we

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obtain the information from FactSet’s SharkWatch dataset where detailed information regarding

the development of the hedge fund activism campaign, such as proxy fight and exempt solicitation,

is also contained.

Before 2001, there are few hedge fund activism targeting events. We exclude two-year events

before and after our sample period (2001, 2003, 2013 and 2014) to incorporate enough auditing

and accounting data of targets firm before and after the events. In the baseline OLS regression and

the difference-in-difference model, we test the difference two years before and after targeting.

Starting with 4,990 hedge fund activists target events during the sample period, we only

include the first-time target event for each firm and require at least one year auditing and

accounting data before and after the event. In total 1,351 hedge fund activism targeting events are

included in our sample. The detailed data selection process is presented in Table 1.

[Insert Table 1 here]

Since 2003, the number of hedge fund intervention events per year has been mostly stable,

although there’s a spike in 2006 and 2007 and a small decline after financial crisis. The distribution

of the events across the years is summarized in Table 2. The distribution is comparable to the prior

literature (Brav et al. 2015).


We extend the events sample to panel data to include observations across all the years. After

merging with auditing data in Audit Analytics and accounting data in Compustat across, the dataset

has 12,293 firm-year observations. For baseline OLS regression, we use the observations two years

before and after the events. The window [-2, +2] indicates the event years t-2, t-1, t, t+1 and t+2,

where t is the event year that the hedge fund activist targets the firm. In total 5,329 observations

are within this 5-year event window.

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3.2. Research Design

To investigate the effect of hedge fund activism on audit fees, we test a dummy variable POST,

which indicates whether year t is before or after hedge fund invention. We run an OLS regression

of the natural logarithm of audit fees (LAUDIT) on POST. This regression model is our baseline.

We limit our sample to two years before, two years after and the event year, totally 5 relative event

years. We run regressions with alternative event windows as robustness checks.

For the testing of Hypothesis 1, we interact the dummy variables with POST. We use the

dummy variables to indicate whether there are significant changes in ROA and LEV. We sort the

firms by the absolute value of changes in ROA from event year t-1 to event year t. We use a dummy

variable ROA_CHG to indicate whether the changes are above the median of the sample. It takes

the value of 1 if the firm is above the median, 0 otherwise. LEV_CHG is defined in a similar

fashion.

For the testing of Hypothesis 2, we use the dummy variables to indicate whether there are

changes of CFO or financial person on the board of directors. CFO_CHG takes the value of 1 if

there’s any CFO changes within the event window [0, +2], and 0 otherwise. CFO_POST is the

interaction between POST and CFO_CHG. FIN_CHG and FIN_CHG_POST are defined in a

similar fashion.

For sensitivity tests, we conduct propensity score matching and difference-in-differences

model. The test clarifies the causality between hedge fund intervention and audit fees. We also test

alternative event windows and include more control variables.

3.3 Summary Statistics

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Table 2, Panel A presents the summary statistics of all the target firms for the 5 event years. In

total 5,329 firm-year observations are in our sample. The continuous variables are winsorized at

+/- 1 percent. We compare the summary statistics of the variables with the prior literature (eg.

Bills, Cunningham and Myers 2015; Carcello et al. 2002) and find similar results. The mean of

AUDITOR is 0.666, which means 66.6% of our sample have Big-N auditors. The high percentage

of the Big-N auditors is because public companies tend to appoint Big-N auditors. The mean of

FOREIGN is 0.396, meaning that 39.6% of our sample have foreign pre-tax income.

Table 2, Panel B presents the summary statistics for the comparison between before targeting

and after targeting. We include the year of being targeted in the “after targeting” subsample. There

are 2,252 observations in the before targeting subsample and 3,077 observations in the after

targeting subsample. We also conduct t test on the means of these two subsamples. The mean

difference of LAUDIT before and after targeting is positively significant (t stat 6.94). This

univariate result is consistent with our hypothesis that audit fees increase after hedge fund

intervention. The mean difference of ROA before and after targeting is negative but not significant.

This result is contradictory to the findings in the prior literature that target firms improve

performance after targeting. However, the mean difference -0.006 is relatively small. The variable

AUDITOR is also significantly positive after intervention. This is consistent with our conjecture

that auditor changes may be one channel for the impact of hedge fund activism on audit fees.

4. Empirical Results

4.1. Baseline OLS Regression (H1)

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We adopt the audit fee model developed by Simunic (1980) and follow prior literature (e.g.,

Johnstone and Be´dard 2003; Gul and Goodwin 2010; Hanlon, Krishnan, and Mills 2012; Bentley

et al. 2013) in selecting the control variables.

𝐿𝐴𝑈𝐷𝐼𝑇𝑖,𝑡 = 𝛽0 + 𝛽1 𝑃𝑂𝑆𝑇𝑖,𝑡 + 𝛽2 𝑅𝑂𝐴𝑖,𝑡 + 𝛽3 𝐿𝑂𝑆𝑆𝑖,𝑡 + 𝛽4 𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽5 𝐴𝑈𝐷𝐼𝑇𝑂𝑅𝑖,𝑡

+ 𝛽6 𝐺𝐶𝑂𝑖,𝑡 + 𝛽7 𝐹𝑂𝑅𝐸𝐼𝐺𝑁𝑖,𝑡 + 𝛽8 𝑅𝐸𝐶𝑉𝑖,𝑡 + 𝛽9 𝐼𝑁𝑉𝐼𝑁𝑇𝑖,𝑡

+ 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐷𝑢𝑚𝑚𝑖𝑒𝑠 + 𝜖𝑖,𝑡 (1)

LAUDIT is measured as the log of audit fees. POST is a dummy variable that takes the value

of one if year t is after hedge fund intervention, and zero otherwise. In this model, we limit our

sample to observations two years before and after targeting. We estimate the equation using OLS

regression and robust standard errors clustered at the company level.

In the model, we control for firm fundamentals ROA, LOSS and SIZE. We expect that ROA has

negative association with audit fees, because auditors charge less risk premium on firms with high

profitability. For the same reason, we expect that LOSS has positive association with audit fees.

We predict positive sign on SIZE because large-size firms need more audit effort. We also control

for AUDITOR and GCO. It has been examined in the prior literature that Big-N auditors charge a

premium on clients for branding image, thus we expect positive coefficient on AUDITOR. We

predict a positive coefficient on GCO since firms with going concern opinions are riskier and need

more audit effort. Following the prior literature, we control for audit complexity using three

proxies: FOREIGN, RECV and INVINT. Firms with foreign operation need more audit effort such

as traveling and translation. RECV and INVINT are proxies for audit task complexity because the

auditing of these items involves more judgements and riskier. We also expect them to be positively

associated with audit fees. For detailed description of the other variables, refer to Appendix A.

We report the OLS regression result in Table 3. Model 1 is OLS regression without control

variables and industry fixed effect. Model 2 includes control variables. Model 3 includes both

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control variables and industry fixed effect. Our variable of interest POST is significantly positive

across three models. The coefficient of POST in model 3 is 0.221 (t stat 13.81). It indicates that

the target firms’ audit fees increase by 22.1% after hedge fund intervention. We find that the signs

of most control variables are as expected, although the coefficient of INVINT is not significant.

The R-squared 0.765 is also comparable to the prior audit fees studies (e.g., Simunic 1980; Stanley

2011; Bentley et al. 2013). This finding in the baseline OLS regression model supports our first

hypothesis that audit fees increase after hedge fund intervention.


4.2. Business Risk and Financial Reporting Resources

In this section, we channel tests. Our variable of interest is the interaction term between POST and

the dummy variables that indicate the level of business risk or lacking in financial resources. The

results are presented in Table 4.


In column (1), the dummy variable ROA_CHG indicates the level of changes in ROA from

event year t-1 to event year t. ROA_CHG takes the value of 1 if the changes are above median,

and 0 otherwise. Our variable of interest is the interaction term ROA_CHG_POST, which is

positively significant at 10% level. It means that for the firms that have high changes in ROA,

audit fees increase even more after hedge fund activists’ targeting. In other words, auditors react

to high operating performance volatility by charging higher audit fees.

In column (2), the dummy variable LEV_CHG indicates the level of changes in LEV from

event year t-1 to event year t. LEV_CHG takes the value of 1 if the changes are above median, and

0 otherwise. Our variable of interest is the interaction term LEV_CHG_POST, which is positively

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significant at 1% level. It means that for the firms that have high changes in LEV, audit fees

increase even more after hedge fund activists’ targeting. In other words, auditors react to high

leverage volatility by charging higher audit fees.

The findings in column (1) and (2) support our second hypothesis. Business risk is the

underlying channel for the positive relationship between hedge fund activists’ intervention and

audit fees.

In column (3) and (4), we test the third hypothesis about the effect of financial reporting

resources. We use financial executive turnovers or financial director turnovers to measure lack of

financial reporting personnel. Our variables of interest are the interaction terms CFO_CHG_POST

and FIN_CHG_POST. In column (3), the coefficient of CFO_CHG_POST is positively significant

at 1% level. It means that auditors react to hedge fund activists’ intervention by charging more

audit fees when the target firms are lacking experienced CFO. The result in column (4) can be

interpreted in the same way. The findings in column (3) and (4) support our third hypothesis.

4.3. Identification

4.3.1. Propensity Score Matching

One may argue that auditors’ reaction to hedge fund activism may result from the firm

characteristics associated with a high likelihood of becoming a hedge fund target firm. To mitigate

this concern, we identify matched target and non-target firms using propensity score technique and

test with difference-in-differences model.

Before matching, we identify a pool of candidate match firms as the public firms that have not

been targeted by hedge fund activists during our sample period. In total 89,092 firm-year

observations are in the pool. We follow recent studies (e.g., Hasan et al. 2014) and use propensity

18

score matching to identify one match firm for each treatment firm. These two groups of firms

ideally have the same firm characteristics except that one group of firms have been targeted by the

hedge fund activists and that the other group of firms haven’t. We identify a non-target control

firm with the closest propensity score in event year t-1.

We use the probit model the estimate the probability of being targeted by hedge fund activists

to obtain the propensity score. We modify the probit model used by Brav et al. (2008) by including

LAUDIT as control. The model can be presented as follows:

𝐷_𝑇𝑎𝑟𝑔𝑒𝑡𝑖,𝑡 = 𝛽0 + 𝛽1 𝐿𝐴𝑈𝐷𝐼𝑇𝑖,𝑡−1 + 𝛽2 𝑀𝑉𝑖,𝑡−1 + 𝛽3 𝑄𝑖,𝑡−1 + 𝛽4𝐺𝑅𝑂𝑊𝑇𝐻𝑖,𝑡−1

+ 𝛽5 𝑅𝑂𝐴𝑖,𝑡−1 + 𝛽6 𝐿𝐸𝑉𝑖,𝑡−1 + 𝛽7 𝐷𝐼𝑉𝑌𝐿𝐷𝑖,𝑡−1 + 𝛽8𝑅𝑁𝐷𝑖,𝑡−1 + 𝛽9𝐻𝐻𝐼𝑖,𝑡−1 + 𝛽10 𝐴𝑁𝐴𝐿𝑌𝑆𝑇𝑖,𝑡−1 + 𝛽11 𝐼𝑁𝑆𝑇𝑖,𝑡−1 + 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐷𝑢𝑚𝑚𝑖𝑒𝑠+ 𝑌𝑒𝑎𝑟 𝐷𝑢𝑚𝑚𝑖𝑒𝑠 + 𝜖𝑖,𝑡 (3)

D_TARGET equals one if the firm is a hedge fund activism target in the year t and zero

otherwise. MV is the log of market capitalization. Q is Tobin’s Q. GROWTH is the growth rate of

sales over the previous year. DIVYLD is the dividend per share. RND is R&D scaled by total asset.

HHI is the Herfindahl-Hirschman index of sales in different business segments as reported by

COMPUSTAT. ANALYST is the number of analysts covering the firm. INST is the institutional

ownership. Industry dummies are defined using 2-digit SIC industries. For detailed description of

the other variables, refer to Appendix A.

The result of the probit model is presented in Panel A, Table 5. The results are mostly

consistent with Brav et al. (2008). Most variables except ANALYST have the same signs of

coefficients as in Brav et al. (2008). Target firms tend to be low growth firms, but are significantly

more profitable. The variables for growth such as q and GROWTH have negative coefficients. The

variable for profitability – ROA – has positive coefficient. The negative coefficient on DIVYLD

indicates that target firms’ dividend payout is relatively lower than peer firms. They are also

19

relatively less competitive (coefficient of HHI is -0.513, significant at 1% level). Target firms tend

to have higher institutional ownership (coefficient of INST is 0.135, significant at 5% level). The

interpretations of these variables are all consistent with the findings in the prior literature.


In the next step, we obtain the predicted value of the probit model as the propensity score.

Without replacement, we match each hedge fund target firm with a control firm that has the closest

propensity score in the same year. Firms that are matched in prior years will be excluded from the

pool of candidate match firms. To ensure that there are no significant differences between

treatment firms and match firms, following Hasan et al. (2014), we use the caliper matching

method, in which caliper refers to the difference in the predicted probabilities between treatment

and match firms. By matching within a caliper of 1%, we are able to identify 812 treatment-match

pairs from this propensity score matching method.

We compare the statistics of the treatment and match firms. The comparison is presented in

Panel B, Table 5. For most variables, the t statistics for the mean difference between the treatment

firms and the match firms are not significant. It means that our matching method gives balanced

treatment and match samples. After matching, we expand the sample to include the proceeding

and succeeding firm-year observations for each treatment and match firm.

4.3.2. Difference-in-Differences Model

One could argue that economy-wide shocks contemporaneous with hedge fund activists

targeting events could also cause changes in audit fees of target firms. To address this concern, we

use the following difference-in-differences model to capture the causal effect of hedge fund

activists targeting events on subsequent audit fees changes:

20

LAUDITi,t=β

0+ β

1 TREAT

i,t + β

2 POST

i,t + β

3 TREAT*POST

i,t + β

4 ROA

i,t+ β

5 LOSS

i,t+

β6

SIZEi,t

+ β7

AUDITORi,t

+ β8

GCOi,t

+ β9

FOREIGNi,t

+ β10

RECVi,t

+

β11

INVINTi,t

+ Industry Dummies + ϵi,t (4)

In the model above, TREAT indicates whether the firm is in the treatment group (target firms)

or control group (non-target firms). POST indicates whether it is the year after the hedge fund

intervention. Consistent with our baseline regression model, we use [-2, +2] event window (5-year

window) for this difference in differences model.

Our variable of interest is TREAT*POST, which is the interaction term between TREAT and

POST. Given that treatment firms have higher audit fees after hedge fund intervention, a positive

(negative) coefficient on this interaction term indicates that hedge fund activists targeting leads to

increases (decreases) in audit fees.

We use the same set of control variables in the difference in differences model. Consistent with

our baseline models, model 1 doesn’t include control variables and industry fixed effect; model 2

includes controls variables but no industry fixed effect; model 3 includes both control variables

and industry fixed effect. The results are reported in Table 6.


The coefficients of TREAT are not significant across the three models. It indicates that our

treatment firms and match firms are not significantly different. The coefficients of POST are still

significantly positive, which is consistent with our baseline regression model. Mostly importantly,

our variable of interest in this difference in differences model is significantly positive at 5% level.

The coefficient of TREAT*POST in model 2 is 0.077 (t stat 2.477). It means that the hedge fund

target firms’ audit fees increase by 7.7% after the intervention, compared with match firms that

21

are not targeted by hedge fund activists. The coefficient of TREAT*POST in model 3 is 0.072 (t

stat 2.349).

These findings confirm our results in the baseline regression model. The model provides strong

evidence on the causal effect of hedge fund intervention on audit fees.

4.4. Robustness Tests

The results above support our first hypothesis that target firms’ audit fees increase after hedge fund

intervention. In this section, we conduct sensitivity tests to provide more solid evidence.

4.4.1. Other Control Variables

One may argue that a few observable variables that are correlated with our variable of interest are

not controlled in our model. To mitigate this concern, we include more control variables in our

model. One may argue that hedge fund activists cause higher audit fees by demanding changes of

auditors. We include AUDITOR_CHG to control for such effect. To control for any financial

constraints and capital market valuation, we include leverage (LEV) and book-to-market ratio (BM).

We also include a set of variables to control for auditors’ characteristics. BUSY indicates whether

the audit report deadline is within auditors’ busy season. We include this variable to proxy auditors’

time constraint. One line of auditing literature argues that auditors’ independence has a significant

effect on audit pricing. We use TENUE, LNFEE_NON and DUMMY_NONAUD to proxy for

auditors’ independence. After including these control variables, we rerun the baseline OLS

regression model and difference-in-differences model. To save space, the results are not tabulated

in this paper. The coefficient of POST is 0.266 (t stat 15.67), which is comparable to our baseline

22

result 0.221 (t stat 13.81). The coefficient of TREAT*POST in difference-in-differences model is

also significantly positive at 10% level. These findings are consistent with the prior results.

4.4.2. Alternative Rolling Windows

We use different rolling windows to test the sensitivity of our empirical results. While we use a 2-

year rolling window in our main analyses in this paper, we test the robustness of the results by

using both a 1-year and 3-year window around the hedge fund intervention, that is, a total period

of 3 year and 7 years, respectively.

Since we define the entire calendar year of being targeted as the event year t and include it into

the post-targeting sample, it may confound the effect of hedge fund activism on audit fees. To

mitigate this effect, we test the result by excluding the event year and test the 1-year, 2-year and

3-year window again. We find that the variables of interest in the baseline regression models and

the difference-in-differences model are still statistically significant and have the same signs of

coefficients. These results suggest that the evidence is robust to the choice of observation period.

4.5. Possible Alternative Explanations

In this section, we rule out other possible explanations for the positive relation between hedge fund

activism and audit fees. The relationship we examine involves three parties: hedge fund activists,

target firms and auditors. We run tests in section 4.5.1 and 4.5.2 to rule out the possible

confounding effects from hedge fund activists and target firms. We also concern that the effect can

be due to regulators’ high attention on hedge fund activism. We test for it in section 4.5.3.

4.5.1. Hedge fund activists’ influence on corporate governance

23

The prior literature has shown that hedge fund activists have significant impact on the target firms

in various aspects. In particular, hedge fund activists aim to change target firms’ capital structure,

change business strategy, sell target firms, or improve governance. By changing the target firms

in these aspects, the hedge fund activists have been proved to create value to shareholders. The

seminal paper Brav et al. (2008) attributes the value creation mostly to the improvement in

governance. The improvement in governance includes hiring more incentivized managers, change

the board chairman, having more independent board members and requesting more information

disclosure. In our context, one can argue that hedge fund activists demand changes in target firms

and cause increases in audit fees. For example, the hedge fund activists may hire the new CEO or

change the chairman of the board, who may have higher standards for audit quality and request

more auditors’ effort. As a result, audit fees may increase.

We test the possibility of this explanation and show the results in Table 7. We focus on the

changes in governance and run four tests, including the changes in directors, changes in

chairperson, changes in CEO and changes in governance. In column (1), DIRECTOR_CHG

indicates whether there’s any director changes within two years (event window [0, +2]) after hedge

fund targeting. In column (2), CHAIR_CHG indicates any chairman changes within two years

after hedge fund targeting. In column (3) indicates any CEO changes within two years after hedge

fund targeting. In column (4), we select a small subsample where the hedge fund activists declare

their goals of targeting the firms. GOV_CHG takes the value of 1 if the hedge fund activists

successfully change the target firms’ governance, and 0 otherwise. In each column, we interact the

variable with POST. For example, DIRECTOR_CHG_POST is the interaction between

DIRECTOR_CHG and POST. The interaction terms are our variables of interest. In each column,

we find no significance in this interaction term. In column (1), the interaction term

24

DIRECTOR_CHG_POST has t statistic 0.755, which is not significant at 10% level. This means

that director changes are not the underlying channel for positive relationship between hedge fund

activism and audit fees. In column (2), the insignificance in CHAIR_CHG_POST means that

chairman changes cannot explain the effect on audit fees either. The findings in column (3) and

column (4) can be interpreted in the similar way. Based on these findings, we conclude that the

influence that hedge fund activists have on the target firms, especially improvement in governance,

does not lead to changes in audit fees.


4.5.2. Target firms’ demand for high audit quality

In this section, we examine the possibility that the target firms demand higher audit quality as a

self-defense to hedge fund activism. The managers of the potential target firms may change the

firms to deter any hedge fund activists. In the process of changing the firms, the managers may

use auditors as a mechanism to identify any potential problems, especially in the financial reporting.

For example, the managers may request the auditors identify any accounting information

disclosure problem, which the hedge fund activists are also able to identify and aim to change in

the target firms. We test this alternative explanation and show the results in Table 8.


We select a subsample in our data, where the target firms disclose whether there’s a proxy fight

against hedge fund activists. The variable of interest is FIGHT, which takes the value of 1 if the

firm discloses a proxy fight, and 0 if the firm discloses “Exempt Solicitation” or no publicly

disclosed activism in the 13D filings. Our variable of interest is the interaction term FIGHT_POST

between FIGHT and POST. We find no statistical significance in FIGHT_POST, which means

25

that the target firms that attempt to fight against the hedge fund activists do not have significantly

higher audit fees than those do not fight. In other words, we find no evidence that target firms

request more audit effort for the purpose of self-defensing.

4.5.3. Regulators’ increased attention

The last alternative explanation that we concern about is regulators’ attention. The recent rise of

hedge fund activism has caught the regulators’ attention. We are concerned that the securities and

exchange commission (SEC) may increase scrutiny on the target firms such as initiating

inspections in the target firms to detect any financial reporting problems. To test this possibility,

we use the Accounting and Auditing Enforcement Releases (AAER) as our measure. AAERs are

“financial reporting related enforcement actions concerning civil lawsuits brought by the

Commission in federal court and notices and orders concerning the institution and/or settlement of

administrative proceedings”2. We obtained the AAERs dataset that is organized by Center for

Financial Reporting and Management at University of California Berkeley. We use two dummy

variables to indicate whether there is any AAER issued. AAER_ANN takes the value of 1 if SEC

issues an AAER for the annual financial report, and 0 otherwise. AAER_QTR takes the value of

1 if SEC issues an AAER for the quarterly financial report, and 0 otherwise. We include each

dummy variable as a control into the regression model.


The results are presented in Table 9. In column (1), the coefficient of AAER_ANN is not

significant, meaning that the SEC inspection to annual report does not have an effect on the audit

fees. The coefficient of POST is unchanged compared to the baseline regression in Table 3. This

2 Refer to SEC AAER website: https://www.sec.gov/divisions/enforce/friactions.shtml

https://www.sec.gov/divisions/enforce/friactions.shtml

26

finding means that the positive relation between hedge fund activism and audit fees is not due to

the SEC inspections to annual report. In column (2), we include AAER_QTR as a control. The

results are similar to column (1) and can be interpreted in the same way.

4.6. How do auditors respond – risk premium or audit effort

Based on our finding that auditors react to the target firms’ business risk, we further examine

specifically how auditors react. DeFond and Zhang (2014) summarize four strategies auditors use

to counter risk: (1) reduce risk by increasing effort; (2) price risk by charging a premium; (3) avoid

risk through client retention and acceptance; and (4) attenuate risk through lobbying. The first two

strategies are related to our topic since they lead to higher audit fees. In this section, we try to

distinguish these two strategies, although it has been difficult to do so in archival research. We do

not have data on audit effort such as working hours. We do not have measures for risk premium

either. However, the prior literature has used audit report lag as a proxy for audit effort. We also

test whether misstatement risk has any impact on the relation between hedge fund activism and

audit fees. The results are presented in Table 10.


In column (1), the dependent variable EFFORT is measured as the number of days between

the date of fiscal year end and the audit report signature date. The variable of interest POST is

statistically significant at 1% level. This means that auditors do react to the business risk by

increasing audit effort. For the test of risk premium, we estimate the misstatement risk using p

score, which is developed by Dechow et al. (2011). It captures each firm’s probability of

misstatement. To better interpret the results, we use a dummy variable P_SCORE to indicate the

level of misstatement risk. It takes the value of 1 if a firm’s probability of misstatement is above

27

the median at the event year t, and 0 otherwise. The results are presented in column (2). The

variable of interest is the interaction term P_SCORE_POST between POST and P_SCORE. The

coefficient is 0.103, significant at 5% level. It means that for the high misstatement risk firms, the

auditors charge significantly higher audit fees. We caution readers that this test cannot answer the

question whether auditors charge high risk premium. We can only interpret that misstatement risk

does matter in the audit pricing. It is possible that auditors react to the risk by both increasing audit

effort and charging risk premium (DeFond and Zhang, 2014).

5. Conclusion

It’s still a debating question in the academic literature as well as in the regulators’ comments

whether hedge fund activists create value. In this paper, we provide a unique perspective from

auditors, who are the external monitors of the target firms. We intend to enrich the literature of

hedge fund activism, in particular its impact on the capital market.

We hypothesize that hedge fund activism leads to higher audit fees. Among the parties

involved that can cause the effect, we identify auditors as the ones that react to the hedge fund

intervention event. We rule out the possible effects from hedge fund activists, target firms and

regulators. The increase of audit fees is due to auditors’ concern about the business risk in the

target firms.

We collect the hedge fund activists targeting events from 2003 to 2012. We select 1,351

targeting events and investigate the effect of these events. According to our baseline OLS

regression model, we find that audit fees increase by 22.1% two years after intervention. Moreover,

the increase of audit fees is more significant when the target firms have high business risk and are

lacking of financial reporting resources. We also find that auditors increase audit fees by spending

more effort, although we cannot directly test whether they charge higher risk premium as well.

28

In sum, the findings improve our understanding of the hedge fund activism targeting events

and more specifically, the impact on audit fees.

29

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Uchida, K., & Xu, P. (2008). US barbarians at the Japan gate: Cross border hedge fund activism.

Work. Pap., Univ. of Kitakyushu/Hosei Univ.

Watts, R. L. (1977). Corporate financial statements, a product of the market and political

processes. Australian journal of management, 2(1), 53-75.

Watts, R. L., & Zimmerman, J. L. (1983). Agency problems, auditing, and the theory of the firm:

Some evidence. The Journal of Law & Economics, 26(3), 613-633.

https://corpgov.law.harvard.edu/2013/02/26/bite-the-apple-poison-the-apple-paralyze-the-company-wreck-the-economy/

https://corpgov.law.harvard.edu/2013/02/26/bite-the-apple-poison-the-apple-paralyze-the-company-wreck-the-economy/

http://www.pwc.com/us/en/10minutes/hedge-fund-activism.html#section04

33

White, M.J. 2015. A Few Observations on Shareholders in 2015. Paper read at 27th Annual

Corporate Law Institute, at Tulane University Law School.

34

Table 1 Sample Description

Panel A: Data Selection Process

Panel B: Yearly Distribution of Hedge Fund Activism Targeting Events

Number of events

All hedge fund activism targeting events from 2003 to 2012 4,990

Include only the first-time targeting for each firm 2,083

Require the target firm to have at least one year auditing

and accounting data before and after the event 1,351

year Events Percent

2003 114 8.4%

2004 131 9.7%

2005 179 13.2%

2006 211 15.6%

2007 223 16.5%

2008 144 10.7%

2009 83 6.1%

2010 100 7.4%

2011 91 6.7%

2012 75 5.6%

1,351 100%

35

Table 2 Summary Statistics – Target Firms

This table presents summary statistics for the variables in our baseline OLS regression model.

The detailed definitions of all variables are provided in the Appendix A. All continuous variables

are winsorized at +/- 1%.

Panel A

Variable N Mean STD Min P10 P50 P90 Max

LAUDIT 5329 13.283 1.307 8.547 11.559 13.309 14.961 18.904

ROA 5329 0.032 0.166 -0.441 -0.212 0.073 0.192 0.256

LOSS 5329 0.260 0.439 0.000 0.000 0.000 1.000 1.000

SIZE 5329 5.557 1.858 2.355 2.969 5.593 8.132 9.084

AUDITOR 5329 0.666 0.472 0.000 0.000 1.000 1.000 1.000

GCO 5329 0.069 0.253 0.000 0.000 0.000 0.000 1.000

FOREIGN 5329 0.396 0.489 0.000 0.000 0.000 1.000 1.000

RECV 5329 0.164 0.154 0.004 0.015 0.127 0.369 0.632

INVINT 5329 0.094 0.116 0.000 0.000 0.038 0.284 0.383

Panel B

Before Targeting After Targeting Difference

Variables N Mean STD N Mean STD

Mean

[-2,2]

t stat

LAUDIT 2252 13.138 1.332 3077 13.389 1.277 0.250*** 6.94

ROA 2252 0.035 0.170 3077 0.029 0.163 -0.006 -1.22

LOSS 2252 0.256 0.436 3077 0.263 0.440 0.007 0.61

SIZE 2252 5.505 1.838 3077 5.595 1.872 0.091 1.76

AUDITOR 2252 0.703 0.457 3077 0.639 0.480 0.064*** 4.94

GCO 2252 0.066 0.249 3077 0.071 0.257 0.005 0.67

FOREIGN 2252 0.372 0.483 3077 0.413 0.492 0.0406** 3.00

RECV 2252 0.162 0.152 3077 0.166 0.156 0.003 0.80

INVINT 2252 0.093 0.116 3077 0.095 0.117 0.003 0.90

36

Table 3 Baseline Regression

This table presents OLS with industry fixed effect regression results. The dependent variable is

LAUDIT, which is the natural logarithm of audit fees. POST is a dummy variable that takes the

value of one if year t is after a firm being targeted by hedge fund activists, and zero otherwise.

We limit our sample within the two-year before and after hedge fund targeting. *, **, *** denote

significance at the 10%, 5% and 1% levels (two-tailed), respectively. We run the OLS regression

clustered by firm. For each variable, we report the OLS regression coefficient, followed by the

robust t-statistic. To conserve space, we do not report the coefficient estimates for the industry

dummies. The detailed definitions of all variables are provided in the Appendix A.

(1) (2) (3)

VARIABLES LAUDIT

[-2, +2]

LAUDIT

[-2, +2]

LAUDIT

[-2, +2]

POST 0.250*** 0.221*** 0.221***

(12.14) (12.98) (13.81)

ROA 0.117 -0.374**

(0.763) (-2.562)

LOSS 0.159*** 0.112**

(2.923) (2.337)

SIZE 0.469*** 0.557***

(39.34) (41.82)

AUDITOR 0.528*** 0.345***

(11.93) (8.831)

GCO 0.231*** 0.177***

(3.776) (3.244)

FOREIGN 0.515*** 0.296***

(14.81) (8.080)

RECV -0.569*** 0.466**

(-4.485) (2.488)

INVINT 0.137 0.0959

(0.887) (0.487)

Industry Fixed Effect NO NO YES

Constant 13.14*** 10.01*** 9.038***

(347.8) (137.9) (93.81)

Observations 5,329 5,329 5,329

R-squared 0.009 0.700 0.765

37

Table 4 Auditors’ Reaction to Business Risk

This table presents OLS with industry fixed effect regression results. The dependent variable LAUDIT is the natural logarithm of

audit fees. ROA_CHG is a dummy variable that takes the value of 1 if a target firm’s ROA change from event year t-1 to t is

above the median of all target firms, 0 otherwise. LEV_CHG is defined in a similar fashion regarding leverage. CFO_CHG

indicates whether the target firm changes CFO within two years after hedge fund intervention. CFO_CHG_POST is the

interaction between CFO_CHG and POST. Other interaction terms are defined in a similar fashion. FIN_CHG indicates whether

the target firm changes the financial expert on the board within two years after hedge fund intervention. *, **, *** denote

significance at the 10%, 5% and 1% levels (two-tailed), respectively. We run the OLS regression clustered by firm. For each

variable, we report the OLS regression coefficient, followed by the robust t-statistic. To conserve space, we do not report the

coefficient estimates for the industry dummies. The detailed definitions of all variables are provided in the Appendix A.

(1) (2) (3) (4)

VARIABLES Business Risk

[-2, +2]

Business Risk

[-2, +2]

Financial Reporting

Resources

[-2, +2]

Financial Reporting

Resources

[-2, +2]

POST 0.199*** 0.180*** 0.188*** 0.189***

(9.286) (8.608) (10.26) (9.774)

ROA_CHG 0.0389

(0.981)

ROA_CHG_POST 0.0565*

(1.790)

LEV_CHG

-0.0718*

(-1.817)

LEV_CHG_POST

0.0861***

(2.752)

CFO_CHG 0.0691

(1.644)

CFO_CHG_POST 0.0970***

(2.700)

FIN_CHG 0.0721*

(1.813)

FIN_CHG_POST 0.0769**

(2.319)

ROA -0.396*** -0.372** -0.355** -0.371**

(-2.678) (-2.538) (-2.437) (-2.549)

LOSS 0.0953* 0.111** 0.107** 0.102**

(1.908) (2.311) (2.256) (2.164)

SIZE 0.561*** 0.559*** 0.554*** 0.553***

(41.62) (40.90) (41.77) (41.72)

AUDITOR 0.330*** 0.339*** -0.342*** -0.341***

(8.254) (8.701) (-8.763) (-8.731)

GCO 0.161*** 0.167*** 0.179*** 0.171***

(2.939) (3.083) (3.306) (3.153)

FOREIGN 0.287*** 0.295*** 0.296*** 0.295***

(7.777) (8.007) (8.149) (8.121)

RECV 0.564*** 0.474** 0.458** 0.464**

(2.951) (2.497) (2.468) (2.497)

INVINT 0.0496 0.0896 0.0986 0.112

(0.251) (0.456) (0.506) (0.571)

Constant 9.032*** 9.060*** 9.282*** 9.296***

(92.28) (89.74) (82.53) (82.55)

Industry Fixed Effect YES YES YES YES

Observations 5,056 5,279 5,329 5,329

R-squared 0.763 0.765 0.767 0.767

38

Table 5 Propensity Score Matching

Panel A: Probit Analysis of Targeting

This table reports the effects of covariates on the probability of being targeted by hedge fund

activists. The dependent variable is a dummy variable equal to one if there is hedge fund

activism targeting the company during the following year (that is, all covariates are lagged by 1

year). *, **, *** denote significance at the 10%, 5% and 1% levels (two-tailed), respectively. We

run the OLS regression clustered by firm. For each variable, we report the OLS regression

coefficient, followed by the robust t-statistic. The detailed definitions of all variables are

provided in the Appendix A.

VARIABLES TARGETING

LAUDIT 0.131***

(10.54)

MV -0.000***

(-9.560)

q -0.042***

(-4.237)

GROWTH -0.065

(-1.616)

ROA 0.062

(0.955)

LEV -0.087

(-1.508)

DIVYLD -3.103***

(-4.423)

RND 0.422**

(2.419)

HHI -0.513***

(-6.929)

ANALYST -0.021***

(-4.692)

INST 0.135**

(2.532)

Constant -3.471***

(-21.16)

Observations 64,211

Pseudo R-squared 0.0314

39

Panel B: Differences between Treatment and Control Groups

In this table, we compare the mean differences between the treatment firms and the match firms.

The treatment firms are firms that are targeted by the hedge fund activists. We use one-to-one

nearest neighbor propensity score match method. To ensure there are no significant differences

between treatment firms and match firms, we use the caliper matching method and require a

caliper of 1% during the match. T statistics and p value for the mean differences are presented.

Mean t-test Variable Treated Control t p>t

LAUDIT 13.408 13.462 -0.9 0.366

MV 867.330 994.260 -1.43 0.154

q 1.733 1.875 -2.35 0.019

GROWTH 0.106 0.127 -1.54 0.123

ROA 0.026 0.028 -0.2 0.839

LEV 0.216 0.214 0.19 0.85

DIVYLD 0.008 0.008 -0.07 0.947

RND 0.052 0.057 -1.23 0.218

HHI 0.316 0.308 1.16 0.246

ANALYST 2.9785 2.8443 0.74 0.461

INST 0.38042 0.38371 -0.21 0.832

40

Table 6 Difference in Differences Model

The table presents difference-in-differences regression results. TREAT is a dummy variable indicating whether it is

in the control group or treatment group. It takes the value of 1 if the firm has been targeted during the sample period

(treatment group), 0 otherwise (control group). POST is a dummy variable that takes the value of one if year t is

after certain years of a firm being targeted by hedge fund activists, and zero otherwise. TREAT*POST is the

interaction term between TREAT and POST. *, **, *** denote significance at the 10%, 5% and 1% levels (two-

tailed), respectively. We run the OLS regression clustered by firm. For each variable, we report the OLS regression

coefficient, followed by the robust t-statistic. To conserve space, we do not report the coefficient estimates for the

industry dummies. The detailed definitions of all variables are provided in the Appendix A.

(1) (2) (3)

VARIABLES LAUDIT

[-2, +2]

LAUDIT

[-2, +2]

LAUDIT

[-2, +2]

TREAT -0.010 0.021 0.007

(-0.148) (0.572) (0.189)

POST 0.356*** 0.246*** 0.250***

(13.78) (11.22) (11.50)

TREAT*POST 0.012 0.077** 0.072**

(0.332) (2.477) (2.349)

ROA -0.390*** -0.345***

(-4.608) (-3.993)

LOSS 0.159*** 0.151***

(3.906) (3.840)

SIZE 0.529*** 0.553***

(52.93) (53.58)

AUDITOR 0.264*** 0.224***

(7.202) (6.405)

GCO 0.211*** 0.258***

(3.771) (4.822)

FOREIGN 0.360*** 0.277***

(12.06) (8.894)

RECV 0.876*** 0.533***

(7.036) (3.840)

INVINT -0.255* 0.139

(-1.875) (0.765)

Constant 13.09*** 9.709*** 9.575***

(268.2) (154.1) (54.49)

Industry Fixed Effect NO NO YES

Observations 7,536 7,205 7,205

R-squared 0.017 0.709 0.733

41

Table 7 Hedge Fund Activists’ Impact This table presents OLS with industry fixed effect regression results. The dependent variable is LAUDIT, which is the natural

logarithm of audit fees. POST is a dummy variable that takes the value of one if year t is after certain years of a firm being

targeted by hedge fund activists, and zero otherwise. DIRECTOR_CHG indicates whether the target firm changes any directors

within two years after hedge fund intervention. CHAIR_CHG indicates whether the target firm changes the chairman within two

years after hedge fund intervention. CEO_CHG indicates whether the target firm changes CEO within two years after hedge fund

intervention. GOV_CHG indicates whether the hedge fund activists aim to change the target firms’ governance. We limit our

sample within the two-year before and after hedge fund targeting. *, **, *** denote significance at the 10%, 5% and 1% levels

(two-tailed), respectively. We run the OLS regression clustered by firm. For each variable, we report the OLS regression

coefficient, followed by the robust t-statistic. To conserve space, we do not report the coefficient estimates for the control

variables and industry dummies. The detailed definitions of all variables are provided in the Appendix A.

(1) (2) (3) (6)

VARIABLES LAUDIT

[-2, +2]

LAUDIT

[-2, +2]

LAUDIT

[-2, +2]

LAUDIT

[-2, +2]

POST 0.195*** 0.225*** 0.213*** 0.0774**

(6.169) (5.554) (11.69) (1.986)

DIRECTOR_CHG 0.197***

(4.379)

DIRECTOR_CHG_POST 0.0272

(0.755)

CHAIR_CHG 0.295***

(5.654)

CHAIR_CHG_POST -0.00940

(-0.217)

CEO_CHG 0.116**

(2.406)

CEO_CHG_POST 0.0222

(0.594)

GOV_CHG 0.0519

(0.608)

GOV_CHG_POST 0.0864

(1.298)

Controls YES YES YES YES

Industry Fixed Effect YES YES YES YES

Constant 9.221*** 9.127*** 9.343*** 10.18***

(81.61) (79.02) (65.80) (39.45)

Observations 5,329 5,329 5,329 905

R-squared 0.769 0.770 0.767 0.784

42

Table 8 Target Firms’ Demand



value of one if year t is after certain years of a firm being targeted by hedge fund activists, and

zero otherwise. FIGHT indicates whether the target firm has any proxy fight. FIGHT_POST is the

interaction term between FIGHT and POST. We limit our sample within the two-year before and

after hedge fund targeting. *, **, *** denote significance at the 10%, 5% and 1% levels (two-

tailed), respectively. We run the OLS regression clustered by firm. For each variable, we report

the OLS regression coefficient, followed by the robust t-statistic. To conserve space, we do not

report the coefficient estimates for the control variables and industry dummies. The detailed

definitions of all variables are provided in the Appendix A.

(1)

VARIABLES LAUDIT

[-2, +2]

POST 0.187***

(4.105)

FIGHT -0.256***

(-2.601)

FIGHT_POST 0.0922

(1.156)

ROA -0.884***

(-2.760)

LOSS -0.0914

(-0.829)

SIZE 0.482***

(13.51)

AUDITOR 0.405***

(4.447)

GCO -0.00963

(-0.0831)

FOREIGN 0.382***

(4.744)

RECV 0.942***

(3.017)

INVINT -0.00399

(-0.00753)

Industry Fixed Effect YES

Constant 11.14***

(52.88)

Observations 1,081

R-squared 0.762

43

Table 9 Regulator’s Attention



value of one if year t is after certain years of a firm being targeted by hedge fund activists, and

zero otherwise. AAER_ANN indicates whether a firm has been issued an AAER letter by SEC

for the annual report. AAER_QTR indicates whether a firm has been issued an AAER letter by

SEC for the quarterly report. We limit our sample within the two-year before and after hedge

fund targeting. *, **, *** denote significance at the 10%, 5% and 1% levels (two-tailed),

respectively. We run the OLS regression clustered by firm. For each variable, we report the OLS

regression coefficient, followed by the robust t-statistic. To conserve space, we do not report the

coefficient estimates for the control variables and industry dummies. The detailed definitions of

all variables are provided in the Appendix A.

(1) (2)

VARIABLES LAUDIT

[-2, +2]

LAUDIT

[-2, +2]

POST 0.221*** 0.221***

(13.80) (13.81)

AAER_ANN -0.0427

(-0.298)

AAER_QTR -0.0766

(-0.569)

ROA -0.374** -0.377***

(-2.562) (-2.590)

LOSS 0.112** 0.110**

(2.339) (2.306)

SIZE 0.557*** 0.558***

(41.74) (41.82)

AUDITOR -0.345*** -0.345***

(-8.829) (-8.841)

GCO 0.178*** 0.177***

(3.252) (3.232)

FOREIGN 0.296*** 0.298***

(8.065) (8.121)

RECV 0.467** 0.464**

(2.490) (2.482)

INVINT 0.0966 0.106

(0.490) (0.538)

Industry Fixed Effect YES YES

Constant 9.383*** 9.381***

(84.65) (84.50)

Observations 5,329 5,347

R-squared 0.765 0.765

44

Table 10 Auditors’ Response – Risk Premium or Audit Effort

The dependent variable EFFORT is the natural log of the number of days between fiscal year end

and the signature date of audit opinion. P_SCORE is a dummy variable that is derived from the

probability of misstatements based on the detection model of Dechow et al. (2011). It takes the

value of 1 if a firm’s probability of misstatement at the event year t is above the median, and 0

otherwise. P_SCORE_POST is the interaction term between P_SCORE and POST. We limit our

sample within the two-year before and after hedge fund targeting. *, **, *** denote significance

at the 10%, 5% and 1% levels (two-tailed), respectively. We run the OLS regression clustered by

firm. For each variable, we report the OLS regression coefficient, followed by the robust t-

statistic. To conserve space, we do not report the coefficient estimates for the control variables

and industry dummies. The detailed definitions of all variables are provided in the Appendix A.

(1) (2)

VARIABLES EFFORT

[-2, +2]

LAUDIT

[-2, +2]

POST 0.0747*** 0.142***

(7.409) (4.826)

P_SCORE -0.139**

(-2.561)

P_SCORE_POST 0.103**

(2.379)

ROA -0.0807 -0.568***

(-1.115) (-2.975)

LOSS 0.0310 0.0589

(1.402) (0.915)

SIZE -0.0253*** 0.549***

(-4.570) (30.66)

AUDITOR -0.0100 0.202***

(-0.558) (3.766)

GCO 0.187*** 0.272***

(6.178) (3.478)

FOREIGN 0.00229 0.285***

(0.128) (6.307)

RECV 0.160** 0.325

(2.358) (1.281)

INVINT 0.0720 0.139

(0.757) (0.538)

Constant 4.360*** 9.248***

(57.22) (84.84)

Industry Fixed Effect YES YES

Observations 5,299 3,463

R-squared 0.100 0.743

45

Appendix A Variable Descriptions

Dependent Variable

LAUDIT Natural logarithm of total audit fees;

Variables of Interest

POST

A dummy variable that takes the value of one if year t is after

certain years of a firm being targeted by hedge fund activists,

and zero otherwise;

TREAT

A dummy variable indicating whether it is in the control

group or treatment group. It takes the value of 1 if the firm

has been targeted during the sample period (treatment group),

0 otherwise (control group);

TREAT_POST Interaction term between POST and TREAT;

Control Variables

ROA Return on total assets = earnings before income tax divided

by total assets: EBITDA/AT;

LOSS A dummy variable that takes the value of one if the firm has

a loss in year t (negative ROA), 0 otherwise;

SIZE Firm size = natural logarithm of total assets;

AUDITOR A dummy variable that takes the value of 1 if the auditor is a

Big 4 CPA firm, 0 otherwise;

GCO A dummy variable that takes the value of 1 if the audit

opinion is going concern, 0 otherwise;

FOREIGN A dummy variable that takes the value of 1 if there is foreign

income (positive PIFO), 0 otherwise;

RECV Receivables divided by total assets: RECT/AT;

INVINT Inventory divided by total assets: INVT/AT;

AUDITOR_CHG A dummy variable that takes the value of 1 if the auditor

changes, 0 otherwise;

LEV Leverage = Long-term debt divided by total asset: DLTT/AT;

BM Book to market ratio: CEQ/(PRCC_F*CSHO);

TENUE The number of fiscal years that the firm has the same auditor;

BUSY Busy season: a dummy variable that takes the value of 1 if

the fiscal year-end is December, 0 otherwise;

LNFEE_NON

Natural logarithm of nonaudit service fees, which are the sum

of audit related fees, benefit plan related fees, financial

information systems design and implementation fees, tax

related fees and other miscellaneous fees;

DUMMY_NONAUD A dummy variable that takes the value of 1 if nonaudit

service fees are greater than zero, 0 otherwise;

46

Other Variables

ROA_CHG

A dummy variable that takes the value of 1 if a target firm’s

ROA change from event year t-1 to t is above the median of

all target firms, 0 otherwise. We define the changes of ROA

as the absolute value of changes in EBITDA/AT from event

year t-1 to t.

ROA_CHG_POST Interaction term of ROA_CHG and POST.

LEV_CHG

A dummy variable that takes the value of 1 if a target firm’s

leverage change from event year t-1 to t is above the median

of all target firms, 0 otherwise. We define the changes of

leverage as the absolute value of changes in DLTT/AT from

event year t-1 to t.

LEV_CHG_POST Interaction term of LEV_CHG and POST.

P_SCORE

A dummy variable that indicates the level of p score. It takes

the value of 1 if the firm’s p score at event year t is above the

median, and 0 otherwise. P score is the probability of

misstatements based on the detection model of Dechow et al.

(2011).

REST = β0 + β1 TOTALACCRUAL + β2 ∆REC + β3 ∆INV

+ β4 SOFT_ASSETS + β5 ∆CSALE + β6 ∆ROA + β7

ISSUANCE + β8 EMP + β9 LEASE + β10 ABRET + β11

LAGABRET + µ

EFFORT Audit effort: natural log of the number of days between fiscal

year end and the signature date of audit opinion.

DIRECTOR_CHG

A dummy variable that takes the value of 1 if the target

company changes any dictors within two years after hedge

fund intervention, 0 otherwise;

CHAIR_CHG


company changes the chairman within two years after hedge

fund intervention, 0 otherwise;

CEO_CHG


company changes the CEO within two years after hedge fund

intervention, 0 otherwise;

CFO_CHG


company changes the CFO within two years after hedge fund

intervention, 0 otherwise;

FIN_CHG


company changes the financial expert on the board within

two years after hedge fund intervention, 0 otherwise;

GOV_CHG

A dummy variable that takes the value of 1 if the hedge fund

aims to change the target firms’ governance, 0 otherwise; We

define the goal of governance change as when hedge funds

state that their goals are: Board Seats (activist group), Add

Independent Directors, Board Representation, Board Control,

or Enhance Corporate Governance.

47

FIGHT

A dummy variable that takes the value of 1 if the target firm

discloses a proxy fight, and 0 if the firm discloses “Exempt

Solicitation” or no publicly disclosed activism in the 13D

filings.

AAER_ANN A dummy variable that takes the value of 1 if a firm has been

issued an AAER letter by SEC for the annual report.

AAER_QTR A dummy variable that takes the value of 1 if a firm has been

issued an AAER letter by SEC for the quarterly report.

Variables for Propensity Score Matching

D_TARGET A dummy variable that takes the value of 1 if the company is

targeted by the hedge fund activists, 0 otherwise;

MV Market capitalization: natural logarithm of PRCC_F*CSHO;

q Tobin’s q = (book value of debt + market value of

equity)/(book value of debt + book value of equity);

GROWTH Growth rate of sales over the previous year: (SALEt – SALEt-

1)/ SALEt-1;

DIVYLD

Dividend yield, defined as (common dividend + preferred

dividends) / (market value of common stocks + book value of

preferred);

RND R&D scaled by total assets;

HHI Herfindahl-Hirschman index of sales in different business

segments as reported by Compustat;

ANALYST The number of analysts covering the company from I/B/E/S;

INST The proportion of shares held by institutions.

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