1

The Differential Consequences of Regulation SHO: The Case of Former

Arthur Andersen Clients

Abstract: We depart from prior research that has examined the effects of Regulation SHO,

which temporarily lifted short-sale constraints for randomly designated stocks, by

distinguishing between ex-Andersen clients and other auditor clients. The demise of

Andersen hurt its clients’ reporting reputation and forced firms to appoint new auditors who

imposed stringent constraints on reporting discretion. We show that the inferences from prior

studies related to two outcome variables—audit fees and earnings management—change

substantially when we separate ex-Andersen clients from other auditor clients. A key

takeaway of our study is that ignoring confounding events such as the Andersen’s demise

provides an incorrect evaluation of the impact of Regulation SHO.

Keywords: Short selling; Audit fees; Regulation SHO

2

The Differential Consequences of Regulation SHO: The Case of Former

Arthur Andersen Clients

I. INTRODUCTION

Theoretical research posits that effective corporate governance mechanisms will

constrain managerial discretion and hence affect firm outcomes (e.g., Jensen and Meckling

1976). However, empirically identifying this relation is problematic due to issues such as

endogeneity and reverse causality.1 In light of these challenges, researchers have used

exogenous shocks to governance structures to identify their impact on firm outcomes. A

prominent example is Regulation SHO, adopted by the Securities and Exchange

Commission, which randomly selected one-third of the stocks on the Russell 3000 Index to

be included in a pilot program. This program eliminated short-sale price tests for the stocks

of these pilot firms from May 2, 2005 to August 6, 2007. Using this setting, empirical

evidence to date documents wide-ranging indirect effects of Regulation SHO on pilot firms.

For example, studies find that Regulation SHO increased audit fees (Hope et al. 2017) and

curbed earnings management (Massa et al. 2015; Fang et al. 2016) of the pilot firms.2

While useful, a challenge involving the Regulation SHO setting is that it occured

close to another consequential event with implications for firm governance: the demise of

Arthur Andersen (referred to as Andersen hereafter), a Big-Six audit firm at that time, in

August 2002. The failure of Andersen engendered three effects on its clients. First, it

tarnished or at least created uncertainty about the reporting quality of its clients (Dyck et al.

2013; Giannetti and Wang 2016). Second, it forced all of Andersen’s clients to involuntarily

change auditors. Third, following the auditor switch, ex-Andersen clients curtailed

1 For example, an independent board of directors is posited to better constrain a firm’s managers and hence

improve firm performance. However, Hermalin and Weisbach (2012) model and explain how firm performance

can influence a manager’s bargaining power in determining the membership of a firm’s board of directors. 2 Recently, Livtak and Black (2017) and Werner (2019) have questioned whether Regulation SHO had any

meaningful effect on short sellers’ ability to take short positions.

3

managerial reporting discretion, paid higher audit fee premiums, and experienced a higher

propensity of receiving a going concern opinion and a higher likelihood of being identified

as having committed fraud (e.g., Cahan and Zhang 2006; Dyck et al. 2013; Krishnan et al.

2007; Srinidhi et al. 2012). In other words, the new auditors of ex-Andersen clients induced

greater accounting conservatism because they perceived these firms to face higher litigation

risk.

The aforementioned findings involving ex-Andersen clients point to potential pre-

treatment differences between pilot and non-pilot firms under Regulation SHO, and raise

questions about the generalizability of the effects of Regulation SHO and the attribution of

Regulation SHO to observed changes such as the rise in audit fees and the reduction in

earnings management. Specifically, they raise at least two important questions: Does

Regulation SHO produce similar effects for both ex-Andersen and non-Andersen pilot

(treatment) firms? If not, how does the impact of Regulation SHO vary between these two

subsets of firms? The answers to these questions are not, ex-ante, obvious.3 However, these

questions are important because ignoring the effects associated with the demise of Arthur

Andersen could lead to incorrect inferences about the effects of Regulation SHO.

In this paper, we shed empirical light on the above questions. Our motivation for

undertaking this inquiry is threefold. First, we aim to better assess the impact of Regulation

SHO by taking into account the pre-treatment differences of firms covered by Regulation

SHO. To this end, we identify ex-Andersen clients and analyze them separately. Second, our

focus on ex-Andersen clients helps delineate firms that are likely to be affected by Regulation

SHO. Short sellers do not randomly target firms but rather target firms that suffer from

3 If the effects of being an ex-Andersen client that is subject to an involuntary auditor change is miniscule, then

the effects of Regulation SHO will not vary between ex-Andersen and the rest of the pilot firms. In contrast, if

the involuntary change in auditor had a significant effect on the ex-Andersen clients, then the effects of

Regulation SHO will markedly differ for the two subsets of pilot firms. In light of these differing viewpoints,

the impact of Regulation SHO on these subsets of firms remains largely an empirical question.

4

questions about their underlying information quality and valuation (Dechow et al. 2001;

Desai et al., 2006). Hence, distinguishing ex-Andersen clients, who faced serious questions

about their reporting quality, from other treatment firms allows us to identify a set of firms

for which the easing of short selling interest under Regulation SHO may engender a

differential impact. Third, we seek to address recent criticisms that prior studies on

Regulation SHO did not take into account the fact that Regulation SHO allowed some firms

to receive partial easing of short selling constraints by suspending the uptick rule outside

regular trading hours (Litvak and Black 2017). We account for this concern by removing

firms that received partial Regulation SHO treatment and redo our primary analyses.

Our empirical inquiry undertakes a broad re-examination of the effects of Regulation

SHO on audit fees, short interest, delay (urgency) in the release of financial statements,

investment efficiency, and earnings management. We depart from prior research by

distinguishing and separately examining the effects of Regulation SHO on ex-Andersen

clients. Our analysis begins by constructing a sample similar to that used by Hope et al.

(2017) with the aim of replicating their study. Constructing a similar sample gives us the

confidence that our findings are unlikely to be due to differences in sample composition. Our

replication using the constructed pooled sample yields findings that are identical to that of

Hope et al. (2017). That is, pilot firms experience larger increases in audit fees during the

Regulation SHO pilot program, but do not experience larger increases in the post-Regulation

SHO period.

Next, we isolate and conduct separate analysis for the ex-Andersen clients and the

rest of the pilot and control firms. We find the effects of Regulation SHO to be different for

pilot firms that were ex-Andersen clients than that for the rest of the pilot firms covered under

Regulation SHO. Specifically, we find that the increase in audit fees due to Regulation SHO

primarily holds for only these ex-Andersen pilot firms. We find no relation for the sub-sample

5

involving non-Andersen clients. To better understand the underlying causal relation

involved, we also examine the short interest of the sample firms and find that the ex-Andersen

pilot firms experienced a heightened level of short interest following the enactment of

Regulation SHO. Additionally, we find Regulation SHO is associated with a decline in the

delay of the release of auditor reports of ex-Andersen pilot firms. Taken together, these

results explain why auditors demanded higher audit fees: the enactment of Regulation SHO

is associated not only with an increase in the short interest of the pilot firms but also with an

increase in the demand to provide timelier audited earnings news to the market. These

findings suggest that the rise of audit fees is attributable to higher audit/litigation risk and

higher audit effort. A key point to note is that the effects of Regulation SHO—on audit fees,

short interest, and audit report lag—hold for only the ex-Andersen subset of pilot firms and

not for the rest of the pilot firms.

When we extend the analysis to examine the real effects of Regulation SHO, we find

that the regulation weakens the investment-stock price relation for the ex-Andersen subset of

pilot firms. This result is in line with our previous findings in that the ex-Andersen firms

experienced a sharper increase in short interest, contributing to a reduction in the optimistic

bias in prices, and consequently attenuating the investment-stock price relation.4 With respect

to earnings management, we find a decline in discretionary accruals only for the non-

Andersen subset of pilot firms during the Regulation SHO period.5 Further analysis reveals

that this effect is driven by non-Andersen subset of pilot firms with higher short interest. In

contrast, we do not find a change in earnings management during the Regulation SHO period

for the ex-Andersen pilot firms, which is consistent with prior research that found ex-

4 Investor pessimism about the ex-Andersen firms can stem from concerns about these firms’ information

environment and this may translate into greater uncertainty about the prospects of the firm. 5 A reduction in earnings management for the non-Andersen pilot firms would imply that there is a reduction

in litigation risk for the auditors. However, it is unlikely that auditors will respond by marking down their audit

fees in response to this change in behavior. Our findings support this assertion.

6

Andersen clients experienced a reduction in earnings management after the involuntary

switch in auditors (Cahan and Zhang 2006).

Taken together, our results highlight the importance of distinguishing ex-Andersen

pilot firms from the rest of the pilot firms covered by Regulation SHO. The markedly

different results for this subset of treatment firms suggest that the confounding event of

Andersen’s demise needs to be accounted for to better evaluate the effects of Regulation

SHO. The reason is that the Andersen collapse also generated governance effects due to the

involuntary change in auditor and the new auditors imposed more stringent constraints on

managerial discretion. In sum, an important takeaway from our study is that ignoring

confounding events such as the demise of Arthur Andersen provides an incorrect evaluation

of the impact of Regulation SHO. Our findings support concerns raised by scholars such as

Werner (2019) on the attribution of several effects to Regulation SHO.

Our findings also contribute to the research on the determinants of audit fees. Prior

research (Choi et al. 2008; Simunic 1980; Hay 2011) has highlighted the role of factors such

as client litigation and business risk in increasing audit fees. However, to date, we have

limited understanding of the changes in audit fees over time. A notable exception is Beck

and Mauldin (2014) who find that CFOs were able to negotiate down a firm’s audit fees

during the 2008-2009 financial crisis. In this study, we find that the easing of short selling

constraints contributed to an increase in audit fees. However, we show that the effects are not

uniform across treatment firms and that they were influenced by changes that produced

corporate governance type effects.

The rest of the paper proceeds as follows. Section II provides background information

and develops our testable predictions. Section III discusses the research design and sample

selection, respectively. Section IV reports our empirical findings, and Section V concludes

the paper.

7

II. BACKGROUND AND PRIOR RESEARCH

Role of Short Sellers

Pownall and Simko (2005) view short sellers as information intermediaries covering

the lower tail of earnings expectations. In their role as information intermediaries, short

sellers are adept at identifying corporate misreporting and frauds before they are widely

known. For example, Desai et al. (2006) find that short interest is significantly higher in the

several months prior to earnings restatements and is concentrated in firms with high accruals.

Karpoff and Lou (2010) similarly find that short interest steadily increases in the 19 months

before public revelations of financial misrepresentation by firms. Prior research also finds

that short sellers use information that is predictive of future returns (Dechow et al. 2001;

Curtis and Fargher 2014; Drake et al. 2011). In other words, short sellers target firms that

have a higher likelihood of future stock price declines.

In the absence of short selling or in the presence of short selling constraints, equity

prices can be overvalued (Miller 1977), and it can take longer for the negative information

to be reflected into stock prices (Diamond and Verrecchia 1987). Overvalued equity, in turn,

can be problematic because overvaluation, by reducing the cost of equity financing, can

induce over-investment (Gilchrist et al. 2005, Grullon et al. 2015).6 However, empirical

evidence on this issue is mixed. While several empirical studies show that easing constraints

on short selling mitigates stock overvaluation (Cohen et al. 2007; Danielsen and Sorescu

2001; Jones and Lamont 2002; Lamont and Thaler 2003), others (e.g., Battalio and Schultz

2006; Boehmer et al. 2013; Beber and Pagano 2013; Crane et al. 2018) find limited support.

Besides the impact on overvaluation, there is limited evidence on the influence of short

6 A contrary viewpoint characterizes short selling as harmful. The underlying argument is that while managers

rely on the feedback provided by the market to make their investment decisions, opportunistic short sellers may

act strategically to lower equity prices with the goal of inducing managers to forgo even potentially profitable

projects (Goldstein and Guembel 2008). In this setting, constraints on short-selling can be beneficial.

8

selling on real decisions due to the endogenous nature of short sales: “short selling activities

could give rise to or result from the underlying characteristics of the corporate sector in the

real economy” (He and Tian 2014). In a similar vein, Grullon et al. (2015) argue that the lack

of convincing evidence on these issues is not surprising given that “firm fundamentals, short

selling, and prices are often jointly determined.”

Regulation SHO

It is due to these shortcomings that Regulation SHO became an important setting to

examine the effects associated with short selling. Prior to Regulation SHO, an uptick rule

restricted short sales during price declines. In July 2004, the Securities and Exchange

Commission (SEC) implemented Regulation SHO by randomly selecting one third of the

Russell 3000 index firms to be part of a pilot program and eliminated all short-sale price tests

for these pilot firms from May 2, 2005 to August 6, 2007. Regulation SHO was intended to

help the SEC understand how these price rules affected market quality and to develop

uniform rules across stock exchanges.

To accounting and finance scholars, Regulation SHO represents an exogenous shock

to short sale constraints and as such, it provides a laboratory setting for better understanding

the effects of short selling. The general view is that Regulation SHO attenuated the short sale

constraints of pilot firms by removing the price test restrictions that impeded short sale

activities. The easing of this short sell constraint was not welcomed by CEOs, CFOs, and

investor relation officers as reflected by the fact that the 2008 New York Stock Exchange

survey found that 85 percent of the survey participants “were in favor of reinstituting the

uptick rule as soon as possible” (Grullon et al. 2015).

Empirical studies show that Regulation SHO resulted in a significant increase in short

sales for stocks in the pilot program compared to those not in the pilot program (Alexander

and Peterson 2008; Boehmer et al. 2008; Diether et al. 2009; SEC 2007). However, recently,

9

researchers (e.g., Livtak and Black 2017; Werner 2019) have noted that studies examining

the indirect effects of Regulation SHO have overlooked the fact that the first detailed

investigation of the first order effects of Regulation SHO by Diether et al. (2009) finds no

evidence of an increase in short interest levels for the pilot firms relative to the control firms.

Werner (2019) adds that causal claims need to be clearly spelled out and validated when

examining the effects of Regulation SHO.

Regulation SHO, Audit fees, and Ex-Andersen Clients

A study of interest that examined the effect of Regulation SHO on a market

participant (auditors) instead of the targeted firms is Hope et al. (2017). Empirically, they

evaluate whether the easing of the short sale constraint on the pilot firms contributed to an

increase in audit fee. Hope et al. (2017, p. 482) note the following:

“Short sellers are among the most sophisticated players in the capital markets

and profit from price declines. As their short-selling activities drive down the

stock price of the targeted firm by incorporating bad news more quickly,

investors who suffer from the price decline are likely to sue the firm's auditor

if any audit-related errors are found to associate with the price decline”

The implication here is that easing of short selling constraints will increase the risk

of litigation or regulatory action against the auditor. Hope et al. (2017) argue that in response

to these increased risks, auditors will raise their audit fees to reflect either the higher audit

effort that this threat will induce or simply the higher risk premium associated with this threat.

Consistent with their argument, Hope et al. (2017) find that the enactment of Regulation SHO

resulted in higher audit fees paid by the pilot firms.

What we consider to be problematic is the presence of ex-Andersen clients in Hope

et al.’s (2017) sample. The demise of the Andersen firm due to the debacle involving Enron

created multiple effects for its clients. First, it raised questions about the quality of their

clients’ reporting quality. Dyck et al. (2013) find that ex-Andersen clients had a higher

likelihood of being identified as having committed fraud. Second, it caused an involuntary

10

change in the auditors of many firms and these auditor changes took place close to the

enactment of Regulation SHO. Third, these auditor changes impacted the reporting behavior

of ex-Andersen clients. For example, Cahan and Zhang (2006) find that the new auditors of

the ex-Andersen clients viewed these clients to involve a “unique source of litigation risk.”

They also find that in response to the higher litigation risk, the new auditors demanded more

conservative reporting as indicated by the smaller magnitude of abnormal accruals and larger

decreases in abnormal accruals following the auditor change. International evidence

involving ex-Andersen clients from 12 countries other than the US mirrors similar evidence

(Srinidhi et al. 2012).7 Using US data, Singer and Zhang (2018) show that the misstatements

of ex-Andersen clients were discovered faster than those of comparable companies that

retained their auditors throughout the misstatement.

The ex-Andersen clients also experienced adverse wealth effects involving their

auditor. In particular, a stream of research has documented negative stock market reaction to

Andersen clients during the events surrounding the collapse of Arthur Andersen (Chaney and

Philipich 2002; Krishnamurthy et al. 2006; Asthana et al. 2011). The implication of this

finding is that there was a sudden decline in the perceived quality of the Andersen audit

(Blouin et al. 2007). Moreover, evidence on the cross-sectional differences in the length of

time ex-Andersen clients took to select a new auditor indicates that Andersen clients with

greater agency conflicts dismissed Arthur Andersen sooner (Barton 2005).

In sum, the ex-Andersen clients experienced events that contributed to pre-treatment

differences between themselves and other firms that were subject to Regulation SHO.

Concerns about the financial reporting quality of ex-Andersen clients, the exogenous change

in the auditors of these firms, and the resulting influence on the incoming auditor to be more

7 Srinidhi et al. (2012, p. 208) find that “following Arthur Andersen's failure in the US, successor Big-N auditors

charged an audit fee premium for ex-Andersen clients compared to existing clients and non-Andersen switch-

ins.” They also find that the earnings quality of ex-Andersen clients is higher after the switch.

11

conservative, raise both internal and external validity concerns about prior findings that point

to Regulation SHO having an effect on audit fees (as well as other variables such as earnings

management). With respect to internal validity, it suggests that any analysis of audit fees that

does not account for the presence of ex-Andersen clients in the context of Regulation SHO

is problematic. With respect to external validity, it is far from clear if one can generalize

Hope et al.’s (2017) conclusion that easing of short selling constraints will induce higher

audit fees. It should also be noted that the issue of ex-Andersen clients raises questions in

relation to prior research that did not specifically account for these firms in examining the

effects of Regulation SHO on earnings management behavior.

Testable Predictions

Extant research does not offer a clear-cut prediction on how Regulation SHO can

impact ex-Andersen clients. To the extent that the market still perceives these firms to suffer

from a high risk of misstatement and this misstatement exposes the new auditor to higher risk

of litigation or regulatory action, particularly during the Regulation SHO regime, then the

ex-Andersen pilot firms will experience an increase in audit fees. If the new auditor has

alleviated the concerns involving misstatements, then Regulation SHO may not have an

impact on these firms. With respect to non-Andersen clients, Hope et al.’s (2017) findings of

higher audit fees during the Regulation SHO period should prevail if their argument—that

Regulation SHO, on average, increased the likelihood of litigation and/or regulatory risk

facing the auditors—holds.

To shed further light on the causal relation involved, it is important to look at the level

of short interest. If concerns about misstatements are in place, then Regulation SHO can

result in a rise in the short interest of the ex-Andersen client firms. Again, if the new auditor

alleviated concerns about misstatement risk, then it will have little bearing on short interest.

With respect to the non-Andersen subset of firms, Regulation SHO could increase short

12

interest given the argument that the easing of short selling constraints will increase the threat

of short selling faced by firms.

We also examine the delay in the issuance of audited financial statements. Increased

pressure by short sellers can induce firms to counter this pessimism by providing timelier

reporting of the actual performance of the firm. We view this relationship to run parallel to

the effect of Regulation SHO on short interest. If ex-Andersen clients face greater short

interest due to concerns about misstatement risk during the Regulation SHO time period, then

these firms will also face greater urgency to be timely in their release of audited financial

statements. A similar urgency may not hold for the non-ex-Andersen firms.

It is also important to re-examine the effects of Regulation SHO on the earnings

management of pilot firms and examine how these effects vary for the subset of ex-Andersen

pilot client firms and the rest of the pilot firms. Fang et al. (2016) find that Regulation SHO

reduced the signed discretionary accruals of pilot firms, particularly during the time period

when the price tests were suspended. However, Cahan and Zhang (2006) have noted that the

new auditors of the ex-Andersen client firms imposed a more conservative reporting on these

firms and ex-Andersen clients decreased earnings management after the involuntary switch

in auditors. Hence, the Fang et al.’s (2016) findings may largely hold only for the subset not

involving the ex-Andersen clients.

Lastly, we examine whether the effects of Regulation SHO on real activities differ

between ex-Andersen client firms and the rest. Grullon et al. (2015) find that Regulation SHO

resulted in a higher short interest for pilot firms and this pessimism led to a downward

revision in prices and a related decline in investments. To the extent that short selling

constraints results in stock prices reflecting an optimistic bias, Regulation SHO, by relaxing

short selling constraints, should reduce the optimistic bias. In other words, Regulation SHO

negatively impacted the stock price-investment relation. If the ex-Andersen client firms are

13

still perceived to be risky and Regulation SHO reduced the optimistic bias in stock prices of

their clients, then Regulation SHO should reduce the investment-stock price relation for this

subset of pilot firms.

In summary, our aim is to examine whether the impact of Regulation SHO differs for

pilot firms that were ex-Andersen clients from that of the rest of the pilot firms. We conduct

a range of tests because there are differences in the perceptions about the ex-Andersen clients

arising from the Andersen’s demise and forced change in auditors. The overall goal is to

improve our understanding of the effects of short selling on auditing, accounting, and real

activities such as investments. We next take the predictions to the data.

III. RESEARCH DESIGN

Sample Selection

Panel A of Table 1 reports the sample selection process. We begin with firms in the

Russell 3000 index as of June 30, 2004 and identify an initial sample of 986 pilot firms and

2,014 non-pilot firms.8 The 986 pilot firms are based on the SEC’s pilot orders and its report

on the pilot program (SEC 2007). We then merge the initial sample of firms with Compustat,

CRSP, and Audit Analytics to obtain the data for the variables required for the analyses and

we drop the observations that have missing values for these variables. We exclude firms in

financial (two-digit SICs between 60 and 69) and utility (two-digit SIC 49) industries because

firms in regulated industries have different characteristics from other firms. Following Fang

et al. (2016), we drop firms that experienced mergers and acquisitions (M&A) after April 30,

2004 to ensure that our results are not confounded by significant changes in ownership

8 Under the SEC’s pilot program, every third stock on the 2004 Russell 3000 index ranked by average daily

trading volume over the June 2003 to May 2004 period within each of the three listing markets (NYSE, Amex,

or NASDAQ-NM) was designated as a pilot stock.

14

structure.9 Our final sample consists of 509 pilot firms and 1,006 non-pilot firms, which is

comparable with Hope et al.’s (2017) sample (538 pilot and 1072 non-pilot firms).

Like Hope et al. (2017), we first derive an unbalanced panel sample covering each

year from 2000 to 2003 (inclusive) and 2005 to 2013 (inclusive). The year 2004 is excluded

because “the pilot firm list was announced on July 28, 2004 but the price tests were not

removed for pilot firms until May 2, 2005” (Hope et al. 2017, p. 484). We also derive two

more panel samples by restricting the time period to the same time period as Fang et al.

(2016), that is, 2001 to 2003 (inclusive) and 2005 to 2010 (inclusive). One of these samples

is an unbalanced panel sample and the other is a balanced panel sample consisting of firms

that have data to calculate firm characteristics over the entire sample period.

[Insert Table 1]

Panels B and C of Table 1 present the distribution of firm-year observations for the

unbalanced panel sample up to 2013 and the balanced panel sample up to 2010. Sample firms

are evenly spread across years, although the number of firms each year are smaller in the

balanced sample.

Descriptive Statistics

Panel A of Table 2 presents the mean statistics as of 2002 for the unbalanced panel

sample covering the period up to 2013. We focus on 2002 because it is before the

announcement of the pilot program and because it is the year when ex-Andersen clients were

forced to change their auditor.10 We also compare pilot and non-pilot firms’ characteristics

by distinguishing between ex- Andersen clients and other auditor clients.

[Insert Table 2]

9 Hope et al. (1997) do not exclude firms that experienced M&As. As a sensitivity test, when we include these

firms in our analyses, our (untabulated) results yield inferences similar to those reported in the paper. 10 In term of a timeline, on June 15, 2002, Andersen was convicted of obstruction of justice for shredding

documents related to its audit of Enron, resulting in the Enron scandal. On August 31, 2002, Andersen agreed

to surrender its CPA licenses and its right to practice —effectively putting the firm out of business.

15

An examination of the mean statistics of several variables for pilot and non-pilot firms

indicates that our sample closely resembles that of Hope et al. (2017). We find non-pilot

firms in our sample paid higher audit fees than pilot firms in 2002. Similarly, Hope et al.

(2017) report higher audit fees for non-pilot firms in the pre-Regulation SHO period. Mean

statistics of other variables for our sample and that of Hope et al. (2017) are also similar.

These similarities in data give confidence that our findings are unlikely to be affected by

differences in sample composition.

Among pilot firms, ex-Andersen clients paid lower audit fees than other auditor

clients. However, there is no difference in audit fees of ex-Andersen and other auditor clients

that were non-pilot firms. It is worth noting that ex-Andersen clients that are pilot firms paid

lower audit fess than their counterparts that were non-pilot firms, even though these firms do

not exhibit statistically significant differences in the mean values for most of the firm

characteristics we examine. Panel B of Table 2 reports similar comparisons for the smaller

balanced panel sample. As in Panel A, ex-Andersen clients that are pilot firms paid lower

audit fees than their counterparts that were non-pilot firms.

IV. EMPIRICAL RESULTS

Results: Short Interest

Although investors were allowed to short sell pilot firms during Regulation SHO

period, short-selling activities could be pursued both before and after Regulation SHO if they

met the uptick rule. To explain, the SEC enforced the uptick rule for any short sales before

and after Regulation SHO in that short selling could not be undertaken when stock prices

were declining (pre period). During Regulation SHO, the uptick rule was lifted for pilot firms

and after the Regulation SHO, the uptick rule was removed for all the stocks (post period).

To shed light on whether ex-Andersen clients that are pilot firms experience an

increase in short-selling interest around Regulation SHO, we compute the mean values of

16

monthly short-selling interest, scaled by number of shares outstanding, and plot the mean

values over time for four subsamples using our 2013 unbalanced and 2010 balanced panel

sample, respectively.11 The four subsamples are ex- Andersen clients (pilot), ex- Andersen

clients (non-pilot), other auditor clients (pilot), and other auditor clients (non-pilot).

For the unbalanced panel sample, Figure 1 shows that except from 2011 to 2013,

short-selling interest of ex-Andersen clients that are pilot firms is above those of other firms

across time, especially during Regulation SHO. Balanced panel sample also exhibits a similar

pattern over time.

[Insert Figure 1]

To examine the effects of Regulation SHO on short selling interest, we apply a

difference-in-differences (DiD) design for the thirteen-year window (2000 to 2003 and 2005

to 2013) around Regulation SHO’s pilot program. Specifically, we estimate the following

model for the full sample and the sample of pilot and non-pilot firms that are separated into

ex-Andersen clients and other auditor clients:

SIRit = α0 + α1Piloti*Duringt+ α2Piloti*Postt + αk∑Controls

+ Firm Fixed Effects + Time Fixed Effects + eit (1)

where SIR is the average of the monthly short interest normalized by the number of shares

outstanding. The pre-pilot period consists of four years before the pilot program (2000 to

2003). As noted previously, the year 2004 is omitted because the SEC announced the list of

pilot firms midway through 2004. Pilot equals one if a firm’s stock is designated as a pilot

stock under Regulation SHO’s pilot program and zero otherwise. During equals one if a

firm’s fiscal year end falls between January 1, 2005 and December 31, 2007, zero otherwise.

Post equals one if a firm’s fiscal year end falls between January 1, 2008 and December 31,

11 Plotting the mean values of monthly short-selling interest, scaled by trading volume, yield a similar pattern.

17

2013, and zero otherwise.12 Controls is a vector of firm characteristics used by Desai et al.

(2006) in explaining short interest. Specifically, we control for firm characteristics such as

firm size, book to market ratio, prior momentum, residual stock return volatility, and a proxy

for earnings management that might affect the shorting decision.13 The subscripts i and t are

denoted for firm i and month t. We also include firm fixed effects to alleviate the time-

invariant firm-level omitted variable problem; it also allows us to control for within firm

variation. Moreover, we include month fixed effects to control for time differences in short

interest.14 The variables are defined in Appendix A. Statistical significance is assessed for

this and all subsequent equations based on test-statistics that use standard errors clustered

by firm (Gow et al. 2010: Petersen 2009).

The regression results estimating equation (1) are reported in Table 3. Column (1)

presents the regression results for the unbalanced sample covering data up to 2013. The

coefficients on both interaction terms, Pilot*During and Pilot*Post, are statistically

significant (p < 0.01), suggesting that pilot firms have larger increases in short interest during

and after the pilot program.

[Insert Table 3]

When we distinguish between ex Andersen clients and other auditor clients, we find

that the coefficients on Pilot*During and Pilot*Post are statistically significant (p < 0.01)

only for pilot firms that are ex-Andersen clients. Moreover, this effect is significantly more

than that of pilot firms that were not ex-Andersen clients.15 This result holds across all

12 While we follow Hope et al. (2017) and use a difference-in-difference design over a 13-year window to ensure

comparability, we acknowledge that drawing inferences over such a long window can be problematic. As a

robustness test, we reestimated all our empirical models by restricting the sample to December 31, 2009, and

our inferences reamin unchanged. 13 Including trading volume and institutional ownership as additional control variables in model (1) did not alter

the inferences reported in the paper. 14 When we replace time fixed effects by During and Post, the results remain the same. 15 To test the significance of the difference in the coefficients between ex-Andersen clients and other auditor

clients, we create an indicator variable equal to 1 for ex Andersen clients, and 0 otherwise, interact the indicator

variable with all variables including fixed effects, and then include these interaction terms along with all the

18

samples—balanced and unbalanced panel. Overall, the results indicate that the increased

short interest of pilot firms during and after Regulation SHO prevailed only for ex-Andersen

clients. In other words, ex-Andersen clients that were pilot firms experienced more short

selling threats during and after Regulation SHO, which is consistent with the argument that

short sellers perceived ex-Andersen clients to have a higher likelihood of misstatements.

Results: Audit Fees

To examine the effects of Regulation SHO on audit fees, we extend the difference-

in-differences test using a multivariate regression model for audit fees. Specifically, we

estimate the following model for the full sample and the sample of pilot and non-pilot firms

that are separated into ex Andersen clients and other auditor clients:

LnAFit = α0 + α1Piloti*Duringt+ α2Piloti*Postt + αk∑Controls

+ Firm Fixed Effects + Time Fixed Effects + eit (2)

where LnAF is the natural logarithm of the audit fees (in millions of U.S. dollars). Controls

is a vector of firm characteristics used by Hope et al. (2017) in explaining audit fees. The

control variables are intended to capture the influence of the client size, client complexity,

and audit characteristics. All other variables are as defined before.

The regression results estimating equation (2) are reported in Table 4.16 In column

(1), the adjusted R2 is 0.939, in line with Hope et al. (2017). Moreover, the coefficients on

control variables are consistent with prior literature (Ashbaugh et al. 2003; Choi et al. 2008)

and they exhibit statistical significance similar to that reported in Hope et al. (2017). Two

exceptions are the coefficients on Quick and AssetGrowth. The coefficients on the two

interaction terms, Pilot*During and Pilot*Post, are of interest. The coefficient on

Pilot*During is statistically significant (p < 0.05) and the coefficient on Pilot*Post is not

variables presented Table 3 in a regression model. The interaction terms between the independent variable and

ex Andersen clients dummy present the difference of the coefficients between ex Andersen clients and other

auditor clients. We perform similar specifications for the other tables. 16 When we replace time fixed effects by During and Post, the inferences remain the same.

19

statistically significant (p > 0.10). Overall, the results, based on the full sample, are consistent

with Hope et al.’s (2017) finding that pilot firms have larger increases in audit fees during

the pilot program, but do not have a larger increase in the post-pilot period.

[Insert Table 4]

A crucial prerequisite for the validity of the DID method is the existence of a parallel

trend in the pre-event period with respect to the outcome of interest (Bertrand et al. 2004).

We check for the existence of a parallel trend using a simple regression framework by

breaking the pre-Regulation SHO period into subperiods.17 If a parallel trend exists between

the pilot and non-pilot firms, we expect the interaction between the pre-event period indicator

variables (e.g., an indicator variable equal to 1 for year 2003, and 0 otherwise) and pilot firms

to be statistically distinguishable from zero. Untabulated results indicate that several pre-

period interaction terms (e.g., indicator variable for year 2003 interacted with pilot firms) are

positive and statistically significant, suggesting that there is an increasing trend in audit fees

for pilot firms even prior to Regulation SHO. In other words, there is a parallel-trend of an

increase in audit fees prior to Regulation SHO.

The results in the remaining columns of Table 4 shed light on the type of firms that

contribute to the audit fees differences between pilot and non-pilot stocks. To test the

robustness of the results, we also present regression results for the unbalanced and balanced

panel samples covering a shorter time period. It is noteworthy that irrespective of whether

we use a balanced or an unbalanced panel sample, the increasing audit fee of pilot firms

during Regulation SHO is not attributable to all pilot firms; instead, the increase is driven by

ex-Andersen clients.

17 Hope et al. (2017) do not report any tests to assess the validity of the parallel trend assumption.

20

Note that the coefficients on Pilot*During are positive and statistically significant

only for ex-Andersen clients. That is, during the pilot program, ex-Andersen clients that are

pilot firms experience a significant increase in audit fees, whereas pilot firms that are other

auditor clients do not.18 Moreover, the coefficients on Pilot*Post are positive and statistically

significant for ex-Andersen clients, while the coefficients on Pilot*Post are negative and

statistically significant for other auditor clients. These changes in audit fees are also

economically significant. Ex-Andersen clients that are pilot firms experience an increase in

audit fees of $30.03 million and $71.4 million during and three years after Regulation SHO

period, respectively.19 In contrast, other auditor clients that are pilot firms experience $76.76

million and $173.42 million decrease in audit fee during and three years after Regulation

SHO period, respectively. The magnitude of the difference in audit fee between ex-Andersen

clients and other auditor clients that are pilot firms is even larger after Regulation SHO Period

(e.g., 0.117 versus 0.175 for the balanced panel sample), which is consistent with the idea

that audit fees are persistent and do not necessarily adjust immediately in a single year (Kacer

et al. 2018).

Results: Audit Report Lag

In this section, we examine whether the Regulation SHO shortens audit delay, a

variable used in prior research to capture the time required to complete fieldwork (Ashton et

al. 1987; Ettredge et al. 2006). Specifically, we estimate the following model:

LNAUDELAYit = α0 + α1Piloti*Duringt+ α2Piloti*Postt + αk∑Controls

+ Firm Fixed Effects + Time Fixed Effects + eit (3)

18 Hope et al. (2017) find that the impact of short-selling threats on audit fees only exists for firms with higher

bankruptcy risk and for firms with managers who are less likely to be disciplined by short-selling threats.

Untabulated results using bankruptcy risk and the extent of CEO discipline as partitioning variables indicate

that the cross-sectional variation documented in Hope et al. (2017) is driven by ex-Andersen clients but not for

other auditor clients. 19 Using the Pilot*During coefficient reported in column (6) for the balanced panel sample, $30.03 million is

computed as 8.9%*average audit fees of $1,249,860 for ex-Andersen clients that are pilot firms in 2004*3 years

of pilot program *90 ex-Andersen clients that are pilot firms. Analogously, $71.4 million is computed as

12.5%*average audit fee of $2,115,441 for ex-Andersen clients that are pilot firms in 2008*3 year post

Regulation SHO*90 ex-Andersen clients that are pilot firms.

21

where the dependent variable, LNAUDELAY, is the natural logarithm of the number of days

from the fiscal year end to date of the auditor’s report.20 We include the same controls as

those in Table 4. All other variables are as defined before.

Table 5 presents the regression results for equation (3). Column (1) presents the

regression results for the unbalanced sample covering the period up to 2013. The coefficients

of both interaction terms, Pilot*During and Pilot*Post, are not statistically significant (p >

0.01), suggesting that pilot firms have no change during and after the pilot program in the

number of days between the date of the auditor’s report and the fiscal year end date. When

we distinguish between ex-Andersen clients and other auditor clients, we find that the

coefficients of Pilot*During and Pilot*Post are negative and statistically significant (p <

0.05) only for pilot firms that are ex-Andersen clients. These results hold across all samples—

balanced and unbalanced panel. Overall, the results indicate that the auditors of ex-Andersen

clients that were pilot firms released their audit reports early during and after Regulation

SHO, suggesting that ex-Andersen clients exhibit greater urgency to be timely in the release

of their audited financial statements. This result complements the evidence in Frankel et al.

(2016) that ex-Andersen clients increased disclosure following the switch and that the returns

of ex-Andersen clients exhibit less concentration around earnings announcements in bad-

news quarters, consistent with timelier release of bad news.

[Insert Table 5]

Results: Signed Discretionary Accruals

20 Glover et al. (2019) suggest that audit delay may be a less useful measure for U.S.-listed firms post-SFAS

165 (ASC 855) because auditors began dating their opinions on the date the financial statements are filed with

the SEC. To address this concern, we restrict our audit delay sample to firms with fiscal year ended before June

15, 2009 (the effective date of SFAS 165) and find that our inferences reamin unchanged.

22

To examine the effects of Regulation SHO on earnings management, we apply a

difference-in-differences (DiD) design for unbalanced and balanced panel samples using the

following model:

Signed DAit = α0 + α1Piloti*Duringt+ α2Piloti*Postt + αk∑Controls

+ Firm Fixed Effects + Time Fixed Effects + eit (4)

where Signed DA is calculated as per Fang et al. (2016). Specifically, it represents the signed

asset-deflated performance-matched discretionary accruals in fiscal year t, computed as the

difference between a firm’s discretionary accruals and the corresponding discretionary

accruals of a matched firm from the same fiscal year and Fama-French 48 industry with the

closest return on assets. A firm’s discretionary accruals are defined as the difference between

its total accruals and the fitted normal accruals derived from a modified Jones (1991) model.21

We use the same control variables as used for equation (2).

The regression results estimating equation (4) are reported in Panel A of Table 6.

Column (1) presents the regression results for the unbalanced sample covering the period up

to 2013. The coefficient of interaction term, Pilot*During, is negative and statistically

significant (p < 0.05), suggesting that pilot firms have larger decreases in discretionary

accruals during the pilot program. Overall, the results, based on the full sample, are consistent

with Fang et al.’s (2016) finding that pilot firms exhibit lower discretionary accrual during

the pilot program.

21 The modified Jones model follows Dechow, Sloan, and Sweeney (1995) and is specified as

𝑇𝐴𝑖,𝑡

𝐴𝑆𝑆𝐸𝑇𝑖,𝑡−1

= 𝛽0 + 𝛽1

1

𝐴𝑆𝑆𝐸𝑇𝑖,𝑡−1

+ 𝛽2

∆𝑅𝐸𝑉𝑖,𝑡 − ∆𝐴𝑅𝑖,𝑡

𝐴𝑆𝑆𝐸𝑇𝑖,𝑡−1

+ 𝛽3

𝑃𝑃𝐸𝑖,𝑡

𝐴𝑆𝑆𝐸𝑇𝑖,𝑡−1

+ 𝜀𝑖,𝑡

Total accruals 𝑇𝐴𝑖,𝑡 are defined as earnings before extraordinary items and discontinued operations (IBC)

minus operating cash flows (OANCF-XIDOC), 𝐴𝑆𝑆𝐸𝑇𝑖,𝑡−1 is total assets at the beginning of year t (AT),

∆𝑅𝐸𝑉𝑖,𝑡 is the change in sales revenue (SALE) from the preceding year, ∆𝐴𝑅𝑖,𝑡 is the change in accounts

receivable (RECT) and 𝑃𝑃𝐸𝑖,𝑡 is gross property, plant, and equipment (PPEGT). The fitted normal accruals

are computed as

𝑁𝐴𝑖,𝑡 = 𝛽0̂ + 𝛽1̂ 1

𝐴𝑆𝑆𝐸𝑇𝑖,𝑡−1

+ 𝛽2̂

(∆𝑅𝐸𝑉𝑖,𝑡 − ∆𝐴𝑅𝑖,𝑡)

𝐴𝑆𝑆𝐸𝑇𝑖,𝑡−1

+ 𝛽3̂

𝑃𝑃𝐸𝑖,𝑡

𝐴𝑆𝑆𝐸𝑇𝑖,𝑡−1

Firm-year-specific discretionary accruals are calculated as 𝐷𝐴𝑖,𝑡 =𝑇𝐴𝑖,𝑡

𝐴𝑆𝑆𝐸𝑇𝑖,𝑡−1− 𝑁𝐴𝑖,𝑡.

23

[Insert Table 6]

When we distinguish between ex-Andersen clients and other auditor clients, we find

that the coefficients of Pilot*During are negative and statistically significant (p < 0.01) only

for pilot firms that are not ex-Andersen clients. In other words, ex-Andersen clients do not

exhibit a change in discretionary accruals during or after Regulation SHO. This finding

highlights the importance of distinguishing between ex-Andersen and non-Andersen clients.

Prior studies have noted that ex-Andersen clients were induced to change their reporting

behavior following the involuntary change in their external auditor. For example, Krishnan

et al. (2007) find that large former Andersen clients were more likely to receive going-

concern opinions, which is consistent with the suggestion that increased litigation risk

associated with the larger ex-Andersen clients led to increased conservatism by the new

auditors. In a similar vein, Cahan and Zhang (2002) find that ex-Andersen clients had lower

levels of and large decreases in abnormal accruals in 2002 relative to a control sample of

clients that were audited by a Big 4 auditor in 2001 and 2002, which is consistent with auditor

conservatism. That is, the successor auditors viewed an Andersen client audit as a unique

source of litigation risk. Thus, the increased auditor conservatism for ex-Andersen clients at

the time of the forced change potentially explains the absence of a change in discretionary

accruals for ex-Andersen clients during and after Regulation SHO. In other words, the

involuntary change in auditor due to the demise of Andersen had a more profound and lasting

impact on firm reporting behavior and hence reducing any potential incremental impact due

to Regulation SHO.

In contrast, for pilot firms that were not ex-Andersen clients, Regulation SHO served

to constrain opportunistic reporting. To explore whether the effects for these pilot firms are

driven by firms with high short selling interest, we focus on these firms and re-estimate

equation (4) separately, for the subsamples with high and low average short interest during

24

Regulation SHO (from 2005 to 2007). We classify firms with the short interest ratio (the

number of shares sold short divided by trading volume) above the sample median as firms

with high short interest, and the rest as firms with low short interest. Panel B of Table 6

reports the results of this estimation. The negative coefficients on Pilot*During are

concentrated in the non-Andersen firms that were shorted more during Regulation SHO.22

For the less shorted non-Andersen firms, the coefficients on Pilot*During are statistically

insignificant. These results indicate that the decline in discretionary accruals during the

Regulation SHO period for the non-Andersen subset of pilot firms is driven by non-Andersen

subset of pilot firms with higher short interest. An implication of our findings in Table 6 is

that the effects of short selling is likely to have a greater impact in the absence of alternative

governance mechanisms.

Results: Investment-Q sensitivity

In this section, we extend the analysis of the effect of Regulation SHO by focusing

on real activities. Specifically, we examine the change in investment-q sensitivity around the

pilot program of Regulation SHO using the following regression model:

Investmentsit = α0 + α1Piloti*Duringt+ α2Piloti*Postt + α3Qit + α4Piloti*Qit +

α5Duringt*Qit + α6Postt*Qit + α7Piloti*Duringt*Qit +α8Piloti*Postt*Qit

+α9Cash Flowit + α10Piloti*Cash Flowit + α11Duringt*Cash Flowit +

α12Postt*Cash Flowit + α13Piloti*Duringt*Cash Flowit

+α14Piloti*Postt*Cash Flowit + αk∑Controlsit + Firm Fixed Effects

+ Time Fixed Effects + eit (5)

where Investments is the firm’s capital expenditures scaled by lagged total assets, Q is a proxy

for investment opportunities, and Cash Flow is a proxy for operating cash flow. All other

variables are defined as before. Following prior research (Edmans et al. 2017), we control

22 Recall that ex Andersen clients experienced a change in auditors who constrained their opportunistic reporting

behavior. Hence, Regulation SHO is not associated with signed discretionary accruals of ex-Andersen pilot

firms, even though short interest inceased for these firms. In contrast, short sellers served as a governance

mechanism for other auditor clients by constraining their reporting behavior. These differences among pilot

firms again point to the importance of distinguishing ex-Andersen clients from other auditor clients in

attributing the effects of Regulation SHO.

25

for firm characteristics such as firm size, firm age, leverage, sales growth, cash, and retained

earnings. The slope coefficients, α3 and α9, represent investment-q sensitivity and investment-

cash flow sensitivity, respectively. Our main variables of interest are the interactions of Pilot

*During*Q and Pilot*During*CashFlow. The slope coefficients α7 and α13 capture the

incremental change in investment-Q and investment-cash flow sensitivity for pilot firms

during Regulation SHO period relative to non-pilot firms. A positive coefficient estimates

of α7 and α13 would indicate that the easing of short selling constraints in the Regulation SHO

regime increases investment-Q and investment-cash flow sensitivity, respectively. However,

Grullon et al. (2015) suggest that short selling lowers stock prices and equity issuance, and

ultimately reduces firm investment. To the extent Regulation SHO reduced the optimistic

bias in stock prices, then Regulation SHO should attenuate the investment-stock price

relation. In this sense, we would expect a significant negative coefficient estimate for α7 and

α13.

Table 7 presents the regression results for equation (5). Column (1) presents the

regression results for the unbalanced sample covering the period up to 2013. Consistent with

the literature, the sensitivity of investment to Q is significant for the full sample; however,

pilot firms have insignificant investment sensitivity to Tobin Q and cash flow. Although, ex-

Andersen clients and other clients that are pilot firms exhibit no significant investment

sensitivity to Tobin Q, as shown by Pilot*Q in columns (2) and (3), Pilot*During*Q is

negative and statistically significant for ex-Andersen clients and other clients. It is

noteworthy is more negative for ex-Andersen clients and the difference of Pilot*During*Q

between ex-Andersen clients and other clients is highly significant in all samples.

Pilot*During*Cash Flow is not significant for ex-Andersen clients and other auditor clients.

The results in Table 7 show a larger reduction in the investment-stock price relation for ex-

Andersen pilot firms during the Regulation SHO period, which is in line with our previous

26

findings in that the ex-Andersen firms experienced a sharper increase in short interest,

contributing to a reduction in the optimistic bias in stock prices and consequently attenuating

the investment-stock price relation.

[Insert Table 7]

Robustness Tests

Litvak and Black (2017) note that the SEC in fact selected the largest 1,000 of the

control group and suspended the uptick rule for trading in these firms’ shares after regular

trading hours. Thus, these firms are partly treated or the uptick rule was applied to them only

during regular trading hours. To make sure our results are not contaminated by these firms,

we exclude them from non-pilot stock sample and replicate our analyses in Tables 3 to 7.

The initial Regulation SHO randomized the sampling process by locating every third

stock ranked by trading volume. However, as largest firms are later systematically selected

for the uptick rule to be eliminated after regular trading hours, pilot firms in the treated group

will eventually be bigger than the remaining control group firms. In other words, the

experiment becomes non-randomized. To ensure causal influence and control for the

potentially confounding influence driven especially by firm size, we apply the Coarsened

Exact Matching (CEM) algorithm (henceforth, CEM; see Iacus et al. 2009) in the first stage

to match firms in the control and treatment groups by size (natural logarithm of market

capitalization). This step allows us to obtain exactly balanced data with very similar

distribution of firm size in the two groups. Then, in the second stage, we perform multivariate

regressions shown in Table 8 using matched pilot and non-pilot groups that have the same

number of firms (369 firms). By default, CEM uses maximal information; thus, the treated

and control groups often have different numbers. We adopt a k-to-k solution, which allows

us to select the same number of treated and control groups without needing to use weights.

Our results remain robust and qualitatively similar to those in Tables 3 to 7.

27

[Insert Table 8]

5. Conclusion

This paper re-examines the effects of Regulation SHO, which eased short selling

constraints for a group of randomly assigned pilot firm stocks by temporarily halting the

short-sale price tests. Prior research finds that these pilot firms experienced an increase in

audit fees and attributes this result to auditors demanding higher fees in the face of heightened

litigation risk due to increased short selling threats. Other research involving this setting finds

that Regulation SHO constrained the earnings management of pilot firms.

While these findings are noteworthy, it ignores a confounding event which also

produced governance type effects. Specifically, prior research ignored the demise of the

Arthur Andersen auditing firm. This event resulted in the involuntary change in the auditors

of the ex-Andersen clients. The new auditors produced governance type effects in that they

constrained earnings management, raised the audit fees, and exhibited an increase in the

likelihood of issuing a going concern opinion. These effects prompt two research questions

examined in this study. First, does the effects of Regulation SHO differ between the ex-

Andersen client firms and the rest of the pilot firms? Second, if so, in what manner do they

differ?

Our analyses separate ex-Andersen clients from the rest of the pilot and control firms

and find the effects of Regulation SHO to be different for the two subsets of treatment firms.

Specifically, we find the increase in audit fees due to Regulation SHO primarily holds for

only the ex-Andersen pilot firms. We find no relation during the Regulation SHO period for

the sub-sample involving non-Andersen clients. Supportive of the hike in the audit fees of

the ex-Andersen subset of pilot fims, we find Regulation SHO is associated with higher short

interest and a decline in the delay of the release of auditor reports for these firms. We fail to

detect a similar relation with respect to the rest of the treatment firms. In terms of the effect

28

of Regulation SHO on the investment-stock price relation, we find that Regulation SHO

weakened the investment-stock price relation of the ex-Andersen subset of pilot firms more

than that of other auditor clients, which is not surprising since the stock prices of these group

of firms incorporated greater investor pessimism as evidenced by the Regulation SHO

induced larger short interest. Separately, we find Regulation SHO is associated with a decline

in discretionary accruals for the non-Andersen pilot firms but not for ex-Andersen pilot firms.

This result is consistent with prior evidence which found the involuntary change in auditors

curbed earnings management of the ex-Andersen client firms.

Prior research has noted that Regulation SHO offers a quasi natural experiment for

identifying the effects of short selling. However, our study cautions that there are

confounding effects that also produce governance effects. Ignoring these confounding events

will lead to incorrect inferences about the effects of regulation that is being studied. Our study

highlights this point by noting that previously documented effect of Regulation SHO on audit

fees largely holds for only a subset of pilot firms. Additional analyses suggest that this

differential effect holds across multiple variables that have been previously studied. Prior

studies have noted that short sale price rules are distortionary and that they should be

permanently suspended. We do not take a position on the efficacy of short sale price rules.

Our focus is on highlighting the differential effects of these regulations in the presence of a

confounding event.

29

References

Alexander, G., and M. Peterson. 2006. (How) do price tests affect short selling? Working

paper, University of Minnesota.

Ashton, R., J. Willingham, and R. Elliott. 1987. An empirical analysis of audit delay. Journal

of Accounting Research 25(2): 275–292.

Asthana, S., S. Balsam, and J. Krishnan. 2011. Audit firm reputation, auditor switches, and

client stock price reactions: The Andersen experience. International Journal of

Auditing 14: 274-293.

Ashbaugh, H., R. LaFond, and B. Mayhew. 2003. Do nonaudit services compromise auditor

independence? Further evidence. The Accounting Review 78 (3): 611-639.

Barton, J. 2005. Who cares about auditor reputation? Contemporary Accounting Research

22: 549–586.

Battalio, R., and P. Schultz. 2006. Options and the bubble. Journal of Finance 61:2071-2072.

Beber, A., and M. Pagano. 2013. Short‐Selling bans around the world: Evidence from the

2007–09 crisis. Journal of Finance 68:343-381.

Beck, M., and E. Mauldin. 2014. Who's really in charge? Audit committee versus CFO power

and audit fees. The Accounting Review 89(6): 2057-2085.

Bertrand, M., E Duflo, and S Mullainathan. 2004. How much should we trust differences-

in-differences estimates? The Quarterly Journal of Economics 119(1): 249-275

Boehmer, E., C. Jones, and X. Zhang. 2013. Shackling short sellers: The 2008 shorting ban.

Review of Financial Studies 26(6): 1363–1400.

Blouin, J., B. Grein, and B. Rountree. 2007. An analysis of forced auditor change: The case

of former Arthur Andersen clients 82(3); 621-650.

Cahan, S., and W. Zhang. 2006. After Enron: Auditor conservatism and ex-Andersen clients.

The Accounting Review 81 (1): 49–82.

Chaney, P, and K. Philipich. 2002. Shredded reputation: The cost of audit failure. Journal of

Accounting Research 40 (Supplement): 1221–1245

Choi, J., J. Kim, X. Liu, and D. Simunic. 2008. Audit pricing, legal liability regimes, and Big

4 premiums: Theory and cross-country evidence. Contemporary Accounting

Research 25 (1): 55-99.

Cohen, L., K. Diether, and C. Malloy. 2007. Supply and demand shifts in the shorting market.

The Journal of Finance 62(5): .2061-2096.

Crane, A., K. Crotty, S. Michenaud, and P. Naranjo. 2018. The causal effects of short-selling

bans: Evidence from eligibility thresholds. The Review of Asset Pricing Studies

https://doi.org/10.1093/rapstu/ray004

Curtis, A. and N. Fargher. 2014. Does short selling amplify price declines or align stocks

with their fundamental values? Management Science 60(9): 2324-2340.

Danielsen, B., and S. Sorescu. 2001. Why do option introductions depress stock prices? A

study of diminishing short sale constraints. Journal of Financial and Quantitative

Analysis 36(4): 451-484.

Dechow, P., A. Hutton, L. Meulbroek, and R. Sloan. 2001. Short-sellers, fundamental

analysis, and stock returns. Journal of Financial Economics, 61(1), 77-106.

Dechow, P., R. Sloan, and A. Sweeney. 1995. Detecting earnings management. The

Accounting. Review 70: 193–225.

Desai, H., S. Krishnamurthy, and K. Venkataraman. 2006. Do short sellers target firms with

poor earnings quality? Evidence from earnings restatement. Review of Accounting

Studies 11: 71-90.

30

Diamond, D., and R. Verrecchia.1987. Constraints on short-selling and asset price adjustment

to private information. Journal of Financial Economics 18(2): 277-311.

Diether, K., K. Lee, and I. Werner, 2009. It’s SHO time! Short-sale price-tests and market

quality. The Journal of Finance 65(1); 37-73.

Drake, M., L. Rees, and E. Swanson. 2011. Should investors follow the prophets or the bears?

Evidence on the use of public information by analysts and short sellers. The

Accounting Review, 86(1): 101-130.

Dyck, A., A. Morse, and L. Zingales. 2013. How pervasive is corporate fraud? Working

paper, University of Toronto.

Edmans. A., S. Jayaraman, J. Schneemeier. 2017. The source of information in prices and

investment-price sensitivity. Journal of Financial Economics 126(1): 74-96.

Ettredge, M., C. Li, and L. Sun. 2006. The impact of SOX section 404 internal control quality

assessment on audit delay in the SOX era. Auditing: A Journal of Practice and Theory

25 (2): 1-23.

Fang, V., A. Huang, and J. Karpoff. 2016. Short selling and earnings management: a

controlled experiment. Journal of Finance 71(3): 1251-1294.

Frankel, R., A. Kalay, R. Sadka, and Y. Zou. 2016. Complementarity between audited

financial reporting and voluntary disclosure: The case of former Andersen clients.

Working paper, Washington University in St. Louis and Columbia University.

Giannetti, M., and T. Wang. 2016. Corporate scandals and household stock market

participation. Journal of Finance 71(6): 2591-2636.

Gilchrist, S., C. Himmelberg, and G. Huberman. 2005. Do stock price bubbles influence

corporate investment? Journal of Monetary Economics 52: 805–27.

Goldstein, I., and A. Guembel. 2008. Manipulation and the allocational role of prices. Review

of Economic Studies 75: 133–64.

Ghosh, A., and R. Pawlewicz. 2009. The impact of regulation on auditor fees: Evidence from

the Sarbanes‐Oxley Act. Auditing: A Journal of Practice & Theory 28(2): 171-197.

Glover, S., J. Hansen, and T. Seidel. 2019. Have regulatory and standard setting changes

weakened the connection between the audit report date and the substantial completion

of audit fieldwork? Working paper, Brigham Young University and Weber State

University.

Gow, I., G. Ormazabal, and D. Taylor. 2010. Correcting for cross-sectional and time-series

dependence in accounting research. The Accounting Review 487-512.

Grullon, G., S. Michenaud, and J. Weston. 2015. The real effects of short-selling constraints.

The Review of Financial Studies 28 (6): 1737-1767.

Hay, D., 2011. Meta-Regression Analysis and the Big Firm Premium (November 24).

Available at SSRN: http://ssrn.com/abstract=1675605.

He, J., and X. Tian. 2014. Short sellers and innovation: Evidence from a quasi-natural

experiment. Working paper, University of Georgia.

Hermalin, B., and M. Weisbach. 2012. Information disclosure and corporate governance.

Journal of Finance 67(2): 195-233.

Hope, O., D Hu and W. Zhao. 2017. Third-Party consequences of short-selling threats: The

case of auditor behavior. Journal of Accounting and Economics 63: 479-498.

Iacus, S., G. King, and G. Porro. 2009. CEM: Software for Coarsened Exact Matching.

Journal of Statistical Software 30(9): 1-27.

Jensen, M., and W. Meckling. 1976. Theory of the firm: Managerial behavior, agency costs

and ownership structure. Journal of Financial Economics 3: 305-360.

Jones, J., 1991. Earnings management during import relief investigations, Journal of

Accounting Research 29: 193–228.

31

Jones, C., and O. Lamont. 2002. Short-sale constraints and stock returns. Journal of

Financial Economics 66(2): 207-239.

Kacer, M., D. Peel, M. Peel, and N. Wilson. 2018. On the persistence and dynamics of Big 4

real audit fees: Evidence from the UK. Journal of Business Finance & Accounting

45, 714-728.

Karpoff, J., and X. Lou. 2010. Short sellers and financial misconduct. The Journal of Finance

65(5): 1879-1913.

Krishnan, J., K. Raghunandan, and J. Yang. 2007. Were Former Andersen Clients Treated

More Leniently Than Other Clients? Evidence from Going-Concern Modified Audit

Opinions Accounting Horizons 21(4): 423–435

Krishnamurthy, S., J. Zhou, and N. Zhou. 2006. Auditor reputation, auditor independence

and the stock market impact of Andersen’s indictment on its clients. Contemporary

Accounting Research 23(2): 465–490.

Lamont, O., and R. Thaler. 2003. Can the market add and subtract? Mispricing in tech stock

carve-outs. Journal of Political Economy, 111(2): 227-268.

Litvak, K., and B. Black. 2017. The SEC’s busted randomized experiment: What can and

cannot be learned. Working paper, Northwestern University.

Massa, M., B. Zhang, and H. Zhang. 2015. The invisible hand of short selling: Does short-

selling discipline earnings management? Review of Financial Studies 28(6): 1701-

1736.

Miller, E., 1977. Risk, uncertainty, and divergence of opinion. Journal of Finance 32: 1151-

1168.

Petersen, M., 2009. Estimating standard errors in finance panel data sets: Comparing

approaches. Review of Financial Studies 22: 435-480.

Pownall, G. and P. Simko. 2005. The information intermediary role of short sellers. The

Accounting Review 80 (3): 941-966.

Securities and Exchange Commission (SEC), 2007, Economic analysis of the short sale price

restrictions under the regulation SHO pilot, Office of Economic Analysis.

Simunic, D., 1980. The pricing of audit services: Theory and evidence. Journal of Accounting

Research 161-190.

Singer, Z., and J., Zhang. 2018. Auditor tenure and the timeliness of misstatement discovery.

The Accounting Review 93(2): 315-338.

Srinidhi, B., M. Hossain, and C. Lim. 2012. The effect of Arthur Andersen’s demise on

clients’ audit fees and auditor conservatism: International evidence. Journal of

International Financial Management and Accounting 23 (3): 208-244.

Werner, I., 2019. Use and abuse of SEC experiments. Presentation for the 2019 Sydney

Microstructure Conference.

32

Figure 1. Plot of short interest over time around Regulation SHO for ex Andersen and other

auditor clients

33

Table 1 Sample Selection and Sample Distribution by Year

This table presents the sample selection in Panel A, sample distribution across 2000 to 2013 for the unbalanced

panel in Panel B and sample distribution across 2000 to 2010 for balanced panel in Panel C. Pilot firms are

firms that were selected to for the Regulation SHO program eliminating short-sales restrictions during 2005 to

2007; otherwise, they are non-pilot firms.

Panel A: Sample Selection

Pilot Firms Non-Pilot Firms Total

1. Russell 3000 companies on June 25,2004 986 2014 3000

2. Matched with CRSP & Compustat 967 1989 2956

3. Matched with Audit Analytics 919 1886 2805

4. Excluding financial and utility firms 673 1351 2024

5. Matched with all control variables 622 1226 1848

6. Drop firms that have experienced M&As 509 1006 1515

Panel B: Sample distribution by year: Unbalanced panel (2000-2013)

Year Freq. Percent Cum.

2000 904 5.48 5.48

2001 1,422 8.62 14.10

2002 1,496 9.07 23.17

2003 1,515 9.18 32.35

2005 1,362 8.26 40.60

2006 1,278 7.75 48.35

2007 1,269 7.69 56.04

2008 1,243 7.53 63.58

2009 1,225 7.43 71.00

2010 1,228 7.44 78.45

2011 1,226 7.43 85.88

2012 1,187 7.19 93.07

2013 1,143 6.93 100.00 16,498 100.00

Panel C: Sample distribution by year: balanced panel (2001 to 2010)

2001 1,055 11.11 11.11

2002 1,055 11.11 22.22

2003 1,055 11.11 33.33

2005 1,055 11.11 44.44

2006 1,055 11.11 55.56

2007 1,055 11.11 66.67

2008 1,055 11.11 77.78

2009 1,055 11.11 88.89

2010 1,055 11.11 100.00 9,495 100.00

34

Table 2. Summary statistics for selected variables in 2002

This table presents the descriptive statistics of selected variables in 2002, the year of ex-Andersen’s sudden collapse. All variables are defined in the Appendix. ** ,

and *** are denoted for p < 0.05 and p < 0.01 (two-sided tests), respectively. Ex-Andersen clients include firms that were clients of Arthur Andersen in 2001 and

2002 and thus were forced to switch to a different auditor because of Andersen’s collapse. Otherwise, they are regarded as Other Auditor Clients. Pilot firms are

firms that were selected to for the Regulation SHO program eliminating short-sales restrictions during 2005 to 2007; otherwise, they are non-pilot firms.

Panel A: Unbalanced panel sample (2000 – 2013)

Pilot Non-Pilot Difference Full

Pilot

Ex-Andersen

Clients

Other Auditor

Clients

Full

non-Pilot

Ex-Andersen

Clients

Other Auditor

Clients

Ex-Andersen Clients –

Other Auditor Clients

Pilot – Non-Pilot

(1) (2) (3) (4) (1) - (2) (3) - (4) (1) - (3) (2) – (4)

N 498 110 388 998 218 780

AF(mil$) 0.863 0.485 0.970 0.981 1.084 0.952 - 0.485*** -0.132 -0.599*** 0.018

LnAF 12.93 12.50 13.05 13.03 13.01 13.03 -0.553*** -0.018 -0.516*** 0.019

Size 6.212 6.087 6.248 6.225 6.240 6.221 -0.161 0.019 -0.153 0.027

Leverage 0.463 0.458 0.464 0.486 0.522 0.476 -0.006 0.046* -0.064** -0.011

BTM 0.526 0.643 0.493 0.539 0.543 0.538 0.150* 0.005* 0.100 -0.045

ROA -0.031 -0.007 -0.037 -0.054 -0.052 -0.055 0.030 0.003 0.044* 0.017

Loss 0.345 0.291 0.361 0.378 0.381 0.377 -0.070 0.004 -0.090 -0.016

CA/TA 0.496 0.480 0.500 0.486 0.451 0.496 -0.020 -0.045** 0.029 0.004

Quick 2.628 2.710 2.604 2.422 1.924 2.561 0.106 -0.637*** 0.786* 0.044

INVREC 0.252 0.255 0.251 0.245 0.238 0.246 0.003 -0.008 0.017 0.005

AssetGrowth 0.112 0.079 0.122 0.115 0.036 0.137 -0.043 -0.101*** 0.042 -0.015

FYEnd 0.363 0.391 0.356 0.345 0.303 0.356 0.035 -0.054 0.088 -0.001

NBusSeg 2.701 2.400 2.786 2.700 2.968 2.626 -0.386* 0.342** -0.568** 0.160

BIG4 0.944 0.882 0.961 0.940 0.894 0.953 -0.080** -0.058*** -0.013 0.009

MNC 0.418 0.345 0.438 0.433 0.422 0.436 -0.093* -0.014 -0.077 0.002

Short

Interest

0.011 0.012 0.011 0.012 0.014 0.012 0.000 0.002 -0.002 -0.001

35

Panel B: Balanced panel sample (2001-2010)

Pilot Non-Pilot Difference Ex-Andersen

Clients

Other Auditor

Clients

Ex-Andersen

Clients

Other Auditor

Clients

Ex-Andersen Clients –

Other Auditor Clients

Pilot – Non-Pilot

Full Pilot (1) (2) Full

Non-Pilot

(3) (4) (1) - (2) (3) - (4) (1) - (3) (2) – (4)

N 376 90 286 679 142 537

AF (mil$) 0.937 0.464 1.086 1.075 1.072 1.075*** -0.622*** -0.004 -0.608*** 0.011

LnAF 12.99 12.52 13.14 13.10 13.07 13.11 -0.620*** -0.040 -0.540*** 0.030

Size 6.431 6.251 6.488 6.464 6.464 6.464 -0.238 0.000 -0.213 0.025

Leverage 0.456 0.465 0.453 0.489 0.501 0.485 0.013 0.015 -0.036 -0.033*

BTM 0.601 0.624 0.594 0.543 0.587 0.532 0.030 0.055 0.037 0.062

ROA -0.016 -0.010 -0.018 -0.021 -0.013 -0.023 0.007 0.010 0.003 0.005

Loss 0.332 0.267 0.353 0.323 0.289 0.331 -0.086 -0.043 -0.022 0.022

CA/TA 0.485 0.483 0.486 0.481 0.456 0.487 -0.002 -0.030 0.027 -0.001

Quick 2.764 2.845 2.738 2.479 1.960 2.616 0.107 -0.656*** 0.885* 0.122

INVREC 0.254 0.269 0.249 0.251 0.249 0.251 0.020 -0.002 0.020 -0.002

Asset Growth 0.076 0.062 0.080 0.107 0.065 0.117 -0.018 -0.052** -0.003 -0.037

FYEnd 0.367 0.400 0.357 0.355 0.310 0.367 0.043 -0.057 0.090 -0.010

NBusSeg 2.795 2.522 2.881 2.956 3.261 2.875 -0.359 0.385** -0.738*** 0.006

BIG4 0.944 0.889 0.962 0.940 0.901 0.950 -0.073** -0.048* -0.013 0.012

MNC 0.447 0.367 0.472 0.499 0.437 0.466 -0.105* -0.029 -0.070 0.006

Short Interest 0.013 0.014 0.012 0.026 0.013 0.013 0.001 0.000 0.000 -0.001

36

Table 3. The impact of Regulation SHO on short-selling activities: Ex-Andersen Clients vs. Other Auditor Clients

This table presents the results of testing the impact of Regulation SHO on short interest. Ex-Andersen clients include firms that were clients of Arthur Andersen in

2001 and 2002 and thus were forced to switch to a different auditor because of Andersen’s collapse. Otherwise, they are regarded as Other Auditor Clients. Pilot

firms are firms that were selected to for the Regulation SHO program eliminating short-sales restrictions during 2005 to 2007; otherwise, they are non-pilot firms.

Columns (1) - (3), (4) and (5), and (6) and (7) present the regression results of unbalanced panel sample (2000 - 2013), unbalanced panel sample (2001 - 2010), and

balanced panel sample (2001 – 2010), respectively. Year 2004 is excluded in estimating the regressions because the pilot firm list was announced on July 28, 2004

but the price tests were not removed for pilot firms until May 2, 2005. The columns next to columns (3), (5), and (7) test the difference between the coefficient

reported in columns (2) and (3), (4) and (5), and (6) and (7), respectively. All variables are defined in the Appendix. t-statistics in parentheses are based on standard

errors clustered by firm. ** , and *** are denoted for p < 0.05 and p < 0.01 (two-sided tests), respectively.

(1) (2) (3) (4) (5) (6) (7)

Unbalanced Panel Sample

2000-2013

Unbalanced Panel Sample

2001-2010

Balanced Panel Sample

2001-2010

Full

Sample

Ex

Andersen

Clients

Other

Auditor

Clients

(2)-(3) Ex

Andersen

Clients

Other

Auditor

Clients

(4)-(5) Ex

Andersen

Clients

Other

Auditor

Clients

(6)-(7)

Pilot*During 0.003*** 0.011*** 0.000 0.011*** 0.012*** 0.000 0.012*** 0.014*** -0.000 0.014***

(3.94) (7.50) (0.13) (6.99) (7.56) (0.28) (7.32) (8.49) (-0.16) (8.24)

Pilot*Post 0.002*** 0.007*** 0.001* 0.005*** 0.012*** 0.000 0.012*** 0.013*** 0.001 0.013***

(3.91) (5.13) (1.66) (3.68) (7.64) (0.08) (7.41) (8.17) (1.17) (7.68)

Ln(MV) 0.003*** 0.002*** 0.003*** 0.002*** 0.006*** 0.001* 0.005***

(14.69) (5.10) (13.92) (3.46) (20.98) (1.73) (16.14)

BTM 0.046 0.273*** -0.006 0.432*** -0.001 0.564*** -2.123***

(1.61) (3.68) (-0.19) (5.49) (-0.02) (5.38) (-11.72)

Return[-12, -1] -0.127*** -0.138*** -0.123*** -0.116*** -0.097*** -0.102*** -0.107***

(-37.92) (-18.21) (-33.19) (-13.25) (-23.49) (-10.56) (-23.30)

StockVolatility 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001***

(42.66) (26.48) (34.54) (17.88) (23.27) (14.99) (23.60)

EarningsManagement 0.002*** 0.010*** 0.002*** 0.004*** 0.000 0.005*** 0.000

(6.61) (6.71) (5.24) (2.59) (0.87) (2.74) (0.41)

Firm FE Yes Yes Yes Yes Yes Yes Yes

Month FE Yes Yes Yes Yes Yes Yes Yes

Observations 163,008 35,140 127,868 24,985 91,317 21,033 75,460

R2 0.533 0.533 0.535 0.590 0.587 0.559 0.562

37

Table 4. The impact of Regulation SHO on audit fees: Ex-Andersen Clients vs. Other Auditor Clients

This table presents the results of testing the impact of Regulation SHO on audit fees. Ex-Andersen clients include firms that were clients of Arthur Andersen in

2001 and 2002 and thus were forced to switch to a different auditor because of Andersen’s collapse. Otherwise, they are regarded as Other Auditor Clients. Pilot

firms are firms that were selected to for the Regulation SHO program eliminating short-sales restrictions during 2005 to 2007; otherwise, they are non-pilot firms.

Columns (1) - (3), (4) and (5), and (6) and (7) present the regression results of unbalanced panel sample (2000 - 2013), unbalanced panel sample (2001 - 2010), and

balanced panel sample (2001 – 2010), respectively. Year 2004 is excluded in estimating the regressions because the pilot firm list was announced on July 28, 2004

but the price tests were not removed for pilot firms until May 2, 2005. The columns next to columns (3), (5), and (7) test the difference between the coefficient

reported in columns (2) and (3), (4) and (5), and (6) and (7), respectively. All variables are defined in the Appendix. t-statistics in parentheses are based on standard

errors clustered by firm. ** , and *** are denoted for p < 0.05 and p < 0.01 (two-sided tests), respectively.

(1) (2) (3) (4) (5) (6) (7)

Unbalanced Panel Sample

2000-2013

Unbalanced Panel Sample

2001-2010

Balanced Panel Sample

2001-2010

Full

Sample

Ex-

Andersen

Clients

Other

Auditor

Clients

(2)-(3) Ex-

Andersen

Clients

Other

Auditor

Clients

(4)-(5) Ex-

Andersen

Clients

Other

Auditor

Clients

(6)-(7)

Pilot*During 0.031** 0.133*** -0.001 0.134*** 0.092*** -0.026 0.118*** 0.089*** -0.029* 0.117***

(2.17) (4.30) (-0.05) (3.92) (2.84) (-1.61) (3.37) (2.69) (-1.66) (3.22)

Pilot*Post -0.001 0.139*** -0.046*** 0.186*** 0.123*** -0.053*** 0.176*** 0.125*** -0.050*** 0.175***

(-0.09) (5.01) (-3.24) (6.06) (3.68) (-3.12) (4.84) (3.75) (-2.91) (4.78)

Size 0.262*** 0.265*** 0.261*** 0.218*** 0.227*** 0.288*** 0.281***

(46.75) (21.96) (41.15) (13.97) (29.21) (16.08) (29.89)

Leverage 0.056*** -0.007 0.073*** 0.077 0.084*** 0.171*** 0.106***

(3.38) (-0.15) (4.06) (1.37) (4.06) (2.65) (4.21)

BTM 0.026*** 0.013 0.032*** 0.023** 0.032*** 0.039*** 0.052***

(5.84) (1.54) (5.96) (2.06) (5.22) (2.84) (6.22)

ROA -0.126*** -0.235*** -0.106*** -0.143*** -0.097*** -0.251*** -0.212***

(-8.47) (-5.99) (-6.62) (-3.29) (-5.62) (-3.88) (-9.05)

Loss 0.078*** 0.053*** 0.082*** 0.064*** 0.080*** 0.055** 0.059***

(9.70) (2.84) (9.16) (2.94) (7.85) (2.34) (5.22)

CA/TA -0.353*** -0.553*** -0.304*** -0.595*** -0.314*** -0.458*** -0.238***

(-10.96) (-7.46) (-8.52) (-6.84) (-7.41) (-4.99) (-5.18)

Quick -0.003* 0.002 -0.004** 0.002 -0.007*** -0.000 -0.009***

38

(-1.91) (0.55) (-2.33) (0.40) (-3.08) (-0.02) (-3.94)

INVREC -0.298*** -0.104 -0.337*** -0.006 -0.271*** -0.064 -0.393***

(-6.01) (-0.94) (-6.10) (-0.05) (-3.95) (-0.46) (-5.26)

AssetGrowth 0.020*** 0.033*** 0.017*** 0.071*** 0.046*** 0.086*** 0.059***

(3.58) (2.89) (2.67) (4.46) (5.21) (5.03) (5.85)

FYEnd -0.019 0.062 -0.049 0.160** -0.112** 0.143 0.005

(-0.55) (0.94) (-1.22) (1.99) (-2.30) (1.61) (0.09)

NBusSeg 0.019*** 0.018*** 0.020*** 0.014** 0.019*** 0.012* 0.017***

(7.45) (3.03) (6.86) (1.98) (5.18) (1.66) (4.54)

BIG4 0.114*** 0.125*** 0.216*** 0.028 0.181*** 0.039 0.134***

(9.37) (5.03) (11.03) (0.89) (7.93) (1.11) (5.53)

MNC 0.103*** 0.082*** 0.111*** 0.086*** 0.102*** 0.072** 0.106***

(9.80) (3.70) (9.33) (3.12) (7.08) (2.55) (6.93)

Short Interest 0.591*** 0.395*** 0.647*** 0.500*** 0.700*** 0.540*** 0.727***

(8.93) (2.91) (8.57) (3.18) (7.95) (3.37) (7.70)

Firm FE Yes Yes Yes Yes Yes Yes Yes

Year FE Yes Yes Yes Yes Yes Yes Yes

Observations 16,495 3,569 12,926 2,602 9,436 2,088 7,407

R2 0.939 0.938 0.940 0.942 0.945 0.947 0.947

39

Table 5. The impact of Regulation SHO on audit report delay: Ex-Andersen Clients vs. Other Auditor Clients

This table presents the results of testing the impact of Regulation SHO on short interest. Ex-Andersen clients include firms that were clients of Arthur Andersen in

2001 and 2002 and thus were forced to switch to a different auditor because of Andersen’s collapse. Otherwise, they are regarded as Other Auditor Clients. Pilot

firms are firms that were selected to for the Regulation SHO program eliminating short-sales restrictions during 2005 to 2007; otherwise, they are non-pilot firms.

Columns (1) - (3), (4) and (5), and (6) and (7) present the regression results of unbalanced panel sample (2000 - 2013), unbalanced panel sample (2001 - 2010), and

balanced panel sample (2001 – 2010), respectively. Year 2004 is excluded in estimating the regressions because the pilot firm list was announced on July 28, 2004

but the price tests were not removed for pilot firms until May 2, 2005. The columns next to columns (3), (5), and (7) test the difference between the coefficient

reported in columns (2) and (3), (4) and (5), and (6) and (7), respectively. All variables are defined in the Appendix. t-statistics in parentheses are based on standard

errors clustered by firm. ** , and *** are denoted for p < 0.05 and p < 0.01 (two-sided tests), respectively.

(1) (2) (3) (4) (5) (6) (7)

Unbalanced Sample

2000-2013

Unbalanced Sample

2001-2010

Balanced Sample

2001-2010

Full

Sample

Ex-Andersen

Clients

Other Auditor

Clients

(2)-(3) Ex-

Andersen

Clients

Other Auditor

Clients

(4)-(5) Ex-Andersen

Clients

Other

Auditor

Clients

(6)-(7)

Pilot*During -1.124 -9.046** 1.203 -10.249* -10.252** -0.355 -9.898* -9.589** 5.311** -14.900*

(-0.51) (-2.24) (0.47) (-1.94) (-2.35) (-0.12) (-1.70) (-2.15) (2.19) (-2.94)

Pilot*Post -2.738 -8.870** -0.827 -8.043* -10.261** -2.485 -7.775 -9.023** 2.211 -11.234**

(-1.40) (-2.46) (-0.36) (-1.71) (-2.27) (-0.85) (-1.29) (-2.00) (0.91) (-2.20)

Size -2.922*** -3.801** -2.758*** -3.393 -2.540* -0.190 -1.620

(-3.43) (-2.43) (-2.75) (-1.58) (-1.88) (-0.08) (-1.22)

Leverage 3.078 8.397 2.450 5.332 5.128 3.641 0.185

(1.22) (1.45) (0.86) (0.69) (1.42) (0.41) (0.05)

BTM 1.116* 0.104 1.578* -2.200 1.688 -0.353 1.489

(1.66) (0.09) (1.91) (-1.46) (1.60) (-0.19) (1.26)

ROA -2.620 -4.868 -2.276 -2.616 -0.054 8.489 -0.803

(-1.17) (-1.05) (-0.89) (-0.49) (-0.02) (0.94) (-0.24)

Loss 3.905*** 3.091 4.114*** 2.318 4.666*** 4.058 3.637**

(3.20) (1.31) (2.91) (0.80) (2.63) (1.27) (2.31)

CA/TA -0.936 -12.309 2.118 -21.203* -3.659 -12.037 10.067

(-0.19) (-1.28) (0.38) (-1.79) (-0.49) (-0.96) (1.55)

Quick -0.351 0.273 -0.473 0.402 -0.494 0.201 -0.516

(-1.35) (0.52) (-1.58) (0.58) (-1.26) (0.29) (-1.54)

INVREC 17.706** 34.644** 13.244 50.636*** -2.815 49.273*** -7.039

(2.35) (2.41) (1.51) (2.76) (-0.23) (2.59) (-0.67)

AssetGrowth 0.238 2.509* -0.413 2.138 -0.577 -1.696 0.474

40

(0.29) (1.68) (-0.43) (1.00) (-0.38) (-0.73) (0.33)

FYEnd 17.628*** 5.576 21.711*** -4.299 32.830*** -2.488 37.125***

(3.48) (0.62) (3.60) (-0.37) (4.00) (-0.19) (4.74)

NBusSeg -0.259 -0.690 -0.133 -1.037 -0.145 -1.002 -0.194

(-0.65) (-0.93) (-0.29) (-1.07) (-0.23) (-1.04) (-0.37)

MNC 0.588 -3.549 2.107 -2.368 2.244 -1.420 -3.423

(0.37) (-1.23) (1.12) (-0.63) (0.90) (-0.37) (-1.60)

BIG4 0.958 -1.255 5.448* -3.020 6.774* -1.420 9.462***

(0.52) (-0.39) (1.76) (-0.77) (1.71) (-0.33) (2.79)

Short

Interest

2.232 -5.351 4.404 -7.559 -3.054 0.239 13.889

(0.23) (-0.31) (0.37) (-0.36) (-0.20) (0.01) (1.05)

Firm FE Yes Yes Yes Yes Yes Yes Yes

Year FE Yes Yes Yes Yes Yes Yes Yes

Observations 16461 3563 12898 2595 9405 2084 7393

R2 0.298 0.260 0.307 0.280 0.344 0.244 0.311

41

Table 6. The impact of Regulation SHO on earnings management: Ex-Andersen Clients vs. Other Auditor Clients

This table presents the results of testing the impact of Regulation SHO on earnings management in Panel A and short sales of other auditor clients in Panel B. Ex-

Andersen clients include firms that were clients of Arthur Andersen in 2001 and 2002 and thus were forced to switch to a different auditor because of Andersen’s

collapse. Otherwise, they are regarded as Other Auditor Clients. Pilot firms are firms that were selected to for the Regulation SHO program eliminating short-sales

restrictions during 2005 to 2007; otherwise, they are non-pilot firms. Columns (1) to (3), (4) to (6), and (7) and (9) present the regression results of unbalanced panel

sample (2000 - 2013), unbalanced panel sample (2001 - 2010), and balanced panel sample (2001 – 2010), respectively. Year 2004 is excluded in estimating the

regressions because the pilot firm list was announced on July 28, 2004 but the price tests were not removed for pilot firms until May 2, 2005. The columns next to

columns (3), (6), and (9) test the difference between the coefficient reported in columns (2) and (3), (4) and (5), and (6) and (7), respectively. All variables are

defined in the Appendix. t-statistics in parentheses are based on standard errors clustered by firm. ** , and *** are denoted for p < 0.05 and p < 0.01 (two-sided tests),

respectively.

Panel A: The impact of Regulation SHO on earnings management

(1) (2) (3) (4) (5) (6) (7)

Unbalanced Sample

2000-2013

Unbalanced Sample

2001-2010

Balanced Sample

2001-2010

Full

Sample

Ex

Andersen

Clients

Other

Auditor

Clients

(2)-(3) Ex

Andersen

Clients

Other

Auditor

Clients

(4)-(5) Ex

Andersen

Clients

Other

Auditor

Clients

(8)-(9)

Pilot*During -0.029** -0.026 -0.030** 0.005 -0.007 -0.035*** 0.028 -0.037 -0.027** -0.010

(-2.43) (-0.97) (-2.26) (0.16) (-0.26) (-2.62) (0.94) (-1.37) (-1.96) (-0.34)

Pilot*Post -0.023** -0.041* -0.019 -0.022 -0.010 -0.021 0.012 -0.031 -0.016 -0.015

(-2.16) (-1.73) (-1.57) (-0.87) (-0.33) (-1.55) (0.38) (-1.13) (-1.14) (-0.52)

Size -0.035*** -0.040*** -0.033*** -0.065*** -0.034*** -0.034** -0.057***

(-7.36) (-3.82) (-6.24) (-4.55) (-5.25) (-2.21) (-7.49)

Leverage 0.029** -0.037 0.042*** -0.035 0.026 -0.046 -0.007

(2.08) (-0.96) (2.80) (-0.69) (1.54) (-0.86) (-0.35)

BTM 0.006 0.002 0.006 0.008 0.010** 0.004 0.014**

(1.53) (0.23) (1.46) (0.83) (1.97) (0.35) (2.13)

ROA 0.242*** 0.147*** 0.265*** 0.150*** 0.225*** 0.403*** 0.271***

(19.45) (4.84) (19.42) (4.31) (15.48) (7.44) (14.58)

Loss -0.042*** -0.065*** -0.035*** -0.098*** -0.034*** -0.051*** -0.026***

(-6.26) (-4.17) (-4.74) (-5.07) (-4.04) (-2.63) (-2.88)

CA/TA -0.033 0.019 -0.048 -0.030 0.005 0.106 0.005

(-1.22) (0.31) (-1.63) (-0.38) (0.13) (1.40) (0.14)

Quick 0.004*** 0.002 0.004*** 0.007 -0.000 -0.003 -0.001

42

(2.80) (0.60) (2.69) (1.56) (-0.08) (-0.78) (-0.39)

INVREC 0.215*** 0.198** 0.218*** 0.316*** 0.158*** 0.267** 0.161***

(5.22) (2.09) (4.77) (2.61) (2.81) (2.33) (2.70)

Asset Growth -0.048*** -0.059*** -0.043*** -0.068*** -0.027*** -0.035** -0.020**

(-10.52) (-5.97) (-8.33) (-4.78) (-3.72) (-2.51) (-2.43)

FYEnd -0.010 0.028 -0.026 -0.006 -0.064* 0.039 -0.048

(-0.37) (0.50) (-0.84) (-0.08) (-1.66) (0.53) (-1.07)

NBusSeg 0.001 0.008* -0.001 0.011* -0.005* 0.006 -0.005

(0.50) (1.70) (-0.40) (1.72) (-1.67) (1.04) (-1.62)

BIG4 0.013 0.005 0.020 -0.010 0.030 0.021 0.042**

(1.25) (0.23) (1.26) (-0.36) (1.61) (0.72) (2.19)

MNC 0.017* 0.007 0.021** -0.020 0.014 -0.035 0.020*

(1.93) (0.36) (2.17) (-0.81) (1.19) (-1.52) (1.66)

Short Interest 0.030 -0.068 0.067 -0.089 0.102 -0.009 0.076

(0.55) (-0.60) (1.09) (-0.64) (1.44) (-0.07) (1.01)

Firm FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Year FE Yes Yes Yes Yes Yes Yes Yes

Observations 16,172 3,507 12,665 2,555 9,259 2,053 7,299

R2 0.173 0.170 0.177 0.197 0.206 0.171 0.183

43

Panel B: The impact of Regulation SHO on earnings management for other auditor clients classified by the

level of average short interest from 2005 to 2007

(1) (2) (3) (4) (5) (6)

Other Auditor Clients

Unbalanced Sample

2000-2013

Unbalanced Sample

2001-2010

Balanced Sample

2001-2010

Less

Shorted

More

Shorted

Less

Shorted

More

Shorted

Less

Shorted

More

Shorted

Pilot*During 0.001 -0.055*** -0.006 -0.062*** -0.012 -0.039**

(0.06) (-3.06) (-0.28) (-3.31) (-0.60) (-2.06)

Pilot*Post 0.008 -0.040** 0.011 -0.046** 0.004 -0.030

(0.44) (-2.51) (0.52) (-2.44) (0.21) (-1.59)

Size -0.045*** -0.024*** -0.040*** -0.029*** -0.054*** -0.058***

(-5.08) (-3.60) (-3.79) (-3.55) (-4.23) (-6.11)

Leverage 0.081*** 0.017 0.103*** -0.005 0.026 -0.012

(2.71) (0.96) (3.04) (-0.23) (0.68) (-0.51)

BTM 0.010 0.003 0.020** 0.004 0.017 0.013

(1.38) (0.65) (2.48) (0.62) (1.40) (1.62)

ROA 0.299*** 0.215*** 0.227*** 0.224*** 0.242*** 0.322***

(16.18) (10.48) (11.77) (9.98) (9.94) (11.00)

Loss -0.036*** -0.037*** -0.033*** -0.038*** -0.032** -0.020*

(-3.18) (-3.80) (-2.65) (-3.28) (-2.38) (-1.66)

AssetGrowth -0.056*** -0.025*** -0.009 -0.048*** -0.002 -0.042***

(-7.83) (-3.32) (-0.91) (-4.44) (-0.20) (-3.48)

CA/TA -0.031 -0.050 0.032 -0.016 0.064 -0.042

(-0.72) (-1.25) (0.64) (-0.32) (1.20) (-0.83)

Quick -0.001 0.008*** -0.001 0.001 -0.004 0.002

(-0.49) (3.77) (-0.47) (0.31) (-1.48) (0.70)

InvRec 0.157** 0.249*** 0.161** 0.143* 0.144 0.166**

(2.27) (4.05) (1.97) (1.84) (1.60) (2.07)

FYEnd -0.045 -0.021 -0.087 -0.052 -0.055 -0.047

(-0.91) (-0.52) (-1.33) (-1.08) (-0.61) (-0.92)

MNC 0.010 0.029** 0.003 0.024 0.014 0.026

(0.63) (2.28) (0.16) (1.46) (0.76) (1.56)

Big4 0.032 0.012 0.037 0.025 0.049 0.039

(1.18) (0.61) (1.20) (1.05) (1.49) (1.62)

NBusSeg 0.000 -0.002 -0.006 -0.004 -0.007 -0.003

(0.06) (-0.58) (-1.50) (-0.90) (-1.59) (-0.74)

Short

Interest

-0.186 0.146** -0.188 0.167** -0.178 0.126

(-1.54) (2.02) (-1.29) (1.98) (-1.14) (1.43)

Firm FE Yes Yes Yes Yes Yes Yes

Year FE Yes Yes Yes Yes Yes Yes

Observations 6160 6505 4508 4751 3402 3897

R2 0.190 0.170 0.218 0.203 0.182 0.189

44

Table 7. The impact of Regulation SHO on investment sensitivity to Tobin Q and Cash Flow: Ex-Andersen Clients vs. Other Auditor Clients

This table presents the results of testing the impact of Regulation SHO investment sensitivity to Tobin Q and cash flow. Ex-Andersen clients include firms that were

clients of Arthur Andersen in 2001 and 2002 and thus were forced to switch to a different auditor because of Andersen’s collapse. Otherwise, they are regarded as

Other Auditor Clients. Pilot firms are firms that were selected to for the Regulation SHO program eliminating short-sales restrictions during 2005 to 2007; otherwise,

they are non-pilot firms. Columns (1) - (3), (4) and (5), and (6) and (7) present the regression results of unbalanced panel sample (2000 - 2013), unbalanced panel

sample (2001 - 2010), and balanced panel sample (2001 – 2010), respectively. Year 2004 is excluded in estimating the regressions because the pilot firm list was

announced on July 28, 2004 but the price tests were not removed for pilot firms until May 2, 2005. The columns next to columns (3), (5), and (7) test the difference

between the coefficient reported in columns (2) and (3), (4) and (5), and (6) and (7), respectively. All variables are defined in the Appendix. t-statistics in parentheses

are based on standard errors clustered by firm. *, ** , and *** are denoted for p < 0.01, p < 0.05 and p < 0.01 (two-sided tests), respectively.

(1) (2) (3) (4) (5) (6) (7)

Unbalanced Panel Sample

2000-2013

Unbalanced Panel Sample

2001-2010

Balanced Panel Sample

2001-2010

Full

Sample

Ex

Andersen

Clients

Other

Auditor

Clients

(2)-(3) Ex

Andersen

Clients

Other

Auditor

Clients

(4)-(5) Ex

Andersen

Clients

Other

Auditor

Clients

(6)-(7)

Pilot*During 0.011*** 0.037*** 0.005 0.033*** 0.038*** 0.001 0.037*** 0.028** 0.009** 0.019*

(2.62) (3.53) (1.02) (3.05) (3.34) (0.22) (3.53) (2.58) (2.01) (1.87)

Pilot*Post 0.004 0.008 0.003 0.005 0.008 -0.003 0.011 -0.003 -0.002 -0.001

(1.07) (0.88) (0.75) (0.54) (0.70) (-0.73) (1.05) (-0.25) (-0.52) (-0.07)

Q 0.004*** 0.005*** 0.003*** 0.002 0.009*** 0.003*** 0.006*** 0.012*** 0.003*** 0.009***

(6.07) (2.61) (5.55) (0.83) (3.41) (4.05) (2.63) (4.67) (4.46) (3.92)

Pilot*Q 0.001 -0.000 0.001 -0.001 0.001 0.000 0.001 0.000 0.001 -0.001

(0.93) (-0.04) (0.80) (-0.41) (0.23) (0.10) (0.22) (0.11) (0.71) (-0.16)

During*Q -0.000 0.000 -0.000 0.001 -0.003 -0.001 -0.003 -0.007** -0.001 -0.006**

(-0.41) (0.16) (-0.53) (0.35) (-1.20) (-0.86) (-1.07) (-2.45) (-0.79) (-2.47)

Post*Q -0.002* -0.002 -0.002* -0.000 -0.004 -0.002* -0.002 -0.008** -0.004*** -0.004

(-1.87) (-0.77) (-1.85) (-0.06) (-1.17) (-1.85) (-0.56) (-2.51) (-3.56) (-1.33)

Pilot*During*Q -0.006*** -0.019*** -0.004** -0.015*** -0.019*** -0.003* -0.016*** -0.013*** -0.004** -0.009**

(-3.88) (-4.21) (-2.19) (-3.38) (-3.95) (-1.76) (-3.71) (-2.74) (-2.31) (-2.10)

Pilot*Post*Q -0.003** -0.006 -0.003* -0.003 -0.008 -0.000 -0.007 -0.001 0.000 -0.002

(-2.14) (-1.42) (-1.73) (-0.80) (-1.25) (-0.15) (-1.37) (-0.22) (0.27) (-0.33)

Cash Flow 0.013*** 0.010 0.014*** -0.004 0.004 0.015*** -0.011 0.018 0.032*** -0.015

(3.93) (1.13) (4.02) (-0.46) (0.40) (4.74) (-1.33) (0.98) (5.59) (-0.88)

Pilot*Cash Flow 0.006 -0.004 0.006 -0.010 0.007 0.000 0.007 -0.014 -0.015* 0.001

(0.94) (-0.20) (0.93) (-0.54) (0.35) (0.08) (0.36) (-0.54) (-1.74) (0.04)

During*Cash Flow -0.007 0.023 -0.010** 0.032* 0.025 -0.014*** 0.038** 0.056** 0.004 0.052**

45

(-1.59) (1.14) (-2.14) (1.75) (1.19) (-3.11) (2.15) (2.29) (0.47) (2.33)

Post*Cash Flow -0.024*** -0.010 -0.028*** 0.018 -0.017 -0.033*** 0.016 -0.014 -0.034*** 0.020

(-4.16) (-0.72) (-4.43) (1.29) (-0.98) (-4.96) (1.02) (-0.70) (-4.35) (1.02)

Pilot*During*Cash

Flow

0.004 0.036 0.001 0.035 0.046 0.008 0.038 -0.019 -0.014 -0.005

(0.39) (0.96) (0.08) (0.99) (1.19) (0.84) (1.13) (-0.44) (-1.14) (-0.14)

Pilot*Post*Cash Flow 0.011 0.039 0.009 0.030 0.052 0.013 0.039 -0.011 0.011 -0.022

(1.13) (1.41) (0.91) (1.11) (1.60) (1.30) (1.35) (-0.33) (0.93) (-0.72)

logME 0.007*** 0.007*** 0.006*** 0.004 0.008*** 0.001 0.009***

(8.45) (3.85) (7.31) (1.44) (8.15) (0.35) (8.07)

logAge -0.036*** -0.033*** -0.036*** -0.007 -0.025*** -0.012 -0.018***

(-13.51) (-4.80) (-12.72) (-0.73) (-7.28) (-1.24) (-4.97)

Leverage -0.016*** -0.017** -0.016*** -0.017* -0.011*** -0.017 -0.001

(-6.02) (-2.15) (-5.62) (-1.66) (-3.59) (-1.55) (-0.37)

Sales Growth 0.017*** 0.013*** 0.019*** 0.014*** 0.013*** 0.017*** 0.011***

(22.92) (8.08) (21.97) (5.53) (12.68) (5.84) (9.72)

Cash -0.037*** -0.048*** -0.035*** -0.053*** -0.040*** -0.048*** -0.042***

(-8.25) (-4.37) (-7.14) (-3.91) (-7.42) (-3.68) (-7.81)

Retained Earning -0.000 -0.001 -0.000 0.001 0.000 0.004* 0.001

(-0.69) (-0.56) (-0.24) (0.54) (0.87) (1.88) (1.33)

Firm FE Yes Yes Yes Yes Yes Yes Yes

Year FE Yes Yes Yes Yes Yes Yes Yes

Observations 16389 3549 12840 2585 9371 2083 7377

R2 0.625 0.675 0.603 0.686 0.661 0.696 0.652

46

Table 8. Robustness tests: The impact of Regulation SHO excluding pilot securities for the after-hours portion of the Pilot

This table presents the results from replicating the tests in Tables 3 to 7 but excluding the stocks that are allowed for short-selling after trading hours from non-pilot

stocks. Pilot firms are firms that were selected to for the Regulation SHO program eliminating short-sales restrictions during 2005 to 2007; non-pilot firms are the

non-pilot stocks excluding those that had uptick rule lifted after trading hours. Ex-Andersen clients include firms that were clients of Arthur Andersen in 2001 and

2002 and thus were forced to switch to a different auditor because of Andersen’s collapse. Otherwise, they are regarded as Other Auditor Clients. Columns (1) -

(3), (4) and (5), and (6) and (7) present the regression results of unbalanced panel sample (2000 - 2013), unbalanced panel sample (2001 - 2010), and balanced panel

sample (2001 – 2010), respectively. Year 2004 is excluded in estimating the regressions because the pilot firm list was announced on July 28, 2004 but the price

tests were not removed for pilot firms until May 2, 2005. The columns next to columns (3), (5), and (7) test the difference between the coefficient reported in

columns (2) and (3), (4) and (5), and (6) and (7), respectively. In all regressions, controls, firms and year fixed effects are included in the same way as in the previous

tables. All variables are defined in the Appendix. t-statistics in parentheses are based on standard errors clustered by firm. *, ** , and *** are denoted for p < 0.01, p

< 0.05 and p < 0.01 (two-sided tests), respectively.

(1) (2) (3) (4) (5) (6) (7)

Unbalanced Panel Sample

2000-2013

Unbalanced Panel Sample

2001-2010

Balanced Panel Sample

2001-2010

Full

Sample

Ex-Andersen

Clients

Other Auditor

Clients

(2)-(3) Ex-Andersen

Clients

Other Auditor

Clients

(5)-(4) Ex-Andersen

Clients

Other Auditor

Clients

(7)-(6)

Panel A: The impact of Regulation SHO on audit fees

Pilot*During 0.039** 0.098** 0.017 0.081* 0.104** 0.016 0.088* 0.100** 0.011 0.089*

(1.97) (2.30) (0.78) (1.67) (2.29) (0.68) (1.75) (2.08) (0.44) (1.70)

Pilot*Post 0.010 0.094** -0.016 0.110** 0.083* -0.032 0.116** 0.093* -0.037 0.130**

(0.56) (2.43) (-0.82) (2.52) (1.75) (-1.36) (2.19) (1.88) (-1.49) (2.40)

Observations 7,874 1,664 6,210 1,222 4,575 1,035 3,690

R2 0.914 0.916 0.914 0.915 0.917 0.912 0.921

47

Panel B: The impact of Regulation SHO on short-selling activities

Pilot*During 0.006*** 0.020*** 0.003*** 0.017*** 0.018*** 0.001 0.016*** 0.017*** 0.000 0.017***

(6.79) (10.17) (2.68) (7.65) (9.12) (1.38) (7.00) (7.94) (0.08) (6.71)

Pilot*Post 0.005*** 0.013*** 0.003*** 0.010*** 0.014*** 0.001 0.013*** 0.014*** 0.001 0.014***

(5.80) (7.19) (2.90) (4.91) (7.42) (0.53) (5.95) (6.84) (0.61) (5.53)

Observations 76,459 15,999 60,460 11,409 43,345 9,855 36,558

R2 0.591 0.608 0.590 0.669 0.629 0.660 0.619

Panel C: The impact of Regulation SHO on audit report delay

Pilot*During 1.247 -13.863** 5.072 -18.935*** -15.692** 3.851 -19.543** -16.225** 7.450** -23.675***

(0.43) (-2.11) (1.57) (-2.64) (-2.30) (1.11) (-2.55) (-2.08) (2.33) (-3.19)

Pilot*Post -1.998 -15.239*** 1.681 -16.920*** -15.428** 1.724 -17.152** -15.776** 4.788 -20.564***

(-0.78) (-2.59) (0.58) (-2.63) (-2.20) (0.48) (-2.17) (-1.99) (1.49) (-2.74)

Observations 7,859 1,657 6,202 1,214 4,561 1,004 3,690

R2 0.263 0.214 0.278 0.237 0.340 0.228 0.314

Panel D: The impact of Regulation SHO on earnings management

Pilot*During -0.031* -0.015 -0.036* 0.021 0.009 -0.046** 0.055 -0.038 -0.041** 0.003

(-1.87) (-0.41) (-1.88) (0.50) (0.22) (-2.43) (1.29) (-1.01) (-2.12) (0.07)

Pilot*Post -0.030** -0.049 -0.026 -0.023 -0.018 -0.037* 0.019 -0.052 -0.034* -0.019

(-2.03) (-1.52) (-1.54) (-0.61) (-0.46) (-1.88) (0.43) (-1.37) (-1.75) (-0.44)

Observations 7,757 1,638 6,119 1,200 4,514 994 3,654

R2 0.170 0.197 0.168 0.216 0.203 0.195 0.195

Panel E: The impact of Regulation SHO on investment sensitivity to Tobin Q

Pilot*During*Q -0.001 -0.013*** 0.001 -0.014*** -0.012*** -0.000 -0.012*** -0.009 -0.002 -0.007

(-1.04) (-3.72) (0.65) (-4.47) (-3.28) (-0.05) (-3.76) (-1.57) (-1.52) (-1.38)

Pilot*Post*Q -0.001 -0.012*** 0.001 -0.012*** -0.009* -0.000 -0.009** -0.007 -0.001 -0.006

(-1.40) (-3.42) (0.78) (-4.21) (-1.88) (-0.13) (-2.20) (-0.97) (-0.46) (-1.01)

Observations 7834 1657 6177 1214 4548 1005 3681

R2 0.612 0.652 0.596 0.683 0.644 0.658 0.646

48

Appendix: Description of Variables

Variable Name Description

Test Variable:

LnAF Natural log of audit fees (in US dollars).

SIR Monthly short interest ratio measured as the ratio of shares in short position to the

total shares outstanding.

Signed DA The signed asset-deflated performance-matched discretionary accruals in fiscal

year t, calculated as a firm’s discretionary accruals minus the corresponding

discretionary accruals of a matched firm from the same fiscal year and Fama-

French 48 industry with the closest return on assets. A firm’s discretionary accruals

are defined as the difference between its total accruals and the fitted normal

accruals derived from a modified Jones (1991) model. The modified Jones model

follows Dechow, Sloan, and Sweeney (1995) and is specified as

𝑇𝐴𝑖𝑡

𝐴𝑆𝑆𝐸𝑇𝑖𝑡−1= 𝛽0 + 𝛽1

1

𝐴𝑆𝑆𝐸𝑇𝑖𝑡−1+ 𝛽2

∆𝑅𝐸𝑉𝑖𝑡 − ∆𝐴𝑅𝑖𝑡

𝐴𝑆𝑆𝐸𝑇𝑖𝑡−1+ 𝛽3

𝑃𝑃𝐸𝑖𝑡

𝐴𝑆𝑆𝐸𝑇𝑖𝑡−1+ 𝜀𝑖𝑡

Total accruals 𝑇𝐴𝑖𝑡 are defined as earnings before extraordinary items and

discontinued operations (IBC) minus operating cash flows (OANCF-XIDOC),

𝐴𝑆𝑆𝐸𝑇𝑖𝑡−1 is total assets at the beginning of year t (AT), ∆𝑅𝐸𝑉𝑖𝑡 is the change in

sales revenue (SALE) from the preceding year, ∆𝐴𝑅𝑖𝑡 is the change in accounts

receivable (RECT) from the the preceding year, and 𝑃𝑃𝐸𝑖𝑡 is gross property,

plant, and equipment (PPEGT). The fitted normal accruals 𝑁𝐴𝑖𝑡 are computed as

𝑁𝐴𝑖𝑡 = 𝛽0̂ + 𝛽1̂ 1

𝐴𝑆𝑆𝐸𝑇𝑖𝑡−1+ 𝛽2̂

(∆𝑅𝐸𝑉𝑖𝑡 − ∆𝐴𝑅𝑖𝑡)

𝐴𝑆𝑆𝐸𝑇𝑖𝑡−1+ 𝛽3̂

𝑃𝑃𝐸𝑖𝑡

𝐴𝑆𝑆𝐸𝑇𝑖𝑡−1

Firm-year-specific signed discretionary accruals are calculated as 𝐷𝐴𝑖𝑡 =𝑇𝐴𝑖𝑡

𝐴𝑆𝑆𝐸𝑇𝑖𝑡−1− 𝑁𝐴𝑖𝑡.

LnAudelay The difference of days between filing date and fiscal year end date.

Investments Capital expenditures scaled by lagged total assets.

Test Variables:

During 1 if a firm’s fiscal year end falls between January 1, 2005 and December 31,

2007, and zero otherwise.

Post 1 if a firm’s fiscal year end falls between January 1, 2008 and December 31,

2013, and zero otherwise.

Pilot 1 if a firm’s stock is designated as a pilot stock under Regulation SHO’s pilot

program, and zero otherwise.

Q Tobin’s Q defined as the ratio of market value of assets (market value of equity

plus book value of assets minus book value of equity) divided by book value of

assets.

Cash flow Cash flows, defined as net income before extraordinary items plus depreciation

and amortization, scaled by total assets.

Control Variables:

Size Natural log of sales.

Leverage Ratio of total liability to total assets.

BTM Ratio of book value of equity to market value of equity.

49

ROA Ratio of net income to total assets.

Loss 1 if net income is negative, and zero otherwise.

CA/TA Current assets to total assets.

Quick Ratio of current assets (excluding inventory) to current liabilities.

INVREC Ratio of inventory and account receivables to total assets.

Asset Growth Annual growth in total assets

FYE 1 if a firm’s fiscal year ends in a month other than December, and zero otherwise.

NBusSeg Natural log of one plus the number of business segments.

Big4 1 if a firm is audited by a Big N auditor, and zero otherwise.

MNC One for firms with foreign operations and zero otherwise

Short Interest The ratio of shares in short position to the total shares outstanding in the month

prior to the start of the fiscal year.

lnMV Natural log of market value of equity. Return[-12, -1] Annual stock returns

Stock Volatility Stock return volatility measured as the standard deviation of market model

residuals (estimated over the past 12 months).

Age One plus current year minus the first year that the firm appears on Compustat.

SalesGrowth Annual growth in sales.

Cash Ratio of cash and short-term investments to total assets.

Retained Earnings Ratio of retained earnings to total assets.