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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
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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.
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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.