Court Stringency and Voluntary Restatements · 3 See the SEC’s Report of Investigation Pursuant...
Transcript of Court Stringency and Voluntary Restatements · 3 See the SEC’s Report of Investigation Pursuant...
Court Stringency and Voluntary Restatements †
C.S. Agnes Cheng
School of Accounting & Finance
The Hong Kong Polytechnic University
Kowloon, Hong Kong
Tel: (852) 2766-7772, E-mail: [email protected]
Henry He Huang
SySyms School of Business
Yeshiva University
New York, NY 10033
Tel: (832) 276-3834, E-mail: [email protected]
Zhen Lei
School of Accounting & Finance
The Hong Kong Polytechnic University
Kowloon, Hong Kong
Tel: (852) 3400-3641, E-mail: [email protected]
Haitian Lu*
School of Accounting & Finance
The Hong Kong Polytechnic University
Kowloon, Hong Kong
Tel: (852) 2766-7065, E-mail: [email protected]
This version: Feb, 2018
† Agnes Cheng thanks Hong Kong Government Research Grant Council - General Research Fund (#590213), and
Haitian Lu thanks the National Natural Science Foundation of China (#71503225) for the support of this project. * Corresponding author. Tel.: +852 2766 7065; Fax: +852 2356 9845. E-mail address: [email protected]
(Haitian Lu).
Court Stringency and Voluntary Restatements
Abstract
This paper studies how district court stringency affects firms’ propensity to admit their
misreporting using different samples of misstated firms. We find robust evidence that firms
headquartered in high dismissal rate (lenient) court jurisdictions are more likely to make voluntary
restatements. Exogenous shock from Supreme Court’s Tellabs decision confirms this effect. We
consider a range of explanations and find the results most easily explained by managers using court
dismissal rates as a heuristic when making voluntary restatement decisions. Our evidence shed
light on the complicated effect of legal environment on financial reporting quality.
Key words: Court stringency, Accounting misreporting, Securities lawsuit, Restatement
JEL Classification Code: M41; K22; G39
1. Introduction
The effect of legal environment on financial reporting quality is an important yet debated
question (Baginski, Hassell and Kimbrough 2002; Field, Lowry and Shu 2005; Rogers and Buskirk
2009; Donelson et al. 2012). Prior work shows managers face asymmetric incentives to disclose
good news but withhold bad news (Basu 1997; Watts 2003; Kothari, Shu, and Wysocki 2009).
Theories of litigation risk suggest that higher perceived legal penalty could, on one hand, deter
non-compliance and prompt pre-emptive disclosure (Skinner 1994, 1997). On the other hand, they
might be counterproductive, as they instill extreme fear of punishment and make disclosure of
wrongdoings unlikely (Pfarrer et al. 2008). Empirically, the average effect of stronger legal
environment on corporate (bad news) disclosure remains unclear.
To shed light on this question, we study the voluntary restatement decision of misreporting
firms. When firms discover accounting misstatements, the federal securities law requires
immediate correction through restatements. 1 In practice, however, due to the catastrophic
consequences of restatements including lawsuits2, voluntary restatements are rare, and many
companies keep strategic silence, gambling that subsequent events would allow them to conceal
the accounting mistakes. The systematic under-correction of accounting mistakes can lower
investors’ confidence in the market. To appreciate its magnitude, Figure 1 shows among the 4,085
defendants of securities lawsuits from 2001 to 2013, only 197 (4.8%) made restatements before
1 The SEC has ruled that “there is a duty to correct statements made in any filing…if the statements either have become
inaccurate by virtue of subsequent events or are later discovered to have been false or misleading from the outset,
and the issuer knows or should know that persons are continuing to rely on all or any material portion of the statements”
(Sec. Act. Rel. 6084, 17 SEC Dock. 1048, 1054 (1979)). The FASB (2005) ASC Topic 250, Accounting Changes and
Error Corrections, states, ‘‘Any error in the financial statements of a prior period discovered after the financial
statements are issued shall be reported as an error correction, by restating the prior-period financial statements.’’
See also Accounting Principles Board Opinion 20; Statement of Financial Accounting Standards (SFAS) No. 16; and
SFAS No. 154 (issued in May, 2005), among others. 2 These consequences include, for example, negative market responses (Palmrose, Richardson, and Scholz, 2004),
increased cost of capital (Hribar and Jenkins, 2004), management turnover (Collins et al. 2009) and the resultant
securities lawsuits (Francis, Philbrick and Schipper, 1994).
the class-end (truth revelation) date. Another 383 (9.4%) made “forced” restatements after being
sued, and the rest 3,505 (85.8%) never restated their financials despite being sued. On the other
hand, the regulators, knowing they are constrained by resources, explicitly encourage and reward
self-reporting of wrongdoings (SEC Seaboard Report 2001)3. In this paper we ask: Does stronger
legal environment induce more voluntary restatements?
To examine the impact of legal environment on voluntary restatements, we take novel
approach by exploiting the variation on the stringency of the district court under which firms are
headquartered. In the U.S., the federal securities law requires all private securities litigations to
be heard first at a federal district court (USDC), which has jurisdiction over a number of counties.
Though plaintiffs can file litigation in any USDC where the defendant firm has a place of business,
multiple filings must be consolidated into one case typically heard by the district court where the
defendant firm is headquartered. Court decisions on whether to dismiss a shareholder litigation are
made by randomly assigned district judges (Bird, 1975; Galasso and Schankerman, 2015), causing
exogenous variation in dismissal rates across courts and over the time.4 Higher dismissal rate
means firms headquartered in the jurisdiction of particular USDC are subject to more lenient
litigation environment.
Our litigation environment measure has advantages over the rule of law indicators used
prior cross-country studies based on World Bank data (La Porta et al. 2006; Kaufmann et al. 2003;
Srinivasan, Wahid, and Yu 2015) by circumventing country-specific idiosyncrasies that affect our
3 See the SEC’s Report of Investigation Pursuant to Section 21(a) of the Securities Exchange Act of 1934 and
Commission Statement on the Relationship of Cooperation to Agency Enforcement Decision (Seaboard Report). Oct
23, 2001, which explained how self-reporting, cooperation, self-policing, and remediation factor into SEC decisions
when considering enforcement actions. 4 Another indicator of litigation environment is the incidence of litigation (Donelson et al. 2012), defined by the
number of filed cases in a district court scaled by the number of headquartered firms under the court’s jurisdiction.
We controlled for this variable in all our regressions.
variables of interest. Specifically, it captures within-country heterogeneities in securities law
enforcement, holding the country’s regulatory environment constant. Moreover, our measure is at
court-level and decided by randomly assigned district judges, which is unlikely affected by
individual firm characters. Finally, it is hard to perceive that firms choose or switch their
headquartering place based on district court dismissal rates5.
Note that litigation environment can simultaneously affect both firms’ likelihood to commit
accounting misstatement, and their propensity to make voluntary restatements. Our paper focuses
on the latter and investigates a sample of firms “known” to have made financial misstatements
(identification method described in Section 3.1). Among these firms, some made voluntary
restatement, that is, restatement before revelation by external agencies. Others either made forced
restatements, or never make restatements. We then ask whether misstated firms headquartered in
more stringent district court jurisdictions are more or less likely to make voluntary restatements.
Why should firms care about their home court stringency when making voluntary
restatement decisions? We argue that firms’ litigation risk heightens when prior misstatement
came to knowledge of the management. In securities litigations, voluntary restatements can be
taken either as evidence of misconduct that are unfavorable to the firm, or as mitigating factors
that help to weaken the claim that managers withheld bad news to keep price distorted, and reduce
potential damages by shortening the class period and number of affected class shareholders.
Central to this discretion is the stringency of the firm’s home court. Court’s dismissal rate, in this
regard, provides managers with a heuristic on the stringency of the court. For example, suppose
5 In our sample, the average years of firms staying in the jurisdiction of one district court since 1996 is 8.2 years,
which exceeds the five-year period that we use to calculate court stringency. On firms’ relocation decision, there are
only 1.9% of all the Compustat firm-years relocated out of the jurisdiction of former district court from 1996 to 2013.
The frequency of headquarter relocations is low and can hardly affect our results.
the district court of North Carolina has the reputation of dismissing 80% of shareholder litigations,
whilst the district court of South Carolina dismissed only 20%. That indicates firms headquartered
in the jurisdiction of district court of South Carolina are under more stringent litigation
environment. Our research design exploits this variation on district court stringency to test its effect
on firms’ voluntary restatement propensity.
Drawn on the largest sample of shareholder litigations from 2000 to 2014 and restatement
records from Audit Analytics (AA), we find misreporting firms headquartered in high dismissal
rate (or lenient) courts are more likely to make voluntary restatements, suggesting a negative
impact of perceived legal stringency on voluntary disclosure of wrongdoing. The effect is both
statistically significant and economically large: One standard deviation increase (decrease) in court
dismissal rate leads to 11.5-18.6% increase (9.6-14% decrease) in the likelihood of voluntary
restatements. Using alternative samples of misstating firms, and having controlled for a battery of
variables in literature including state fixed effect, the result remains robust.
Two pieces of evidence suggests the positive correlation between voluntary restatement and
court dismissal rate is not an artifact of measurement error or other randomness. First, we find
firms are more sensitive to more recent dismissal rates than remote ones. Second, the effect is more
pronounced for firms headquartered in less busy than busy courts. Both evidence are consistent
with the availability heuristic theory in social psychology, which states that agents have tendencies
to overestimate the likelihood of events with greater availability in memory, which can be
influenced by how recent the memories are, or how unusual or emotionally charged they may be
(Sherman and Corty 1984; Strack 1985; Schwarz et al. 1991).
Our causal evidence comes from the landmark case of Tellabs v. Makor (hereafter “Tellabs”).
Tellabs represents one of the Supreme Court’s first effort to clarify the strong inference standard
of Scienter, a core element to plead securities litigations. The effect of Tellabs is that it
“homogenizes” federal courts’ pleading standards (Choi and Pritchard, 2012), thus tightens the
stringency of district courts under previously lenient Circuits. Using difference-in-differences, we
first find that after Tellabs, district courts under previously lenient Circuits decreased their
dismissal rates relative to the control courts. Next, we show misstated firms under pre-Tellabs
lenient courts also decrease their voluntary restatement propensity after Tellabs relative to the
control firms. This evidence suggests that misstated firms deliberately alter their restatement policy
in response to (exogenous) changes in court stringency.
Our results suggest that misreporting firms actively consider their home court stringency
when choosing their restatement policy. Whether this strategy works, in the sense that it avoids
shareholder litigations or helps to reduce the legal and market penalties, is clearly a tougher
question to answer. This is because we do not observe what the legal and market consequence
would be if the voluntary restating firms chose to keep silent. Instead, we take indirect approach
by comparing the outcomes of voluntary restating firms headquartered in lenient and stringent
courts, relative to their control firms. Interestingly, our event study on defendant firms reveals no
significant differences of market reactions on the class-end date and case-filing date between
voluntary restating and control firms headquartered in lenient or stringent courts. On the other
hand, we find no evidence that voluntary restating firms in lenient courts enjoy better court
advantages in terms of dismissal outcomes, settlement amount, or unconditional cost amount.
Taken together, our tests suggest that firms’ response to court stringency is not more psychological
than based on rational economic analysis.
We see three contributions of this paper to the literature. First is our study on voluntary
restatements as the main construct. The large accounting and finance literature tends to use
restatement frequency to infer the magnitude of financial misreporting (Burns and Kedia 2006;
Efendi et al. 2007). However, As Srinivasan, Wahid, and Yu (2015) note, while lower level of
restatements can represent an absence of errors, it can also indicate a lack of detection and
disclosure. Given a large number of misstatements are concealed, it is important to understand
how institutional factor contributes to firms’ voluntary restatements. Though this paper is not the
first to differentiate voluntary and forced restatements (Pfarrer et al. 2008; Marciukaityte,
Szewczyk, and Varma 2009), to our best knowledge it presents by far the largest and cleanest
samples of misstated firms that made either voluntary, forced, or no restatements.
Second, we add to the understanding of how legal environment affect firms’ voluntary
disclosure. In the accounting literature, the question “why firms voluntarily disclose bad news”
was asked in important work including Skinner (1994, 1997), Kothari, Shu, and Wysocki (2009),
Rogers and Buskirk (2009), and Donelson et al. (2012), etc. The consensus is that firms actively
consider litigation risk when making bad news disclosure such as earnings warnings (Field, Lowry
and Shu 2005) and management forecast (Baginski, Hassell and Kimbrough 2002). Specifically
on restatements, Srinivasan, Wahid, and Yu (2015) find U.S. listed firms headquartered in weak
rule-of-law countries have low restatement frequency than firms from strong rule-of-law countries
despite higher earnings management levels. Our paper, however, focuses on firms’ voluntary-
rather than overall restatement frequency. Other than legal environments, Lin and Huang (2015)
find both firm-level managerial incentives (CEO tenures) and governance characteristics (board
independence) affect voluntary restatements. Finally, Pfarrer et al. (2008) show firms’ voluntary
restatements are positively induced by the behavior of industry leaders and peer groups, but
negative induced by formal sanctions.
Last but not the least, we enrich the law and accounting literature by proposing novel,
court-based measure on firms’ legal environments. Prior work focuses on the laws “on paper” and
implicitly assume that they are enforced with full strength.6 Unlike these studies, we focus on the
role of courts as law enforcers, and the resultant variations in litigation environment on
headquartering firms. In economics literature, court and judge stringencies are used to study, for
example, the effect of incarceration on individual’s earnings prospect (Kling, 2006), patent
protection on corporate innovation (Galasso and Schankerman, 2015), and bankruptcy rules on
lending behaviors (Dobbie, Goldsmith-Pinkham, and Yang, 2016), etc. We extend this approach
to examine the effect of court stringency on firms’ restatement policy.
The rest of this paper proceeds as follows: Section 2 describes the institutional setup relevant
to our analysis. Section 3 presents sample selection, data and descriptive statistics. Section 4
presents empirical results. Section 5 presents robustness tests. Section 6 concludes.
2. Restatements and Litigation Environment in the U.S.
2.1. The Restatement Process
The observation of a restatement (voluntary or forced) is a joint outcome of two stages:
First, firms made misstatements in financial reporting that involve accounting errors or
irregularities. Second, upon discovery, the management make the decision on whether, when, and
how to issue a restatement. This paper explicitly focuses on the second stage. As Palmrose,
Richardson, and Scholz (2004) illustrate, the company, the SEC, an independent auditor or a
6 For example, many papers use staggered adoption of anti-takeover (Business Combination) laws by U.S. states as
shocks to corporate governance (Garvey and Hanka, 1999; Bertrand and Mullainathan, 2003; Wald and Long, 2007;
Atanassov, 2013).
combination thereof, can detect misreporting.7 Upon discovery of misstatements, the management
face statutory duty to take corrective restatements8. However, the decision is often strategic. Some
firms make preemptive restatement before revelation by external agencies, some firms make
forced restatements after being caught up; others never make restatements. Conditional on issuing
restatements, the mediums of report can differ (Files, Swanson, and Tse, 2009). Some restatements
are reported in press release or series of press releases, some are in the Form 8-K filings with the
SEC, and some by the filing of amended financials (10-Ks). The information provided in such
disclosure, such as accounting issues involved and circumstances underlying the restatement, as
well as specificity level, also vary (Palmrose, Richardson, and Scholz 2004).
2.2. Securities Lawsuit as Deterrence Mechanism
As public enforcement by the SEC is resource constrained, in the U.S., the private securities
lawsuit pursuant to the SEC 10b-5 anti-fraud provision plays unique roles in compensating victims,
and deterring frauds (See Habib et al. 2014 for a review). The rule prohibits any fraud or deceit in
connection with the purchase or sale of any security. The plaintiff under this rule are shareholders,
and the defendants include the firm and any person involved in the fraudulent activity. Successful
pleading of a securities lawsuit depends on the plaintiff’s evidence on each of the legal elements:
7 For example, the company can identify misstatements through internal audits and other internal control procedures,
such as period-end closing processes, policy reviews, and mechanisms that solicit and investigate complaints from
employees. Occasionally, the SEC requests a restatement after reviewing company filings. Finally, when auditors
discover that previously issued financial statements contain misrepresentations, GAAS requires that they advise the
client to make appropriate disclosures, and to take the necessary steps to ensure this occurs (AICPA, 2002, Section
AU 561). 8 The SEC has ruled that “there is a duty to correct statements made in any filing…if the statements either have become
inaccurate by virtue of subsequent events or are later discovered to have been false or misleading from the outset,
and the issuer knows or should know that persons are continuing to rely on all or any material portion of the statements”
(Sec. Act. Rel. 6084, 17 SEC Dock. 1048, 1054 (1979)). The FASB (2005) ASC Topic 250, Accounting Changes and
Error Corrections, states, ‘‘Any error in the financial statements of a prior period discovered after the financial
statements are issued shall be reported as an error correction, by restating the prior-period financial statements.’’
See also Accounting Principles Board Opinion 20; Statement of Financial Accounting Standards (SFAS) No. 16; and
SFAS No. 154 (issued in May, 2005), among others.
(1) Materiality; (2) Misrepresentation or omission; (3) Scienter (defendant’s fraudulent intent or
recklessness); (4) Reliance; (5) Causation; and (6) Damages.
It is worthwhile to illustrate below the typical stages of a securities class action lawsuit. In the
first stage, a plaintiff files a lawsuit and asks to be the lead plaintiff. A 60-day clock for any
individual or entity to file paperwork with the court asking to be the lead plaintiff is triggered when
the first securities lawsuit is announced. After the deadline, the court reviews all pleadings and
appoints the lead plaintiff and lead counsel.
In the second stage, plaintiffs’ counsel files their amended consolidated complaint, and the
defendants then have a deadline to file their motion to dismiss. A motion to dismiss is essentially
an argument by the defendants that, even if all facts alleged in the complaint were true, they are
insufficient to give rise to liability under SEC Rule 10b-5. The court then decides, based on both
plaintiff’s complaints and defendant’s motions, whether to uphold plaintiff’s Rule 10b-5 claim. If
yes, the court enters an order denying defendant’s motion to dismiss, which then gives class
plaintiffs the right to obtain “discovery” from the defendant. Passing the motion to dismiss is the
pivotal stage in securities lawsuits, for the costs of litigation increases substantially if the plaintiffs
claim is not dismissed by court. Because almost all cases end up either dismissed or settled, prior
literature uses whether the plaintiff passes the motion to dismiss as proxy for “win” (Choi, 2007;
Choi, Nelson, and Pritchard, 2009; Dyck, Morse, and Zingales, 2010).
If plaintiff survives the motion to dismiss then it enters the third stage of discovery. Discovery
typically involves requests for document production, admissions, and depositions of officers,
employees, experts and third parties. Once completed, plaintiff must seek class certification. If
granted by court, the case officially becomes a securities class action. At this point, defendants can
face great liability if the case goes to trial and the often likely outcome is a settlement.
In the final stage, the plaintiff and defendant’s attorney often negotiate a settlement. The
settlement must seek court’s preliminary and final approval. Once approved, the claims
administrator takes over to receive the settlement fund, sends out court-approved notices to the
investor class, receives and processes the claims and distributes the settlement funds. The whole
process takes a typical 3-4 years to complete.
2.3. Court Jurisdictions and Splits in Pleading Standards
Under the Securities and Exchange Act, federal courts are given exclusive jurisdiction to hear
securities lawsuits.9 There are 94 district courts (5 outside the main territory), 13 circuit (appellate)
courts, and the Supreme Court throughout the country. Each district court has geographical
jurisdiction over a number of counties.10 All federal judges receive appointment by the President
and have lifetime tenure. Each district court has at least one judge, some busy courts such as
Southern District of New York and Central District of California each has 28 judges.
The assignment of cases to federal judges is on a rotational or, more often, random basis (Bird,
1975; Galasso and Schankerman, 2015). 11 Appeals against district court rulings go to its
corresponding circuit court. There are twelve appellate courts dividing the country into different
circuits12. The 9th circuit court in California, for example, has 29 appellate judges, overseeing 13
district courts. Circuit courts in the U.S. are influential lawmakers for their ability to set legal
9 See Section 27 of the 1934 Securities Exchange Act. 10 For geographical jurisdiction of federal district courts, see PACER: https://www.pacer.gov/psco/cgi-bin/county.pl. 11 Though there might be a need to assign more specialized and complex cases to more experienced judge, “to
implement a program that would attempt to assign cases according to the relative abilities of the judges in a district
is understandably unpopular” (Bird, 1975, pp. 483). Moreover, many courts see a danger in fostering judge
specialization, because if certain judges in a district become experts to whom cases in particular areas of the law
always would be assigned, it deprives other judges of the opportunity, provided by random selection, to gain expertise
in that legal area. See Bird (1975). 12 The thirteenth court of appeals is the United States Court of Appeals for the Federal Circuit, which has nationwide
jurisdiction over certain appeals based on their subject matter.
precedent with minimal supervision by the Supreme Court.13 Figure 2 visualizes the geographical
jurisdiction of federal district courts and their corresponding circuit courts. Colors and numbers
indicate their average dismissal rate and standard deviation over our sample period.
Attorneys, commentators and scholars have long recognized the divergent pleading standards
among courts in securities lawsuits. The split centers on the pleading standard of Scienter, a core
legal element to plead a 10b-5 claim. The element of Scienter requires plaintiffs to “state with
particularity facts giving rise to a strong inference that the defendant acted with the required state
of mind.” 14 It is well known that hard evidence of Scienter is difficult to obtain prior to discovery,
and in practice whether plaintiff’s evidence can satisfy Scienter depends largely on the pleading
standard of the relevant court. For example, drawn from representative Circuit Court decisions
legal scholars divide federal courts into three groups: The 1st, 4th, 6th, and 9th circuits adopted a
“preponderance” standard which is pro-defendant firms; the 2nd, 8th, 10th and 11th circuits adopted
an “equal inference” standard positioned in the middle; the 3rd and 7th circuits adopted a
“reasonable person” standard which is pro-plaintiffs (Choi and Pritchard 2012).
3. Sample and Data
3.1. Data and Sample Distribution
We purchased the Securities Class Action Services (SCAS) database from RiskMetrics’
Institutional Shareholder Services (ISS) to identify all securities lawsuits filed in federal courts.15
13 This is particularly the case for securities lawsuits: On average, securities cases make up less than 1% of Supreme
Court’s docket, or about 1.5 cases per year, making circuit courts the de facto final arbiter (Pritchard, 2011). 14 See Exchange Act § 21D(b)(2), 15 U.S.C. 78u-4(b)(2). 15 Another (free) database popular for securities lawsuit study is the Stanford Law School Securities Class Action
Clearinghouse (SCAC) database. However, as Karpoff et al. (2017) observe, the filing date on the SCAC database
postdates the time at which investors first learn of the purported misconduct that triggers the litigation by an average
of 150 calendar days. Since the identification of our sample relies crucially on the key event dates, we purchase the
commercial database whose primary purpose is to assist institutional investors that have a claim in securities lawsuits,
and supplement any missing variables using SCAC.
The SCAS database is used in accounting and finance studies such as Cheng et al. (2010) and
Donelson et al. (2012). It offers detailed portfolio views of securities lawsuits including plaintiffs,
defendants, court, allegations, class periods, claim deadline dates, claims administrator details and
pertinent related data since 1982. To make up any missing values in the SCAS, we hand collect
additional data from the Stanford Law School Securities Class Action Clearinghouse (SCAC)
database. To merge with Compustat records, we hand collect the GVKEY from Compustat to each
lawsuit case. The two data cleaning procedures leave us with 3,363 unique cases with valid
GVKEY and non-missing data on federal filing date, dismissal date or settlement date (if the case
is closed), class-start date, class-end date and allegations. For the construction of our court
dismissal rate, we utilize 6,976 unique cases with valid case name, federal filing date and case
status information from both SCAS and SCAC.
The restatement data come from Audit Analytics (AA). For restatement identification, we
exclude firms labelled in AA as “Res Clerical Errors” since we are interested in accounting
irregularities. All financial statement variables are from Compustat, and the stock trading data
come from CRSP.
Our objective is to identify a group of firms that made accounting mistakes, some made
voluntary restatements and others kept strategic silence. In practice, whether or not a firm has
misreported earnings can only be identified through evidences ex post such as firms’ own
restatements, the SEC sanctions, or court trial outcomes. As the SEC is resource constrained, and
most securities litigations end up with settlements rather than trial, this paper employs the
following methods to identify misreporting firms.
Sample of Defendant Firms with alleged both US GAAP and Rule 10b-5 violations
Our first sample of misstated firms contains those sued by shareholders in securities litigation
(hereafter “Sample of Defendant Firms” or “SDF”). Some of the defendant firms made restatement
before the class-end date, which is the date when the corrective disclosure that triggers the lawsuit
was revealed to the market (Kellogg, 1984; Griffin, Grundfest, and Perino, 2004; Gande and Lewis,
2009). We identify these firms as voluntary restating firms. For the control sample, we further
screen the remaining defendant firms, and require the causes of action to include both US GAAP
violations and Rule 10b-5 violations.
Appendix B.1 summarizes our screening process. For voluntary restating firms, we start with
3,363 lawsuits whose GVKEY, federal filing date, class periods and dismissal date (settlement
date) are identifiable, and 11,377 restatement records merged from Audit Analytics (AA)
restatement database and Compustat annual financial database. By matching the lawsuit class
periods and restating periods, we obtain 789 non-duplicated defendant firms with non-error-based
restatement. We then identify voluntary restating firms to be those that make restatement before
class-end date, and obtain 275 observations.
For control firms, we merge 3,363 lawsuits with Compustat annual financial database and
obtain 3,175 non-duplicated defendant firms. Excluding those with voluntary restatement, we
obtain 2,921 observations without voluntary restatement. We then require our defendant firms to
be alleged of both GAAP violations and Rule 10b-5 violations, leaving 928 observations.
Eliminating observations without valid variables in our tests and requiring fiscal-end date to be
between December 31, 2000 to December 31, 2013, we obtain a final sample comprising 393
defendant firms from 2001 to 2013, with 111 voluntary restating firms and 282 control firms
without voluntary restatement.
Sample of Restating Firms: Defendant firms with restatements
The second sample of misstated firms contains defendant firms with (voluntary or forced)
restatements (hereafter “Sample of Restating Firms” or “SRF”). Our voluntary restating firms are
the same 111 with the SDF sample. For control firms, we require our defendant firms to have made
corresponding restatement on or after the class-end date (i.e. “forced” restatements). Intuitively,
forced restating firms constitute the best counterfactual group, for they indicate these firms ought
to, but did not make voluntary restatement. Our final SRF comprises 300 observations from 2001
to 2012, with 111 voluntary restating firms and 189 control firms. Appendix B.2 summarizes the
screening process for SRF.
Some of the defendant firms also receive the SEC sanctions. To ensure that our 111 voluntary
restating firms are indeed voluntary restaters, we further compare their restatement filing date with
the SEC enforcement date (if any), which we obtain from the SEC’s Accounting and Auditing
Enforcement Releases. We find 8 related SEC enforcements, but none is before the restatement
date. Therefore, it is safe to conclude that all 111 observations in our SDF and SRF are all voluntary
restatements.
3.2. Explanatory Variables
Our key explanatory variable is court dismissal rate, defined by the number of securities cases
dismissed within five years prior to a firm’s fiscal year end in the federal district court where the
firm is headquartered, divided by total such cases filed in the same period and court:
𝑑𝑖𝑠𝑚𝑖𝑠𝑠𝑎𝑙𝑖,𝑡−4→𝑡 =𝑛𝑜_𝑑𝑖𝑠𝑚𝑖𝑠𝑠𝑎𝑙𝑖,𝑡−4→𝑡𝑛𝑜_𝑓𝑖𝑙𝑖𝑛𝑔𝑠𝑖,𝑡−4→𝑡
(1)
where, 𝑛𝑜_𝑑𝑖𝑠𝑚𝑖𝑠𝑠𝑎𝑙𝑖,𝑡−4→𝑡 is the number of cases dismissed within the five years prior to the end
of fiscal year 𝑡 of firm 𝑖 handled by the district court where firm 𝑖 is headquartered; and
𝑛𝑜_𝑓𝑖𝑙𝑖𝑛𝑔𝑠𝑖,𝑡−4→𝑡 is the number of cases filed within the five years prior to the end of fiscal year
𝑡 of firm 𝑖 handled by the district court where firm 𝑖 is headquartering.
Two notes on this explanatory variable are in order: First, note it may take several years for
some case to reach any sort of resolution, while other cases may be dismissed much faster.
Therefore, cases dismissed within five years may not exactly correspond to cases filed during the
same period. However, to account for the fact that different district courts have very different
number of firms and lawsuit filings, we believe this scaling method is reasonable. In the robustness
tests, we provide results using alternative estimation of court dismissal rate taking into account the
lag between case filing and dismissal dates. Second, it is possible for a few less-busy district courts
to have no case filed in the five-year period but are some cases dismissed in the same period. In
such cases we assign value 1 to the court dismissal rate.
Our key assumption, that securities lawsuits are typically heard by the district court that the
defendant firm is headquartered, requires validation. Two statutory provisions: 28 U.S.C. § 1404(a)
and 1406(a) provide legal basis for this claim. Section 1404(a) protects parties and witnesses from
an undue expenditure of time and money. Because of the nature of claims in securities lawsuits,
substantially all witnesses and sources of proof are likely to be located at the firm’s headquarters.
Section 1406(a) provides the transfer of a case brought in an improper forum. Plaintiffs that file
suits at inappropriate courts are either dismissed based on the doctrine of forum non conveniences,
or transferred to the district court of the defendant firm’s headquarter. Cox, Thomas, and Bai (2009)
report their interview with plaintiffs’ counsels who consistently reflect that it is impractical for
them to engage in forum shopping due to the strong likelihood that their choice of a venue other
than the defendant firm’s principal place of business will be immediately followed by a successful
defendant’s motion to relocate the suit. Hence, rather than engaging in in futile act, they file suit
initially in the defendant company’s home district court.
We proceed to verify this assumption using actual lawsuit and court data in our sample.
Within the 393 observations in our Sample of Defendant Firms, 343 (or nearly 90%) of the cases
are heard only heard by the defendant firms’ headquartering district court, providing validity to
our assumptions. Table 1, Panel A displays the distribution of our two samples by district courts,
and Panel B reports their yearly distribution. The top three busiest district courts are California
(Northern), California (Central), and New York (Southern).
Our control variables follow the literature on litigation risk and restatement. We first include
court filing rate, defined as the total number of securities cases filed within five years prior to a
firm’s fiscal year end in the federal district court where the firm is headquartered, divided by total
number of Compustat firms in the same period and court. Our firm-level controls include the
natural logarithm of total assets, leverage ratio, and book-to-market ratio. Following the work on
restatements (Files, Swanson, and Tse, 2009; Srinivasan, Wahid, and Yu, 2015) we take ROA,
sales growth and last year stock return as control variables for firm performance. We further
control the stock trading activities by including previous-year stock return volatility, market risk
factor loading (beta), stock turnover, and stock return skewness (Kim and Skinner, 2012). Finally,
to account for the strength of governance and monitoring system we include whether the firm is
audited by a Big 4 auditing firm (Srinivasan, Wahid, and Yu, 2015). All variables are defined in
Appendix A, and winsorized at 1% level, except for restating dummy, court dismissal rate and
court filing rate.
Table 2, Panel A and B summarizes the descriptive statistics of variables in our SDF and SRF,
respectively, and compares characters of restated and non-restated firms in each sample.
For SDF (Panel A), the average court dismissal rate is 35.9%, and the average court filing
rate is 15.7%. Mean log total assets (firm size) is 7.42, leverage 23.5% of total assets, and book-
to-market ratio at 0.651. The average ROA is -3.0% of total assets, and sales growth rate at 17.7%.
Average daily return volatility is 3.6%, skewness 0.157, and annual turnover at 2,893. 71.0% of
the firms are audited by the Big 4 auditing firms. Comparing voluntary restating firms with control
firms in SDF, we find that voluntary restating firms have significantly higher court dismissal rate
and lower court filing rate, and other characteristics are almost similar. This lends us confidence
that our control firms are a good match for voluntary restating firms.
For SRF (Panel B), the average dismissal rate is 36.7%, and the average court filing rate is
16.0%. Mean log total assets (firm size) is 7.22, leverage 25.3% of total assets, and book-to-market
ratio at 0.58. The average ROA is -3.3% of total assets, and sales growth rate at 22.4%. Average
daily return volatility is 3.6%, skewness 0.133, and annual turnover at 2,991. 76.0% of the firms
are audited by the Big 4. Compared with firms in SDF, firms in SRF have lower accounting returns
but higher growth. Comparing voluntary restating firms with forced firms in SRF, we find that
voluntary restating firms have higher court dismissal rate, lower court filing rate, and marginally
higher skewness. Other characteristics are almost similar.
Univariate analysis of the two samples reveals that court dismissal rate and court filing rate
significantly distinguish voluntary restating firms with control firms. A higher court dismissal rate
and a lower court filing rate are associated with a higher propensity to voluntarily restate,
indicating that firms are more likely to make voluntary restatement when the court is more lenient
and when the risk to be sued is lower, i.e. a lenient legal environment. Section 4 presents
comprehensive results in regression analysis.
4. Empirical Results
4.1. Baseline Model
To test the predictive power of court dismissal rate on misstating firms’ propensity to make
voluntary restatement, we propose the following probit model:
𝑅𝑒𝑠𝑡𝑎𝑡𝑖𝑛𝑔𝑖,𝑡+1
= 𝛽0 + 𝛽1𝑑𝑖𝑠𝑚𝑖𝑠𝑠𝑎𝑙𝑖,𝑡−4→𝑡 + 𝛽2𝑓𝑖𝑙𝑒𝑑𝑖,𝑡−4→𝑡 + 𝛽3𝑙𝑒𝑣𝑖,𝑡 + 𝛽4𝑙𝑛𝑎𝑡𝑖,𝑡
+ 𝛽5𝑟𝑒𝑡𝑢𝑟𝑛𝑖,𝑡 + 𝛽6𝑅𝑂𝐴𝑖,𝑡 + 𝛽7𝑠𝑎𝑙𝑒𝑠𝑔𝑟𝑡ℎ𝑖,𝑡 + 𝛽8𝑠𝑡𝑑𝑖,𝑡
+ 𝛽9𝑡𝑢𝑟𝑛𝑜𝑣𝑒𝑟𝑖,𝑡 + 𝛽10𝑠𝑘𝑒𝑤𝑛𝑒𝑠𝑠𝑖,𝑡 + 𝛽11𝐵𝑖𝑔4𝑖,𝑡 + 𝛽12𝑏𝑡𝑚𝑖,𝑡
+ 𝛽13𝑏𝑒𝑡𝑎𝑖,𝑡 + 𝜀𝑖,𝑡
(2)
where, 𝑅𝑒𝑠𝑡𝑎𝑡𝑖𝑛𝑔𝑡+1 is an indicator variable that takes 1 if the firm makes voluntary restatement
before class-end date16; 𝑑𝑖𝑠𝑚𝑖𝑠𝑠𝑎𝑙𝑖,𝑡−4→𝑡is the court dismissal rate for the headquartering firm;
𝑓𝑖𝑙𝑒𝑑𝑖,𝑡−4→𝑡 is the court filing rate for the headquartering firm;𝑙𝑒𝑣𝑡 is the book leverage at the end
of fiscal year t; 𝑙𝑛𝑎𝑡𝑡 is the natural logarithm of total assets at the end of fiscal year t; 𝑟𝑒𝑡𝑢𝑟𝑛𝑡 is
the annual total return over fiscal year t; 𝑅𝑂𝐴𝑡 is the return on total assets in the fiscal year t;
𝑠𝑎𝑙𝑒𝑠𝑔𝑟𝑡ℎ𝑡 is sales growth from fiscal year t-1 to t; 𝑠𝑡𝑑𝑡 is the daily return volatility over fiscal
year t; 𝑡𝑢𝑟𝑛𝑜𝑣𝑒𝑟𝑡 is the turnover over fiscal year t; 𝑠𝑘𝑒𝑤𝑛𝑒𝑠𝑠𝑡 is the return skewness over fiscal
year t; 𝐵𝑖𝑔4𝑡 is an indicator variable that takes 1 if the firm has a Big-4 auditor firm as its auditor;
𝑏𝑡𝑚𝑡 is the book-to-market ratio at the end of fiscal year t; and 𝑏𝑒𝑡𝑎𝑡 is the market risk factor
loading from CAPM model, estimated from the monthly stock returns of the 5-year period before
the most recent fiscal year end. We control for the state fixed effects because some states have
16 Note that for the counterfactual firms in Sample of Defendant Firms and Sample of Restating Firms, we take their
class-end date as the hypothetical restating date and the latest fiscal year up to the class-end date as fiscal year t in
equation 2, since the restating firms and counterfactual firms are matched by lawsuit and class-end year in these two
samples.
more than one district court and we need to disentangle the effect of court stringency from the
unobservable state level economic, social and political effects. We also control for industry fixed
effects and year fixed effects.
Table 3 reports the impact of court dismissal rate on the likelihood of misstating firms issuing
voluntary restatement using SDF (Column 1 and 2) and SRF (Column 3 and 4). Panel A presents
the probit regression results, and Panel B exhibits the change in the likelihood of voluntary
restatement with one-standard deviation increase (decrease) in court dismissal rate according to
the probit regression results. Industry fixed effects and year fixed effects are included in all of the
four regressions. State fixed effects are included in the regressions in Column 1 and 3. The industry
and year fixed effects filter out time-varying and industry-level shocks that may affect the
restatement decision, and the state fixed effect control for state-wide differences in business and
regulatory environment.
We find court dismissal rate exhibits a significantly positive impact on the likelihood of
misreporting firms issuing voluntary restatements (p=0.0019, 0.0016, 0.0151 and 0.0016 for the
four regressions respectively). Remarkably, court dismissal rate is the only variable that has both
statistical significance and consistent economic magnitude across all the four regressions.
Specifically, Panel B shows that one-standard deviation increase in court dismissal rate (lowered
court stringency) leads to 18.56%, 11.48%, 25.36% and 24.90% increase in voluntary restatement
propensity for the four regressions in Column 1, 2, 3 and 4, compared to the average restating rate
of 28.2% and 37.0% for the two samples, respectively. One standard deviation in dismissal rate
amounts to the difference in the rates between Illinois (Northern) and California (Northern) district
courts. Hypothetically, if a firm moves from Illinois (Northern) to California (Northern), ceteris
paribus, its voluntary restatement propensity would increase by 18.56% according to SDF, which
increases over 65% of its voluntary restatement rate.
ROA, sales growth, beta, turnover, return volatility, and Book-to-Market all exhibit
insignificant effect on voluntary restatement. Surprisingly, court filing rate has no significant effect
on voluntary restatement, probably because the litigation incidence is determined by other factors
and controlling for firm characteristics and fixed effects could absorb its statistical significance.
Also, having a Big-4 auditor has little impact on voluntary restatement, probably because auditors
are concerned about their own legal and reputational penalties when their audited firm made
accounting mistakes (Seetharaman, Gul, and Lynn, 2002; Hope and Langli, 2010). Overall, our
baseline model analysis supports the defensive disclosure hypothesis.
4.2. The Availability Heuristic of Court Dismissal Rates
To test whether court dismissal rates indeed affect managers’ estimate of their litigation
environment, we employ the famous availability heuristic theory in social psychology (see
Sherman and Corty 1984; and Strack 1985, for reviews). One of the most widely shared
assumption in decision-making holds that people estimate the probability of an event by the ease
with which instances or associations come to mind (Tversky and Kahneman 1973). It follows,
managers have tendencies to overestimate the likelihood of events with greater “availability” in
memory, which can be influenced by how recent the memories are, or how unusual or emotionally
charged they may be (Schwarz et al. 1991).
The availability heuristic enables cross-sectional tests to validate our key explanatory variable.
If court dismissal rates indeed provide headquartering firms with an impression of stringency in
litigation environment, we expect to find (1) recent court dismissal rates to have larger impact than
distant court dismissal rates; and (2) court dismissal rate in less busy district courts yield larger
emotional charge, therefore larger impact, than dismissal rate in busier district courts.
Table 4 and 5 present evidence supporting these hypotheses. Table 4 compares the impact of
recent court dismissal rate with that of distant court dismissal rate on voluntary restatement. For
recent dismissal rate, we use the 2-year court dismissal rate calculated from the most recent 2 years’
in the firm’s headquartering district court. For distant dismissal rate, we use the 3-year court
dismissal rate calculated from year 3 to year 5 in the firm’s headquartering district court. Column
1 and 4 shows that impact of recent dismissal rate on firm’s voluntary restating likelihood remains
significantly positive (p=0.0008 and 0.0037 for Column 1 and 4, respectively), whereas the impact
of distant dismissal rate is less significant (p=0.0394 and 0.0403 for Column 2 and 5, respectively).
More clearly, when we regress firm’s voluntary restating likelihood on both the recent and distant
dismissal rate, the impact of distant dismissal rate is insignificant for both samples, but the impact
of recent dismissal rate remains very significant (p=0.0055 and 0.0279 for Column 3 and 6,
respectively). For economic magnitude, the impact of recent dismissal rate is also larger than that
of distant dismissal rate for both samples.
Table 5 partitions our sample firms into those headquartered in the top 5 active courts and the
rest. From the raw data of dismissal information, the top 5 active courts are California (Central),
California (Northern), Florida (Southern), Massachusetts and New York (Southern), issuing 48%
of the total dismissal decisions ever made since 1982. Column 1 and 2 presents the cross-sectional
tests with the interaction term between Court Dismissal Rate and Top Active Courts Dummy.
Column 3 to 6 display the subsample regressions, with Column 3 and 4 presenting the subsample
without top 5 active courts and Column 5 and 6 displaying the subsample with only top 5 active
courts. Due to the small size of sample in the subsample tests, we only include year-fixed effects
in Column 3 to 6.
We find that while court dismissal rate exhibits significantly positive impact on the voluntary
restatements in the subsample without top 5 active courts, the impact of court dismissal rate is
insignificant for the subsample with only top 5 active courts. However, the coefficients for the
interaction term between Court Dismissal Rate and Top Active Courts Dummy for both samples
are insignificant, which might be explained by the insignificant but large coefficient for the court
dismissal rate for the subsample with only top 5 active courts in Column 5 and 6.
Taken together, Table 4 and 5 present evidence consistent with managers’ availability heuristics.
Our tests suggest that (1) our court dismissal rate variable has information content of court
stringency; (2) the positive sensitivity of voluntary restatement to court dismissal rate found in
Table 3 is unlikely to be an artifact of measurement error or other randomness. Randomness
cannot explain why recent dismissal rates have larger impact than remote ones, or why dismissal
rates in less busy courts (which are easier to estimate) have larger impact than those in busier
courts (which are harder to estimate).
4.3. Shock in Court Stringency: Examining the Effect of Tellabs v. Makor
To provide evidence that firms’ voluntary restatements are caused by court stringency, we
exploit the 2007 case of Tellabs, Inc v. Makor Issues & Rights, Ltd. The case was originally
dismissed by the district court of Northern Illinois, reversed by the 7th circuit court upon appeal17,
further appealed to the Supreme Court which granted certiorati18, and finally judge Posner of the
17 See 437 F.3d 588, 602 (7th Cir. 2006) 18 See Tellabs, Inc. v. Makor Issues & Rights, Ltd. 551 U.S. 308 (2007).
7th circuit court rendered final ruling following Supreme Court’s clarified pleading standard.19 We
choose Tellabs because it is one of the Supreme Court’s first efforts to clarify the key legal element
in 10b-5 lawsuits: the strong inference standard for pleading Scienter, a “mental state embracing
intent to deceive, manipulate, or defraud.”20
Prior to Tellabs, there is longstanding confusion in the federal courts as to what is required of
scienter allegations in order to defeat a motion to dismiss under the Private Securities Litigation
Reform Act (PSLRA) of 1995.21 Different courts of appeals followed their own approach, and
monitor by the Supreme Court is close to nonexistent (Westerland et al., 2010). For example, the
1st, 4th, 6th, and 9th circuit courts adopted a “preponderance” standard that is most favorable to
defendant. It requires the inference that the defendants had the requisite Scienter (fraudulent intent
or recklessness) to be the most plausible when compared with competing inference of “No
Scienter”. The 2nd, 8th, 10th and 11th circuit and DC District Court adopted an “equal inference”
standard that requires at least a “tie” of competing inference of Scienter and No Scienter. Lastly,
the 3rd and 7th circuits adopted a “reasonable person” standard that is most favorable to plaintiffs.
It only requires the court to look at the plausibility of the plaintiff’s allegations, without requiring
the assessment of competing inferences (Choi and Pritchard, 2012).
Importantly, the Supreme Court’s ruling on Tellabs in 2007 clarifies what is required for the
plaintiff to plead Scienter. It held that plaintiffs shall survive a motion to dismiss “only if a
reasonable person would deem the inference of [culpable state of mind] cogent and at least as
compelling as any opposing inference one could draw from the facts alleged”.22 This stance of the
Supreme Court thus mimics the middle, “equal inference” standard, which is more stringent than
19 See Makor Issues & Rights, Ltd. v. Tellabs, Inc., F.3d, No. 04-1687, 2008 WL 151180 (7th Cir. Jan. 17, 2008). 20 See Ernst & Ernst v. Hochfelder, 425 U.S. 185, 193 (1976) 21 22 See Tellabs, Inc. v. Makor Issues & Rights, Ltd. 551 U.S. 308 (2007), at 324.
the “preponderance” standard adopted by the 1st, 4th, 6th, and 9th circuit. To the extent that lower
courts make decisions anticipating upper court’s tendency and risk of reversal (Gulati, Choi and
Posner, 2012; Choi, Gulati, and Posner, 2016), the effect of Tellabs is that it homogenizes what is
required for plaintiffs to establish scienter across all circuits. Specifically, we hypothesize that the
Tellabs decision exogenously increase the pleading standard of federal courts that previously
adopted a “pro-defendant” standard of scienter relative to other federal courts.
We follow this conjecture to design a quasi-difference-in-differences test. Using Tellabs as a
shock on court pleading standard, the first difference is firms’ voluntary restatement likelihood
between the post-event period and pre-event period. The second difference is that under pre-event
lenient Circuits relative to that of district courts under pre-event non-lenient circuits. Note this
“diff-in-diff” design in our analysis is different from the original diff-in-diff test, which requires
the control group to be unaffected by the treatment. Here our control group comprising of misstated
firms headquartered in non-lenient courts are also affected, but not in the direction of the treatment
group. To the extent that the Supreme Court’s decision is unpredictable, which affects the pleading
standards of its Circuits in different directions, we believe the relevant changes in court dismissal
rate between treatment group and control group are also exogenous, thus our research design is
still valid.
Following Choi and Pritchard (2011), we categorize district courts under the 1st, 4th, 6th, and
9th circuit as “pre-event lenient circuit” (treatment observations), and district courts under other
circuits as control observations. We choose the 6-year window (3-year pre- and 3-year post-
Tellabs), taking into account the fact that it takes time for managers of misstated firms to learn
about their accounting mistakes and deliberate on restatement decision in response to altered
pleading standard. As the Tellabs case spans 2006 through 2007, these two years are excluded
from our event window. Thus, the pre-event period for the 6-year window is from January 2003
to December 2005; and the post-event period for the 6-year window is from January 2008 to
December 2010.
To see whether Tellabs decision has homogenizing effect on federal courts, we first check the
court dismissal rate for the district courts under the lenient (the “preponderance” standard) circuits
and that under other circuits pre- and post-Tellabs case. The results are shown in Panel A of Table
6. Consistent with Choi and Pritchard (2012), we find the court dismissal rates for the district
courts under the treatment circuits decreases right after the Tellabs case while those under the
control circuits increase after Tellabs.
Panel B of Table 6 presents the results of the diff-in-diff analysis, i.e., the change in voluntary
restatement likelihood after the Tellabs case under different court stringency. Column 1 and 2 use
SDF and Column 3 and 4 use SRF. To eliminate potential bias of sample truncation when including
control variables, we only control for industry fixed effects in regressions for Column 1 and 3,
while we still perform regressions with full controls in Column 2 and 4. Remarkably, after Tellabs,
the voluntary restatement probability of the lenient circuit firms significantly decreased relative to
the control group. The coefficient estimate of the interaction term between pre-event lenient circuit
dummy and post-event dummy is significantly negative (p=0.0484, p=0.0301, p=0.0300 and
0.0997 in Column 1 to 4, respectively). Taken together, our result provides preliminary causal
evidence that firms adjust their voluntary restatement policy in response to (exogenous) changes
in court stringency.
4.4. Court Stringency and Legal / Market Outcomes of Voluntary Restatements
So far, we have shown that misreporting firms are more likely to make voluntary restatements
when they face a more lenient court. Whether this strategy “works”, in the sense that it results in
better legal and market outcomes, is another question. Our working hypothesis is the following: If
a strategy of voluntary restatement in lenient court jurisdictions is indeed effective, than we should
find the legal and market outcomes of voluntary restaters in lenient courts to be “better” than in
non-lenient courts.
For a start, we cannot hope to compare the likelihood of litigation between voluntary restaters
in lenient and stringent courts, because our definition of voluntary restatement requires firms to be
litigated. We can, however, test: (1) whether lawsuits against voluntary restaters are easier to be
dismissed in more lenient courts; (2) whether voluntary restaters pay less settlement amount in
more lenient courts; and (3) the market reactions upon key event dates for voluntary restaters in
lenient and stringent courts.
Specifically, we conduct the following regression model in this section:
𝐿𝑒𝑔𝑎𝑙/𝑀𝑎𝑟𝑘𝑒𝑡𝑂𝑖 = 𝛾0 + 𝛾1𝑉𝑅𝑖 + 𝛾2𝐿𝐶𝑖 + 𝛾3𝐿𝐶𝑖 × 𝑉𝑅𝑖 + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝜀𝑖, (3)
where, 𝐿𝑒𝑔𝑎𝑙𝑂𝑖 is the lawsuit outcome measures, which include (1) indicator of dismissal, (2)
settlement amount scaled by the defendant firm’s total assets, and (3) the unconditional legal cost,
which equals to the settlement amount scaled by the defendant firm’s total assets when the motion-
to-dismiss is denied and 0 otherwise. 𝑀𝑎𝑟𝑘𝑒𝑡𝑂𝑖 including 3-day (-1, +1) cumulative abnormal
return (CAR) around class-end date and case-filing date. 𝑉𝑅𝑖 is a dummy variable that takes 1 if
the defendant firm in case 𝑖 makes voluntary restatement and 0 otherwise. 𝐿𝐶𝑖 is a dummy variable
that takes 1 if the court dismissal rate of the headquartering court falls into the top quarter and 0 if
the court dismissal rate of the headquartering court falls into the bottom quarter. To control for the
severity of the case we include the stock return during the class period and the natural logarithm
of the length of the class period. Merging the daily stock trading data from CRSP and the filing
dates and class-end dates from SCAS reduces our SDF firms to 149 observations and SRF firms
to 116 observations.
Table 7 Panel A present the result on legal outcomes. Column 1 and 2 show the probability of
being dismissed, Column 3 and 4 show the settlement amount, and Column 5 and 6 show the
unconditional legal cost. Consistent with our prediction, the interaction term between Voluntary
Restating Dummy and Lenient Court Dummy is positive in dismissal, negative in settlement
amount, and negative in unconditional cost amount, suggesting voluntary restating firms in lenient
courts are more likely to win the litigation, pay less settlement amount, and incur less litigation
cost than those in stringent courts. However, none of these interaction effects is statistically
significant.
Panel B of Table 7 presents the result on market outcome. We focus on the share price reaction
around two key dates for SDF and SRF. The first is the “class end date” which is the date when
the truth that “corrected” the stock price was allegedly revealed to the market (Kellogg, 1984;
Griffin, Grundfest, and Perino, 2004; Gande and Lewis, 2009). The second event date is the “case
filing date”, which is the date when the first plaintiff filed the lawsuit in a district court. We
examine both event dates because the class end date was the first time that market knows about
the misreporting, and studies that only focus on filing date returns tend to underestimate the true
economic costs associated with securities lawsuits (Gande and Lewis, 2009). Column 1-4 of show
the 3-day (-1, +1) cumulative abnormal return (CAR) around class-end date and case-filing date
on SDF, and Column 5-8 on SRF using regression model in (3). We find insignificant coefficients
for the interaction term between Voluntary Restating Dummy and Lenient Court Dummy (all p-
values>0.1), indicating that the benefit on the market reactions around class-end date and case-
filing date of making voluntary restatement under lenient courts is not different from that under
stringent courts. There is weak evidence that voluntary restatements are beneficial for misstated
firms, indicated by the marginally significant positive coefficient for the Voluntary Restatement
Dummy in Column 5 (p-value=0.0648).
4.5. Discussion
Results in Table 7 suggest making voluntary restatement in lenient courts can gain some
advantage for the firm, but such advantage is not statistically significant between lenient and
stringent courts. This result, however, shall not be taken as evidence defying our baseline results
for two reasons:
First, note our empirical design only allows us to use litigated firms. It is possible that a strategy
of making voluntary restatement in lenient courts prevents shareholder litigation more in lenient
than stringent courts. For example, using a simultaneous equations methodology, Field, Lowry,
and Shu (2005) find that voluntary disclosure of bad news does not trigger, and even deters certain
types of litigation. If that is the case, such strategy might be successful.
Second, as this paper argues, court dismissal rates are taken as a heuristic, or mental short cut
for court stringency. It is possible that managers’ responsiveness to court dismissal rates reflect
their cognitive biases, such as the availability heuristics that we demonstrate in earlier test. It is
therefore not surprising that we do not find a voluntary restatement strategy to work significantly
better in lenient than stringent courts. However, it is important to note that none of these
interpretations affects the validity of the fact we establish: misstated firms are more likely to make
voluntary restatement in lenient than stringent courts.
5. Robustness Check
5.1. Alternative Measure of Court Dismissal Rate
One shortcoming in the construction of our key explanatory variable, court dismissal rate, is
that cases dismissed within five years may not exactly correspond to cases filed during the same
period. However, to account for the fact that different district courts have different number of
headquartering firms and lawsuit filings, we believe this scaling method is reasonable. To address
this issue empirically, this subsection tests our baseline hypothesis using alternative court dismissal
rate that takes into account the gap between case filing date and case dismissal date.
𝑑𝑖𝑠𝑚𝑖𝑠𝑠𝑎𝑙𝑖,𝑡−5.5→𝑡−1.5 =𝑛𝑜_𝑑𝑖𝑠𝑚𝑖𝑠𝑠𝑎𝑙𝑖,𝑡−4→𝑡𝑛𝑜_𝑓𝑖𝑙𝑖𝑛𝑔𝑠𝑖,𝑡−5.5→𝑡−1.5
(5)
where, 𝑛𝑜_𝑑𝑖𝑠𝑚𝑖𝑠𝑠𝑎𝑙𝑖,𝑡−4→𝑡 is the number of cases dismissed within the five years prior to the end
of fiscal year 𝑡 of firm 𝑖 handled by the district court where firm 𝑖 is headquartered; and
𝑛𝑜_𝑓𝑖𝑙𝑖𝑛𝑔𝑠𝑖,𝑡−5.5→𝑡−1.5 is the number of cases filed within the five years ended 18 months prior to
the end of fiscal year 𝑡 of firm 𝑖 handled by the district court where firm 𝑖 is headquartered. The
period of 18 months is the average gap between the filing date and dismissal date for our dismissed
cases from 1996 to 2013 in the SCAS database. Panel A of Table 8 compares the alternative court
dismissal rate with our original court dismissal rate in all Compustat firm-years between 2000 and
2013. Our alternative court dismissal rate is insignificantly different from our original court
dismissal rate (p=0.2538), confirming that our original court dismissal rate well captures court
stringency despite the gap between filing period and dismissal period.
We re-run the baseline regression analysis in Equation 2 with this alternative court dismissal
rate. Panel B of Table 8 reports the results using SDF (Column 1 and 2) and SRF (Column 3 and
4). We find court dismissal rate still exhibits a significantly positive impact on the propensity of
misreporting firms issuing voluntary restatement (p=0.0013, 0.0004, 0.0008 and 0.0002 for the
four regressions, respectively). This evidence supports that the gap between case filing date and
case dismissal date does not contaminate our results.
5.2. Material Weakness Sample and External Validity
This paper’s research design relies on lawsuits to identify voluntary restating firms, which has
the advantage of internal validity. However, in practice only a proportion of misstated firms are
sued in securities lawsuits. To test whether our baseline findings that firms in lenient courts are
more likely to make voluntary restatement is generalizable to larger sample of misstated firms, this
section employs an alternative sample of firms that receive the material weakness opinion from
their auditors.
Our material weakness sample is constructed based on the internal control reports on the
material weakness pursuant to SOX 404. After the SOX, there is auditor responsibility to identify
“Material Weakness (MW)” in the internal control of the firm following Section 404, which is
approved on June 5, 2003 and mandatorily enforced after April 15, 2005. Studies show firms who
receive MW opinion have high restatement propensity and are likely to continue having
misstatements in the following two years after receiving MW opinions (e.g. Myllymäki, 2013).
Therefore, firms that receive MW opinion could be good candidate for us to construct alternative
sample of misstated firms not based on ex post lawsuits.
Appendix B.3 presents the selection process of our material weakness sample. First, we
restrict our sample to 149,223 SOX 404 disclosure records from Audit Analytics SOX 404
database with opinion fiscal year in the period of 2003 to 2013. Then, we merge the SOX 404
records with Compustat firm-years by CIK and ensure that the Compustat datadate is within the
3-year window (-1,+1) taking SOX 404 opinion fixcal year as year 0 (Myllymäki, 2013), leaving
23,625 observations with at least 1 item of material weakness reported. By combining the 23,625
material weakness records with restatement database from Audit Analytics, we obtain 24,084
observations with or without restatement records23. We select the firms with non-accounting-error
restatement and restating date within 1-year period after restating period as our voluntary restating
firms, and the remaining as control firms. Finally, by matching the sample with SCAS, CRSP and
Compustat variables in the later tests, we obtain a final sample comprising 6,436 observations
from January 1, 2003 to December 31, 2013, with 1,591 voluntary restating firm-years and 4,845
control observations. Table 9, Panel A displays the distribution of material weakness sample by
district courts, Panel B reports their yearly distribution, and Panel C reports the summary statistics
of major variables in the material weakness sample.
Panel D reports empirical results using our material weakness sample. Consistent with our
baseline result, court dismissal rate exhibits a significantly positive impact on the likelihood of
material weakness firms issuing voluntary restatements (p=0.0426 and 0.0015 two regressions
respectively). Column (3) displays the results of diff-in-diff analysis in the Tellabs case. After
Tellabs, the likelihood of making voluntary restatements of firms in the pre-event lenient circuits
decreased significantly compared with that of firms in the pre-event “equal inference” circuits,
which is indicated by the significantly negative coefficient estimate of the interaction term between
pre-event lenient circuit and post-event dummy.
Column (4) of Panel D presents result of an interesting placebo test on error-based restatements.
Unlike accounting irregularities which are often related to fraud, accounting errors are mainly due
23 The number of observations (24,084) in matched result exceeds 23,625 because some MW firms made multiple
restatements on different periods of a fiscal year.
to accidental omissions which are unlikely to support a 10b-5 lawsuit. This key difference allows
us to use propensity of voluntary restatement on accounting errors as placebo. Intuitively, if firms’
restatement policy is indeed affected by their home court stringency in 10b-5 lawsuits, which, by
legal criteria, is only relevant to fraud, then we should not expect to find court dismissal rate to
have significant effect on restatement propensity for accounting errors. As predicted, Column (4)
of Panel D shows that the court dismissal rate exhibits an insignificantly positive impact on the
likelihood of material weaknss firm restating accounting errors (p=0.2882). This evidence, albeit
indirect, lends some support to our argument that firms actively consider their home court
stringency when making irregularity-based restatement.
6. Conclusion
This paper provides the first evidence that district court stringency affects misreporting firms’
propensity to admit their accounting mistakes through restatements. We find strong and robust
evidence that more stringent legal environment makes misreporting firms unlikely to make
voluntary restatements. This strategy, however, appears not rewarded by the market or the court.
This result sheds light on the long-debated question on the effect of institutional environment on
voluntary disclosure of bad news. Whilst stronger legal environment may deter firms’ misreporting,
it also deters misreporting firms’ tendency to admit their mistake. Future studies on the impact of
legal environment on financial reporting quality will need to account for this complication in their
conclusions.
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Appendix A. Variable Definition and Construction
=1 if Voluntary
Restating
= indicator variable, equals to 1 if the firm restated its financial statements
before class-end date in Sample 1 and 2 or within 1 year after restating
period in Sample 3 and is not labelled in Audit Analytics as “Res Clerical
Errors”, 0 otherwise
Court Dismissal Rate = court dismissal rate is the number of dismissed cases in the Federal
District Court that the firm belongs to in the 5 years before the most recent
fiscal year end, scaled by the number of cases filed during the same period
in the Court
Court Filing Rate = court filing rate is the number of filed cases in the Federal District Court
that the firm belongs to in the 5 years before the most recent fiscal year
end, scaled by the number of Compustat firms during the same period in
the Court
Recent Court Dismissal
Rate
= recent court dismissal Rate is a 2-year court dismissal rate calculated from
the most recent 2 years’ dismissal number in the Federal District Court
that the firm belongs to scaled by the filing number in the corresponding
period
Distant Court Dismissal
Rate
= recent court dismissal Rate is a 3-year court dismissal rate calculated from
the dismissal number in the Federal District Court that the firm belongs to
in the period 3 years from the most recent fiscal year end to 5 yeasr from
the most recent fiscal year end scaled by the filing number in the
corresponding period.
Log Total Assets = natural Logarithm of total assets (log(at))
Leverage = leverage, total debt scaled by total assets ((dltt+dlc)/at)
ROA = return on assets, net income scaled by total assets (ni/at)
Sales Growth = the different between sales in the most recent fiscal year and pervious year
divided by the sales in pervious year
Last Year Stock Return = compounded gross return over the most recent fiscal year
Beta = beta from CAPM model, estimated from the monthly stock returns of the
5-year period before the most recent fiscal year end
Return Volatility = return volatility, standard deviation of the daily return within the most
recent fiscal year
Turnover
(in 1000s)
= 1-(1-TURN)n, where turn is average daily trading volume divided by the
number of shares outstanding and n is the number of trading days in the
most recent fiscal year
Skewness = the third moment of the return distribution over the most recent fiscal year
Book-to-Market = the book value of equity (CEQ) plus book value of deferred taxes (TXDB)
divided by market value (PRC*SHROUT/1000), measured at the most
recent fiscal year end.
=1 if Auditor is Big 4 = indicator variable, equals to 1 if the firm is audited by big 4, 0 otherwise
Pre-Event Lenient
Circuit Dummy
= indicator variable, equals to 1 if the firm is in the jurisdiction of district
courts under the 1st, 4th, 6th, and 9th circuit following Choi and Pritchard
(2011), 0 otherwise
Lenient Court Dummy = indicator variable, equal to 1 if the court dismissal rate of the observation
is in the top quarter of court dismissal rate for all compustat firms, 0
otherwise
Filing Date
CAR (-1,+1)
= 3-day [t-1, t+1] cumulative abnormal return around the Federal case filing
date, calculated by the cumulative return of the defendant firm’s stock
over the event window minus the cumulative return of the CRSP value-
weighted return including dividends over the event window
Revealing Date
CAR (-1,+1)
= 3-day [t-1, t+1] cumulative abnormal return around the accounting
misconduct revealing date, calculated by the cumulative return of the
defendant firm’s stock over the event window minus the cumulative return
of the CRSP value-weighted return including dividends over the event
window
=1 if Dismissed = indicator variable in Sample 1 and 2, equal to 1 if the case is dismissed, 0
otherwise
Settlement Amount = the total settlement amount scaled by total assets
Unconditional Legal
Cost Amount
= the total settlement amount scaled by total assets if the case is settled, 0
otherwise
Class-period Return = The cumulative stock return over the class period
Log Class Length = The natural logarithm of the number of days over the class period
=1 if Sued = indicator variable in Sample 3, equals to 1 if the firm is filed with class
action lawsuits as defendant after making voluntary restatement and the
lawsuit’s class period overlaps with the restating period, 0 otherwise
Appendix B.1 Selection of Defendant Firms Sample
This table presents the selection process of our defendant firms sample, where each observation is a
defendant firm in securities class action lawsuits (SCA). Voluntary restating firms are those with
restatement before class-end date, and control firms are those without voluntary restatement but alleged
with both GAAP violation and Rule 10b-5 violation.
Sample Selection Procedures No. of Obs.
Voluntary Restating Firms
Begin with securities class action lawsuit defendant firms whose GVKEY, federal
filing date, class periods and dismissal date (settlement date) are identifiable; 3,363
Merge with 11,377 restatement records from Audit Analytics; 789
Eliminate 514 observations with restatement on or after class-end date; 275
Eliminate observations without valid variables in our tests and require fiscal-end
date to be between December 31, 2000 to December 31, 2013. 111
Control Firms without Voluntary Restatement
Begin with securities class action lawsuit defendant firms whose GVKEY, federal
filing date, class periods and dismissal date (settlement date) are identifiable; 3,363
Merge with Compustat annual financial database; 3,170
Exclude 249 observations with restatement before class-end date; 2,921
Require defendant firms to be alleged of both GAAP violations and Rule 10b-5
violations; 928
Eliminate observations without valid variables in our tests and require fiscal-end
date to be between December 31, 2000 to December 31, 2013. 282
Total Sample of Defendant Firms 393
Voluntary Restating Firms 111
Control Firms without Voluntary Restatement 282
Appendix B.2 Selection of Restating Firms Sample
This table presents the selection process of our restating firms sample, where each observation is an SCA
defendant firm with restatement. Voluntary restating firms are defined and selected using the same method
in Appendix B.1. For control firms, we require our defendant firms to have made corresponding restatement
on or after the class-end date. The final sample comprises 300 observations from 2001 to 2012, with 111
voluntary restating firms and 189 control firms.
Sample Selection Procedures No. of Obs.
Begin with securities class action lawsuit defendant firms whose GVKEY, federal
filing date, class periods and dismissal date (settlement date) are identifiable; 3,363
Merge with 11,377 restatement records from Audit Analytics; 789
Take 275 observations with restatement before class-end date as voluntary
restating firms and 514 observations with restatement on or after class-end date as
control firms;
Eliminate observations without valid variables in our tests and require fiscal-end
date to be between December 31, 2000 and December 31, 2013. 300
Total Sample of Restating Firms 300
Voluntary Restating Firms 111
Control Firms without Voluntary Restatement 189
Appendix B.3 Selection of Material Weakness Firms Sample
This table presents the selection process of our material weakness firms sample, where each observation is
a firm-year with material weakness (MW) from auditor’s opinion. We define voluntary restating
observations as those that made non-error-based restatement within 1 year after restating period, and the
remaining as control observations. The final sample comprises 6,436 firm-year observations from January
1, 2003 to December 31, 2013, with 1,591 voluntary restating observations and 4,845 control observations.
Sample Selection Procedures No. of
Obs.
Begin with SOX 404 disclosure records from Audit Analytics SOX 404 database with
opinion fiscal year 2004 to 2013 149,223
Merge with Compustat firm-years with three criteria: 1) CIK is matched; 2) at least 1
material weakness item is reported; and (3) the Compustat datadate is within the 3-
year window (-1,+1) taking SOX 404 opinion fiscal year as year 0
23,625
Combine with restatement records from Audit Analytics restatement database 24,084
Eliminate observations without valid variables in our tests 6,436
Total Sample 6,436
Voluntary Restating Firms 1,591
Control Firms 4,845
Figure 1 Trends in Voluntary Restatement for Securities Class Action Defendants
This figure presents the trends in voluntary restatement among securities class action (SCA) defendants.
We assign the value series of lawsuits to the left vertical axis while the right axis indicates the proportion
of voluntary restatements. Voluntary restatements are defined as SCA lawsuit defendants that make non-
accounting-error restatement before class-end date (misstatement revealing date). Involuntary restatements
are defined as SCA lawsuit defendants that make non-accounting-error restatement on or after class-end
date. Other SCA lawsuits are the SCA lawsuits excluding voluntary restatements and involuntary
restatements.
16 25 26 16 32 27 6 9 15 12 9 4 0
42 3161
4355
2624 17 22 16 21 16 4
752
274 226 306
148
102213
294226 233 238 241 257
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
18.0%
20.0%
0
100
200
300
400
500
600
700
800
900
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
# of Voluntary Restatements # of Involuntary Restatements
# of Other SCA Defendants % of Voluntary Restatements
Figure 2 Federal District Court Dismissal Rate on 10b-5 securities lawsuits (2001-2013)
This figure displays the variation of court dismissal rate among district courts and volatility of court dismissal rate within each district court.
According to the five-year court dismissal rate calculated in Appendix A, we take average of each court’s dismissal rate in the period between
December 31, 2000 and December 31, 2013 and calculate the standard deviation of court dismissal rate for each court in this period. Then, we rank
all the courts according to their average court dismissal rate into three tertiles and mark them on the map with three different colors. The numbers in
the map represent the standard deviation of court dismissal rate for each court and indicate the volatility of dismissal rate within each district court.
Figure 3 Event Window in the Tellabs Case
This figure displays the event window of 2006 Tellabs case, which harmonized court stringency during
2006 and 2007, and increases stringency of district courts under pre-event lenient circuits relative to that of
district courts under pre-event middle-stringency circuits. We exploit this homogenizing effect on the
likelihood of making voluntary restatement in a diff-in-differences test. The first difference is between the
post-event period and pre-event period. The diff-in-diff is between the pre-event lenient circuit courts and
pre-event middle stringency circuit courts. Following Choi and Pritchard (2011), we categorize district
courts under the 1st, 4th, 6th, and 9th circuit as “pre-event lenient circuit” (treatment observations), and
district courts under other circuits as control observations. We choose the 6-year window (3-year pre- and
3-year post-Tellabs), taking into account the fact that it takes time for managers of the misreporting firms
to know about their accounting mistakes and to deliberate on their restatement decision in response to
altered pleading standard. As the Tellabs decision spans 2006 through 2007, these two years are excluded
from our event window. Thus, the pre-event period for the 6-year window is from January 2003 to
December 2005; and the post-event period for the 6-year window is from January 2008 to December 2010.
Table 1 Sample Distribution
This table reports the distributions of our two samples. Panel A display the distribution of our two
samples by Federal Courts. Panel C exhibits the distribution of our two samples by year.
Panel A Distribution of Samples by Court
Sample of Defendant Firms Sample of Restating Firms =1 if Restating =1 if Restating
Federal Court N. of Obs. % Mean Std. Dev. N. of Obs. % Mean Std. Dev.
USDC - Alabama (Northern) 4 1.0% 25.0% 50.0% 3 1.0% 33.3% 57.7%
USDC - Arizona 9 2.3% 33.3% 50.0% 9 3.0% 33.3% 50.0%
USDC - California (Central) 34 8.7% 32.4% 47.5% 30 10.0% 36.7% 49.0%
USDC - California (Eastern) 1 0.3% 100.0% . 1 0.3% 100.0% .
USDC - California (Northern) 45 11.5% 37.8% 49.0% 36 12.0% 47.2% 50.6%
USDC - California (Southern) 12 3.1% 16.7% 38.9% 9 3.0% 22.2% 44.1%
USDC - Colorado 7 1.8% 14.3% 37.8% 6 2.0% 16.7% 40.8%
USDC - Connecticut 10 2.5% 30.0% 48.3% 9 3.0% 33.3% 50.0%
USDC - Delaware 2 0.5% 0.0% 0.0%
USDC - District of Columbia 4 1.0% 50.0% 57.7% 2 0.7% 100.0% 0.0%
USDC - Florida (Middle) 10 2.5% 20.0% 42.2% 4 1.3% 50.0% 57.7%
USDC - Florida (Southern) 8 2.0% 12.5% 35.4% 6 2.0% 16.7% 40.8%
USDC - Georgia (Northern) 10 2.5% 10.0% 31.6% 3 1.0% 33.3% 57.7%
USDC - Idaho 2 0.5% 50.0% 70.7% 1 0.3% 100.0% .
USDC - Illinois (Northern) 19 4.8% 21.1% 41.9% 18 6.0% 22.2% 42.8%
USDC - Indiana (Northern) 1 0.3% 0.0% .
USDC - Indiana (Southern) 5 1.3% 20.0% 44.7% 7 2.3% 14.3% 37.8%
USDC - Kentucky (Eastern) 1 0.3% 100.0% . 2 0.7% 50.0% 70.7%
USDC - Louisiana (Eastern) 2 0.5% 50.0% 70.7% 2 0.7% 50.0% 70.7%
USDC - Maryland 6 1.5% 0.0% 0.0% 1 0.3% 0.0% .
USDC - Massachusetts 14 3.6% 57.1% 51.4% 17 5.7% 47.1% 51.5%
USDC - Michigan (Eastern) 7 1.8% 42.9% 53.5% 5 1.7% 60.0% 54.8%
USDC - Michigan (Western) 2 0.5% 0.0% 0.0% 1 0.3% 0.0% .
USDC - Minnesota 9 2.3% 22.2% 44.1% 4 1.3% 50.0% 57.7%
USDC - Missouri (Eastern) 1 0.3% 0.0% .
USDC - Missouri (Western) 4 1.0% 25.0% 50.0% 3 1.0% 33.3% 57.7%
USDC - Nebraska 3 0.8% 100.0% 0.0% 3 1.0% 100.0% 0.0%
USDC - Nevada 3 0.8% 33.3% 57.7% 3 1.0% 33.3% 57.7%
USDC - New Hampshire 1 0.3% 0.0% . 1 0.3% 0.0% .
USDC - New Jersey 15 3.8% 26.7% 45.8% 10 3.3% 40.0% 51.6%
USDC - New York (Eastern) 9 2.3% 22.2% 44.1% 6 2.0% 33.3% 51.6%
USDC - New York (Southern) 32 8.1% 18.8% 39.7% 25 8.3% 24.0% 43.6%
USDC - New York (Western) 3 0.8% 33.3% 57.7% 2 0.7% 50.0% 70.7%
USDC - North Carolina
(Middle) 2 0.5% 0.0% 0.0% 1 0.3% 0.0% .
USDC - North Carolina (Western)
3 0.8% 0.0% 0.0%
USDC - Ohio (Northern) 8 2.0% 50.0% 53.5% 7 2.3% 57.1% 53.5%
USDC - Ohio (Southern) 9 2.3% 11.1% 33.3% 7 2.3% 14.3% 37.8%
USDC - Oklahoma (Northern) 1 0.3% 0.0% .
USDC - Oklahoma (Western) 1 0.3% 100.0% . 1 0.3% 100.0% .
USDC - Oregon 4 1.0% 50.0% 57.7% 3 1.0% 66.7% 57.7%
USDC - Pennsylvania (Eastern)
9 2.3% 55.6% 52.7% 9 3.0% 55.6% 52.7%
USDC - Pennsylvania
(Middle) 1 0.3% 100.0% . 1 0.3% 100.0% .
USDC - Pennsylvania
(Western) 1 0.3% 0.0% . 1 0.3% 0.0% .
USDC - Rhode Island 1 0.3% 0.0% .
USDC - South Carolina 1 0.3% 0.0% .
USDC - Tennessee (Eastern) 2 0.5% 0.0% 0.0% 1 0.3% 0.0% .
USDC - Tennessee (Middle) 4 1.0% 0.0% 0.0% 1 0.3% 0.0% .
USDC - Tennessee (Western) 2 0.5% 0.0% 0.0% 1 0.3% 0.0% .
USDC - Texas (Eastern) 8 2.0% 37.5% 51.8% 7 2.3% 42.9% 53.5%
USDC - Texas (Northern) 5 1.3% 20.0% 44.7% 1 0.3% 100.0% .
USDC - Texas (Southern) 5 1.3% 40.0% 54.8% 8 2.7% 25.0% 46.3%
USDC - Texas (Western) 6 1.5% 33.3% 51.6% 4 1.3% 50.0% 57.7%
USDC - Utah 2 0.5% 50.0% 70.7% 3 1.0% 33.3% 57.7%
USDC - Virginia (Eastern) 9 2.3% 22.2% 44.1% 7 2.3% 28.6% 48.8%
USDC - Washington (Eastern) 2 0.5% 0% 0% 1 0.3% 0.0% .
USDC - Washington (Western) 8 2.0% 13% 35% 4 1.3% 25.0% 50.0%
USDC - Wisconsin (Eastern) 5 1.3% 20% 45% 2 0.7% 50.0% 70.7%
Total 393 300
Panel B: Distribution of Samples by Year
Sample of Defendant Firms Sample of Restating Firms
Year N. of Obs. % of Obs. N. of Obs. % of Obs.
2001 60 15.3% 47 15.7%
2002 52 13.2% 44 14.7%
2003 56 14.2% 48 16.0%
2004 54 13.7% 34 11.3%
2005 42 10.7% 29 9.7%
2006 25 6.4% 15 5.0%
2007 26 6.6% 15 5.0%
2008 27 6.9% 16 5.3%
2009 17 4.3% 10 3.3%
2010 16 4.1% 14 4.7%
2011 11 2.8% 16 5.3%
2012 5 1.3% 12 4.0%
2013 2 0.5%
Total 393 300
Table 2 Summary Statistics
This table reports the summary statistics of variables in the two samples and the univariate analysis between voluntary restating firms
and control firms. Panel A and Panel B display the results of Sample of Defendant Firms and Sample of Restating Firms, respectively.
All variables are as defined in the appendix A, and variables are winsorized at 1% level except for voluntary restating indicator, court
dismissal rate and court filing rate. The superscripts, ***, **, and * denote the 1%, 5%, and 10% levels of significance, respectively.
Panel A: Summary Statistics of Sample of Defendant Firms
Sample of Defendant Firms Control Firms Voluntary Restating
Firms
N=393 N=282 N=111
(1) (2) (3)
Mean Std
Dev. Median Mean Std Dev. Mean Std Dev. (3) - (2) T-stat
=1 if Voluntary Restating 0.282 0.451 0.000
Court Dismissal Rate 0.359 0.221 0.348 0.337 0.211 0.415 0.238 0.079 3.04***
Court Filing Rate 0.157 0.200 0.097 0.166 0.217 0.134 0.146 -0.032 -1.68*
Log Total Assets 7.415 2.218 7.382 7.507 2.261 7.179 2.096 -0.328 -1.37
Leverage 0.235 0.217 0.198 0.234 0.219 0.237 0.213 0.003 0.13
ROA -0.030 0.218 0.020 -0.033 0.214 -0.022 0.227 0.010 0.41
Sales Growth 0.177 0.409 0.110 0.172 0.403 0.190 0.425 0.019 0.4
Last Year Stock Return 1.118 0.745 0.985 1.099 0.756 1.167 0.719 0.068 0.84
Beta 1.506 1.090 1.306 1.464 1.078 1.611 1.119 0.147 1.18
Return Volatility 0.036 0.019 0.031 0.036 0.019 0.034 0.019 -0.002 -1.15
Turnover (in 1000s) 2.893 2.498 2.072 2.956 2.547 2.733 2.374 -0.222 -0.82
Skewness 0.157 1.206 0.236 0.095 1.134 0.316 1.364 0.221 1.51
Book-to-Market 0.651 0.777 0.459 0.654 0.769 0.644 0.801 -0.010 -0.11
=1 if auditor is Big 4 0.710 0.454 1.000 0.709 0.455 0.712 0.455 0.002 0.05
Panel B: Summary Statistics of Sample of Restating Firms
Sample of Restating Firms Control Firms Voluntary Restating Firms
N=300 N=189 N=111
(1) (2) (3)
Mean Std Dev. Median Mean Std Dev. Mean Std Dev. (3) - (2) T-stat
=1 if Voluntary Restating 0.370 0.484 0.000
Court Dismissal Rate 0.367 0.213 0.352 0.338 0.193 0.415 0.238 0.077 2.91***
Court Filing Rate 0.160 0.209 0.096 0.175 0.237 0.134 0.146 -0.041 -2.09**
Log Total Assets 7.216 2.133 7.027 7.238 2.160 7.179 2.096 -0.059 -0.23
Leverage 0.253 0.226 0.227 0.263 0.232 0.237 0.213 -0.026 -1
ROA -0.033 0.230 0.019 -0.040 0.232 -0.022 0.227 0.018 0.64
Sales Growth 0.224 0.468 0.106 0.244 0.491 0.190 0.425 -0.054 -1
Last Year Stock Return 1.153 0.765 1.004 1.144 0.793 1.167 0.719 0.023 0.26
Beta 1.491 1.057 1.300 1.421 1.015 1.611 1.119 0.190 1.47
Return Volatility 0.036 0.018 0.032 0.037 0.018 0.034 0.019 -0.003 -1.4
Turnover (in 1000s) 2.991 2.582 2.130 3.143 2.691 2.733 2.374 -0.409 -1.37
Skewness 0.133 1.250 0.207 0.026 1.168 0.316 1.364 0.290 1.87*
Book-to-Market 0.581 0.699 0.437 0.544 0.632 0.644 0.801 0.100 1.13
=1 if auditor is Big 4 0.760 0.428 1.000 0.788 0.410 0.712 0.455 -0.077 -1.46
Table 3 Impact of Court Dismissal Rate on Propensity of Voluntary Restatement
This table reports the impact of court dismissal rate on the likelihood of headquartering firms making
voluntary restatement conditioning on accounting misstatements. Panel A reports the probit regression
results. Panel B reports the change in voluntary restatement rate with one-standard deviation increase
(decrease) in court dismissal rate. In both panels, Column 1 and 2 display the results for defendant firms
sample, and Column 3 and 4 exhibits the results for restating firms sample. All variables are defined in the
appendix A. Numbers in parentheses represent t-values. The superscripts, ***, **, and * denote the 1%, 5%,
and 10% levels of significance, respectively.
Panel A: Probit Regression Results =1 if making voluntary restatement
Sample of Defendant Firms Sample of Restating Firms
VARIABLES (1) (2) (3) (4)
Court Dismissal Rate 2.238 1.424 1.908 1.855
(3.13)*** (3.19)*** (2.45)** (3.2)***
Court Filing Rate 0.062 0.095 -0.551 -0.268 (0.09) (0.2) (-0.65) (-0.52)
Log Total Assets -0.151 -0.109 -0.092 -0.066 (-2.36)** (-2)** (-1.2) (-1.01)
Leverage 0.648 0.843 0.475 0.411 (1.31) (1.94)* (0.79) (0.75)
ROA 0.18 0.24 0.262 0.239 (0.37) (0.54) (0.5) (0.5)
Sales Growth 0.076 -0.035 -0.086 -0.125 (0.32) (-0.16) (-0.34) (-0.55)
Last Year Stock Return -0.118 -0.113 -0.446 -0.328 (-0.78) (-0.81) (-2.37)** (-2)**
Beta 0.074 0.012 0.103 0.094
(0.59) (0.1) (0.72) (0.73)
Return Volatility -14.425 -7.126 -7.239 -9.789 (-1.56) (-0.9) (-0.71) (-1.09)
Turnover (in 1000s) 0.032 -0.027 -0.1 -0.084 (0.69) (-0.68) (-1.87)* (-1.82)*
Skewness 0.131 0.133 0.151 0.14
(1.51) (1.77)* (1.33) (1.49)
Book-to-Market -0.166 -0.038 -0.072 -0.016
(-0.99) (-0.28) (-0.32) (-0.08)
=1 if auditor is Big 4 0.239 0.233 -0.063 -0.075 (1.06) (1.21) (-0.23) (-0.31)
Constant -9.133 -8.283 -2.321 -1.684 (0) (0) (-1.51) (-1.48)
No. of Obs 393 393 300 300
No. of Timely Restating Firms 111 111 111 111
No. of Culpable Firms 282 282 189 189
Pseudo R-sq. 30% 21% 35% 29%
Time F.E. Yes Yes Yes Yes
Industry F.E. Yes Yes Yes Yes
State F.E. Yes No Yes No
Panel B: Change in Voluntary Restatement Rate with 1-Std. Dev. Change in Court Dismissal Rate
Incremental Impact on Voluntary Restatement Rate
Sample of Defendant Firms Sample of Restating Firms
(1) (2) (3) (4)
1 Std. Dev. Increase in CDR 18.56% 11.48% 25.36% 24.90%
1 Std. Dev. Decrease in CDR -14.04% -9.59% -5.71% -5.35%
Time F.E. Yes Yes Yes Yes
Industry F.E. Yes Yes Yes Yes
State F.E. Yes No Yes No
Table 4 Recency Effect
This table presents the recency effect of managers using court dismissal information by comparing the
impact of recent dismissal information on firm’s voluntary restating likelihood and the impact of distant
dismissal information. For recent dismissal information, we use the 2-year court dismissal rate calculated
from the most recent 2 years in the firm’s headquartering district court. For distant dismissal information,
we use the 3-year court dismissal rate calculated from the year 3 to year 5 in the firm’s headquartering
district court. All variables are as defined in the appendix A. Numbers in parentheses represent t-values.
The superscripts, ***, **, and * denote the 1%, 5%, and 10% levels of significance, respectively.
=1 if making voluntary restatement
Sample of Defendant Firms Sample of Restating Firms
VARIABLES (1) (2) (3) (4) (5) (6)
Recent Court Dismissal Rate 0.712 0.671 0.734 0.655 (3.39)*** (2.8)*** (2.93)*** (2.21)**
Distant Court Dismissal Rate 0.676 0.133 0.789 0.223 (2.07)** (0.34) (2.06)** (0.49)
Court Filing Rate -0.248 -0.35 -0.227 -1.069 -1.002 -1.017 (-0.35) (-0.49) (-0.32) (-1.26) (-1.18) (-1.19)
Log Total Assets -0.119 -0.144 -0.121 -0.103 -0.112 -0.102 (-1.85)* (-2.3)** (-1.87)* (-1.31) (-1.44) (-1.3)
Leverage 0.633 0.672 0.637 0.605 0.533 0.597 (1.28) (1.37) (1.28) (0.98) (0.87) (0.96)
ROA 0.151 0.244 0.133 -0.012 0.222 -0.009 (0.31) (0.51) (0.28) (-0.02) (0.43) (-0.02)
Sales Growth -0.037 0.008 -0.032 -0.203 -0.213 -0.201 (-0.15) (0.04) (-0.13) (-0.76) (-0.82) (-0.76)
Last Year Stock Return -0.113 -0.079 -0.11 -0.435 -0.397 -0.426 (-0.74) (-0.53) (-0.72) (-2.26)** (-2.1)** (-2.2)**
Beta 0.056 0.101 0.059 0.066 0.134 0.071
(0.43) (0.81) (0.46) (0.45) (0.93) (0.48)
Return Volatility -11.312 -15.851 -12.127 -12.899 -18.272 -13.831 (-1.23) (-1.72)* (-1.28) (-1.16) (-1.63) (-1.22)
Turnover (in 1000s) 0.02 0.027 0.022 -0.082 -0.068 -0.079 (0.44) (0.59) (0.47) (-1.49) (-1.24) (-1.42)
Skewness 0.127 0.14 0.127 0.146 0.187 0.145
(1.44) (1.6) (1.44) (1.26) (1.65) (1.26)
Book-to-Market -0.136 -0.168 -0.14 0.132 0.078 0.129
(-0.79) (-0.99) (-0.81) (0.55) (0.33) (0.54)
=1 if auditor is Big 4 0.282 0.313 0.282 -0.056 -0.113 -0.067 (1.22) (1.37) (1.22) (-0.18) (-0.39) (-0.22)
Constant -8.397 -8.015 -8.425 -2.989 -2.677 -3.145 (0) (0) (0) (-1.71)* (-1.46) (-1.73)*
No. of Obs 375 375 375 290 290 290
No. of Timely Restating Firms 108 108 108 108 108 108
No. of Culpable Firms 267 267 267 182 182 182
Pseudo R-sq. 30% 29% 30% 38% 37% 38%
Time F.E. Yes Yes Yes Yes Yes Yes
Industry F.E. Yes Yes Yes Yes Yes Yes
State F.E. Yes Yes Yes Yes Yes Yes
Table 5 Cross-Sectional Tests: Most Active Courts vs. Less Active Courts
This table reports the cross-sectional test on the impact of court dismissal rate on the likelihood of firm
issuing voluntary restatement conditioning on that they committed accounting mistakes between top active
courts and the rest. California (Central), California (Northern), Florida (Southern), Massachusetts and New
York (Southern) are the top 5 active courts in the SCAS database in terms of making dismissal decisions.
All variables are as defined in the appendix A. Numbers in parentheses represent t-values. The superscripts, ***, **, and * denote the 1%, 5%, and 10% levels of significance, respectively.
=1 if making voluntary restatement w/o Top 5 Active Courts Top 5 Active Courts
Sample of Defendant
Firms
Sample of Restating
Firms
Sample of Defendant
Firms
Sample of Restating
Firms
Sample of Defendant
Firms
Sample of Restating
Firms
VARIABLES (1) '(2) (3) (4) (5) (6)
Court Dismissal Rate 2.313 1.744 0.744 1.311 2.336 2.628 (3.12)*** (2.18)** (1.73)* (2.26)** (1.33) (1.23)
× Top Active Courts Dum. -0.377 1.536
(-0.24) (0.84)
Top Active Court Dummy 0.796 -1.093
(0.96) (-1.11)
Court Filing Rate -0.698 0.433 -1.605 -1.18 0.116 -0.676 (-0.69) (0.36) (-0.9) (-0.48) (0.13) (-0.63)
Log Total Assets -0.171 -0.102 -0.071 -0.007 0.015 0.087 (-2.58)** (-1.29) (-1.31) (-0.1) (0.16) (0.77)
Leverage 0.747 0.46 0.904 0.064 -0.589 -0.907 (1.47) (0.75) (1.99)** (0.11) (-0.74) (-1)
ROA 0.155 0.377 -0.129 0.394 -0.371 -2.777 (0.31) (0.72) (-0.23) (0.65) (-0.44) (-2.23)**
Sales Growth 0.07 -0.081 0.205 0.152 -0.183 -0.709 (0.29) (-0.32) (0.7) (0.55) (-0.61) (-1.81)*
Last Year Stock Return -0.132 -0.466 0.143 -0.036 -0.216 -0.235 (-0.87) (-2.41)** (0.83) (-0.19) (-1.03) (-0.84)
Beta 0.072 0.131 0.001 0.109 0.206 0.395 (0.56) (0.89) (0) (0.67) (1.28) (2.01)**
Return Volatility -15.863 -8.131 -2.886 -4.867 -11.534 -30.27 (-1.7)* (-0.8) (-0.33) (-0.52) (-0.81) (-1.41)
Turnover (in 1000s) 0.036 -0.101 -0.062 -0.103 0.007 -0.004 (0.77) (-1.89)* (-1.16) (-1.56) (0.11) (-0.05)
Skewness 0.122 0.172 0.093 0.135 -0.162 0 (1.39) (1.48) (1.16) (1.41) (-0.94) (0)
Book-to-Market -0.164 -0.054 0.029 0.001 0.064 0.211 (-0.98) (-0.24) (0.19) (0) (0.3) (0.69)
=1 if auditor is Big 4 0.271 -0.08 0.079 -0.58 0.175 0.731 (1.19) (-0.29) (0.39) (-2.26)** (0.5) (1.81)*
Constant -9.129 -2.196 -6.164 -0.798 -6.911 -8.959 (0) (-1.41) (0) (-0.95) (0) (0)
No. of Obs. 393 300 260 186 133 114 No. of Timely Restating Firms
111 111 68 68 43 43
No. of Culpable Firms 282 189 192 118 90 71
Pseudo R-sq. 36% 36% 8% 15% 27% 38%
Time F.E. Yes Yes Yes Yes Yes Yes
Industry F.E. Yes Yes No No No No
State F.E. Yes Yes No No No No
Table 6 The Tellabs Case: Diff-in-Diff Analysis
This table reports the results for the difference-in-difference tests for the Supreme Court’s Tellabs case,
which homogenizes federal courts’ pleading standards for Scienter in 10b-5 lawsuits and increases the
stringency of pre-Tellabs lenient circuit courts. Following Choi and Pritchard (2011), we categorize district
courts under the 1st, 4th, 6th, and 9th circuit as “pre-event lenient circuits” (treatment observations), and
district courts under other circuits as control observations. We choose the 6-year window (3-year pre- and
3-year post-Tellabs), taking into account the fact that it takes time for managers of the misreporting firms
to learn their accounting mistakes and deliberate on the restatement decision in response to altered pleading
standard. As the Tellabs decision spans 2006 through 2007, these two years are excluded from our event
window. Thus, the pre-event period is from January 2003 to December 2005; and the post-event period is
from January 2008 to December 2010. Panel A displays the change in court dismissal rate right after the
Tellabs case under different court stringency. Panel B presents the change in voluntary restatement
likelihood after the Tellabs case under different court stringency. The superscripts, ***, **, and * denote the
1%, 5%, and 10% levels of significance, respectively.
Panel A: Change in Court Dismissal Rate after the Tellabs Case under Different Court Stringency
Court Dismissal Rate
No. of Obs. Mean First-Order Diff. Diff-in-Diff
USDCs under Lenient Circuits
Pre-Tellabs 22 45.76% -5.99% -12.25%
(5.13) (-0.69) (-1.58)
Post-Tellabs 20 39.78%
(4.76)
USDCs under Non-Lenient Circuits
Pre-Tellabs 30 30.06% 6.26%
(4.42) (0.89)
Post-Tellabs 31 36.32%
(4.98)
Panel B: Change in the Likelihood of Voluntary Restatement after the Tellabs Case under Different Court
Stringency
=1 if making timely restatement Sample of Defendant Firms Sample of Restating Firms
VARIABLES (1) (2) (3) (4)
Pre-Event Lenient Circuit Dummy 0.61 0.721 0.538 0.465 (2.15)** (2.21)** (1.62) (1.18)
Post-Event Dummy -0.149 0.241 0.018 -0.309 (-0.41) (0.48) (0.04) (-0.46)
Pre-Event Lenient Circuit × Post-Event Dummy -1 -1.281 -1.444 -1.399 (-1.97)** (-2.19)** (-2.2)** (-1.65)*
Log Total Assets -0.193 -0.225 (-2.21)** (-2.02)**
Leverage 2.431 2.458 (3.31)*** (2.38)**
ROA 2.024 2.203 (1.94)* (1.69)*
Sales Growth -0.42 -0.073 (-1.06) (-0.14)
Last Year Stock Return -0.042 -0.469 (-0.22) (-1.93)*
Beta 0.101 0.189
(0.65) (1.07)
Return Volatility -8.226 2.602 (-0.77) (0.18)
Turnover (in 1000s) -0.011 -0.007 (-0.17) (-0.08)
Skewness 0.143 0.242
(1.28) (1.67)*
Book-to-Market -0.003 -0.121
(-0.01) (-0.36)
=1 if auditor is Big 4 0.547 0.223 (1.7)* (0.53)
Constant -1.405 -1.207 -1.378 -0.332 (-2.04)** (-1.11) (-1.97)* (-0.26)
No. of Obs 217 212 155 151
No. of Timely Restating Firms 61 60 61 60
No. of Culpable Firms 156 152 94 91
Pseudo R-sq. 25% 32% 34% 42%
Industry F.E. Yes Yes Yes Yes
Table 7 Legal and Market Outcomes of Voluntary Restating Firms under Different Court Stringency
This table exhibits lawsuit outcomes between voluntary restating firms and control firms under different court stringency. Panel A reports the market
reaction: the 3-day cumulative abnormal return ((-1,+1) CAR) around the case filing date and around the accounting misconduct revealing date.
Panel B displays the legal outcomes: dismissal rate, settlement and unconditional legal cost. The difference of lawsuit outcomes between voluntary
restating firms and control firms are indicated by the variable, “=1 if Voluntary Restating”; and the impact of court stringency on this difference is
indicated by the interactive variable, “× Lenient Court Dummy”. In this analysis, we only include observations with top-quarter dismissal rate
(Lenient Court Dummy=1) or with bottom-quarter dismissal rate (Lenient Court Dummy=0). All variables are as defined in the appendix A. Numbers
in parentheses represent t-values. The superscripts, ***, **, and * denote the 1%, 5%, and 10% levels of significance, respectively.
Panel A: Legal Outcomes
=1 if Dismissed Settlement Amount Unconditional Cost Amount
Sample of
Defendant Firms
Sample of
Restating Firms
Sample of
Defendant Firms
Sample of
Restating Firms
Sample of
Defendant Firms
Sample of R
estating Firms Variables (1) (2) (3) (4) (5) (6)
=1 if Voluntary Restating -0.59 -0.65 19.3 8.294 11.982 14.869 (-1.28) (-1.07) (1.08) (0.38) (0.93) (0.96)
× Lenient Court Dummy 0.90 1.38 -18.893 -62.149 -16.652 -19.372 (1.36) (1.54) (-0.45) (-1.11) (-0.84) (-0.81)
Lenient Court Dummy -0.2 -0.973 23.918 24.698 1.947 12.517 (-0.38) (-1.02) (0.9) (0.52) (0.13) (0.58)
Court Filing Rate 0.311 -0.445 46.988 88.441 33.909 60.972
(0.49) (-0.51) (2.02)** (3.15)*** (2.07)** (2.92)***
Leverage -1.297 1.198 -14.695 12.061 -11.17 -2.009 (-1.44) (0.94) (-0.45) (0.26) (-0.53) (-0.07)
Log Total Assets 0.132 -0.11 -18.069 -27.961 -9.609 -15.589 (1.33) (-0.78) (-4.68)*** (-5.18)*** (-4.39)*** (-4.93)***
ROA 1.074 3.033 -127.505 -125.565 -113.031 -132.956 (0.97) (1.29) (-3.56)*** (-3.27)*** (-4.58)*** (-5)***
Sales Growth -0.231 -1.029 13.698 23.804 3.219 17.54 (-0.52) (-0.99) (0.77) (1.29) (0.3) (1.42)
Beta 0.31 0.332 -6.801 -7.709 -6.092 -5.281 (0.01) (-0.65) (-0.74) (-0.58) (-1.02) (-0.64)
Class-period Return 0.139 0.779 1.822 6.218 -5.789 -14.317
(0.39) (1.26) (0.1) (0.26) (-0.58) (-1.01)
Log Class Length -0.294 -0.562 5.307 9.676 0.864 0.26 (-1.5) (-1.75)* (0.6) (0.52) (0.16) (0.03)
Return Volatility 0.16 16.211 -94.773 165.459 68.451 -305.023 (0.01) (0.67) (-0.13) (0.14) (0.17) (-0.61)
Turnover (in 1000s) 0.021 -0.008 -8.811 -10.912 -4.052 -3.815
(0.24) (-0.06) (-2.49)** (-2.44)** (-1.81)* (-1.38)
Skewness 0.112 -0.016 1.293 -12.586 3.036 -1.558
(0.73) (-0.08) (0.16) (-1.09) (0.8) (-0.3) =1 if auditor is Big 4 0.31 0.332
(0.89) (0.42)
Book-to-Market 0.004 -0.073 -7.912 -32.753 -10.485 -10.239 (0.02) (-0.2) (-0.77) (-1.89)* (-1.81)* (-1.21)
Constant 6.631 3.845 132.787 235.259 93.519 128.161
(0) (1.24) (1.29) (1.61) (2.09)** (2)**
No. of Obs 184 137 107 73 184 137
Time F.E. Yes Yes Yes Yes Yes Yes Industry F.E. Yes Yes No No No No
Pseudo R-sq (Adj. R-sq) 74% 79% 35% 51% 27% 36%
Panel B: Market Reaction around Class-end Date and Case-filing Date
Cumulative Abnormal Return (-1,+1) Sample of Defendant Firms Sample of Restating Firms Class-end Date Case-filing Date Class-end Date Case-filing Date
Variables (1) (2) (3) (4) (5) (6) (7) (8)
=1 if Voluntary Restating 0.06 0.07 0.001 -0.027 0.072 0.06 -0.013 -0.044
(1.65) (1.57) (0.03) (-0.7) (1.87)* (1.2) (-0.4) (-1.09)
× Lenient Court Dummy -0.03 0.063
0.03 0.068
(-0.47)
(1.14) (0.38)
(1.22)
Lenient Court Dummy 0.011 0.019 -0.011 -0.028 -0.071 -0.081 0.024 -0.002 (0.27) (0.44) (-0.32) (-0.76) (-1.37) (-1.39) (0.59) (-0.04)
Court Filing Rate 0.091 0.092 0.036 0.034 0.123 0.122 0.109 0.107 (1.73)* (1.75)* (0.81) (0.77) (1.91)* (1.89)* (2.09)** (2.06)**
Leverage -0.111 -0.109 0.039 0.033 -0.074 -0.072 -0.015 -0.009 (-1.62) (-1.57) (0.66) (0.57) (-0.94) (-0.9) (-0.24) (-0.15)
Log Total Assets 0.013 0.013 0.006 0.007 0.008 0.009 0.005 0.006 (1.83)* (1.75)* (0.95) (1.1) (0.85) (0.88) (0.6) (0.74)
ROA 0.13 0.129 -0.087 -0.084 0.10 0.099 -0.17 -0.165 (1.4) (1.38) (-1.09) (-1.06) (1.12) (1.14) (-2.44)** (-2.37)**
Sales Growth -0.04 -0.036 -0.016 -0.015 -0.021 -0.021 -0.063 -0.064 (-1.1) (-1.12) (-0.61) (-0.56) (-0.62) (-0.63) (-2.31)** (-2.34)**
Beta -0.013 -0.014 -0.014 -0.013 0.011 0.013 -0.003 0.001 (-0.71) (-0.75) (-0.91) (-0.81) (0.51) (0.56) (-0.14) (0.07)
Class-period Return 0.021 0.021 0.026 0.027 0.06 0.06 0.031 0.032 (0.61) (0.61) (0.91) (0.92) (1.42) (1.43) (0.92) (0.95)
Log Class Length 0.009 0.009 -0.016 -0.016 0.032 0.031 0.005 0.004 (0.5) (0.5) (-1.05) (-1.04) (1.46) (1.43) (0.28) (0.22)
Return Volatility 2.078 2.077 0.854 0.857 1.353 1.33 0.039 -0.018 (1.51) (1.5) (0.73) (0.73) (1.01) (0.98) (0.04) (-0.02)
Turnover (in 1000s) -0.004 -0.004 0.001 0.001 -0.004 -0.004 0.003 0.003 (-0.52) (-0.52) (0.23) (0.24) (-0.47) (-0.46) (0.44) (0.46)
Skewness -0.016 -0.016 -0.004 -0.005 -0.019 -0.019 -0.014 -0.015 (-1.25) (-1.23) (-0.4) (-0.44) (-1.33) (-1.34) (-1.21) (-1.29)
Book-to-Market -0.057 -0.057 0.008 0.008 -0.05 -0.051 0.032 0.031
(-2.76)*** (-2.76)*** (0.45) (0.49) (-1.83)* (-1.84)* (1.43) (1.38)
Constant -0.377 -0.379 -0.029 -0.025 -0.407 -0.401 -0.109 -0.095
(-1.82)* (-1.82)* (-0.17) (-0.14) (-2.28)** (-2.23)** (-0.76) (-0.66)
No. of Obs 151 151 151 151 115 115 115 115
Time F.E. Yes Yes Yes Yes Yes Yes Yes Yes
Adj. R-squared 13% 12% -7% -7% 15% 15% 3% 4%
Table 8 Robustness Check with Alternative Court Dismissal Rate
This table reports the impact of alternative court dismissal rate on the likelihood of firm issuing restatement
conditioning on that they committed accounting mistakes. Our alternative court dismissal rate is defined by
the number of securities cases dismissed within five years prior to a firm’s fiscal year end in the federal
district court where the firm is headquartered, divided by total such cases filed in the same court in the five
years lagged 18 months to the period used to calculate the number of cases dismissed. The period of 18
months is the average gap between the filing date and dismissal date for our dismissed cases from 1996 to
2013 in the SCAS database. Panel A compares the alternative court dismissal rate with our original court
dismissal rate in all Compustat firm-years between 2000 and 2013. Column 1 and 2 display the probit
regression results of Sample of Defendant Firms; and Column 3 and 4 present the results of Sample of
Restating Firms. All variables are as defined in the appendix A. Numbers in parentheses represent t-values.
The superscripts, ***, **, and * denote the 1%, 5%, and 10% levels of significance, respectively.
Panel A: Difference between original Court Dismissal Rate and Alternative Court Dismissal Rate
Obs. Mean Std. Error t-Statistic
Original Court Dismissal Rate (OCDR) 44,250 0.438 0.001 337.57
Alternative Court Dismissal Rate
(ACDR) 42,187 0.436 0.001 349.67
ACDR - OCDR -0.001 0.001 -1.14
Panel B: Probit Regression Results with Alternative Court Dismissal Rate
=1 if making voluntary restatement
Sample of Defendant Firms Sample of Restating Firms
VARIABLES (1) (2) (3) (4)
Alternative Court Dismissal Rate 2.3 1.807 2.932 2.314
(3.24)*** (3.55)*** (3.39)*** (3.8)***
Court Filing Rate -0.362 -0.143 -0.611 -0.551
(-0.51) (-0.3) (-0.75) (-1.09)
No. of Obs 393 393 300 300
No. of Timely Restating Firms 111 111 111 111
No. of Culpable Firms 282 282 189 189
Pseudo R-sq. 69% 28% 43% 36%
Intercept Yes Yes Yes Yes
Controls Yes Yes Yes Yes
Time F.E. Yes Yes Yes Yes
Industry F.E. Yes Yes Yes Yes
State F.E. Yes No Yes No
Table 9 Robustness Check with Material Weakness Sample
This table reports the robustness check with our material weakness sample, which is selected with a group
of firms that report material weakness in their internal control reports. For detailed sampling method please
see Appendix B.3. Panel A exhibits the distribution of the material weakness sample by district courts.
Panel B displays the distribution of the sample by year. Panel C reports the summary statistics used in the
analysis. Panel D presents the results for the major tests using our material weakness sample. Specifically,
Column 1 and 2 of Panel D reports the impact of court dismissal rate on the likelihood of firm issuing
voluntary restatement conditioning on that they committed accounting mistakes; Column 3 displays the
results for the diff-in-diff analysis during the Tellabs case; and Column 4 exhibits the impact of court
dismissal rate on the likelihood of firm issuing restatement of restating accounting errors as a placebo test.
All variables are as defined in the appendix A. Numbers in parentheses represent t-values. The superscripts, ***, **, and * denote the 1%, 5%, and 10% levels of significance, respectively.
Panel A: Distribution of Material Weakness Sample by Court
=1 if making voluntary restatement
Federal Court N. of Obs. % Mean Std. Dev.
USDC - Alabama (Middle) 5 0.1% 20.0% 44.7%
USDC - Alabama (Northern) 28 0.4% 42.9% 50.4%
USDC - Alaska 25 0.4% 32.0% 47.6%
USDC - Arizona 96 1.5% 18.8% 39.2%
USDC - Arkansas (Western) 15 0.2% 20.0% 41.4%
USDC - California (Central) 595 9.2% 25.2% 43.5%
USDC - California (Eastern) 18 0.3% 66.7% 48.5%
USDC - California (Northern) 637 9.9% 28.9% 45.4%
USDC - California (Southern) 177 2.8% 21.5% 41.2%
USDC - Colorado 124 1.9% 25.0% 43.5%
USDC - Connecticut 177 2.8% 26.6% 44.3%
USDC - Delaware 11 0.2% 9.1% 30.2%
USDC - District of Columbia 48 0.7% 29.2% 45.9%
USDC - Florida (Middle) 100 1.6% 16.0% 36.8%
USDC - Florida (Northern) 17 0.3% 0.0% 0.0%
USDC - Florida (Southern) 158 2.5% 21.5% 41.2%
USDC - Georgia (Northern) 189 2.9% 23.8% 42.7%
USDC - Idaho 13 0.2% 38.5% 50.6%
USDC - Illinois (Central) 1 0.0% 0.0% .
USDC - Illinois (Northern) 283 4.4% 31.8% 46.7%
USDC - Indiana (Northern) 33 0.5% 9.1% 29.2%
USDC - Indiana (Southern) 37 0.6% 27.0% 45.0%
USDC - Iowa (Northern) 6 0.1% 0.0% 0.0%
USDC - Iowa (Southern) 1 0.0% 0.0% .
USDC - Kansas 26 0.4% 11.5% 32.6%
USDC - Kentucky (Eastern) 27 0.4% 48.1% 50.9%
USDC - Kentucky (Western) 26 0.4% 26.9% 45.2%
USDC - Louisiana (Eastern) 20 0.3% 25.0% 44.4%
USDC - Louisiana (Middle) 3 0.0% 33.3% 57.7%
USDC - Louisiana (Western) 15 0.2% 0.0% 0.0%
USDC - Maryland 123 1.9% 28.5% 45.3%
USDC - Massachusetts 352 5.5% 20.2% 40.2%
USDC - Michigan (Eastern) 117 1.8% 27.4% 44.8%
USDC - Michigan (Western) 8 0.1% 25.0% 46.3%
USDC - Minnesota 104 1.6% 22.1% 41.7%
USDC - Mississippi (Southern) 2 0.0% 100.0% 0.0%
USDC - Missouri (Eastern) 9 0.1% 55.6% 52.7%
USDC - Missouri (Western) 20 0.3% 40.0% 50.3%
USDC - Montana 6 0.1% 0.0% 0.0%
USDC - Nebraska 34 0.5% 26.5% 44.8%
USDC - Nevada 51 0.8% 23.5% 42.8%
USDC - New Hampshire 21 0.3% 4.8% 21.8%
USDC - New Jersey 285 4.4% 20.7% 40.6%
USDC - New Mexico 10 0.2% 50.0% 52.7%
USDC - New York (Eastern) 101 1.6% 18.8% 39.3%
USDC - New York (Northern) 3 0.0% 0.0% 0.0%
USDC - New York (Southern) 334 5.2% 22.5% 41.8%
USDC - New York (Western) 43 0.7% 25.6% 44.1%
USDC - North Carolina (Eastern) 26 0.4% 11.5% 32.6%
USDC - North Carolina (Middle) 56 0.9% 17.9% 38.6%
USDC - North Carolina (Western) 41 0.6% 14.6% 35.8%
USDC - Ohio (Northern) 139 2.2% 18.7% 39.1%
USDC - Ohio (Southern) 100 1.6% 36.0% 48.2%
USDC - Oklahoma (Northern) 23 0.4% 17.4% 38.8%
USDC - Oklahoma (Western) 9 0.1% 55.6% 52.7%
USDC - Oregon 95 1.5% 14.7% 35.6%
USDC - Pennsylvania (Eastern) 131 2.0% 27.5% 44.8%
USDC - Pennsylvania (Middle) 12 0.2% 25.0% 45.2%
USDC - Pennsylvania (Western) 49 0.8% 28.6% 45.6%
USDC - Puerto Rico 21 0.3% 14.3% 35.9%
USDC - Rhode Island 5 0.1% 0.0% 0.0%
USDC - South Carolina 26 0.4% 23.1% 43.0%
USDC - South Dakota 6 0.1% 16.7% 40.8%
USDC - Tennessee (Eastern) 31 0.5% 12.9% 34.1%
USDC - Tennessee (Middle) 36 0.6% 36.1% 48.7%
USDC - Tennessee (Western) 10 0.2% 10.0% 31.6%
USDC - Texas (Eastern) 139 2.2% 25.9% 44.0%
USDC - Texas (Northern) 87 1.4% 25.3% 43.7%
USDC - Texas (Southern) 261 4.1% 29.1% 45.5%
USDC - Texas (Western) 69 1.1% 23.2% 42.5%
USDC - Utah 45 0.7% 11.1% 31.8%
USDC - Vermont 2 0.0% 0.0% 0.0%
USDC - Virginia (Eastern) 220 3.4% 26.8% 44.4%
USDC - Virginia (Western) 34 0.5% 35.3% 48.5%
USDC - Washington (Eastern) 10 0.2% 0.0% 0.0%
USDC - Washington (Western) 122 1.9% 26.2% 44.2%
USDC - Wisconsin (Eastern) 62 1.0% 27.4% 45.0%
USDC - Wisconsin (Western) 35 0.5% 31.4% 47.1%
Total 6,436
Panel B: Distribution of Material Weakness Sample by Year
Year N. of Obs. %
2003 337 5.2%
2004 754 11.7%
2005 1038 16.1%
2006 939 14.6%
2007 715 11.1%
2008 581 9.0%
2009 453 7.0%
2010 416 6.5%
2011 370 5.7%
2012 395 6.1%
2013 438 6.8%
Total 6436
Panel C: Summary Statistics of Material Weakness Sample
Whole Sample Control Observations Voluntary Observations
N=6436 N=4845 N=1591
(1) (2) (3)
Mean Std Dev. Median Mean Std Dev. Mean Std Dev. (3) - (2) T-stat
=1 if Voluntary Restating 0.247 0.431 0.000
Court Dismissal Rate 0.457 0.265 0.444 0.458 0.269
0.455 0.255
-0.003 -0.38
Court Filing Rate 0.139 0.153 0.103 0.139 0.153
0.139 0.153
0.000 -0.06
Log Total Assets 5.958 1.889 5.854 5.826 1.869
6.363 1.892
0.537 9.85***
Leverage 0.209 0.228 0.147 0.205 0.227
0.222 0.230
0.016 2.45**
ROA -0.068 0.249 0.006 -0.074 0.257
-0.049 0.223
0.025 3.69***
Sales Growth 0.113 0.389 0.062 0.111 0.396
0.119 0.366
0.008 0.76
Last Year Stock Return 1.094 0.664 0.990 1.069 0.663
1.170 0.662
0.100 5.24***
Beta 1.449 0.982 1.281 1.439 0.979
1.477 0.991
0.038 1.32
Return Volatility 0.036 0.021 0.030 0.037 0.021
0.033 0.019
-0.004 -7.58***
Turnover (in 1000s) 1.845 2.028 1.254 1.827 2.037
1.902 1.998
0.075 1.29
Skewness 0.439 1.370 0.332 0.463 1.401
0.363 1.271
-0.100 -2.65***
Book-to-Market 0.745 0.893 0.540 0.730 0.865
0.792 0.973
0.062 2.26**
=1 if auditor is Big 4 0.570 0.495 1.000 0.535 0.499 0.676 0.468 0.141 10.25***
Panel D: Empirical Results with Material Weakness Sample
=1 if making voluntary restatement
=1 if restating
accounting error
VARIABLES (1) (2) (3) (4)
Court Dismissal Rate 0.179 0.183 0.176
(2.03)** (2.43)** (1.06)
Pre-Event Lenient Circuit Dummy
0.721
(2.21)**
Post-Event Dummy
0.241
(0.48)
Pre-Event Lenient Circuit × Post-Event Dummy
-1.281
(-2.19)**
No. of Obs 6,436 6,436 3,050
4,845
No. of Timely Restating Firms 1,591 1,591 788
113
No. of Culpable Firms 4,845 4,845 2,262
4,732
Pseudo R-sq. 8% 7% 7% 7%
Other Controls Yes Yes Yes
Yes
Time F.E. Yes Yes No
Yes
Industry F.E. Yes Yes Yes
No
State F.E. Yes No No No