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: afagnes@polyu.edu.hk

Henry He Huang

SySyms School of Business

Yeshiva University

New York, NY 10033

Tel: (832) 276-3834, E-mail: henry.huang@yu.edu

Zhen Lei

School of Accounting & Finance

The Hong Kong Polytechnic University

Kowloon, Hong Kong

Tel: (852) 3400-3641, E-mail: arthur.lei@connect.polyu.hk

Haitian Lu*

School of Accounting & Finance

The Hong Kong Polytechnic University

Kowloon, Hong Kong

Tel: (852) 2766-7065, E-mail: haitian.lu@polyu.edu.hk

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: haitian.lu@polyu.edu.hk

(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