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Litigation Risk, Short Sales, and Informed Trading: The Case of Securities Class Action Lawsuits

NORRIS L. LARRYMORE*

* Quinnipiac University. For contributions to an earlier version of this work, I am grateful to Heather Tookes of Yale University and graduate assistants Darwin Choi and Robert Giandalone of Yale University and Quinnipiac University, respectively. A particular debt of gratitude is owed to Ravi Jagannathan, and Robert Korajczyk for helpful comments. I thank Quinnipiac University for sabbatical support, Wayne Lee and the University of Arkansas finance department for research assistance, and Northwestern University for graciously hosting me as a visiting research scholar.

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ABSTRACT

This study uses class action lawsuit filing dates from the Stanford Law School Securities

Class Action Clearinghouse and a market model framework to examine the differential

impact on stock prices and trade volume of publicly traded firms over the period 1996 to

2008. Specifically, an empirical investigation is performed to determine whether security

prices change in anticipation of these class action lawsuit announcements, suggesting

informed trading. Results show a significant downward drift in share prices before,

during, and after the announcement, providing the first evidence of a link between stock

prices and value-relevant private information associated with and preceding class action

lawsuit filings. The results are robust across major exchanges, bull and bear markets,

economic expansions and contractions, rule changes, and filing districts.

JEL classification: G12; G13; G14; D8

Keywords: event study; informed trading; litigation risk; short selling

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In 2003, the Washington Legal Foundation (WLF), a non-profit, public interest law and

policy center based in Washington, D.C., found evidence suggesting that trial attorneys who file

class action lawsuits may be selectively providing short sellers and others with information,

regarding when the lawsuit against a publicly traded company will be filed with the court 1. With

the information provided by trial lawyers, short sellers may be given an advantage to trade for

arbitrage profits by anticipating the subsequent negative effect of the news released in the lawsuit

filing. However, to my knowledge, no study has substantiated or refuted this claim.

1 Kamenar, 2003 From a Statement of Washington Legal Foundation before the Subcommittee on Capital Markets, Insurance, and Government Sponsored Enterprises of the Committee on Financial Services of the U.S. House of Representatives on “The Long and Short of Hedge Funds: Effects of Strategies for Managing Market Risk” The Relationship between Short Sellers and Trial Attorneys, Daniel J. Popeo, Chairman and General Counsel and Paul D. Kamenar, Senior Executive Counsel May 22, 2003.

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Safieddine and Wilhelm (1996), which built on Korajczyk, Lucas, and McDonald (1991)

examine the Security and Exchange Commission (SEC) adoption of Rule 10b-21 on August 25,

1988, which addresses manipulative short-selling prior to seasoned equity offerings. They find

that in seasoned equity offerings, higher levels of short selling are related to lower expected

proceeds from the issuance of new shares. Other studies have also sought to link short selling to

specific events. Christophe, Ferri, and Angel (2004) find that negative earnings surprises are

preceded by abnormal short selling. They provide evidence of informed trading in pre-earnings

announcement short-selling by revealing that abnormal short-selling is significantly linked to

post-announcement stock returns. Christophe, Ferri, and Hsieh (2010) find abnormal short

selling before analyst downgrades are publicly released. Desai, Krishnamurthy, and

Venkataraman (2006) find that short sellers are able to anticipate earnings restatements. Francis,

Venkatachalam, and Zhang (2005) show that short sellers are able to predict downward analyst

forecast revisions. However, the gravity of informed trading prior to lawsuit announcements has

neither attracted similar attention by the SEC nor by academia. Thus, this paper seeks to fill that

gap and determine whether there is information contained in class action lawsuit announcements

and if so, whether the market response may be attributed to short selling.

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This study is important because trial lawyers, who may be unaffiliated outsiders, may disrupt

the orderly flow of equity markets with information about imminent legal actions against a firm,

causing valuations and credit ratings to cascade, raising capital costs. More seriously, market

values may be impacted by artificial claims. Lawsuits may be filed for the sole purpose of

producing artificial conditions that benefit traders taking short positions. Specifically, if a trial

lawyer has private information about a valid or invalid legal action pending against a company,

then she might try to exploit this knowledge to gain a profit or to protect clients or friends against

loss to the detriment of the uniformed general population of investors. If she or one of her

associates passes this privileged information to dealers, trading shares of this firm, a criminal act

might have occurred. However, it is extremely difficult to identify this type of (illegal) insider

trading because the information transmission is exogenous to the firm and communication

between the trial lawyer and the dealer is unobservable. For these reasons, it may be difficult to

ascertain whether trial lawyers engage in tipping and even more difficult to identify who the

“tippee” may be that does the trading.2

2 See footnote 12 in Bhattacharya and Daouk (2002)

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A second purpose of this paper is to determine through use of the market model whether

short selling matters, which is an open question in an ongoing debate. Some argue that short

selling is not value creating while others contend that short selling improves market efficiency.

By analyzing short selling activity and measuring negative stock price reactions in the

preannouncement period, privileged information about the legal action may be revealed. Pre-

event trading behavior may be discernible, but it may be difficult to identify underlying motives.

Because there may be competing explanations for short selling, there is a reluctance to attribute

causality. For example, some pre-event short selling may be related to needs for liquidity or

some other factors unrelated to the lawsuit, which may trigger a market reaction and thus

contaminate results.

Because there are many actors involved in a legal investigation and a legal action, it is not

surprising that information regarding an impending lawsuit leaks. While market changes

attributed to trading invalid nonpublic information is expected, the extended effect on the market

of such unsubstantiated information should be negligible due to mean reversion. However, valid

announcements of short sales, investigations, and legal actions may be more informative signals,

resulting in a stronger initial market reaction and subsequent price deterioration.

The rest of the paper is organized as follows. Section I encompasses other related academic

work. In Section II, a theoretical analysis is provided. A discussion the data and method used to

test the research question appears in Section III. Section IV expands the empirical analysis with

results from tests. Section V contains some tests to measure the robustness of our findings.

Implications of the study are described in Section VI. In Section VII, we present the summary

and conclusion.

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I. Literature Review

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The Private Securities Litigation Reform Act of 1995 was enacted to discourage misleading

forward-looking statements pertaining to a security. From a practical perspective, prior to the

Private Securities Litigation Reform Act of 1995, Mitchell and Netter (1994) discussed the use

of the event study methodology in Supreme Court securities fraud cases, including an insider

trading case. This study begins in 1996, after the enactment of the Litigation Reform Act, with

the start of the Stanford Law School Securities Class Action Clearinghouse database.

Early scholarly works in market microstructure focused on issues of a stochastic nature of

supply and demand while later works explored inventory-based and information-based

explanations to determine price and volume. For example Ho and Stoll (1981) examine

optimization of prices through stochastic pricing. However, stochastic modeling does not fit this

study because stochastic modeling depends on stationarity of supply and demand and prices in

this study are dynamic, exhibiting a temporal downward trend. Following Garman (1976) and

Easely and O’Hara (1987), this model focuses on the optimizing behavior of three market

participants: informed traders, dealers, and uniformed traders.

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From an empirical standpoint, our investigation offers a unique opportunity to integrate a

proven event study methodology and two strands of literature: informed trading and short selling.

There is a vast array of empirical research along all three lines. Seminal works in event study

methodology include the semi-strong form test for market efficiency by Fama, Fisher, Jensen

and Roll (1969), using stock splits as an event and the Ball and Brown (1968) market model

measurement of sensitivity to earnings announcements. Amid applications of an event study

methodology in finance literature are Fidrmuc, Goergen, and Renneboog, (2006) and Hirschey

and Zaima (1989). We also find numerous papers that address the interaction between stock

prices and trade volume useful. Included among them are Acharya and Johnson (2007), Cao,

Chen, and Griffin (2005), Chan, Chung, and Fong (2002), Christophe, Ferri, and Angel (2004),

Easley, O’Hara, and Srinivas (1998), Hasbrouck (1991), Jones, Kaul, and Lipson (1994), and

Pan and Poteshman (2003).

II. Theoretical Framework

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Here is the intuition. Suppose the stock market consists of three participants with differential

information about a firm’s litigious offense. These market participants are both informed and

uninformed. Generally, from the sell side of the market, informed market participants are

defined as short sellers and put traders who receive primary private information. Brokers,

dealers, and other intermediaries are less well informed and receive secondary private

information by observing the trades of the informed traders and learning from the market. The

dealer knows the informed trader has more information. From the buy side of the market,

uniformed market participants are described as individual and institutional investors who have

only public information. Certainly, one might argue that not all individual and institutional

investors are uninformed. That is correct, but the distinction is made here mostly for illustration

and brevity.

In this model, only informed traders have primary private information, which is either

created through their expertise or through tips from inside connections. These informed traders

cannot internally cross orders, but instead, they must handle all of their transactions through the

dealer. Here it is assumed that the dealer is monopolistic and the market maker at the center of

every trade. With only one dealer, there is no interdealer trading.

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A number of assumptions are made regarding dealers. For simplification, dealer is the

designation given to brokers, dealers, and broker/dealers. As noted, the assumption is that the

dealer is the sole intermediary and can either act as an agent or trade on her own account.

Following Garman (1976), the dealer sets the ask price, pa, the price at which she will fill orders

from investors wishing to buy shares of a firm and the bid price, pb, the price offered to fill

orders from investors wishing to sell shares. All orders are completed at the end of the day. The

market spread, pa-pb, represents the compensation taken by the dealer for bearing risk and widens

with information asymmetry. The dealer is assumed to be risk averse and sets the market

clearing price, pt, that is roughly the average of the bid-ask prices.

(1)

Although the dealer faces market variability as well as price variability, she manages her

cash, inventory of stock, and bid-ask prices to optimize her position and to avoid bankruptcy.

For example, suppose the probability of stock going out from the dealer is (pa) and the

probability of stock coming in is (pb). Under severe conditions, the dealer may run out of cash

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if (2)

and run out of stock

if

(3)

Here it is assumed that the dealer’s portfolio includes only cash and stock. Let the cash and

stock held by the dealer be designated Ic and Is, respectively. The end of day inventory position

can be positive or negative. Because litigation represents bad news, the dealer will most likely

buy more stock than she sells and carry a positive inventory, which is not necessarily bad for the

dealer. Because she holds an adequate inventory of cash and stock to handle all transactions,

there is no chance of her failure. This no failure assumption relaxes restrictions on short sales

such as cash reserve requirements and quantity limits on short sale positions. Although

simplistic, the unbounded assumption is not completely unrealistic because a dealer may borrow

cash or stock or to some degree, undertake naked short selling. Concern for cash or stock

shortages usually arise in a severe bear or bull market, respectively, but examination of such a

market environment is outside the scope of this model and this study. The market in this model

is initially assumed to be in equilibrium.

The actual market mechanisms used to accomplish trades are varied. Hasbrouck (2007)

points to the continuous limit order market as being important, but also indicates that along with

others, auction and dealer markets are employed. These mechanisms may operate in parallel or

as hybrids. The actual mechanism of the market is outside the central focus of this study.

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An inventory framework for short selling was described above. However, short selling

around a lawsuit may be viewed, as well, from an information perspective. Because both an

inventory and an information approach are used to describe the sequence of trade for market

participants around a lawsuit announcement, a good starting point may be to discuss the

information perspective.

As earlier stated, the dealer knows the informed trader has superior information, so the dealer

always loses when trading with the informed trader. With regard to lawsuit announcements, the

informed trader short sells in the initial transaction. To avoid bankruptcy, the dealer has to offset

losses when liquidity trading with the uniformed trader, which is accomplished through

adjustment of the bid-ask spread. For example, let us say that from the dealer’s perspective,

equals the probability of an informed trade and equals the probability of an

uninformed trade. Following Copeland and Galai (1983), this study formulates the dealer

decision problem under information asymmetry. First designate the generally known market

price p for stock in some trading density f(p). The dealer’s expected gain from instantaneous

trading with the informed trader is given by the following.

(4)

The probability that some uninformed trader will buy is and the probability that

some uninformed trader will sell is . The probability that the trader does neither is

.

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The dealer’s expected gain from discrete trading with the uninformed trader is given by the

following.

(5)

The dealer’s overall objective function is given by the following equation.

+

(6)

The objective function displays the important of the spread and how the dealer benefits from

a higher ask price and a lower bid price. As long as there is a probability that some investors are

informed the spread will never be zero.

Having setup the model, here are the initial assumptions in this theoretical framework. The

reader may find it useful to refer to figures 1 and 2, which portray the timeline and sequential

trading described below. At a given time t=t0, all market participants have the same information,

Ɵ0, which is the key determinant of valuation and is impounded in the price. That is, all market

participants have the same fundamental valuation of the firm, and movements in firm prices are

independent. However, for a number of reasons, such as different buyer and seller perspectives,

there may be many prices at a single instant, but the volatility is negligible, so equilibrium prices

exist. At t=t0, this study characterizes the stabilized, market-clearing price as p t, which follows a

random walk, a martingale.

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At time t=t1, an externality is introduced in the form of new private information, Ɵ1, which

creates differential information among market participants. It triggers a sequential trading

process that leads to a new equilibrium price. This new price is the true price, but only the

informed trader has knowledge of it. The new valuation results from private information

conveyed to the informed trader through some litigious offense, such as product failure,

corporate corruption, contagion. Such information may be conveyed by a trial lawyer or

someone on the legal staff that is drafting a lawsuit. Another source of the new private

information may be the trader’s superior skill in spotting firm offenses leading to lawsuits. The

informed trader speculates on this private information, Ɵ1, of a pending lawsuit. This private

information, Ɵ1, is viewed as reliable, but incomplete because there is uncertainty in the degree

and timing of the lawsuit announcement. However, the high potential of the lawsuit claim being

awarded is enough to lower the informed participant’s assessment of the firm’s value from the

current market price, pt. The informed participant discounts the fundamental value of the firm

and shorts its stock. The initial transaction is taken immediately and completely at t=t1. From an

information perspective, at t=t1, the dealer’s objective function takes the following form.

(7)

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Because the uniformed trader’s buying and selling offsets in equilibrium, the part of the

dealer’s objective function that relates to equation (5) drops off and because the informed trader

is selling only the dealer’s purchases are relevant from equation (4). The dealer loses money at

t=t1 because the market price p>pb(p). Sell orders submitted to the market cause temporary order

imbalances and the dealer’s supply of stock to exceed demand.

The increase in the dealer’s inventory of stock lowers the cash in her portfolio. The larger

inventory of stock in and her observations of informed trader actions cause the dealer to adjust

downward her true value of the firm. Accordingly, the dealer lowers her bid price and ask price,

shifting the market-clearing price downward in order to find buyers. Price is adjusted until there

is no excess supply and a new equilibrium evolves. Although the dealer is not as informed as the

informed trader, the willingness of the informed trader to incur a cost for their positions provides

the dealer some assurance of the validity of their positions. The short seller borrows stock at a

fee and the put trader pays a premium. These are valid signals.

At this time, the uninformed participant is unaware of the private information, Ɵ1 and the

unusual trading activity. Consequently, he has not reformulated an opinion of the stock and does

not change his portfolio.

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However, at some time t=t2, which may be an instant of time, Δt, beyond t=t1, say t=t1+Δt,

and which could exist anywhere within the time interval t1 <t< t3, the uniformed participant

observes extraordinary trading and downward price movement and decides to sell. Using a

discrete time multi-period model may not be the best way to explain what happens in this

interval, but the complexity of asynchronous trading defies another approach. This historical

price and volume information, Ɵ2, is observed and acted upon by uninformed investors through

market orders that are placed through the dealer randomly through the interval. There are more

sellers than buyers, so prices fall. Limit orders and margin calls further exacerbate declining

prices. In contrast to uniformed investors, informed participants, having already taken a position

at time t=t1, find no value in information, Ɵ2, and exercises their option not to trade. They take

no action except possibly leak more bad news. At t=t2, the dealer’s objective function takes the

following form because there is some buying and selling among uniformed investors and no

trading with informed investors. That is, f(p)=0.

With prices falling, the dealer continues to buy more that sell, but manages to extract positive

gain through the spread, as prices decline. Stock inventory continues to grow and net stock in

exceeds net stock out,

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At time t=t3, all market participants are informed of the lawsuit through an announcement

released to the public. It conveys information, Ɵ3, with certainty, validating the time and

magnitude of the claim. However, there remains a small degree of uncertainty regarding the

outcome of the claim, which contributes to differential analyses of the announcement. Although

both informed and uninformed participants receive the same public information at the same time,

their interpretations differ dramatically. This misalignment of beliefs causes actions taken by

informed and uninformed investors to offset and the price to stabilize.

To informed investors, information, Ɵ3, brings certainty in timing and magnitude of the

claim, which was provided in the private information, Ɵ1. The informed participants assess

whether the initial price had been discounted accurately and whether the current price level

impounds the potential loss in firm value. If the suit and potential for the award are larger than

expected, the informed participants may delay buying the stock and closing their speculative

positions, in order to exact additional gains. However, if the suit and potential for award are less

than expected, the informed participants may immediately buy the stock to close their short

positions. In contrast, to uninformed participants, the information conveyed in the

announcement of the lawsuit, Ɵ3, has entirely negative implications. The uninformed

participants expect prices to retreat further and feel compelled to sell their shares, which have

already taken a beating. Because the information in the lawsuit announcement, which is revealed

at time t=t3, is a surprise to relatively few, the downward pressure on prices is weak and offset by

the upward pressure of short sellers buying shares to close their speculative positions.

Consequently, prices converge shortly after the announcement. From an inventory perspective,

that is . From an information perspective, the dealer’s objective function

goes to zero as the two terms below offset.

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III. Data and Research Methodology

Data

This study investigates and tests the market response to lawsuit filings announcements and

the relationship between that market reaction and the information content of informed trading.

Primary data on firm litigations, daily stock market prices, announcement dates, and transaction

dates are extracted from two sources. For daily stock market prices, the source used is the

Center for Research in Security Prices (CRSP) database, which is accessed through WRDS. For

legal announcements, lawsuits, and settlements, the Stanford Law School Securities Class Action

Clearinghouse database is used. A distinction is drawn between transaction and announcement

dates because although this information is released to the public through online data sources on

the transaction date, it usually does not come to the attention of the broader investing population

until it appears in the Wall Street Journal on the announcement date, which normally is the next

day. Occasionally, these dates are the same. Because the filing dates are electronically available

and Wall Street Journal announcement dates may require hand collection, the former may be

more efficient, and consistent. Thus, we favor and employ the Stanford filing dates over the

Wall Street Journal announcement dates.

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Table I provides an example of descriptive statistics available from Stanford Law School

Securities Class Action Clearinghouse in cooperation with Cornerstone Research. The Securities

Class Action Clearinghouse provides detailed information relating to the prosecution, defense,

and settlement of federal class action securities fraud litigation. The Clearinghouse maintains an

Index of Filings of issuers that have been named in federal class action securities fraud lawsuits

since passage of the Private Securities Litigation Reform Act of 1995.

Sample Description

Table I provides a comprehensive breakdown of this study’s sample, which consists of

litigation filings from Stanford Law School Securities Class Action Clearinghouse by company

name, exchange name, ticker, filing date, and jurisdiction in quarterly files from January 1, 1996

through December 31, 2008. The period of the sample was constrained by the databases. The

Stanford Law data started in 1996 and the CRSP data was current through the end of 2008. A

sample of the data provided in the first quarter of 1996 is presented in Appendix A for

illustrative purposes to give the reader a sense of the data.

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We merge the quarterly files appearing on the Stanford Clearinghouse website to construct a

database of all filings. From the database, which includes information from the various major

stock exchanges, AMEX, NASDAQ, NYSE, OTC, pink sheets, and private securities, we

exclude private issuers and those not listed in the Center for Research in Security Prices (CRSP)

database. Because we obtain stock market data and two-digit Standard Industrial Classifications

(SICs) from CRSP and CRSP consists of firms listed on the AMEX, NASDAQ, and NYSE, our

sample includes only these publicly traded firms, which reduces the filings in our sample from

the initial 2875 to 2594. In a number of instances, CRSP firm data is incomplete, which further

reduces the sample size 2231. However, because the sample is fairly large, the power of our

tests should be strong and we can address this issue with a high degree of confidence.

As noted, a descriptive summary of this data is presented in Table I. Table I Panel A shows

the frequency of filings over time for the total sample of data subsequent to the passage of the

Private Securities Litigation Reform Act of 1995 (PSLRA) through December 31, 2008. To our

knowledge, this is the most comprehensive database on securities class action litigations. Shown

in Table I, Panel B is the frequency of filings for our sample, which consists of firms listed only

on the AMEX, NASDAQ, and NYSE, due to the limitations of CRSP data. Interestingly, the

filings spike on NASDAQ firms in 2001 and peak on AMEX and NYSE firms in 2002, which

follow in the wake of the notorious Enron and WorldCom failures.

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Subsets of the data are constructed to control for unrelated factors and the market response of

within these subgroups is examined. For example, announcements of investigations that precede

the filing of a class action lawsuit may moderate market reactions to the announcement on the

filing date. A negative price changes due to pre-filing investigation announcements may be

misinterpreted as resulting from short sales and vice versa. Tetlock (2007) finds that high levels

of negative media attention forecast downward market prices, higher trading volume, and lower

returns. Using the search feature of the ProQuest Newspapers database, which scans a wide

array of both national and international newspapers, we search the 20-day window preceding the

filing date to identify other announcements and contemporaneous factors in the event window

that might have confounding effects on stock prices. The actual public record provided by the

Stanford Law database is employed for information in the post 20-day window. The intuition is

that the market reaction to the lawsuit will be more informative without the preceding

investigation announcements because the investigation leading to the class action lawsuit

gradually informs the market, so information is incrementally impounded in the stock prices.

The frequency of news releases associated with an investigation should diminish the market

impact of each subsequent release. Only a handful of prior news releases were identified.

Research Design

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We employ an event study methodology to test the market response to lawsuit

announcements. In equity market event studies, the Brown and Warner (1980, 1985) market

model methodology is widely used. The underlying assumption of this method is that the market

is somewhat efficient and that investors react in a timely manner to event announcements. The

abnormal return, designated ARit, for firm i on day t, which removes the portion of variance due

to the market, is calculated by the following equation.

(8)

In the above equation, Rit is the return for firm i on day t, Rmt is the market return for the

CRSP value-weighted market index on day t, are the ordinary least squares (OLS) estimate

of firm i’s market model parameters from the regression of the firm’s return on the market return

during the estimation period.

(2)

where t = -200, …, -21. We follow Campbell, Lo, and MacKinlay (1997) in employing the

value-weighted market CRSP index (with daily dividend reinvestment) for Rmt. As in Campbell,

Lo, and MacKinlay (1997) the estimation period is the interval, defined as days from t = -200

through t = -21, which is 180 trading-days, where day t = 0 is the filing date. Finally, ε it is the

error term, which is often called the residual.

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Because pre-event information leakage and a post-event information monitoring effect is

expected, we extend the event window 20 days in either direction (-20, +20) around the event

date, which is the day that the lawsuit is filed. Thus, we examine the cumulative abnormal return

over two event periods: a forty one-day window, from t = -20 to t= +20, and a two-day window, t

= 0 and t = +1. Because litigation is a lengthy process, it is likely that additional price

movements will follow the event day, as information unfolds. The cumulative abnormal return

for firm i over the interval t = d1 to t = d2 is given by the equation (9).

(9)

(10)

where N is the number of lawsuits in the sample.

The null hypothesis is that stock price activity surrounding the lawsuit announcement, as

measured by the CAR, are unaffected by legal actions taken against the firm.

H0: Stock prices are unrelated to lawsuit filings announcements.

The parametric test statistic used to determine whether the null hypothesis has been violated

is provided in the following equation.

24

(11)

Two additional parametric tests are offered by Campbell, Lo, and MacKinlay (1997), and

employed in Fidrmuc, Goergen, and Renneboog, (2006):

and (12)

(13)

The s(CARi) is the sample standard deviation of the individual cumulative abnormal return

over the event window t1 to t2 and is calculated as follows. L i is the number of observations in

the estimation period.

(14)

(15)

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(16)

The si is the sample standard error from the market model regression over the estimation

interval. As noted, Li is the number of observations in the estimation period. For our study, L i is

180 with an estimation period T0i = -200 to T1i = -21.

In an event study, the decision to use either one of these two additional tests depends on

whether CAR variances are similar or dissimilar. If security variances fluctuate with similar

abnormal returns, then J2 may be the test offering the higher power because it gives more weight

to securities with the lower abnormal return variances. However, Campbell, Lo, and MacKinlay

(1997) suggest that if the variance of the CAR is not different between the securities, then the

choice of J1 or J2 will not yield very different results. Fidrmuc, Goergen, and Renneboog, (2006)

use J1. An absolute value of J1 exceeding 1.96 would be significant in rejecting the null

hypothesis of a zero abnormal return.

Since lawsuits and their respective filing dates across securities are unrelated, we assume that

the abnormal returns are uncorrelated, making the market model an attractive methodology.

Nevertheless, we check for clustering to assure that CAR windows are broadly dispersed. As

suggested in Fidrmuc, Goergen, and Renneboog, (2006), for hypotheses tests over intervals

exceeding one day, autocorrelation of abnormal returns is taken into consideration.

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If we reject the null hypothesis and find that litigation announcements have a significant

impact on stock prices, then validating these results requires affirmation of them with alternative

methodologies or examination and refutation of alternative explanations for stock price

movement in the preannouncement period. Acharya and Johnson (2007) offer an alternative

approach in determining information availability in the preannouncement period. Acharya and

Johnson (2007) argue that in the event study context, unusual volume activity may indicate news

“leakage.”

Empirically connecting volume and the lawsuit announcement would support the null

rejection. Employing event study methodology, we examine trade volume in the 41-day window

against volume in the estimation period. Other studies discuss the relationship between volume

and stock prices (Cao, Chen, and Griffin (2005), Chan, Chung, and Fong (2002), Easley, O’Hara,

and Srinivas (1998), Hasbrouck (1991), and Pan and Poteshman (2003)). To further examine the

link between stock price and volume, we employ an estimation of the following bivariate VAR

model, following Chan, Chung, and Fong (2002) and Hasbrouk (1991) which closely resembles

the simple causal model described in Granger (1969). The high frequency of our data and the

sampling period supports the use of this model in explaining the relationship.

(17)

(18)

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where rt represents the average holding period return of the stocks in our sample on day t, and z t

represents the trading volume in total number of shares sold by the firms in our sample on day t.

All the return and trade volume series are standardized by subtracting the mean and dividing by

the standard deviation of the day. We assume that the error terms in the two equations, ε 1,t and

ε2,t , are jointly and serially uncorrelated with mean zero. As in Chan, Chung, and Fong (2002),

in equation (15), the contemporaneous volume and 6 lags for the explanatory variables are used.

Here, other factors that may otherwise be expected to affect prices are ignored. Table III reports

the regression coefficients for the contemporaneous volume and the six lags and t-statistics (in

italics).

Basically, the idea is to determine whether trades precede price changes, but the reverse

casual effect may be found. However, when one variable is not clearly leading the other, or a

distinct causality cannot be ascribed, and rt causes zt and zt causes rt, it is said that feedback is

occurring.

The results of this test are expected to be consistent with the results of Christophe, Ferri, and

Angel (2004) that unusual short selling precede stock price declines and the findings of Desai,

Ramesh, Thiagarajan, and Balachandran (2002) that heavy short interest activity precedes

negative abnormal returns. At best, interesting patterns may be found, matching the Senchack

and Starks (1993) finding that links short interest to negative abnormal returns.

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The following information, taken from Chan, Chung, and Fong (2002), is the summary of

interrelationship between quote returns and net trade volume. Chan, Chung, and Fong (2002)

employ net-trade volume, which is defined as buyer-initiated trading volume minus seller-

initiated trading volume. From CRSP, daily trading volume is extracted, which is defined as the

total number of shares sold on that day, which we would expect to match the magnitude of the

CCF net-trade volume in the event window with a change in sign. In the event window, we

expect buyer-initiated trading volume to be negligible relative to seller-initiated trading volume,

rendering a negative net-trade volume versus our positive trading volume in shares sold.

From the summary of interrelationship between quote returns and net trade volume [Chan,

Chung, and Fong (2002)]

Dependent variables

Explanatory variables

Contemporaneous terms (if applicable) + Lagged terms

Stock returns Net-trade volume

Stock returns Negative Positive

Stock net-trade volume Negative Positive

29

If no significant impact of lagged stock returns on volume is found, the investors may not be

rebalancing their positions following changes in stock prices. Such a result would suggest that

another reason exists for observed investor rebalancing. One reason may be that short-sales

activity preceding a lawsuit filing might have significant information content and the value of

any private information that triggers them is probably large.

IV. Empirical Results

Descriptive

30

Figure 3 plots the cumulative average market-model abnormal return for information

contained in 2,231federal class action securities fraud lawsuit filings over a 41-day event

window. Figure 3 provides a dramatic confirmation of pre-filing trading and its failure to revert

afterward validates the litigation risk. Before the filing date, t < 0, the CAAR plots show to what

extent the market anticipates the forthcoming announcement. The gradual deterioration in prices

is an indication that share prices may be informationally inefficient. Otherwise, one would

observe a sharp drop in share prices and immediate price stability at the lower level, which

actually does not occur until a few days after the filing date. One might ask why the market does

not incorporate this information efficiently. Of course, one answer may be that the market is not

informed efficiently. Information about the impending lawsuits may not be dispersed uniformly,

trickling into the public piecemeal, and may not be fully known until it is filed. Another

rationale may be that pessimism grows daily, leading to more pessimism. It could also mean that

dealers are gradually lowering their prices to extract gains as prices cascade. An alternative

explanation may be that traders slowly react to information, as suggested in Tetlock (2007).

As noted in the theoretical framework, investors may be asymmetrically informed with

insiders trading on well-specified private information that by definition is not fully revealed and

outsiders pessimistically trading on falling asset prices that are observable. It is unclear which is

the dominant contributing factor because the downward drift may be due to unfolding

information associated with an unraveling investigation or it may be due to more intense

performance monitoring of asset prices. Apparently, for whatever reason, beliefs are being

revised continuously, underscoring the uncertainty linked to litigation through the filing date.

Normally, as more investors become informed, the less valuable it is to be informed. That does

not appear to be the case here.

31

After the filing date, t > 0, the continued downward price drift suggests that even the lawsuit

filing does not appear to provide sufficient information for the complete price adjustment. A

possible explanation may be that after the filing date, investors are able to monitor the court

proceedings firsthand through publicly available court records and trade accordingly.

Alternatively, in the preceding period, investors had to rely on inside information, media

speculation, and technical analysis. Obviously, from the results seen in figure 3, both insiders

and outsiders can profitably short sell after the filing date.

The most important finding emerging from these tests is the apparent risk-free profit

opportunity both before and after the filing date. Investors who make it a practice to short sell

stock of a firm named in a lawsuit on the filing date can take an apparent risk-free profit over a

20-day postannouncement period.

Test Results of the Hypothesis

Based on the results described in Table II and in the figure 3, the null hypothesis that stock

prices are independent of class action lawsuit filings is rejected. The results show an expected

pattern of prices drifting lower across the board before the filing date and also afterward. The

lawsuit filing introduces some certainty by putting an explicit dollar figure on the worse case

scenario for the firm, making valuation estimates of the firm’s future cash flow possible. If the

characteristics of the suit lead to an assessment that the stock is undervalued, informed traders

unwind their short positions to correct for initial overreactions, sending the stock price higher

and vice versa. While the informed trade out of their short positions, the uninformed, first

learning the details of the lawsuit in the filing announcement, precipitate a selling pattern that

continues well beyond the announcement date.

32

These preliminary findings that class action lawsuit filings negatively impact value over the

41-day window and positively impact value over the 2-day window are consistent with the

notions of market inefficiency and information asymmetry because these reactions are not

instantaneous and complete. However, the cause of the loss in value is not completely clear.

There may be a number of factors operating in concert to bring about the observed behavior in

share prices. For example, the downward drift in valuation may be due to informed trading,

short sales, or some type of predisclosure information, such as news of an investigation or a pre-

filing notice of “intent to file.” Moreover, once price precipitation begins, even herding may

become a factor.

V. Robustness

33

Table II shows the results of testing a number of sample subsets that were thought to provide

insight. Generally, the results were robust throughout the tests. Panels A and B reveal that it is

immaterial whether value-weighted or equally-weighted market returns are employed in the

abnormal return calculation. Panel C reveals that there may be something distinct in the way

orders are handled on the American Stock Exchange (AMEX). Panel G shows the widening of

spreads after Regulation SHO became effective and Panels H reveals spreads narrowing after the

suspension of the uptick rule. Panel I contradicts the popular belief that removal of the uptick

rule contributed to market volatility. As expected, spreads widen in bear markets, offering

dealers broader opportunities for positive returns. The tightening of spreads seen in Panel N and

Panel O, during economic contractions, may be the result of restrictions on credit. Finally, the

very tight spreads shown the Southern District of New York (Panel Q), where the largest number

of class action lawsuits are written, may reflect a more competitive dealer market and lower

information asymmetry.

Separately, Acharya and Johnson (2007) offer an alternative approach in determining

information availability in the preannouncement period. Acharya and Johnson (2007) argue that

in the event study context, unusual volume activity may indicate news “leakage.” Then, using

this argument, our event study tests empirically connecting abnormal volume and the lawsuit

announcement would support the null rejection.

34

Extending our analysis of the relationship between stock returns and trading volume, we base

both the event study and VAR analysis on the number of trades. Jones, Kaul, and Lipson (1994)

find that price movements are more strongly related to trades than to trading volume.

Fortunately, the CRSP database provides the number of trades for NASDAQ firms, which makes

up the majority of our sample. The same information is not available on CRSP for

NYSE/AMEX firms, but the analysis of NASDAQ firms should provide enough information to

ascertain whether price movements in an investigatory environment are linked to trading

behavior and that is what is most important. Table III shows the results of our VAR tests, which

conclude without a strong link between volume and stock prices for our sample. Thus, we rule

out trading volume as an explanation of our results.

VI. Implications

35

The results of our study may call into question the results of numerous previous studies that

have not controlled for the impact of lawsuits. For example, we may be able to show that

controlling for lawsuits in studies that examine the market reaction of short sales might overturn

their results. Given the sizable negative stock returns preceding lawsuit announcements, lawsuit

filings may be a major factor that has been overlooked in equity market studies. The impact of

the lawsuits may explain a significant part of the equity market collapse in 2000. For example,

in fall 2000 (September 13 through December 12), the period chosen by Christophe, Ferri, and

Angel (2004) to construct a sample of 913 NASDAQ firms, there were 35 lawsuits or roughly

4%. In an equally weighted portfolio, 4% of the firms with sizable double-digit negative returns

can significantly bias the overall performance of the portfolio. Moreover, any study that

examines earnings, such as Christophe, Ferri, and Angel (2004), and includes firms with lawsuits

in their sample could be affected by our results because companies might have understated their

earnings in anticipation of extraordinary expenses related to lawsuits. Surely, a pending lawsuit

is a target characteristic in firm selection by short sellers. Because lawsuits are not considered

under firm characteristics, fundamental analysis and studies that consider earnings

announcement effects based on firm characteristics should control for lawsuits. To validate their

results, all of these analyses and studies might have to control for the impact of write-downs

linked to impending lawsuits.

36

Additionally, the effects of lawsuits on stock prices might have altered the performance

calculations of numerous mutual fund and hedge fund portfolios that hold securities named in

lawsuits, particularly those portfolios heavily weighted in NASDAQ firms in 2001.

Subsequently, it may be the subject of widespread interest that these effects might have been

impounded in the results of mutual fund and hedge fund performance studies, which may explain

part of the difference between mutual fund under performance and hedge fund over performance

versus index funds. That is, we presume that mutual fund studies might have been adversely

affected by the inclusion in their sample of firms with pending lawsuits and that the inclusion of

these firms may explain some of the mutual fund underperformance. Similarly, part of hedge

fund over performance may be explained by the inclusion of these firms in hedge fund studies

because hedge funds have fewer constraints on short selling and use of derivatives. Thus,

depending on the information available to the traders, hedge funds may adopt investment

strategies that coincide with the strong downward momentum of the pending lawsuit firms and

benefit from negative news. When informed of a pending lawsuit, a hedge fund manager may

take a short position in that stock, a short position in a call option, a long position in a put option,

or any combination of the three, exploiting the deterioration of the stock price. Presumably, the

spread between mutual fund performance and hedge fund performance should increase

monotonically with the number of lawsuits in a period. Of course, the caveat is that not all hedge

funds trade options.

VII. Summary and Conclusion

37

Determining whether unusual price movement or trading in a firm’s stock precedes the

lawsuit filing date is the primary focus of this paper. Not only does this study find abnormal

trading activity on the lawsuit filing date, but extraordinary trading in anticipation of the lawsuit

filing announcement. In sum, lawsuit announcements reveal private information and the sizable

movement of stock prices reflect the litigation risk. The secondary purpose is to explain the

preannouncement stock price effect. Through investigation of the possible existence of value-

relevant private information associated with and preceding class action lawsuit announcements,

this paper contributes to the understanding of asset price movements linked to short selling,

providing the first evidence of this interaction.

38

Appendix: A

As a sample, the table below provides a description of Statistics available from Stanford Law School Securities Class Action Clearinghouse in cooperation with Cornerstone Research for the first quarter of 1996. Similar data are available for each subsequent quarter through the current period in 2007. The Securities Class Action Clearinghouse provides detailed information relating to the prosecution, defense, and settlement of federal class action securities fraud litigation. The Clearinghouse maintains an Index of Filings of issuers that have been named in federal class action securities fraud lawsuits since passage of the Private Securities Litigation Reform Act of 1995.

  Litigation Name Exchange Ticker Date Court

1 Unitech Industries, Inc. NASDAQ UTIIQ 01/10/1996 D. Arizona

2 ABS Industries, Inc. NASDAQ ABSI 01/19/1996 N.D. Ohio

3Donaldson, Lufkin & Jenrette Securities Corporation : Rickel Home Centers Notes

Other PRIVATE 01/26/1996 S.D. New York

4 Touchstone Software Corporation OTC-BB TSSW 01/26/1996 C.D. California

5 Cephalon, Inc. NYSE CEPH 01/29/1996 E.D. Pennsylvania

6 Silicon Graphics, Inc. NYSE SGI 01/29/1996 N.D. California

7 Mustang Development Corporation PRIVATE PRIVATE 02/01/1996 C.D. California

8 NuMed Home Health Care, Inc. NASDAQ NUMD 02/01/1996 M.D. Florida

9 Chantal Pharmaceutical Corporation NYSE CHTL 02/07/1996 C.D. California

10 Firefox Communications, Inc. Other FFOX 02/23/1996 N.D. California

11 HighwayMaster Communications Inc. NASDAQ HWYM 02/23/1996 S.D. New York

12 Health Management, Inc. I NASDAQ HMIS 02/29/1996 E.D. New York

13 Alliance Semiconductor Corporation NASDAQ ALSC 03/04/1996 N.D. California

14 FTP Software, Inc. NASDAQ FTPS 03/14/1996 D. Massachusetts

15 Pinnacle Micro, Inc. NYSE PNCL 03/15/1996 C.D. California

16 New York Life Insurance Company Other D.NZG 03/18/1996 S.D. Florida

17 Bollinger Industries, Inc. OTC-BB BOLL.OB 03/22/1996 N.D. Texas

18NationsBank Corporation : Dean Witter U.S. Government Securities Trust

Other DWD 03/26/1996 M.D. Florida

19 Fleming Companies, Inc. : Common Stock NYSE FLM 03/28/1996 W.D. Oklahoma

20 Wellcare Management Group, Inc. NASDAQWELLE [DELISTED]

03/29/1996 N.D. New York

39

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42

Table IDescriptive Statistics: Frequency of Case Filings

Panel A shows data subsequent to the passage of the Private Securities Litigation Reform Act of 1995 (PSLRA) through December 31, 2008 by year. The test sample consists of securities class action lawsuits filed in U.S. Federal Courts from January 1, 1996 through December 31, 2008, as documented in the Stanford Law School Securities Class Action Clearinghouse database. To our knowledge, this is the most comprehensive database on securities class actions. Because we obtain daily stock market data from the Center for Research in Security Prices (CRSP) and it consists of firms listed on the AMEX, NASDAQ, and NYSE, our sample, as described in Panel B, includes only these firms, which reduces the filings in our sample to 2875 from 2594. Further screening of unusable observations, leaves us with the sample of 2231 observations shown in Panel C. Panel D and Panel E describe this sample by Standard Industrial Classification (SIC) and by court, respectively.

Panel A: All Filings

Year AMEX NASDAQ NYSE OTC-BB Other Private Pink-Sh Unclassified Total1996 3 58 27 7 11 6 0 0 1121997 3 88 59 13 7 4 0 0 1741998 6 140 64 14 8 8 0 0 2401999 10 104 73 17 3 2 0 0 2092000 4 126 64 19 2 0 0 1 2162001 3 409 62 13 4 4 0 2 4972002 11 108 111 24 8 4 1 1 2682003 9 103 90 13 3 6 0 1 2252004 8 110 92 12 3 4 4 2 2352005 6 86 73 8 2 1 0 6 1822006 6 56 46 4 1 2 2 1 1182007 2 97 75 0 0 2 1 0 1772008 6 91 105 3 5 11 1 0 222Total 77 1576 941 147 57 54 9 14 2875

Panel B: Initial Filings in Sample

Year AMEX NASDAQ NYSE OTC-BB Other Private Pink-Sh Unclassified Total1996 3 58 27 0 0 0 0 0 881997 3 88 59 0 0 0 0 0 1501998 6 140 64 0 0 0 0 0 2101999 10 104 73 0 0 0 0 0 1872000 4 126 64 0 0 0 0 0 1942001 3 409 62 0 0 0 0 0 4742002 11 108 111 0 0 0 0 0 2302003 9 103 90 0 0 0 0 0 2022004 8 110 92 0 0 0 0 0 2102005 6 86 73 0 0 0 0 0 1652006 6 56 46 0 0 0 0 0 1082007 2 97 75 0 0 0 0 0 1742008 6 91 105 0 0 0 0 0 202Total 77 1576 941 0 0 0 0 0 2594

43

Panel C: Usable Filings in Sample

Year AMEX NASDAQ NYSE OTC-BB Other Private Pink-Sh Unclassified Total1996 2 47 22 0 0 0 0 0 711997 0 77 54 0 0 0 0 0 1311998 4 122 54 0 0 0 0 0 1801999 7 93 67 0 0 0 0 0 1672000 3 77 56 0 0 0 0 0 1362001 3 391 58 0 0 0 0 0 4522002 10 88 96 0 0 0 0 0 1942003 4 93 79 0 0 0 0 0 1762004 4 92 86 0 0 0 0 0 1822005 4 74 63 0 0 0 0 0 1412006 5 49 45 0 0 0 0 0 992007 1 67 69 0 0 0 0 0 1372008 6 61 98 0 0 0 0 0 165Total 53 1331 847 0 0 0 0 0 2231

Panel D: Usable Filings in Sample by SICSIC SIC CODE DESCRIPTION AMEX NASDAQ NYSE TOTAL

700 AGRICULTURAL SERVICES 0 2 0 2

1000 METAL MINING 3 3 5 11

1200 COAL MINING 0 0 3 3

1300 OIL AND GAS EXTRACTION 3 4 15 22

1400 NONMETALLIC MINERALS, EXCEPT FUELS 0 0 2 2

1500 GENERAL BUILDING CONTRACTORS 0 0 8 8

1600 HEAVY CONSTRUCTION, EX. BUILDING 0 0 4 4

1700 SPECIAL TRADE CONTRACTORS 0 0 3 3

2000 FOOD AND KINDRED PRODUCTS 1 7 16 24

2100 TOBACCO PRODUCTS 0 0 1 1

2200 TEXTILE MILL PRODUCTS 0 4 2 6

2300 APPAREL AND OTHER TEXTILE PRODUCTS 0 3 9 12

2500 FURNITURE AND FIXTURES 0 1 1 2

2600 PAPER AND ALLIED PRODUCTS 0 1 3 4

2700 PRINTING AND PUBLISHING 0 13 10 23

2800 CHEMICALS AND ALLIED PRODUCTS 7 106 60 173

2900 PETROLEUM AND COAL PRODUCTS 0 0 6 6

3000 RUBBER AND MISC. PLASTICS PRODUCTS 1 9 6 16

3100 LEATHER AND LEATHER PRODUCTS 0 4 1 5

3200 STONE, CLAY, AND GLASS PRODUCTS 0 1 1 2

3300 PRIMARY METAL INDUSTRIES 0 4 4 8

3400 FABRICATED METAL PRODUCTS 1 5 4 10

3500 INDUSTRIAL MACHINERY AND EQUIPMENT 2 72 37 111

3600 ELECTRONIC & OTHER ELECTRIC EQUIPMENT 2 201 53 256

3700 TRANSPORTATION EQUIPMENT 0 4 20 24

3800 INSTRUMENTS AND RELATED PRODUCTS 4 57 33 94

3900 MISCELLANEOUS MANUFACTURING INDUSTRIES 1 11 3 15

4000 RAILROAD TRANSPORTATION 0 0 1 1

4100 LOCAL AND INTERURBAN PASSENGER TRANSIT 0 1 0 1

Panel D (Continued): Usable Filings in Sample by SIC

44

SIC SIC CODE DESCRIPTION AMEX NASDAQ NYSE TOTAL

4200 TRUCKING AND WAREHOUSING 0 4 1 5

4400 WATER TRANSPORTATION 0 2 5 7

4500 TRANSPORTATION BY AIR 0 1 2 3

4600 PIPELINES, EXCEPT NATURAL GAS 0 1 1 2

4700 TRANSPORTATION SERVICES 1 4 1 6

4800 COMMUNICATION 3 89 25 117

4900 ELECTRIC, GAS, AND SANITARY SERVICES 1 6 46 53

5000 WHOLESALE TRADE^DURABLE GOODS 4 29 10 43

5100 WHOLESALE TRADE^NONDURABLE GOODS 2 17 16 35

5200 BUILDING MATERIALS & GARDEN SUPPLIES 0 1 1 2

5300 GENERAL MERCHANDISE STORES 0 1 8 9

5400 FOOD STORES 0 1 4 5

5500AUTOMOTIVE DEALERS & SERVICE STATIONS 0 1 4 5

5600 APPAREL AND ACCESSORY STORES 0 6 6 12

5700 FURNITURE AND HOMEFURNISHINGS STORES 0 5 2 7

5800 EATING AND DRINKING PLACES 0 14 3 17

5900 MISCELLANEOUS RETAIL 1 27 13 41

6000 DEPOSITORY INSTITUTIONS 1 20 59 80

6100 NONDEPOSITORY INSTITUTIONS 0 22 29 51

6200 SECURITY AND COMMODITY BROKERS 5 16 46 67

6300 INSURANCE CARRIERS 0 13 62 75

6400 INSURANCE AGENTS, BROKERS, & SERVICE 0 1 9 10

6500 REAL ESTATE 0 2 3 5

6700 HOLDING AND OTHER INVESTMENT OFFICES 1 10 56 67

7000 HOTELS AND OTHER LODGING PLACES 0 5 4 9

7200 PERSONAL SERVICES 0 5 8 13

7300 BUSINESS SERVICES 4 414 50 468

7500 AUTO REPAIR, SERVICES, AND PARKING 0 1 3 4

7800 MOTION PICTURES 0 5 4 9

7900 AMUSEMENT & RECREATION SERVICES 0 7 6 13

8000 HEALTH SERVICES 2 39 28 69

8100 LEGAL SERVICES 0 0 1 1

8200 EDUCATIONAL SERVICES 0 12 3 15

8300 SOCIAL SERVICES 0 1 2 3

8700 ENGINEERING & MANAGEMENT SERVICES 3 33 14 50

8800 PRIVATE HOUSEHOLDS 0 0 1 1

8900 SERVICES, NEC 0 3 0 3

  TOTAL 53 1331 847 2231

45

Panel E: Usable Filings in Sample by Filing JurisdictionCourt AMEX NASDAQ NYSE TOTAL

Alabama N.D. 2 1 3

Arizona D. 22 6 28

Arkansas E.D. 3 3

Arkansas W.D. 1 1

California C.D. 1 71 55 127

California N.D. 2 179 39 220

California S.D. 1 42 9 52

Colorado D. 23 8 31

Connecticut D. 1 12 21 34

Delaware D. 8 7 15

District Columbia D. 1 5 7 13

Florida M.D. 2 27 9 38

Florida N.D. 1 1

Florida S.D. 6 35 27 68

Georgia M.D. 1 1

Georgia N.D. 34 19 53

Idaho D. 4 4

Illinois N.D. 28 41 69

Illinois S.D. 1 1 2

Indiana S.D. 5 7 12

Iowa S.D. 1 1 2

Kansas D. 3 3

Kentucky E.D. 3 3

Kentucky W.D. 2 6 8

Louisiana E.D. 3 8 11

Louisiana W.D. 1 1

Maine D. 1 1 2

Maryland D. 12 7 19

Massachusetts D. 4 70 23 97

Michigan E.D. 9 11 20

Michigan W.D. 2 2

Minnesota D. 28 21 49

Mississippi S.D. 3 3

Missouri E.D. 5 6 11

Missouri W.D. 3 5 8

Nebraska D. 4 5 9

Nevada D. 1 9 5 15

New Hampshire D. 6 3 9

New Jersey D. 4 36 38 78

New Mexico D. 5 4 9

New York E.D. 1 31 32 64

New York N.D. 2 2

New York S.D. 14 415 210 639

New York W.D. 4 1 5

Panel E (Continued): Usable Filings in Sample by Filing JurisdictionCourt AMEX NASDAQ NYSE TOTAL

46

North Carolina E.D. 2 1 3

North Carolina M.D. 7 3 10

North Carolina W.D. 1 2 3

Ohio N.D. 1 5 15 21

Ohio S.D. 9 11 20

Oklahoma N.D. 1 1

Oklahoma W.D. 2 5 7

Oregon D. 2 5 1 8

Pennsylvania E.D. 1 31 21 53

Pennsylvania M.D. 1 1

Pennsylvania W.D. 1 4 9 14

Puerto Rico D. 1 1

Rhode Island D. 1 1 2 4

South Carolina D. 1 2 2 5

South Dakota D. 1 1 2

Tennessee E.D. 1 3 4

Tennessee M.D. 7 8 15

Tennessee N.D. 1 1

Tennessee W.D. 2 2 4

Texas E.D. 6 3 9

Texas N.D. 2 24 31 57

Texas S.D. 3 12 25 40

Texas W.D. 9 2 11

Utah D. 10 1 11

Vermont D. 1 1

Virgin Islands D. 1 1

Virginia E.D. 8 16 24

Virginia W.D. 1 3 4

Washington E.D. 2 2 4

Washington W.D. 2 21 8 31

West Virginia S.D. 2 2

Wisconsin E.D. 4 5 9

Wisconsin W.D. 2 4 6

Total 53 1331 847 2231

47

Table IIMean Cumulative Abnormal Returns and Precision Weighted Cumulative Average Abnormal Return

Table II shows the percentage market adjusted mean cumulative abnormal return and precision weighted cumulative average abnormal return (CAAR) for a sample of class action lawsuit announcements from 1996 through 2008. Unless otherwise noted the returns are calculated on a value-weighted market portfolio. Panels show these returns for a number of subsets. The t-statistic based on the cross sectional standard error as calculated by EVENTUS. The pre-event (-20,-1), event (0,+1), and post-event (+2,+20) windows are measured in days. N represents the number of observations.

Panel A: COMPOSITE

Days N

Mean Cumulative

Abnormal Return

Precision Weighted

CAARCross Sectional

t-statistic(-20,-1) 2231 -14.03% -21.23% -19.824***(0,+1) 2214 -2.85% -3.41% -11.016***

(+2,+20) 2212 -1.45% -1.30% -2.559**

Panel B: COMPOSITE with equally weighted market return

Days N

Mean Cumulative

Abnormal Return

Precision Weighted

CAARCross Sectional

t-statistic(-20,-1) 2231 -14.94% -22.11% -21.398***(0,+1) 2214 -2.92% -3.47% -11.320***

(+2,+20) 2212 -2.33% -2.22% -4.157***

Panel C: AMEX

Days N

Mean Cumulative

Abnormal Return

Precision Weighted

CAARCross Sectional

t-statistic(-20,-1) 53 -26.59% -35.12% -5.728***(0,+1) 50 -4.99% -4.51% -2.787***

(+2,+20) 50 -4.39% -6.47% -1.134

Panel D: NASDAQ

Days N

Mean Cumulative

Abnormal Return

Precision Weighted

CAARCross Sectional

t-statistic(-20,-1) 1331 -13.44% -20.12% -13.415***(0,+1) 1319 -3.13% -3.78% -9.246***

(+2,+20) 1318 -1.79% -2.06% -2.387**

Panel E: NYSE

Days N

Mean Cumulative

Abnormal Return

Precision Weighted

CAARCross Sectional

t-statistic(-20,-1) 847 -14.18% -18.08% -14.912***(0,+1) 845 -2.29% -2.49% -5.570***

(+2,+20) 844 -0.75% -0.28% -0.845*, **, *** Significant at the 10, 5, and 1 percent levels, respectively, for a two-tailed test.

48

Table II (Continued)Mean Cumulative Abnormal Returns and Precision Weighted Cumulative Average Abnormal Return

Table II shows the percentage market adjusted mean cumulative abnormal return and precision weighted cumulative average abnormal return (CAAR) for a sample of class action lawsuit announcements from 1996 through 2008. Unless otherwise noted the returns are calculated on a value-weighted market portfolio. Panels show these returns for a number of subsets. The t-statistic based on the cross sectional standard error as calculated by EVENTUS. The pre-event (-20,-1), event (0,+1), and post-event (+2,+20) windows are measured in days. N represents the number of observations.

Panel F: Pre-Regulation SHO (Effective September 7, 2004)

Days N

Mean Cumulative

Abnormal Return

Precision Weighted

CAARCross Sectional

t-statistic(-20,-1) 1642 -14.37% -21.66% -16.656***(0,+1) 1627 -3.00% -3.54% -9.800***

(+2,+20) 1626 -1.37% -1.38% -1.943*

Panel G: Post Regulation SHO (Effective September 7, 2004 through April 30, 2005)

Days N

Mean Cumulative

Abnormal Return

Precision Weighted

CAARCross Sectional

t-statistic(-20,-1) 97 -16.60% -20.09% -7.049***(0,+1) 97 -2.33% -3.16% -2.438**

(+2,+20) 97 -2.56% -2.64% -1.940*

Panel H: Uptick Rule Suspension Pilot (May 1, 2005 through April 30, 2006)

Days N

Mean Cumulative

Abnormal Return

Precision Weighted

CAARCross Sectional

t-statistic(-20,-1) 128 -12.78% -15.76% -5.206***(0,+1) 127 -2.39% -2.18% -2.964***

(+2,+20) 127 -0.43% 0.24% -0.363

Panel I: Post Uptick Rule Suspension (Effective July 3, 2007through September 15, 2008 )

Days N

Mean Cumulative

Abnormal Return

Precision Weighted

CAARCross Sectional

t-statistic(-20,-1) 194 -14.28% -19.70% -6.010***(0,+1) 193 -2.98% -2.79% -3.503***

(+2,+20) 193 -1.70% -0.12% -0.942

Panel J: Post Lehman Collapse ( September 15, 2008 )

Days N

Mean Cumulative

Abnormal Return

Precision Weighted

CAARCross Sectional

t-statistic(-20,-1) 53 -14.38% -23.18% -2.816***(0,+1) 53 -1.10% -1.99% -0.457

(+2,+20) 52 -1.19% 2.82% -0.229*, **, *** Significant at the 10, 5, and 1 percent levels, respectively, for a two-tailed test.

Table II (Continued)Mean Cumulative Abnormal Returns and Precision Weighted Cumulative Average Abnormal Return

49

Table II shows the percentage market adjusted mean cumulative abnormal return and precision weighted cumulative average abnormal return (CAAR) for a sample of class action lawsuit announcements from 1996 through 2008. Unless otherwise noted the returns are calculated on a value-weighted market portfolio. Panels show these returns for a number of subsets. The t-statistic based on the cross sectional standard error as calculated by EVENTUS. The pre-event (-20,-1), event (0,+1), and post-event (+2,+20) windows are measured in days. N represents the number of observations.

Panel K: Equity Bull Markets

Days N

Mean Cumulative

Abnormal Return

Precision Weighted

CAARCross Sectional

t-statistic(-20,-1) 1453 -12.71% -19.77% -14.669***(0,+1) 1443 -2.72% -3.49% -9.064***

(+2,+20) 1445 0.11% -0.29% 0.172

Panel L: Equity Bear Markets

Days N

Mean Cumulative

Abnormal Return

Precision Weighted

CAARCross Sectional

t-statistic(-20,-1) 796 -16.40% -23.69% -13.678***(0,+1) 789 -3.23% -3.16% -6.689***

(+2,+20) 785 -4.12% -3.42% -4.022***

Panel M: Economic Expansions

Days N

Mean Cumulative

Abnormal Return

Precision Weighted

CAARCross Sectional

t-statistic(-20,-1) 1737 -16.30% -22.16% -20.693***(0,+1) 1723 -3.04% -3.41% -10.305***

(+2,+20) 1721 -2.03% -1.74% -3.219***

Panel N: Economic Contractions

Days N

Mean Cumulative

Abnormal Return

Precision Weighted

CAARCross Sectional

t-statistic(-20,-1) 494 -6.05% -11.63% -3.918***(0,+1) 491 -2.17% -2.67% -4.057***

(+2,+20) 491 0.56% 1.46% 0.432

Panel O: Economic Contraction 2008 Only

Days N

Mean Cumulative

Abnormal Return

Precision Weighted

CAARCross Sectional

t-statistic(-20,-1) 165 -11.90% -15.85% -5.012***(0,+1) 164 -2.33% -2.40% -2.029**

(+2,+20) 163 -1.29% 0.30% -0.544*, **, *** Significant at the 10, 5, and 1 percent levels, respectively, for a two-tailed test.

Table II (Continued)Mean Cumulative Abnormal Returns and Precision Weighted Cumulative Average Abnormal Return

50

Table III shows the percentage market adjusted mean cumulative abnormal return and precision weighted cumulative average abnormal return (CAAR) for a sample of class action lawsuit announcements from 1996 through 2008. Unless otherwise noted the returns are calculated on a value-weighted market portfolio. Panels show these returns for a number of subsets. The t-statistic based on the cross sectional standard error as calculated by EVENTUS. The pre-event (-20,-1), event (0,+1), and post-event (+2,+20) windows are measured in days. N represents the number of observations.

Panel Q: Southern District New York

Days N

Mean Cumulative

Abnormal Return

Precision Weighted

CAARCross Sectional

t-statistic(-20,-1) 639 -5.55% -13.44% -4.285***(0,+1) 636 -2.03% -3.09% -3.983***

(+2,+20) 635 0.27% 0.75% 0.266

Panel R: Northern District California

Days N

Mean Cumulative

Abnormal Return

Precision Weighted

CAARCross Sectional

t-statistic(-20,-1) 220 -19.76% -22.14% -8.438***(0,+1) 218 -4.29% -4.34% -4.606***

(+2,+20) 218 2.35% 2.45% 1.164*, **, *** Significant at the 10, 5, and 1 percent levels, respectively, for a two-tailed test.

51

Table IIIRegression analysis of the relationship between daily returns and trading volume

Table III shows the results of a regression analysis of the relationship between daily returns and trading volume. Following Chan, Chung, and Fong (2002) and Hasbrouk (1991), this table presents estimation of the following bivariate VAR model:

(1)

(2)

where rt represents the average holding period return of the stocks in our sample on day t, and z t represents the trading volume in total number of shares sold by the firms in our sample on day t. All the return and trade volume series are standardized by subtracting the mean and dividing by the standard deviation of the day. We assume that the error terms in the two equations, ε 1,t and ε2,t , are jointly and serially uncorrelated with mean zero. Lag 0 measures the simultaneity. As in Chan, Chung, and Fong (2002), in equation (1), we use the contemporaneous volume and 6 lags for the explanatory variables, and report the regression coefficient for the contemporaneous and the six lags and t-statistics (in italics).

Dependent

Panel A: AMEX Explanatory variablesLagged stock returns Lagged stock trading volume

Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6 Lag 0 Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6

Stock returns 0.3313 0.0223 0.5896** -0.4545* -0.2315 0.1620 -0.0101** 0.0122** -0.0109** 0.0028 -0.0042 0.0062 -0.0037

1.6813 0.1007 2.5742 -2.0100 -0.9788 0.7497 -2.2348 2.2607 -2.3137 0.5733 -0.8465 1.3193 -0.9725

Trade volume -9.2679 -18.604* 21.5606** -1.0079 -4.2668 -3.5472 0.8662*** -0.2379 0.0157 -0.1542 0.3182 -0.2149

-1.0236 -1.9344 2.2138 -0.0948 -0.3850 -0.3500 4.9465 -1.1025 0.0677 -0.6691 1.5163 -1.2514

Dependent

Panel B: NASDAQ Explanatory variables

Lagged stock returns Lagged stock trading volume

Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6 Lag 0 Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6

Stock returns 0.0357 0.0871 -0.1702 -0.0486 0.0864 -0.1214 -0.0054** -0.0033 0.0011 -0.0038 0.0046 -0.0034 0.0019

0.1714 0.3979 -0.8093 -0.2504 0.4377 -0.6372 -2.6230 -0.9788 0.3124 -1.0019 1.2877 -0.9634 0.6800

Trade volume -1.7628 3.7314 16.3564 -3.8532 -17.9752 -25.1338 1.2601*** -0.5534 0.4519 -0.0835 -0.0885 -0.4052

-0.0810 0.1631 0.7539 -0.1902 -0.8863 -1.3106 5.3885 -1.5264 1.1803 -0.2224 -0.2403 -1.4745

*, **, *** Significant at the 10, 5, and 1 percent levels, respectively, for a two-tailed test.

52

Dependent

Panel C: NYSE Explanatory variablesLagged stock returns Lagged stock trading volume

Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6 Lag 0 Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6

Stock returns 0.2008 -0.1061 0.1966 0.2048 0.1494 0.1379 -0.0141*** 0.0037 0.0007 0.0013 0.0067 0.0006 0.0017

0.9045 -0.4585 0.8464 0.8611 0.5856 0.5611 -2.9956 0.5006 0.0929 0.1720 0.8405 0.0774 0.3285

Trade volume -9.6580 -12.6724 1.9219 3.8509 -6.9551 7.9552 1.0155*** -0.4755 0.2170 -0.2906 0.1610 0.0772

-0.9805 -1.2505 0.1827 0.3582 -0.6065 0.7227 4.0632 -1.4230 0.6164 -0.8164 0.4529 0.3216

*, **, *** Significant at the 10, 5, and 1 percent levels, respectively, for a two-tailed test.

53

Figure 1 Display of the sequential trading process from an inventory

approach.

54

Figure 2 Display of the sequential trading process from an inventory

approach.

55

56