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  1. 1. Comments Welcome 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. 1
  2. 2. 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 2
  3. 3. 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 court1. 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. 3
  4. 4. 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. 4
  5. 5. 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) 5
  6. 6. 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. 6
  7. 7. I. Literature Review 7
  8. 8. 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. 8
  9. 9. 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 9
  10. 10. 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. 10
  11. 11. 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. ~ = p a + pb pt 2 (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 λ a (pa) and the probability of stock coming in is λb (pb). Under severe conditions, the dealer may run out of cash 11
  12. 12. if p a λ a ( p a ) < p b λb ( p b ) (2) and run out of stock if λ a ( p a ) > λb ( p b ) (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. 12
  13. 13. 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. 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, π I ( p ) equals the probability of an informed trade and 1 − π I ( p ) 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. 13
  14. 14. ∞ pb ∫pa ( p a − p ) f ( p)dp + ∫ ( p − pb ) f ( p )dp 0 (4) The probability that some uninformed trader will buy is π UB ( p ) and the probability that some uninformed trader will sell is π US ( p ) . The probability that the trader does neither is π UN ( p ) . The dealer’s expected gain from discrete trading with the uninformed trader is given by the following. π UB ( p )( p a − p ) +π US( p )( p − p b ) + π UN (0) (5) The dealer’s overall objective function is given by the following equation. ∞ pb g (•) = π I ( p)[ ∫ ( p a − p ) f ( p )dp + ∫ ( p − pb ) f ( p )dp ] + pa 0 (1 − π I ( p))[π UB ( p)( p a − p ) +π US( p )( p − pb ) + π UN (0)] (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. 14
  15. 15. 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 pt, which follows a random walk, a martingale. 15
  16. 16. 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, Ɵ 1, 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, p t. 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. pb ( p ) g (•) = π I ( p ) ∫ ( pb ( p) − p ) f ( p )dp (7) 0 16
  17. 17. 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. ∑λ a ( p a ) < ∑ λb ( p b ) 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, Ɵ 1 and the unusual trading activity. Consequently, he has not reformulated an opinion of the stock and does not change his portfolio. 17
  18. 18. 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. g (•) = (1 − π I ( p ))[π UB ( p )( p a − p) +π US( p)( p − pb ) + π UN (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, ∑λ a ( p a ) < ∑ λb ( p b ) 18
  19. 19. 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. 19
  20. 20. 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 ∑λ a ( p a ) = ∑ λb ( p b ) . From an information perspective, the dealer’s objective function goes to zero as the two terms below offset. ∞ g (•) = π I ∫ ( p a − p ) f ( p) dp + (1 − π I ( p ))[π UB ( p )( p a − p)] pa ( p ) III. Data and Research Methodology 20
  21. 21. 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. 21
  22. 22. 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. 22
  23. 23. 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. 23
  24. 24. 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 24
  25. 25. 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. ˆ ARi ,t = Ri ,t − (α i + β i R m,t ) ˆ (8) In the above equation, Rit is the return for firm i on day t, Rmt is the market ˆ ˆ return for the CRSP value-weighted market index on day t, α i , β i 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. Rit = α i + β i Rmt + ε it (2) 25
  26. 26. 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. 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). d2 CARi = ∑ ARit t = d1 (9) N ∑ CAR i (10) CAAR = CAR = i =1 N where N is the number of lawsuits in the sample. 26
  27. 27. 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. 1 N ∑ CARi N i =1 t CAAR = s (CARi ) N (11) Two additional parametric tests are offered by Campbell, Lo, and MacKinlay (1997), and employed in Fidrmuc, Goergen, and Renneboog, (2006): CAAR J1 = and s(CAAR) (12) 27
  28. 28. N ( Li − 4) 1 N CARi J2 = Li − 2 N ∑ s(CAR ) i =1 i (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. Li is the number of observations in the estimation period. ∑ t2 s (CARi ) = t =t1 si2 (14) 1 ∑ ∑ N t2 s (CAAR ) = i =1 t =t1 si2 N2 (15) 1 ∑ 1i ( Ri,t − α i − βˆi Rm,t ) 2 T si = ˆ Li − 2 t =T0 i (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, Li is 180 with an estimation period T0i = -200 to T1i = -21. 28
  29. 29. 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 J 1 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. 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.” 29
  30. 30. 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. rt = a1 rt −1 + ... + a p rt − p + b0 z t + b1 z t −1 + ... + b p z t − p + ε 1,t (17) z t = c1 rt −1 + ... + c p rt − p + d1 z t −1 + ... + d p z t − p + ε 2,t (18) 30
  31. 31. where rt represents the average holding period return of the stocks in our sample on day t, and zt 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, ε1,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. 31
  32. 32. 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. 32
  33. 33. 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. 33
  34. 34. 34
  35. 35. From the summary of interrelationship between quote returns and net trade volume [Chan, Chung, and Fong (2002)] D Explanatory variables Contemporaneous terms (if e applicable) + Lagged terms p N e e n t d S - e t t n o r t c a v k d a e r r i e v a t o b u l l r u e n m s s e S N t e o g c a k t i P 35 r v o e e s t i
  36. 36. 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 36
  37. 37. 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). 37
  38. 38. 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. 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. 38
  39. 39. 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. 39
  40. 40. 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 40
  41. 41. 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. 41
  42. 42. 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 42
  43. 43. 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. 43
  44. 44. 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 44
  45. 45. 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 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. 45
  46. 46. 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 Donaldson, Lufkin & Jenrette Securities 3 Other PRIVATE 01/26/1996 S.D. New York Corporation : Rickel Home Centers Notes 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 NationsBank Corporation : Dean Witter U.S. 18 Other DWD 03/26/1996 M.D. Florida Government Securities Trust 19 Fleming Companies, Inc. : Common Stock NYSE FLM 03/28/1996 W.D. Oklahoma WELLE 20 Wellcare Management Group, Inc. NASDAQ 03/29/1996 N.D. New York [DELISTED] 46
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  50. 50. Table I Descriptive 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 Total 1996 3 58 27 7 11 6 0 0 112 1997 3 88 59 13 7 4 0 0 174 1998 6 140 64 14 8 8 0 0 240 1999 10 104 73 17 3 2 0 0 209 2000 4 126 64 19 2 0 0 1 216 2001 3 409 62 13 4 4 0 2 497 2002 11 108 111 24 8 4 1 1 268 2003 9 103 90 13 3 6 0 1 225 2004 8 110 92 12 3 4 4 2 235 2005 6 86 73 8 2 1 0 6 182 2006 6 56 46 4 1 2 2 1 118 2007 2 97 75 0 0 2 1 0 177 2008 6 91 105 3 5 11 1 0 222 Total 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 Total 1996 3 58 27 0 0 0 0 0 88 1997 3 88 59 0 0 0 0 0 150 1998 6 140 64 0 0 0 0 0 210 1999 10 104 73 0 0 0 0 0 187 2000 4 126 64 0 0 0 0 0 194 2001 3 409 62 0 0 0 0 0 474 2002 11 108 111 0 0 0 0 0 230 2003 9 103 90 0 0 0 0 0 202 2004 8 110 92 0 0 0 0 0 210 2005 6 86 73 0 0 0 0 0 165 2006 6 56 46 0 0 0 0 0 108 2007 2 97 75 0 0 0 0 0 174 2008 6 91 105 0 0 0 0 0 202 Total 77 1576 941 0 0 0 0 0 2594 50
  51. 51. Panel C: Usable Filings in Sample Year AMEX NASDAQ NYSE OTC-BB Other Private Pink-Sh Unclassified Total 1996 2 47 22 0 0 0 0 0 71 1997 0 77 54 0 0 0 0 0 131 1998 4 122 54 0 0 0 0 0 180 1999 7 93 67 0 0 0 0 0 167 2000 3 77 56 0 0 0 0 0 136 2001 3 391 58 0 0 0 0 0 452 2002 10 88 96 0 0 0 0 0 194 2003 4 93 79 0 0 0 0 0 176 2004 4 92 86 0 0 0 0 0 182 2005 4 74 63 0 0 0 0 0 141 2006 5 49 45 0 0 0 0 0 99 2007 1 67 69 0 0 0 0 0 137 2008 6 61 98 0 0 0 0 0 165 Total 53 1331 847 0 0 0 0 0 2231 Panel D: Usable Filings in Sample by SIC SIC SIC CODE DESCRIPTION AMEX NASDAQ NYSE TOTAL 700 AGRICULTURAL SERVICES 0 2 0 2 100 0 METAL MINING 3 3 5 11 120 0 COAL MINING 0 0 3 3 130 0 OIL AND GAS EXTRACTION 3 4 15 22 140 0 NONMETALLIC MINERALS, EXCEPT FUELS 0 0 2 2 150 0 GENERAL BUILDING CONTRACTORS 0 0 8 8 160 0 HEAVY CONSTRUCTION, EX. BUILDING 0 0 4 4 170 0 SPECIAL TRADE CONTRACTORS 0 0 3 3 200 0 FOOD AND KINDRED PRODUCTS 1 7 16 24 210 0 TOBACCO PRODUCTS 0 0 1 1 220 0 TEXTILE MILL PRODUCTS 0 4 2 6 230 0 APPAREL AND OTHER TEXTILE PRODUCTS 0 3 9 12 250 0 FURNITURE AND FIXTURES 0 1 1 2 260 0 PAPER AND ALLIED PRODUCTS 0 1 3 4 270 0 PRINTING AND PUBLISHING 0 13 10 23 280 0 CHEMICALS AND ALLIED PRODUCTS 7 106 60 173 290 0 PETROLEUM AND COAL PRODUCTS 0 0 6 6 300 0 RUBBER AND MISC. PLASTICS PRODUCTS 1 9 6 16 310 0 LEATHER AND LEATHER PRODUCTS 0 4 1 5 51
  53. 53. 0 610 0 NONDEPOSITORY INSTITUTIONS 0 22 29 51 620 0 SECURITY AND COMMODITY BROKERS 5 16 46 67 630 0 INSURANCE CARRIERS 0 13 62 75 640 0 INSURANCE AGENTS, BROKERS, & SERVICE 0 1 9 10 650 0 REAL ESTATE 0 2 3 5 670 0 HOLDING AND OTHER INVESTMENT OFFICES 1 10 56 67 700 0 HOTELS AND OTHER LODGING PLACES 0 5 4 9 720 0 PERSONAL SERVICES 0 5 8 13 730 0 BUSINESS SERVICES 4 414 50 468 750 0 AUTO REPAIR, SERVICES, AND PARKING 0 1 3 4 780 0 MOTION PICTURES 0 5 4 9 790 0 AMUSEMENT & RECREATION SERVICES 0 7 6 13 800 0 HEALTH SERVICES 2 39 28 69 810 0 LEGAL SERVICES 0 0 1 1 820 0 EDUCATIONAL SERVICES 0 12 3 15 830 0 SOCIAL SERVICES 0 1 2 3 870 0 ENGINEERING & MANAGEMENT SERVICES 3 33 14 50 880 0 PRIVATE HOUSEHOLDS 0 0 1 1 890 0 SERVICES, NEC 0 3 0 3 TOTAL 53 1331 847 2231 53
  54. 54. Panel E: Usable Filings in Sample by Filing Jurisdiction Court 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 Jurisdiction Court AMEX NASDAQ NYSE TOTAL 54
  55. 55. 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 55
  56. 56. Table II 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 A: COMPOSITE Mean Precision Cumulative Weighted Cross Sectional Days N Abnormal Return CAAR 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 Mean Precision Cumulative Weighted Cross Sectional Days N Abnormal Return CAAR 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 Mean Precision Cumulative Weighted Cross Sectional Days N Abnormal Return CAAR 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 Mean Precision Cumulative Weighted Cross Sectional Days N Abnormal Return CAAR 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 Mean Precision Cumulative Weighted Cross Sectional Days N Abnormal Return CAAR 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. 56
  57. 57. 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) Mean Precision Cumulative Weighted Cross Sectional Days N Abnormal Return CAAR 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) Mean Precision Cumulative Weighted Cross Sectional Days N Abnormal Return CAAR 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) Mean Precision Cumulative Weighted Cross Sectional Days N Abnormal Return CAAR 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 ) Mean Precision Cumulative Weighted Cross Sectional Days N Abnormal Return CAAR 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 ) Mean Precision Cumulative Weighted Cross Sectional Days N Abnormal Return CAAR 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 57