Do Internet Stock Message Boards Influence Trading? Evidence from Heavily Discussed Stocks with No Fundamental News

Transcription

1 Journal of Business Finance & Accounting Journal of Business Finance & Accounting, 38(9) & (10), , November/December 2011, X doi: /j x Do Internet Stock Message Boards Influence Trading? Evidence from Heavily Discussed Stocks with No Fundamental News SANJIV SABHERWAL, SALIL K. SARKAR AND YING ZHANG Abstract: This study extends the literature on the information content of stock message boards. To better understand the effect of online postings on trading activities and reduce the error due to stocks with small message board followings, we examine stocks with no fundamental news and high message posting activity. Such stocks tend to be of small firms with weak financials. For those stocks, we find a two-day pump followed by a two-day dump manipulation pattern among online traders, which suggests that an online stock message board can be used as a herding device to temporarily drive up stock prices. We also find that online traders credit-weighted sentiment index, but not the number of postings, is positively associated with contemporaneous return and negatively predicts the return next day and two days later. Also, absolute sentiment is negatively related with contemporaneous and next day s intraday volatility and positively related with the proportion of volume in small-sized trades. We conclude that message board sentiment is an important predictor of trading-related activities. Keywords: internet stock message board, online trading, investor sentiment, noise trader, naïve bayesian, text classifier 1. INTRODUCTION The use of the Internet for stock trading and information collection and sharing has been growing rapidly. 1 In addition to trading via online discount brokers, traders often The first and second authors are from the Department of Finance and Real Estate, University of Texas at Arlington. The third author is from the Department of Finance, Dolan School of Business, Fairfield University, USA. They thank an anonymous referee, Peter F. Pope (editor), Bobby Alexander, Benoit Ballay, Yen-Ling Chang, Craig A. Depken, John G. Gallo, Larry J. Lockwood, Giao X. Nguyen, Wikrom Prombutr and Peggy E. Swanson for helpful comments and suggestions. They also thank Malcolm Bain, Philip Gagner, Tianqiang Li, Huaping Shen, Adam Stickland, Ellen Stranberg and Guohua Zhang for providing technical support. All remaining errors are the authors own. (Paper received April 2010, revised version accepted July 2011) Address for correspondence: Ying Zhang, Department of Finance, Dolan School of Business, Fairfield University, Fairfield, Connecticut 06824, USA. yzhang1@fairfield.edu 1 Rubin and Rubin (2010) discuss the characteristics of the Internet that distinguish it from traditional information sources and have contributed to the rapid increase in its use. Orens et al. (2010) focus on the, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA. 1209

2 1210 SABHERWAL, SARKAR AND ZHANG seek information online from paid sources such as ValueLine and Zacks and free sources such as stock message boards and chat rooms. Among the free sources, stock message boards (e.g., Yahoo! Finance, Raging Bull and TheLion.com) are popular for learning other investors opinions about specific stocks or the market. There are numerous message boards where potential investors post, read and reply to messages. Not only the number of such boards but also the number of participants on these boards has exploded. For example, TheLion.com, a message board aggregator web site, tracks over 100 million message postings from more than 25,000 message boards and attracts over 250 million page views and two million monthly visitors as of June With their increasing prominence, stock message boards have been getting the attention of practitioners, policymakers, and researchers since the late 1990s. The message board sentiments are closely watched by investors who seek inputs to enhance their trading profits. However, there is a concern that participants may post messages designed to improve their positions at the expense of other investors. Such trading behavior is known as pump-and-dump (Antweiler and Frank, 2004; and Jiang et al., 2005). 2 Many low priced stocks are subject to stock manipulation because of their thin float and lack of broad ownership (Wysocki, 1999; and Aggarwal and Wu, 2006), and regulators are concerned about this issue. The Securities and Exchange Commission (SEC) and the Federal Trade Commission (FTC) are especially interested in tracking the activities on online message boards in order to prevent internet investment scams and protect investors interests. Among researchers, studies such as van Bommel (2003) and Eren et al. (2009) provide theoretical models of the manipulation of stock prices by spreading rumors. In this paper, we first argue that an online stock message board can be used to manipulate the price of a vulnerable stock with small market capitalization and weak fundamentals. Specifically, an online message board can be used as a herding mechanism to temporarily drive up the stock price; subsequently, the stock is sold and the price falls. We then examine if the empirical evidence supports this argument. Our argument, described in detail in the next section, is based on the theoretical model in van Bommel (2003). Another issue we examine is how online message posting activities influence shortterm security trading. Although previous studies document that online talk is not just noise, it remains unclear whether message board activities influence trading activities or is it the other way around. Also, previous studies do not find a relation between investor sentiment on the message boards and future stock returns. In this paper, we revisit the issue of the causality between online posting activities and trading activities, including online investor sentiment s ability to predict subsequent returns. Further, as discussed above, we empirically examine the use of an online message board to pump and dump stocks. We also examine the characteristics of stocks likely to be targeted by online traders for their pump and dump strategy. The approach we use for investigating the above issues has some important differences from other studies. Specifically, we examine stocks for which there is no material news and the message board activity is high. If there is material news about use of Internet by firms as a separate source for disclosing non-financial information and find evidence of economic relevance of such disclosure. 2 Following links provide information about SEC enforcement actions against internet fraud and manipulation: internetenforce.htm

3 DO INTERNET STOCK MESSAGE BOARDS INFLUENCE TRADING? 1211 a stock, market activities are likely to influence message board activities instead of the other way around. Therefore, in earlier studies, which do not specifically control for news, evidence is confounded by the presence of stocks with material news. By examining stocks with no material news, we are able to better understand the effect of online postings on market trading activities. Also, consistent with Tumarkin (2002), who suggests that only stocks with high message volume should be included in the sample in order to reduce error introduced by stocks with small bulletin board followings, we only include stocks with high message posting activity. For this purpose, we use TheLion.com, a novel data source that provides a list of the ten most heavily discussed stocks on each trading day. In our sample, we include these most heavily discussed stocks, instead of subjectively selecting a research sample that could include stocks without a significant online following. In constructing a measure of online investor sentiment, we include messages with and without a self-reported sentiment. We use a text classifier to assess the sentiment of a message in which it is not self-reported. TheLion.com has a reward system in which a poster earns reputation credit from other users for posting quality messages. This enables us to use a credit-weighted measure of investor sentiment. While there is no sample selection bias in our study, one caveat is that our sample, which includes heavily discussed stocks without any material news, mainly has small cap stocks with weak financials. 3 Therefore, while this study enhances our understanding of the influence of online message board activities on trading activities in such stocks, the findings of the study should not be generalized to larger firms with strong financials. The main findings of this study are as follows. First, the empirical evidence is consistent with our pump and dump argument. There is a significant positive abnormal return on the day a stock is heavily discussed on the message board and also on the preceding day. There are significant negative abnormal returns on the next two days. These abnormal returns suggest a two-day pump followed by a twoday dump stock manipulation pattern among online traders. There are no long-term economic effects of the pumping as the stock price reverts to the pre-pump level within a few days. Second, online traders prefer non-financial stocks with a low price, a high trading volume, a small float, and a low price-to-book ratio. We also find that online posting activities affect online trading activities. Online traders sentiment index on the day a stock is heavily discussed is positively related with the return on that day and negatively related with the return on the following day. This is consistent with our event study results suggesting a pump and dump pattern. Of the three posting activity measures examined (sentiment index, disagreement index, and the number of messages), sentiment index dominates in explaining returns. Further, absolute sentiment helps explain volatility and the proportion of volume in small-sized trades. These results show that message board sentiment is an important predictor of trading-related activities. The rest of the paper is organized as follows. The next section discusses the main issues and hypotheses. The design-related features of this study are discussed in Section 3. Section 4 includes details of sample construction, data sources, and sample characteristics. Section 5 provides event study results. In Section 6, we look at online investors trading incentives and characteristics of targeted stocks. Section 7 examines 3 This is expected as such stocks are the ones most vulnerable to online manipulation.

4 1212 SABHERWAL, SARKAR AND ZHANG if posting activities can explain contemporaneous and subsequent trading activities. Section 8 discusses the robustness of our results and Section 9 concludes. (i) Pump and Dump 2. ISSUES AND HYPOTHESES Several studies have examined the manipulation of stock markets. Most of these studies, such as van Bommel (2003), Eren et al. (2009) and Kyle and Viswanathan (2008), are theoretical. There are a few empirical studies, including Jiang et al. (2005), Khwaja and Mian (2005) and Aggarwal and Wu (2006). None of these studies empirically examine online stock manipulation through a pump and dump scheme. In this paper, we suggest that an online stock message board can be used as a herding mechanism to temporarily drive up prices; the stock is then dumped and the price falls. Our arguments, based on the theoretical model in van Bommel (2003), are as follows. An influential poster, with a high reputation and many followers on an online message board, builds a long position in a target stock. 4 The target stock is likely to be a small firm with weak financials and small institutional ownership since such a stock is easier to manipulate via an online message board. After building a long position, the poster pumps the stock on the message board by posting messages about the stock with a bullish sentiment. As in the bluffing variant of the model in van Bommel, the poster need not be informed and he could just be bluffing that he is trading on information. Due to the poster s reputation and the strong sentiment in his messages, his followers herd and trade based on his advice, thus moving the price up. Outside traders (non-subscribers to the message board) such as chartists and momentum traders are influenced by the price movement and also take a position. Within a day or two, the price likely overshoots the trigger point for the influential poster to reverse his position. When the poster knows the price is overshooting, he dumps the stock and his followers then do the same. Both the poster and his followers profit at the expense of the outside traders. 5 Based on the above arguments, we propose the following hypothesis. Initially when a stock is heavily talked about (pumped) on a message board, there are positive abnormal returns in it. Negative abnormal returns in the stock follow as the stock is then dumped. (ii) Trading Preferences of Online Traders A basic issue is the attributes of stocks likely to be targeted by online traders for their pump-and-dump strategy. Wysocki (1999) examines which stocks attract message postings. He finds that message-posting volume is related to firm characteristics. While Wysocki examined number of messages and our focus is on trading preferences, 4 An influential poster may work alone. Or, similar to Khwaja and Mian (2005) in which brokers in an emerging market collude in a pump and dump stock price manipulation scheme, two or more influential posters may collaborate. The collaboration could occur through private communication channels such as and instant messenger. 5 As the pump and dump works for the message board subscribers, it reinforces the original poster s reputation.

5 DO INTERNET STOCK MESSAGE BOARDS INFLUENCE TRADING? 1213 the findings in Wysocki indicate that online traders have a preference for certain types of stocks. Accordingly, we suggest that a firm s characteristics affect the trader s preference for that firm s stock. Maximizing trading profits is typically the primary incentive for trading a stock (Lakonishok and Smidt, 1986). Korczak et al. (2010) discuss that the abnormal return associated with a corporate news announcement reflects for the trading profit realized (foregone) by an insider if he chose to trade (not to trade) ahead of the news. In this study, we use abnormal returns as a proxy for trading preferences. Earlier studies such as Fama and French (1992) and Daniel and Titman (1997) show that a firm s fundamental factors significantly explain its stock returns. Other studies such as Brock et al. (1992) and George and Hwang (2004) document the significant power of technical characteristics in explaining security returns. Wysocki (1999) finds that both fundamental and technical characteristics of a stock affect the message-posting volume in that stock. In view of the above studies, we propose that trading preferences of online traders are related to the fundamental and technical characteristics of the stock. (iii) Short-Term Effect of Online Activities on Trading Activities An extant interesting issue is whether online message posting activities on a given day influence short-term security trading on that day and the next day or two. Though previous studies document that online talk is not just noise, the causality between message board postings and trading activities is not without controversy. While some studies find significant predictive effects of the number of messages on trading activities, the studies generally find no significant predictive effects of investor sentiment. Wysocki (1999) suggests that online posters can influence public opinion about a firm. Tumarkin and Whitelaw (2001) study Internet service stocks. They find that on days with abnormally high posting activity, changes in online investor opinion are correlated with contemporaneous abnormal industry-adjusted returns and trading volume but do not predict future returns and volume. It is ambiguous whether activity on the message board causes or is the result of abnormal returns on the stock. Similar findings are reported in Tumarkin (2002). Using data from Yahoo! Finance and Raging Bull message boards, Antweiler and Frank (2004) suggest that the causality for volatility is more from message boards to market. But Koski et al. (2007) find the opposite. Antweiler and Frank also find that high number of messages predicts negative subsequent returns. However, investor sentiment, though positively related with contemporaneous returns, does not predict subsequent returns. Using data from Motley Fool, Das et al. (2005) report that sentiment does not apparently predict returns, but returns drive sentiment instead. Gu et al. (2006) rebut this argument and suggest that there are informed posters on stock message boards whose information is not fully incorporated into market prices. Das and Chen (2007) find that tech sector s aggregate sentiment can predict the level of the sector s aggregate index but not of individual stocks. Overall, prior studies conclude that online talk is not just noise. However, it remains unclear whether message board activities influence market activities or is it the other way around, and whether online investor sentiment predicts future returns. The lack of clarity is puzzling, especially for the relation between online investor sentiment

6 1214 SABHERWAL, SARKAR AND ZHANG and subsequent returns. In this paper, we argue that the evidence in earlier studies is confounded by the presence of stocks with material news. If there is material news about a stock, market activities are likely to influence message board activities instead of the other way around (Klibanoff et al., 1998; and Chan, 2003). However, in the absence of material news, message board activities are likely to influence market activities, especially for the small-cap stocks examined in this paper. Accordingly, we propose the following hypothesis. For stocks with no material news, online posting activities (the number of messages, sentiment, and disagreement) affect short-term trading activities (returns, volatility, volume and the bid-ask spread). 3. STUDY DESIGN (i) Use of a Chat-Room-Like Message Board Aggregator Our data source is TheLion.com, a chat-room-like message board aggregator. Unlike message boards such as Yahoo! Finance that list messages on a separate webpage for each stock, TheLion.com lists messages reverse chronologically on a single page. Also, it follows a reward system in which posters earn credits from other users for posting quality messages. This system alleviates the problem stated in Vilardo (2004) that the same poster may post under multiple IDs. It also enables the construction of a creditweighted sentiment index. (ii) Control for Surrounding Material News In previous studies, evidence regarding the effect of message board activities is confounded by the presence of news. In this study, we examine only those firms for which there is no material news at the time they are included in the sample. 6 Our sample allows for a clearer examination of the effect of message board activities. For identifying material news, we use Ravenpack news database, an up-to-date and reliable source affiliated with Dow Jones News. 7 It collects comprehensive news from all the major news sources, including the Wall Street Journal, Reuters Newswires, Press Release Wires, and many others. (iii) Sample of most heavily discussed stocks The main advantage of using TheLion.com is that it provides a list of the ten most heavily discussed stocks on each trading day. We include these most heavily discussed stocks in our sample. Thus, we don t have to subjectively select a research sample that could possibly include stocks without a significant online following and suffer from the noise and error introduced by such stocks (Tumarkin, 2002). In the absence of material news, the most actively discussed stocks are likely to be small-cap stocks 6 Material news is news that is likely to affect the value of the firm s securities or influence investors decisions. Material news includes information such as unusual corporate events (for example, mergers, divestitures, significant changes in management, downsizing, FDA approvals, and new product introductions), earnings announcements, analyst recommendations, stock splits, etc. Technical news such as 200-day moving average alerts and abnormal volume reports are not considered material news. 7 RavenPack is a leading provider of news analytics and machine-readable content. The company specializes in linguistic analysis of high volume, real-time news from high-end newswire services. All the news for any firm at any specific time can be identified using their database. For more details, please see

7 DO INTERNET STOCK MESSAGE BOARDS INFLUENCE TRADING? 1215 with weak fundamentals and low institutional holdings as those stocks are more easily influenced by online postings. This study will shed more light on the role and influence of online message boards for such stocks. (iv) Inclusion of Messages with and without a Self-Reported Sentiment During the late 1990s and early 2000s, online message posters were not able to directly disclose their sentiment (such as buy, hold, or sell). As a result, many previous studies, such as Antweiler and Frank (2004) and Das and Chen (2007), need to use a text classifier to assess the sentiment of each message before constructing a sentiment index. However, these days many message boards provide a sentiment indicator for posters to explicitly disclose their sentiment on a voluntary basis. For instance, Yahoo! Finance allows a poster to choose one of the following sentiments: strong buy, buy, hold, sell, strong sell, or not disclose (by default). TheLion.com offers two more sentiments: short and scalp. 8 Though online posters may now disclose their sentiment, not all of them do so. Some studies such as Gu et al. (2006) and Cook and Lu (2009) use only the messages with a self-reported sentiment. However, Antweiler and Frank (2004) argue that to construct a sentiment index with a minimal bias, it is necessary to include all messages from the same message board. Accordingly, in this paper, we include messages without a self-disclosed sentiment also, and use a text classifier to assess the sentiment scores of those messages. (v) Text Classifier Following Antweiler and Frank (2004), we employ the Naïve Bayesian (NB) text classifier to assess the sentiment score of messages without a self-reported sentiment. 9 Additional details of our procedure are included in Appendix A. For all messages, including those with and without a self-reported sentiment, we follow Tumarkin and Whitelaw (2001), Tumarkin (2002) and Zhang and Swanson (2010), and code sentiment scores as 3 for short, 2 for strong sell, 1 for sell, 0 for hold and scalp, +1 for buy, and +2 for strong buy. (vi) Credit-Weighted Sentiment and Disagreement Indexes TheLion.com has a credit system for posters, with the credit score being a proxy for the poster s reputation and influence. Therefore, we are able to construct a creditweighted sentiment index, as suggested in some other studies (Gu et al., 2006; and Cook and Lu, 2009). Our formula for the sentiment index is similar to Antweiler and Frank (2004), except that our index is credit-weighted, with the log of a poster s credit being the poster s weight. 10 Each message has a sentiment score from Short ( 3) to Strong Buy (+2), which is either provided by its poster or assessed with our NB text classifier. We first compute 8 Scalping implies the intent to buy and sell quickly to make a day trade profit without any specific sentiment. 9 For details of text classifiers, in general, please see Sebastiani (2002) and Das and Chen (2007). For details of the NB algorithm, please see McCallum and Nigam (1998) and Lewis (1998). 10 The relation between credit score and its impact on the message board is likely to be non-linear. Therefore, we use a log transformation to capture the decreasing marginal effect of the poster s credit.

8 1216 SABHERWAL, SARKAR AND ZHANG the bullishness ratio R i t follows: based on all sentiment scores for stock i on event day t as R i t = M Optimistic M Pessimistic = PositiveScore i NegativeScore i = t N i,o p =1 t N i,p q =1 log(creditscore p ) x i,o p log(creditscore q ) x i,p q where N i,o t is the number of messages with an optimistic sentiment (buy or strong buy) on stock i on day t; N i,p t is the number of messages with a pessimistic sentiment (sell, strongsellorshort);x is the sentiment score; and creditscore is each poster s credit score. Following the transformation in Antweiler and Frank (2004), our sentiment index for stock i on day t is: Sentiment i t = R i t 1 log ( ) NMessages i (2) Rt i t + 1 where Ri t 1 is a standardized bullish index bounded by Rt i +1 1and1andNMessagesi t is the total number of messages for stock i. Antweiler and Frank (2004, p. 1268) further propose a reduced form of standard deviation of sentiments as a disagreement proxy. In their setup, sentiment is either 1or 1, with an equal weight allocation. Because our sentiment score ranges from 3 to +2, our disagreement index is a credit-weighted standard deviation of the sentiment scores for the stock i. We first calculate each message k s weight and then the disagreement index: log ( ) creditscore i k w i k = NMessages k=1 Disagreement i t = NMessages k=1 log ( creditscore i k w i k ) NMessages (x ik k=1 w i k x i k where x k is the sentiment score associated with message k and NMessages k=1 w i k x i kis the credit- weighted mean score for the stock. 11 (i) Sample Construction 4. SAMPLE AND DATA We download real-time messages from TheLion.com from July 18, 2005 to July 18, These messages are downloaded daily after the close of trading. Tumarkin 11 Note that Disagreement i t [0, + ] and is not correlated with Sentiment or the standardized bullishness index. Therefore, we do not expect multicollinearity to be an issue in a model with both Sentiment and Disagreement. 12 We download each message s detailed information such as the associated stock symbol, time posted, poster s name, message length, and message content. The content of each message is also saved in a ) (1) (3) (4)

9 DO INTERNET STOCK MESSAGE BOARDS INFLUENCE TRADING? 1217 (2002) suggests that only stocks with a sufficient number of messages should be included. Thus, on each trading day we retain only those stocks that are among the top ten stocks by number of messages on that day. So, the event day for a stock is the day it is among the top ten stocks. To ensure sufficient posting activity, we also require that the sample stocks have at least 30 messages during the event day (from 4:00 p.m. Eastern time the previous day to 4:00 p.m. Eastern time on the trading day). We additionally require that the stocks not be over-the-counter bulletin board (OB) or pink sheet (PK) stocks; have a share price above $1 to avoid excess transaction costs; and have at least five different posters in order to prevent hyping by one or two dominant posters. 13 Finally, to avoid any contamination from news, we exclude stocks with material news on or within two days before and after the event day. Some stocks occur more than once in our sample. We include those multiple occurrences as separate observations if there is a gap of at least two trading days between any two occurrences. Otherwise, we include only the first occurrence. Our sample consists of 80 observations, accounted for by 64 distinct stocks. (ii) Data Sources For controlling news, we use the Ravenpack news database described in Section 3. We obtain financial and trading data from the Center for Research in Security Prices (CRSP), Trade and Quote database (TAQ), Compustat, and Thomson Financial. We use the standard procedures to filter the TAQ data. Specifically, we exclude the following: trades and quotes outside of the regular trading hours (9:30 a.m. to 4:00 p.m. Eastern time): opening and closing trades and quotes; regional trades and quotes; trades marked as irregular; negative bid or ask quotes; and spreads that are negative or in excess of 40% of the bid-ask midpoint. (iii) Sample Characteristics Our sample has 64 stocks, for which we download a total of about 12,000 messages. Because we exclude stocks with material news, most of these messages are related to trading. To provide a qualitative feel for their contents, we provide examples of messages in Table 1 for each of the six self-reported message sentiment categories. These messages, like the sample messages in Wysocki (2000) and Antweiler and Frank (2004), are short and in the form of conversational discourse. Admati and Pfleiderer (2001) point out that unlike newspaper articles or financial reports, forum messages are style free, short, and in an informal dialogue-like format. The messages in our study have the same characteristics. The details of our 64 distinct sample stocks are included in Appendix B. Among all the stocks in our sample, 31.25% (20 out of 64) are in the technology sector, only 18.75% (12 out of 64) have options listed, and most are listed on NASDAQ (58 out of 64). separate text file. These text files are used for building the text classifier, sample training, and classification evaluation. 13 Technically, a poster can register multiple screen names. However, the reputation system of Thelion.com reduces the probability of forum participants doing so. Posts written by authors with more aggregate credits are more likely to be read and paid attention to. Registering multiple accounts would reduce the accumulated reputation for any particular account. Thus, posters have little incentive to register multiple usernames.

10 1218 SABHERWAL, SARKAR AND ZHANG Table 1 Examples of Messages Sentiment Message Number Stock Symbol Poster s ID Posting Time Message Content Strong Buy IPII dreadknought :02:27 People still trying to short stock here, HEDGE funds will punish that soon, IMO ODMO OTCBBKing :10:24 HUGE volume spike New HOD of $ FORD looktothefuture :35:07 Its about to run, get in today OLAB dreadknought :34:31 Adding a few more dont let the shorts win here. Buy CAFE GOPHERBROK :08:40 Buying opportunity thia AM after yesterday selloff BIDU mtmover :02:03 Down a little but gaining ground,still looks good for a run, imo NXXI Goingin :49:45 This week $3? ARTW dreadknought :06:11 Looks cheap here esp. if BEETS could be a new energy tie in 980k floater Hold SYNX hy hawarya :38:45 Acting peculiarly SMTX toughmarket :06:59 Still undervalued CHCI GOPHERBROK :56:43 Nice article by Street.com $12 and $16 target OLAB Lionmaster :02:59 Tons of shorting OLAB here next CHNR? Sell COOL windy :41:45 Out all COOL 1.34 from 1.25 nice. Thanks HEB TheLaker :47:46 Out Heb rest of heb here Congrats all ENWV glamba :39:13 Shorted ENWV at 14 this AM...will cover at BIDU KATN :12:43 Tech thats still defying reality, needs to fall hard imo. Strong Sell CAFE Traderspot :18:35 Seems like CAFE failed to appeal and not even pinks taking them in. Very bad SMTX EPR :42:45 Out at 3.5 from PPTV danshadow :40:03 VOLUME IS DEAD NVAX mumic :58:27 Looks like back to 2.80 from here... Short JRJC jjkool :45:14 Short into earnings CTIB Napolion :44:35 Re: Plan to cover and the HOKU tjtrader :40:27 Negative Motley Fool article on HOKU just out afterhours. Looks like a good short IPII Lionmaster :36:56 Short 1k shr here $20.32, way too fast here, target $16.

11 DO INTERNET STOCK MESSAGE BOARDS INFLUENCE TRADING? 1219 Table 2 provides summary statistics of the sample. The table discloses some interesting characteristics of stocks targeted by online investors. Panel A includes a summary of fundamental characteristics. The average market equity is above three times the book equity, suggesting that these stocks are more likely to be characterized as growth stocks. The mean ROE is 24.92% but the median is 2.80%. The profit margins (mean of %, median of 0.03%) and quarterly revenue growth rates (mean of %, median of 6.80%) are weak. The mean operating cash flow is $9,420. Overall, these summary statistics suggest that stocks targeted by online investors have weak fundamentals and may be vulnerable to pump-and-dump trading. Table 2 Summary Statistics Variable Mean Std. Dev. Minimum Median Maximum Panel A: Summary of Fundamental Aspects of 64 Stocks Price-to-book ratio ROE (%) Profit margin (%) Quarterly revenue growth (%) Debt/Equity Operating cash flow ( 000 $) Panel B: Summary of Technical Aspects of 64 Stocks Prior three-month volume (in millions) Floating shares (in thousands) Stock price ($) Held by institutions (%) Short-sell ratio Options listed (binary) Technology sector (binary) Panel C: Summary of Trading and Posting Activities for 80 Observations Number of messages Number of posters Bullishness index [ 1,1] Sentiment index [, + ] Disagreement index [0, + ] Raw return on posting day (%) CRSP equally-weighted return (%) PowerShare QQQ return (%) Volume-weighted volatility Trading volume (in millions) Number of trades (in thousands) Small trades (<=$3,750) proportion Medium trades proportion Large trades (>=$30,000) proportion Normalized spread Notes: Our sample includes a total of 80 observations, accounted for by 64 distinct stocks. Event day is the day on which a stock appears in the list of ten most actively discussed stocks on that day. We include two observations of the same stock if they are at least two trading days apart. The summaries are based on the statistics on the event day.

12 1220 SABHERWAL, SARKAR AND ZHANG The summary of technical aspects in Panel B shows a low trading volume in the three months prior to the event day (mean of 0.90 million shares, median of 0.29 million shares). The range of average daily trading volume is from a minimum of merely 40,000 shares to a maximum of 8.65 million shares. Similar to the low trading volume, average float is small with a mean of 14,430 shares and a median of 7,200 shares. The stock price ranges from $1.43 to $97.90, with the mean being fairly low at $8.44. Wysocki (1999) reports that online investors chase stocks with low institutional holdings in order to generate a bigger impact on the stock price. Our sample also reflects this attribute with the average institutional holding being only 20.36% and the median being even lower at 11.50%. The short-sell ratio indicates that it takes investors an average of 2.32 days to cover the current short interest. Panel C includes a summary of trading and posting activities. Consistent with previous literature (Tumarkin and Whitelaw, 2001; and Tumarkin, 2002), we find that on average, online posters are bullish with a mean bullishness ratio of 0.65 (zero implies a neutral opinion) and a mean credit-weighted sentiment index of The credit-weighted disagreement index (mean of 1.03) shows that investors express diverse opinions (disagreement index > 0) although a majority of investors are optimistic. The average event-day return on our sample stocks is 13.65%. In contrast, the CRSP equally-weighted average return on these days is about zero and the average return on PowerShare QQQ fund, a proxy for the NASDAQ composite index, is 0.20%. The average trading volume in a stock on the event day is 4.60 million shares compared with the average prior three-month volume of just 0.9 million shares per day. Thus, the trading volume is much larger when heavy online talk occurs. We also report the percentage of small-, medium-, and large-sized trades on the event day. We follow Lee and Radhakrishna (2000) to classify trades. A trade is classified as small if it is worth $3,750 or less and large if it is worth more than $30, Small trades account for 78% of all trades, suggesting that the trading volume surge is likely induced by small-sized trades. 5. EVENT STUDY We have argued in Section 2 that if an online message board is used as a herding mechanism to temporarily drive up prices, initially there would be significant positive abnormal returns in a stock when it is heavily talked about (pumped) on a message board. Subsequently, there would be negative abnormal returns when the stock is dumped. To test this argument, we measure abnormal returns using the standard event study methodology. 15 We include the methodology details in Appendix C. In Table 3, we report the results of daily event study for abnormal returns and volumes from day t 5 todayt+5, with day t as the event day when the stock is heavily discussed among posters. For a side-by-side comparison, the table also includes online posting statistics. Cumulative abnormal return and volume are also computed for two 14 Lee and Radhakrishna (2000) define a small trade as <=$2,500 and a large trade as >$20,000 based on data. From to 2007, the Consumer Price Index has increased by 48.7%, or about 50%. We take this inflation into account and accordingly our cutoffs are 50% greater than Lee and Radhakrishna s. Further, Lee and Radhakrishna find that dollar-value based proxies are generally less noisy than tradesize based proxies in separating small and large investor transactions. Therefore, we use dollar-value based proxies in this paper. 15 Please see Strong (1992) for a guide to event study methodology and procedures for modeling abnormal returns.

13 DO INTERNET STOCK MESSAGE BOARDS INFLUENCE TRADING? 1221 Table 3 Return and Volume Event Study and Posting Statistics Panel A: Daily Day AAR (%) Patell Z Cowan Z CAAR (%) AAV (%) Patell Z Cowan Z CAAV (%) NMessages NPosters Sentiment Disagreement Panel B: Longer Windows Window CAAR (%) Patell Z Cowan Z Window CAAV (%) Patell Z Cowan Z ( 30, 1) ( 30, 1) > (+1, +30) (+1, +30) > Notes: We use a 255-day estimation window starting on t-300, wherever possible, and ending on t 46. We require three days as the minimum length of the estimation window. AAR is the average abnormal return, CAAR is the cumulative average abnormal return, AAV is the average abnormal volume, and CAAV is the cumulative average abnormal volume. Patell Z-statistic is based on Patell (1976) test statistic and Cowan Z-statistic is based on Cowan (1992) nonparametric generalized sign test. NMessages represents the average number of messages and NPosters represents the average number of posters. Sentiment represents the average credit-weighted sentiment index and Disagreement represents the average credit-weighted disagreement index., and denote two-tailed significance at the 1%, 5% and 10% levels, respectively.

14 1222 SABHERWAL, SARKAR AND ZHANG wider windows: (t 30, t 1) and (t+1, t+30). The event study results and posting statistics are shown pictorially in Figures 1 and 2, respectively. On the event day t, both average abnormal return (AAR) and average abnormal volume (AAV) are significantly higher (13.93% and %, respectively). The average number of messages (44.41) and the average number of posters (11.55) also proliferate on that day. Meanwhile, average sentiment jumps from 0.25 on day t 1 to 2.44 on event day t and disagreement drops from 2.01 on day t 1 to1.03ondayt. Both AAR and AAV are also significantly higher on day t 1 (4.91% and %, respectively). DeMarzo et al. (2003) argue that online investors are likely to build their positions prior to posting messages. Our finding of significant positive abnormal Figure 1 Abnormal Return and Volume Notes: We use a 255-day estimation window starting on t-300, wherever possible, and ending on t-46. We require three days as the minimum length of the estimation window. AAR is the average abnormal return, CAAR is the cumulative average abnormal return, AAV is the average abnormal volume, and CAAV is the cumulative average abnormal volume.

15 DO INTERNET STOCK MESSAGE BOARDS INFLUENCE TRADING? 1223 Figure 2 Online Posting Statistics Notes: Average posting statistics are based on 80 observations. NMessages represents the average number of messages and NPosters represents the average number of posters. Sentiment is the average credit-weighted sentiment index and Disagreement is the average credit-weighted disagreement index. NMessages [30, + ] on day t, the event day, and NMessages [0, + ] on other days. NPosters [5, + ] ondayt and NPosters [0, + ] on other days; Sentiment [, + ]; and Disagreement [0, + ]. return on day t 1 supports this argument. The two-day period of days t 1 andt can be characterized as a pumping period with overly optimistic and herding behavior, as documented in Shiller (1995) and Hirschey et al. (2000). The heavy trading volume continues on days t+1 (AAV of %) and t+2 (AAV of %) but the price drops significantly (AAR t +1 of 1.65%, and AAR t +2 of 2.06%). The post-event period of days t+1 and t+2 can be described as a two-day dumping period. The average sentiment changes from positive or bullish on day t

16 1224 SABHERWAL, SARKAR AND ZHANG (2.44) to negative or bearish on day t+1 ( 1.72). The trading on the days after t+2 is associated with a mild price drop and smaller volume and is possibly due to some outside traders (non-subscribers to the message board) liquidating their positions. In almost 30 days, the price reverts back to the pre-event level. Thus, there are no longterm economic effects of the message board activity. Overall, the event study findings support our hypothesis that that initially when the stock is heavily talked about (pumped) on a message board, there are positive abnormal returns in the stock, followed by negative abnormal returns when the stock is dumped. 6. TRADING INCENTIVES AND TARGET STOCK CHARACTERISTICS We now test our second hypothesis that the trading preferences of online traders, as proxied by a stock s abnormal returns, are related to the fundamental and technical characteristics of the stock. We choose fundamental and technical characteristics as per the suggestions in Wysocki (1999) and test them in separate models. Earlier studies such as Fama and French (1992) and Daniel and Titman (1997) show that a firm s fundamental factors significantly explain its stock returns. Based on these studies, our cross-sectional regression model for fundamental variables is as follows. CAR i t 1,t 2 = β 0 + β 1 PTB i t 1 + β 2 ROE i t 1 + β 3 PRM i t 1 + β 4 QRG i t 1 + β 5 DTE i t 1 + β 6 OCF i t k=7 β k D i k 6 + ε i. (5) CAR i t 1,t 2 is the cumulative abnormal return on stock i from t 1 to t 2 based on the Scholes and Williams (1977) market model, PTB is the price-to-book ratio, ROE is the return on equity, PRM is the profit margin percentage, QRG is the quarterly revenue growth rate, DTE is the debt-to-equity ratio, and OCF is the operating cash flow. We control for industry effects using six industry dummy variables, D 1 to D 6, corresponding to consumer goods, financial, healthcare, industrial goods, services, and technology industries. Basic materials is the reference industry. We estimate the model separately for three CAR windows (0, 0), (0, 10) and (0, 30), where 0 is the event day. The t- statistics in all our regressions are adjusted based on the heteroskedasticity-consistent covariance matrix developed by White (1980). In Panel A of Table 4, we find that the fundamental variables lack a cross-sectional explanatory power for CAR except for the price-to-book ratio being significant in the (0, 30) window (β = 0.022, t-stat = 2.17). We also find that online investors have some preference for consumer goods stocks (β (0,0) = 0.237, t-stat = 1.89) and technology stocks (β (0,30) = 0.278, t-stat = 1.74). Previous studies such as Brock et al. (1992) and George and Hwang (2004) also document the significant power of technical characteristics in explaining returns. Our cross-sectional regression model for technical variables is as follows. CAR i t 1,t 2 = β 0 + β 1 AVO i t 1 + β 2 FLS i t 1 + β 3 PRI i t 1 + β 4 INS i t 1 + β 5 SSI i t 1 + β 6 OPT i t k=7 β k D i k 6 + ε i (6)

17 DO INTERNET STOCK MESSAGE BOARDS INFLUENCE TRADING? 1225 Table 4 Trading Incentives and Target Stock Characteristics CAR 0,0 CAR 0,10 CAR 0,30 Dependent Variable Coeff. t-stat Coeff. t-stat Coeff. t-stat Panel A: Fundamental Aspects Intercept PTB ROE PRM QRG DTE OCF D ConsumerGoods D Financial D Healthcare D IndustrialGoods D Service D Technology R-squared Panel B: Technical Aspects Intercept AVO FLS PRI INS SSI OPT D ConsumerGoods D Financial D Healthcare D IndustrialGoods D Service D Technology R-squared Notes: This table reports the results of two sets of cross-sectional regressions. The dependent variable CAR i t 1,t 2 is the cumulative average abnormal return between time t 1 and t 2. In the model based on fundamental variables, PTB is the price-to-book ratio, ROE is the return on equity, PRM is the profit margin percentage, QRG is the quarterly revenue growth rate, DTE is the debt-to-equity ratio, and OCF is the operating cash flow. In the model based on technical variables, AVO is the log of average prior 3-month volume, FLS is the log of the number of floating shares, PRC is the log of stock price, INS is percentage institutional holdings, SSI is the short-sell interest, and OPT is a dummy variable that assumes a value of one if the stock has options listed and zero otherwise. Six industry dummy variables are used. An industry dummy variable assumes a value of one if the firm belongs to that industry and zero otherwise. The t-statistics are adjusted based on the heteroskedasticity-consistent covariance matrix developed by White (1980)., and denote two-tailed significance at the 1%, 5% and 10% levels, respectively. AVO is the average prior 3-month trading volume, FLS is the log of the number of floating shares, PRI is the log of the stock price, INS is the institutional holding percentage, SSI is the short-sell interest, OPTis a binary variable with a value of one if the stock has options listed and zero otherwise, and D 1 to D 6 are six industry dummy variables as discussed above.

18 1226 SABHERWAL, SARKAR AND ZHANG Panel B of Table 4 shows that the abnormal return on event day 0 is significantly positively related to the average prior 3-month trading volume (β = 0.066, t-stat = 1.73) and significantly negatively related to the log of the number of floating shares (β = 0.088, t-stat = 1.68) and the log of the stock price (β = 0.096, t-stat = 1.89). Thus, stocks with a high trading volume, a small float, and a low price are more likely to be targeted by online traders. These findings support Kumar and Lee (2003), who document that individual investors have a special interest in low-priced small cap stocks, and Wysocki (1999), who finds that online traders have a greater trading incentive for stocks with a higher trading volume. We also find that financial stocks are less preferred by online investors (β = 0.954, t-stat = 2.28). We further examine CAR 0,10 and CAR 0,30 as the dependent variables and find similar results. Overall, we find partial support for our second hypothesis. While the fundamental aspects other than the price-to-book ratio are irrelevant to online investors trading incentives, the technical aspects are not. 7. SHORT-TERM EFFECTS OF ONLINE POSTING ACTIVITIES ON TRADING ACTIVITIES We now examine whether message posting activity, as reflected in the number of messages, aggregate sentiment, and aggregate disagreement among investors, can (a) explain contemporaneous return, volatility, volume, and spread, and (b) predict these variables. Previous studies find a good explanatory and predictive power of the number of messages, but not of sentiment and disagreement indices. However, in contrast to previous studies that use non-weighted indices, we use credit-weighted indices. The higher a poster s reputation, the greater is the weight given to the poster s sentiment. Also, we include messages with and without a self-reported sentiment, with the sentiment of messages without a self-reported sentiment assessed using a text classifier. We follow Antweiler and Franks (2004) contemporaneous and time sequencing lead-lag regression models. We first discuss the contemporaneous models followed by the lead-lag predictive models. For both these sets of regression models, we first calculate the various endogenous variables, including the daily raw return, volumeweighted volatility, proportion of volume induced by small trades, and bid-ask spread. We then test each of them as an endogenous variable in separate regression models. (i) Contemporaneous Regressions In the contemporaneous models, the endogenous variables mentioned above and the message board activity variables are on the same-day basis. The contemporaneous relation between posting activities and the stock s raw return is tested using the following model: 16 R i t = β 0 + β 1 Sentiment i t + β 2Disagreement i t + β 3NMessages i t + β 4Size i t + β 5MktRet t + β 6 R i t 1 + u t (7) 16 In order to generate comparable results, we follow Antweiler and Frank (2004) and use raw return instead of market-adjusted return. This market return is included as a control variable. We also run tests using market-adjusted return, with market return not included as a control variable in the model. The results are consistent.