1 Why Pay Attention to Stock Message Boards? 2 a Variety of Stock

Notes

1 Why Pay Attention to Stock Message Boards?

1. The term quality of life (QOL) references the general well-being of individuals and societies. 2. www.stocktwits.com 3. http://www.empathica.com/retail2012 4. http://www.accenture.com/us-en/Pages/insight-shopper-preferences.aspx 5. For stocks priced under $1, add 0.5 percent of the principal value to the $7 commission. 6. http://www.sec.gov/News/PressRelease/Detail/PressRelease/1365171513574. Regulation Fair Disclosure is a regulation that was promulgated by the SEC in August 2000. The rule mandates that all publicly traded companies must disclose material information to all investors at the same time. 7. http://www.sec.gov/news/press/2000-135.txt 8. http://www.ftc.gov/opa/2007/06/wholefoods.shtm 9. Penny stocks are usually unlisted, highly speculative, and usually selling for a dollar or less. 10. Asset liquidity refers to how quickly an asset can be converted into cash without a significant loss in value. 11. For the definition of short selling, please visit http://www.sec.gov/answers/ shortsale.htm. Not all the stocks can be shorted. In order to sell the stock short, you must have margin privileges on your brokerage account. Your broker must have available shares to lend to you. You cannot sell short a stock which is under $5. At any time, you must maintain enough capital in your account to place a buy to cover order on your short position to return shares you borrowed to your broker.

2 A Variety of Stock Message Boards

1. Day traders, as defined by the SEC, “rapidly buy and sell stocks through- out the day in the hope that their stocks will continue climbing or falling in value for the seconds to minutes they own the stock, allowing them to lock in quick profits.” Please see http://www.sec.gov/answers/daytrading.htm 268 Notes

2. Trade types include buy, sell, buy to cover, and sell short. Order types include market, limit, stop, stop limit, and trailing stop. 3. Based on Fama and French (1995) and Carhart (1997) research, there are four widely recognized systematic risk factors: stock market premium factor (Mktrf), size factor (SMB), style factor (HML), and momentum factor (UMD). 4. http://www.sec.gov/news/press/2011/2011-268.htm 5. http://www.sec.gov/news/headlines/intmm.htm 6. The ClearStation, a semiprivate chat room, is now an E*TRADE community. Chatters need to be E*TRADE customer to use the chat room. 7. http://www.sec.gov/news/headlines/tokyojoe2.htm 8. http://www.sec.gov/news/headlines/intmm.htm 9. The “Short” sentiment suggests short selling while the “Scalp” sentiment implies buying and selling quickly with the intent of a day trade profit but without any specific sentiment. 10. Poster’s credit score is a proxy for the poster’s reputation. Such reputation systems have been adopted in a wide range of online applications, including auction sites such as eBay.com, reseller sites such as Amazon.com and file sharing sites such as YouTube and Flickr. For a detailed explanation of the validity and effectiveness of an online reputation system, see Ghose, Ipeirotis, and Sundararajan (2006). 11. http://hotcopper.com.au/posts.asp?fid=303 12. On Yahoo! Finance, if a message board for a company you are interested in does not exist, you can create one. 13. http://www.siliconinvestor.com/subject.aspx?subjectid=6136 14. One can also sort the board by messages so that all messages will be listed reverse chronologically based on posting time. Sorting by topics is the default setting. 15. Big-Boards, which tracked the most active message boards and forums on the Web, used to be a popular Board of Boards, but it is no longer in service.

3 About Stock Message Board Posters

1. An ISP is a business or organization that offers user access to the Internet and related services, such as Comcast, Verizon, AOL, etc. 2. For example, http://www.thelion.com/bin/disclaimer.cgi and http://ragingbull.com/about 3. To further understand stock “pump-and-dump” manipulation, visit http:// www.sec.gov/investor/pubs/pump.htm 4. In July 1998, the SEC formed the Office of Internet Enforcement, a unit created to eliminate securities fraud occurring over the Internet. 5. Here is an example of companies and individuals who were charged with security fraud: http://www.sec.gov/news/headlines/intmm.htm 6. https://bulk.resource.org/courts.gov/c/F3/318/318.F3d.465.01-1120.html 7. http://www.thelion.com/bin/disclaimer.cgi 8. http://www.sec.gov/news/digest/1996/dig112096.pdf Notes 269

9. www.sec.gov/news/press/2000-135.txt and www.ftc.gov/opa/2007/06/ wholefoods.shtm 10. http://www.sec.gov/litigation/litreleases/lr15953.txt 11. https://www.sec.gov/litigation/admin/33-7885.htm 12. http://www.sec.gov/litigation/admin/3-9768.txt 13. http://www.sec.gov/litigation/litreleases/lr15855.txt 14. https://www.sec.gov/news/digest/dig090903.txt 15. www.hitwise.com and www.claritas.com 16. In finance, a long position in a security means the holder of the position owns (bought) the security and will profit if the price of the security goes up. In contrast, a short position means that the holder of the position does not own but borrowed the security from the broker and will profit if the price of the security goes down. Going long is the more conventional practice of investing and is contrasted with going short. 17. In the literature, when constructing a sentiment index, a common practice is to assign +2 to “Strong Buy,” +1 to “Buy,” 0 to “Hold” or “Scalp,” –1 to “Sell,” –2 to “Strong Sell,” and –3 to “Short.”

4 Why Do People Post Messages on Stock Message Boards?

1. In the United States, the SEC defines a penny stock as a security that trades below $5 per share, is not listed on a national exchange, and fails to meet other specific criteria. See http://www.sec.gov/answers/penny.htm

5 Modeling the Value of a Stock Message Board

1. A text classifier uses its algorithm to assign a sentiment score to a non-self- disclosed message. Details of text classifiers will be discussed in the next chapter. 2. SPY is SPDR S&P 500 Exchanged Traded Fund while DIA is SPDR Dow Jones Industrial Average Exchanged Traded Fund. 3. 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. The Short sentiment suggests short selling while the Scalp sentiment implies buying and selling quickly with the intent of a day-trade profit but without any specific sentiment. Raging Bull also requires the author to specify his or her short-term and long-term sentiments (Tumarkin and Whitelaw 2001). 4. To be more realistic, the value of a message could also depend on the author’s reputation or credibility. Logically, a high-ranked author’s message should contribute a higher value than that of a low-ranked author’s, ceteris paribus. 270 Notes

For example, when two social media accounts give out the same information and same recommendation, a higher value should be given to an Associated Press account than an unknown individual’s account. Another example is, one author’s credit score is 100 and the other author has just 1 credit score. If both of them are correct, we can define the value of a message’s value to be 100(+1) = +100 for the former author and 1(+1) = +1 for the latter one. But to keep my derivation simple, I define the value is +1 unit if the message’s sentiment is right and –1 unit otherwise. 5. Shorting is the practice of selling securities or other financial instruments that are not currently owned, with the intention of subsequently repurchas- ing them (“Buy to cover”) at a lower price. 6. In reality, it is possible for an author to have one or more messages deleted by the administrator. However, it is unlikely for one author’s messages to be consistently deleted over a considerable amount of time by the administrator because this type of author would be blocked by the message board. Thus,

we do not consider a case with a negative bA in the model. 7. A reputation-recognition mechanism could be implemented either manda- torily by the forum design or voluntarily by all participants in the message board. An effective voluntary reputation-recognition mechanism would occur when an author makes a correct forecast and other participants grant him or her positive credit, and vice versa.

8. Rank’s upper limit 1/|2aF| can be set by the forum developer. Each author’s rank needs to be updated in real time based on his/her cumulative forecast accuracy. 9. http://www.sec.gov/news/headlines/intmm.htm and http://www.sec.gov/ news/headlines/tokyojoe2.htm

6 How to Measure Stock Message Boards’ Activities?

1. Although the Yahoo! Finance message board currently allows posters to disclose their sentiments, it did not launch its self-disclosed sentiment function until June 2001. Thus, several earlier studies faced data restrictions. For example, studies using year 2000 messages from Yahoo! Finance as their data sample required researchers to hand-classify (hand-code) messages to obtain the training data set. Hand-classifying means researchers manually read the content of a message and assigned a sentiment score to the message based on the researchers’ interpretation. 2. This comparison must be based on the identical testing sample and same testing procedure and, more importantly, each text classifier is tested inde- pendently. Due to the different algorithms, one text classifier’s attributes (accuracy, standard error) are unaffected by another classifier’s attributes. As long as we design an identical testing sample and use the same testing methods, the sequence of comparison will not affect the text classifier’s ranking. 3. Although there is the chance that some posters paste articles or news, most messages posted on forums appear to be conversational discourse. Notes 271

7 Patterns in Stock Message Board Posting Activities

1. Although I cannot totally exclude the possibility that some messages are posted by institutional investors or their agents, the chance of such incidence is extremely low. 2. According to the SEC (http://www.sec.gov/answers/penny.htm), the term “penny stock” generally refers to a security issued by a very small company that trades at less than $5 per share. Penny stocks generally are quoted over-the-counter, such as on the OTC Bulletin Board (which is a facility of FINRA) or OTC Link LLC (which is owned by OTC Markets Group, Inc., formerly known as Pink OTC Markets Inc.); penny stocks may, however, also trade on securities exchanges, including foreign securities exchanges. 3. Currently, WallStreetPit does not allow over-the-counter bulletin board (OB) or pink sheet (PK) stocks. OB and PK stocks are posted on a separate message board: http://thelion.com/bin/forum.cgi?tf=pinks_and_bb

8 Online Talk: Does It Matter At All?

1. Some researchers apply a postevent window to their Event studies. Cowan, Nayar, and Singh (1990) suggested a postevent window to mitigate any bias induced by the preevent price momentum.

9 Trading Strategies Based on Stock Message Board Information

1. Not all the stocks have option trading. Some stocks, such as illiquid penny stocks, do not have stock options while most large-cap stocks do.

10 Legal Issues Associated with Stock Message Board Posting

1. www.sec.gov/news/press/2000-135.txt 2. www.ftc.gov/opa/2007/06/wholefoods.shtm 3. http://www.sec.gov/news/headlines/tokyojoe2.htm 4. http://www.gpo.gov/fdsys/pkg/USCOURTS-paed-2_05-cv-05725/content- detail.html 5. “John Doe” is used as a placeholder name for a party whose true identity is unknown or must be withheld in a legal action, case, or discussion. 6. http://www.sec.gov/litigation/litreleases/lr16684.htm 7. http://www.sec.gov/litigation/litreleases/lr16439.htm 8. http://www.sec.gov/litigation/litreleases/lr16620.htm 9. http://www.sec.gov/litigation/litreleases/lr15953.txt 272 Notes

10. http://www.sec.gov/litigation/admin/3-9768.txt 11. www.siliconinvestor.com 12. http://www.sec.gov/litigation/admin/33-7885.htm 13. For example, Yahoo collects personal information when you register with Yahoo. Yahoo automatically receives and records information from your computer and browser, including your IP address, Yahoo cookie information, software and hardware attributes, and the page you request. 14. For more detail information about the SEC, visit http://www.sec.gov. 15. http://www.thelion.com/bin/forum.cgi?tf=yingzhangtradingboard&msg=1 1&cmd=r&t= 16. http://www.sec.gov/rules/interp/34-42728.htm 17. https://www.sec.gov/news/headlines/intmm.htm 18. http://www.sec.gov/divisions/marketreg/bdguide.htm 19. https://www.sec.gov/news/press/pressarchive/1998/98-117.txt 20. http://www.sec.gov/complaint.shtml 21. http://www.sec.gov/about/offices/owb/reg-21f.pdf

11 Whisperers Versus Analysts and Implications for Market Efficiency

1. There are also many security research firms that provide financial analysts’ recommendations on fixed-income securities, foreign currencies, and commodities among other financial assets. 2. www.thomsonreuters.com 3. www.investors.com 4. www.morningstar.com 5. www.spoutlookonline.com 6. www.starmine.com 7. www.valueline.com 8. Ranks are announced online at 8:00 a.m. EST on Tuesday if Monday is a public holiday. 9. www.valuengine.com 10. www.wisi.com 11. www.zacks.com 12. http://thomsonreuters.com/press-releases/022014/Thomson-Reuters-Adds- Unique-Twitter-and-News-Sentiment-Analysis-to-Thomson-Reuters-Eikon

12 Alternative Information on the Internet

1. http://www.pivotinc.com 2. www.theice.com 3. www.prophetalerts.com 4. www.thinkorswim.com 5. https://www.sec.gov/about/laws/sa33.pdf Notes 273

6. https://www.sec.gov/news/press/2007/2007-34.htm (stet) 7. caps.fool.com 8. www.freerealtime.com 9. krugman.blogs.nytimes.com 10. www.cramers-mad-money.com 11. Jim Cramer’s disclaimer is “Always do your own research as these are recommendations and I make no guarantees. No one cares about your money more than you do!” 12. http://seekingalpha.com 13. http://www.alexa.com/siteinfo/twitter.com 14. Regulation FD is the regulation of fair disclosure, which prevents the selec- tive disclosure of information by publicly traded companies and other issuers. Regulation FD provides that when an issuer discloses nonpublic information to certain individuals or entities (e.g., securities market professionals, stock analysts, or holders of the issuer’s securities who may trade on the basis of the information), the issuer must make public disclosure of that information. In this way, the new rule aims to promote full and fair disclosure. 15. www.statisticbrain.com reports 58 millions tweets per day on January 1, 2014. 16. http://www.google.com/trends 17. By default, the search starts with the whole world. 18. http://www.youtube.com/user/ZacksInvestmentNews 19. http://www.youtube.com/user/TheMotleyFool 20. http://www.youtube.com/user/SchiffReport 21. www.investopedia.com/simulator 22. www.mocktrading.com 23. http://www.marketwatch.com/game/ 24. http://www.stocktrak.com/ 25. http://www.tradestation.com/trading-technology/tradestation-platform/ execute/simulator 26. The ten sectors are: energy, basic materials, industrials, cyclical consumer goods and services, noncyclical consumer goods and services, financials, health care, technology, telecommunications services, and utilities. 27. http://bbs.jrj.com.cn/ 28. http://guba.sina.com.cn/ 29. http://club.business.sohu.com/ 30. http://guba.eastmoney.com 31. http://bbs.ruoshui.com/ 32. http://finance.yahoo.com/mb/EWJ/ 33. http://www.msci.com/resources/factsheets/index_fact_sheet/msci-japan- index.pdf 34. http://boards.thisismoney.co.uk/shares-stock-markets/ 35. http://www.iii.co.uk/ 36. http://investorshub.advfn.com/Iraqi-Dinar-Discussion-Board-IQD-7851/ 37. http://www.shareswatch.com.au/blog/category/stockmarket/ 38. http://www.usmessageboard.com/economy/145252-istanbul-stock- exchange.html 274 Notes

39. http://thelion.com/bin/forum.cgi?tf=pinks_and_bb 40. http://investorshub.advfn.com/boards/boards.aspx?cat_id=140 41. http://boards.fool.com/bonds-fixed-income-investments-100135.aspx 42. http://boards.fool.com/talk-about-treasury-bonds-113234.aspx 43. http://mmb.moneycontrol.com/stock-message-forum/currencies/ comments/509163 44. http://forums.babypips.com/ 45. http://boards.fool.com/ggt-lets-talk-currency-119328.aspx 46. http://thelion.com/bin/forum.cgi?tf=commodities 47. boardreader.com/fp/InvestorVillage_Stock_Message_69381/ Commodities_Message_Board_for_InvestorVI_13265021.html 48. http://thelion.com/bin/forum.cgi?tf=writing_options 49. http://boards.fool.com/futures-and-commodity-trading-114666.aspx 50. http://investorshub.advfn.com/Commodity-Futures-Trading-9144/

14 The Future of Stock Message Boards

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absolute privacy, 40, 42, 173 American Online (AOL), 39, 41, absolute sentiment, 148, 150, 152, 207–8 168, 259 AMEX, 38, 140, 245 academic researchers, 16, 24, 48 anonymity, 16, 20, 27, 39, 41, 173, Accenture, 7 179–80, 184–5, 237–8, 252 accounting information, 152, 258 AOL Instant Messenger (AIM), acquaintances, 22 207–8 Activetrader, 23–5, 42, 208 Apparel Manufacturing Associates administrator, 42–4, 49, 54–5, 71, Inc. (APPM), 210 82, 121, 246 Apple (AAPL), 11, 23, 33–8, 274 advanced users, 246 applied statistics, 249 advantageous selection, 67–70, 238 arbitrage, 160–1, 225, 240 adverse selection, 28, 67, 69–76, 82, arbitrager, 225, 240 237–8, 257 arbitrary numbers, 77, 80 advertising, 4, 22, 29, 216, 228, 246 artificial intelligence, 15, 89, 227, ADVFN, 38, 85, 247 249 aggregate level, 13–14, 169, 205, Associated Press, 11, 270 227, 232 attention-worthy, 19, 33 aggregation, 161, 167, 244, 255 Australian Securities Exchange aggregators, 12, 245 (ASX), 29, 32, 150, 221, 256 agreement index, 109 auto-correlated errors, 156, 265 Aite Group, 200 autocorrelation, 141, 152, 190 Alexa, 213 auto-regression, 156, 265 algorithm, 10, 49, 82, 90–7, 102–7, average abnormal return (AAR), 121, 148, 167, 238, 251, 254 134, 139, 144, 161, 163, 204 alias, 22, 41–5, 51, 55–6, 63–4, 174, average abnormal volume (AAV), 140 184–5, 209, 231, 235, 239, 241 average message length, 15, 51, 145, allegations, 41, 60, 176 147, 153 Allegheny Energy, Inc., 175, 229 Aziz-Golshani, 176–7 all-in-one, 12, 14 alternative hypothesis, 143 Babypips, 223 Amazon, 5, 46, 82, 121, 201–2, 238 bandwagon, 59, 164 American Depositary Receipt Barack Obama, 188 (ADR), 220 barometer, 153, 197 286 Index bashers, 51, 153–4 China (Chinese), 13, 65, 220, 260–2 benchmark, 138–40, 191, 203, 223, Chi-square, 106, 107 253 Christopher P. Hastings, 175 bid-ask spread, 15, 68, 151–3, 255 civil suits, 181 Big-boards, 246 class imbalance, 92–3 biotech, 13, 37 class-assignment, 105 Blog.com, 2 ClearStation, 247 Blogger, 210–11 client computer, 90 Bloomberg, 8, 10, 157, 166, 220 clientele effect, 21 blue-chip stocks, 59 Clifton G. Swiger, 175, 229 board of boards, 20–1, 36, 268 CNBC, 211, 220 BoardCentral, 13, 36, 38, 244–5 CNNMoney, 8, 220 bogus, 185, 231 coefficients, 138, 141–2, 152 Bond Funds, 13, 124, 216 cognitive psychology, 62 botnet, 58 collaboration, 4, 23, 54 broad ownership, 16, 83 collusion, 51 brokerage firms, 50, 61, 201, 205, commission, 8, 9, 124, 162, 182 208, 213, 227 commodities traders, 214 bulletin board (OB) stocks, 124 Commtouch, 209 bullishness index, 108, 148, 150–1 Communications Decency Act of Bureau of the Public Debt, 222 1996, 41 buy-and-hold, 46, 58–9, 126, 130, co-movement, 21 235 Company representatives, 43, 45, buzz, 38, 60, 245 219, 229 complaints, 182 California law, 180 compulsory posting, 57, 65 Callaway Golf Company, 64 computational linguistic, 90, 130 Carl Icahn, 11 computer network, 21 caucasian, 46–7 computer science, 249 causality (causation), 111, 149, 150, conflict of interest, 31, 179 155–6, 168, 253, 264 confusion matrix, 106, 107 Cayman Atlantic, 10, 157 constraints, 60, 88, 98–9, 102 Center for Research in Security content community, 3 Prices (CRSP), 126 continuous regime, 81 Charles Schwab, 228 contrarian, 133, 160–1, 225, 240 chartists, 159 contributors, 21, 193, 207, 211 Chartpattern, 26 control variables, 141–2, 151 chatters, 12, 20, 22–3, 207, 247 corporate governance, 154, 225, cheap talk, 16, 22, 28, 257 229, 253 Chicago Board Options Exchange corporate reputations, 154, 253 (CBOE), 168, 224 correction, 133, 163 Chicago Mercantile Exchange correlation, 9, 21, 111, 123, 129, (CME), 223 145–6, 165, 171, 261–2 children in household, 45–6 Cramers-Mad-Money, 211 Index 287 credit-weighted disagreement index, Dow Jones Industrial Average 111 (DJIA), 9, 11, 19, 145, 171, 214 credit-weighted sentiment index, 14, due diligence, 63, 65, 184–5, 236 49, 110, 146, 259 dummy, 141–3 criminals, 180, 182 dump-and-pump, 61, 59 critical value, 74 duration of time, 108 cross-sectional regression, 81, 147, 265 earnings announcements, 142, 152, cumulative average abnormal return 191, 215–16, 225, 228, 251, 258, (CAAR), 139 261 cumulative average abnormal earnings forecasts, 198, 201, 203 volume (CAAV), 140 earnings per share (EPS), 131, 196, currencies, 10, 13, 218–19, 222–3 199 customer reviews, 4–7 Eastmoney, 220, 260 cybersmear, 64–5 e-banking, 1 eBay, 82, 121, 238 damage control, 44, 229 e-commerce, 1, 283 Danielle Tierney, 200 economists, 53, 211, 225 data mining, 246 efficacy, 55–6, 91, 93, 107, 203, 265 data services firms, 246 efficient market hypothesis (EMH), data-providing companies, 227 189–92, 202–5 David A. Wood, 44, 178 email, 30, 182, 210 day traders, 15, 20–7, 50, 165, 236, Empathica, 7 253, 257 endogeneity, 155–6, 264, 265 dead cat bounce, 60 endogenous, 140–1 defamation, 39, 41, 180 Enron, 43, 63, 153, 252 degrees of freedom, 107 equally weighted, 29, 240 Department of the Treasury, 222 estimation window, 138–9 derivation, 74, 82 ethics, 83 Derwent Capital Markets, 9, 15, ethnicity, 45–7 157 E-Trade, 208–9, 227, 228, 247 detractors, 154 Eugene B. Martineau, 44, 178 dialogue-like, 23, 50, 106, 113 event study, 133–8, 140–5, 160–4, disclaimer, 41, 87, 113, 183–5, 209, 191, 204, 234 221 event trading, 162–4, 205 disclosure, 30, 32, 142, 178–83, evolvement, 68–9, 71, 84 202, 273 Excel, 215 discount brokers, 8–9, 208, 227 Exchanged Traded Funds (ETFs), discrepancy, 146, 199 13, 124 dispersion, 107, 148, 201, 240, 254, execution, 165–6, 256 258 exogenous, 140–3 Division of Enforcement, 181–2 Expectation maximization (EM), Dodd-Frank Wall Street Reform and 94–5 Consumer Protection Act, 188 expiry day, 142 288 Index exponential function, 81 frequent posters, 42, 51–2 exponential growth, 155, 166, 228 freshmen, 49 external search engine, 244 fringe stocks, 16, 146 extrinsic, 55 full-service brokers, 8 function-service sites, 2 Facebook, 2–4, 7, 9–11, 21, 36, 84, fundamental analysis, 189–90, 203, 169, 212–13 235 Facebook-style social networking fundamental characteristics, 51 website, 158, 213 Futures, 168, 194, 218, 223–4, 243 Fama, 189 fear index, 168 G7 countries, 201 Federal Bureau of Investigation GDP, 162 (FBI), 40, 182, 187 gender, 45, 47 Federal Funds Rate, 162 George Charles Pappas, 176 Federal Trade Commision (FTC), gimmicks, 59, 61 10, 15, 17, 40, 44, 83, 174, 183, Glenn R., 46, 201 239 Goldman Sachs, 193 feedback, 3, 5, 14, 49, 54, 102, 121, Google Domestic Trends, 219 227 Google Finance, 9, 84, 219–20, Financial and Economic Attitudes 244, 246 Revealed by Search (FEARS), 216 Google.com, 2 financial community, 14, 19, 49, 63, gossip, 63–5 225, 229 gross national happiness (GNH), financial crisis, 118, 142 158, 170, 213 financial institutions, 44–5, 63, gurus, 5, 42, 57, 122, 175, 184 227–8, 235, 246 Financialchat, 23–4, 208 habits, 7, 42, 50, 115, 118 First Amendment, 41, 65, 173, hand-code, 92 179–80, 182, 211 harassment, 22 First Call, 193, 196–7, 202, 250–1 HealthGrades, 5 first derivative, 74, 77, 79 hedge funds, 9–10, 13, 15, 157, 167, Fixed-Income Security, 222 200, 205, 211, 214, 227, 232 flam, 55–6, 185 hedger, 223, 225, 240 flowchart, 90–1 herding, 16–19, 57, 62, 68–9, 131, follow risk, 238 133, 150, 158–9, 202, 232–3, Ford, 215 235, 251, 259 Form 10-Q, 8 high-frequency trading (HFT), Form 8-K, 8 10–11, 157, 167 Forward Contracts, 218, 223–4 high-quality messages, 68–9 fraudsters, 40, 62–3, 124, 180, Hitwise and Claritas, 46 184–6, 231, 252 “Hold” recommendation, 130 free speech, 39, 173 holiday, 117, 141–2, 152 freedom of speech, 179, 182, 238 Honda, 215 FreeRealTime, 38, 211 Hong Kong, 65, 220 Index 289 hosts, 41–3, 46, 84, 221, 227, 246 institutional investor sentiment, 237 hot stocks, 21, 209, 242 institutional investors, 10, 13–15, HotCopper, 29–32, 35, 63, 144, 49, 59, 167, 193–6, 200, 225, 150, 167, 221, 243, 254, 256, 262 227, 232, 257, 262 hotspots, 225 intangible asset, 153 household income, 45–6 intent, 28, 55–6, 89, 238 hucksters, 179 interaction term, 141–3 human interpretation, 28, 89, 92 Interactive Investor website, 221 hype, 26, 40, 58–9, 126, 174–5, 208 internal search engine, 239, 242, 244 hyperlink, 29, 35, 59, 114, 184 Internet Discussion Site (IDS), 144, 167 IBM, 22, 59, 71 Internet Protocol (IP), 16, 22, 32, ICE Chat, 208 39–41, 56, 118, 173, 239 identity, 39–42, 55, 173, 175, 180 Internet service provider (ISP), 39, illusion, 185 180 IMTrader, 208 Internet technology, 155 Index Funds, 124, 170, 189 Internet-based applications, 3, 9 individual investor sentiment, 237 InTheMoneystocks, 26 industrial sector, 13 intraday, 21, 29, 144, 149–50, 166– industry-adjusted returns, 143, 191, 8, 254, 259 232, 252 intrinsic, 55, 165 inelastic, 59 intuition, 48 inexperienced traders, 165 investment advice, 183–4, 209, 215, inferior author (IA), 67, 158 252 influential, 11, 58, 83, 158, 165, investment banks, 10, 13, 167, 193, 201, 232, 253, 255 200, 213, 227 influx, 23, 59, 69, 165, 244 investment horizon, 170–1 information asymmetry, 69, 74, 82, Investopedia, 213, 218 152, 258 investor relations, 8, 149, 178, 225, information diffusion, 216 229 information leakage, 133 Investor Village, 38, 244 Information Management Associate InvestorHub, 228 (IMA), 26, 175 InvestorLinks, 33 information-service sites, 1–2 Investor’s Business Daily (IBD), 178, infrequent posters, 42 193 Initial Public Offering (IPO), 216, InvestorsHub, 33, 38, 47, 85, 244, 228, 243 247 in-sample, 92–3 InvestorVillage, 228 insider trading, 182, 190 Iraqi Dinar Discussion Board, 221 insiders information, 43, 63–4, Istanbul Stock Exchange 184–5, 190, 202, 230–1 Discussion, 221 Instant Messenger (IM), 207–8 institutional holdings, 40, 49, 128, J.P.Morgan Chase, 213 131, 150, 165, 231, 249–50 Japan (Japanese), 13, 220, 221 290 Index

Jason A. Greig, 177 manipulation, 16, 26–8, 40–4, Jim Cramer, 211 54, 57–8, 62, 65, 83, 131–2, John Doe, 175, 180 154, 174, 177, 186–7, 205, 235, John Mackey, 10, 44, 64, 174, 229 252–4, 259 joint probability, 104 Mapquest, 2 Jonathan G. Lebed, 173 margin, 60–1 JRJ forum, 220 marginal forecast accuracy, 74–5 justice, 180 marginal learning curve, 77, 79–80 marginal posting rate, 74, 77 keystone, 29, 114 market makers, 44 K-nearest neighbor (KNN), 94, 97 market model, 138–9 Korea (Korean), 13, 220, 261 market news, 14, 32, 36, 40, 142–4, Kullback-Leibler divergence (KL), 147, 156, 160, 163–5, 168, 219, 94, 96 234, 265 Market Pulse, 245 law enforcement, 39, 42, 173, Market Stream, 245 180–2 market timing, 201, 204 lawsuits, 10, 39, 44, 83, 173, 175, Marketwatch, 10, 20, 38, 158, 166, 180 213, 218, 220 lead-lag, 145, 151 marriage, 53, 246–7 learning path, 68–9 masters, 5, 42 left-hand side (LHS), 140 maturity, 84, 222 legal team, 43, 229 maximizing trading profits, 54, 56 lengthy messages, 23, 50, 51, 114 Maximum entropy (ME), 94, 98–9, less informed, 159 107 libelous, 39, 41, 61, 180 mean reversion, 157 life cycle, 84–5 mean sentiment, 111, 197 life-service sites, 2–3 measurement error, 156, 261, 265 Likefolio, 157 media coverage, 142 linear functions, 81 median sentiment, 197 linear relationship, 73, 75–7, 80 Mega cap, 128 LinkedIn, 4, 84 mega sites, 3 Lionmaster, 120, 243 membership growth rate, 79–80 listing, 141–2 memberships, 12, 26, 35, 73, 79–80, log-transformed, 140 84, 87–8, 228, 238, 245–6 long-short, 161, 170, 258 mergers and acquisitions (M&A), long-term, 27, 29, 32, 57, 68–9, 71, 61, 67, 85, 149, 246–7 84, 157, 160, 165, 203, 242, 252, methodology, 48, 134, 138, 162, 269 205, 234, 262 lurkers, 6, 52, 217 microblog, 3, 94, 147, 156, 158, 212–13, 260, 265 Mad Money, 211 microcap, 16, 26, 48, 128, 131, management information system, 144, 146, 150, 159, 177, 186, 249 231, 234 Index 291

Microsoft (MSFT), 59, 108 nonreply, 28, 36, 93–4, 123–4 Microsoft Access, 87–91 non-skillful traders, 25 millisecond, 10, 167, 256 NPosters, 110, 131–2 MIT, 250 null hypothesis, 137, 140, 143, 147 mobile apps, 241 number of posters, 13, 84, 108, 110, Mocktrading, 218 131–2, 140 moderation tools, 238–9 NYSE, 38, 140, 245 moderators, 22–3, 29, 42, 120, 208, 239, 259 off-hour, 114, 219 momentum traders, 21, 50, 159, offline speech, 179 164–6, 240, 242 omitted variables, 156, 265 momentum, 21, 50, 157, 159, 164– OmniGene Diagnostics, Inc. 6, 240, 242, 258 (OMGD), 43, 231 Moneycontrol, 223 one-stop shopping, 243 moral hazard, 67, 82–3, 238 online dating, 1 Morgan Stanley High-Tech Index online gaming, 1 (MSH), 167, 255 online investors, 12–14, 17, 20, 22, Morgan Stanley, 193 42, 46, 50–1, 71, 125, 135, 169, Morningstar, 8, 193–4, 220 202, 233, 235, 250 motivations, 42, 53–8, 61–5 online shopping, 1 Motley Fool, 9, 38, 167, 202, 211, online speech, 173, 179 216, 222–3, 244 online trading and investment mouth, 6, 50, 54, 67, 130 simulator (OTIS), 218 MSCI Japan Index, 221 online trading simulators, 218 MSN Money, 220 Operation Spamalot, 210 multiassets board, 243 opinion leaders, 6–7 mutual funds, 181, 191, 193–4, options, 13, 129, 142, 168–9, 194, 216, 222, 245 218, 223, 230, 243 Myspace, 2, 210 Ordinaries Index, 150 outlier, 120 Naïve Bayesian (NB), 91, 94, 99, out-of-sample, 91–3 107, 166, 253, 256–9 outstanding shares, 126–9 Nanocap, 128 overfitting, 93 NASDAQ, 38, 130, 141–2, 150, over-the-counter (OTC), 13, 36, 177, 186, 220, 245 124, 177–8, 186, 221, 223, 245 NASDAQ-100 Index, 150 overvalue, 48, 62, 131, 190 Naver, 220, 261 neophytes, 22 panel regression, 81, 265 Netflix, 214, 231 Patel Z-test, 138–40 new media, 2–4 Paul Krugman, 211 NMessages, 131–2 pecuniary rewards, 51, 257 nonfinancial, 53–4, 148, 159 penny stocks, 13, 22, 25, 59–60, nonlinear, 78, 80–2 124, 174–6, 184–6, 209, 221–2 nonnegative integer, 73, 75–6 perpetrators, 180, 210, 279 292 Index personal attacks, 22 pump-and-dump, 10, 25–6, 40–3, Peter Schiff, 217 62, 127, 144, 154, 158–60, 165, pharmaceuticals, 143 174, 179–86, 210, 231, 235, 252 phishing, 180 pumpers, 58–9 pink sheet, 13, 124, 186, 210, 221 plain text, 241 quality of life (QOL), 1, 2, 267 podcast, 3, 6 Quantcast, 45, 47 policymakers, 14–16, 23, 83, 124, 137, 265 racism, 22 popularity, 9, 29, 35–6, 49, 56, 67, Raging Bull, 9, 12, 33, 38, 47, 85, 84, 89, 108, 124, 217, 257 143, 167, 228 poster disagreement, 13, 145 RAINBOW, 90 poster sentiment, 13, 145–8, 160–8, random walk, 190 232, 234, 253 rank r, 67, 69, 74–7 practitioners, 14, 16, 23, 124, 137, RateMyProfessors, 5 157–8, 168, 196, 209, 227, 252, Real Estate Investment Trusts 265 (REITs), 124 predictor, 9, 146, 160, 201, 214, realized losses, 61 234, 259 realized volatility, 149, 150, 168 PredictWallStreet, 10, 158, 166 reciprocal, 48 premium user, 246 reciprocity, 55 price-to-book (P/B), 148 recognition, 55, 68–70, 74–80, 82, price-to-earnings (P/E), 49, 257 94, 106, 142 privacy, 40, 42, 173, 175 recommendation revisions, 201, 205 private information, 190, 226, 230, red flags, 184 251, 284 reduction, 137, 152, 258 private stock message boards, 12 Reed Hastings, 214, 231 Probabilistic indexing (PRIND), referral program, 246 94–5, 100 registration, 22, 27, 33, 39, 52, probability distribution, 96–8, 105 72–3, 84, 120, 122, 208, 240 probability score, 90, 104 Regulation Fair Disclosure profanity, 22 (Regulation FD), 9, 213, 230 profit taken, 62, 133, 160 regulators, 16, 39, 43, 45, 54, 82, profit-related motivations, 65 153, 174, 209, 225, 230–3, 239, progressive membership program, 252, 256 245 reputation system, 28, 42, 49, 51, promoters, 25, 177, 184, 186–7 58, 121, 256, 257 ProphetAlerts, 208 reputation-recognition system, psychologies, 54, 62, 126 69–70, 74, 79 public opinion, 153, 156, 250, 261, residual, 139, 141 264 retail investors, 25, 44–5, 124, 141, public stock message boards, 12, 147, 170, 193, 215, 262 44, 254 Reuters, 10, 38, 157, 166, 193, 220 publicly traded company, 8, 43, rewards, 49, 51, 121, 246 219–20, 228 right-hand side (RHS), 140–1 Index 293 risk factors, 25 short selling, 50, 60–1, 125, 159, Rocker Management, 200 179, 205, 235, 267–9 rudeness, 22 short-and-cover, 58–60 Rudy Nutrition (RUNU), 25 short-term traders, 21, 33, 50, 60, rumormongers, 63, 153, 256 165 runs tests, 190 Siliconinvestor, 33–5, 37, 46–7, 178, Ruoshui, 220 261 simulation, 218 sample selection, 155, 159, 256, 264 simultaneity, 156, 265 Scalp, 28, 89, 119, 179–80 Sina, 220, 262 Schedule 15G, 186 sincere investors, 63 Scholes and Williams estimates, 138 Singapore, 65 Scottrade, 8, 213, 228 skillful traders, 25 screen names, 16, 22–3, 27–8, 42, small-size trades, 148–9 88, 118–20, 175 social network, 3, 9–10, 158, 212– search engine, 1, 21, 36, 239–45 13, 217 Search Volume Index (SVI), 169–70, Socialpicks, 10, 158, 166 215–16 Societe Anonyme, 26, 175 Section 17(b) of the Securities Act of sockpuppet, 55–6, 58, 65 1933, 210 Sohu, 220 Securities and Exchange spam, 25, 40, 45, 48, 56–7, 65, 180, Commission (SEC), 8, 83 209–10 security brokers, 44 speculate (speculation, speculative), security fraud, 26, 39–40, 54, 59, 21, 26, 156, 211, 223, 258 131, 173, 179–83, 188, 233 stagnation, 137 SeekingAlpha, 10, 38, 203, 211–12, stale information, 2, 23 244 stand-alone, 85, 246–7 self-control, 165 Standard & Poor’s, 194 self-promotion, 55–6 standardized unexpected earnings sell off, 58, 60, 182, 235 (SUE), 198–9 semistrong form of EMH, 190, 192, StarMine, 194, 196 202–5 start-up, 84 sequential analysis, 106 statistical learning theory, 90 serial dependencies, 190 Stock Roach, 38 server computer, 90 Stockhouse, 38, 47 share price volatility, 149, 150, 262 stock-picking, 184, 192, 201, 205, Shareswatch Australia, 221 209 shell companies, 182 Stockpkr, 10, 158, 166 Short Message Service (SMS), 207, StockReads, 29 213 stock-specific message boards, 12, short sales, 15, 52, 60 51 short sell limitations, 50, 61, 125, Stocktrak, 218 159, 235 StockTwits, 4, 158, 166, 200, 260 short sellers, 51, 60–1, 154, 249, straddle, 169 256 strangers, 22, 26, 186–7 294 Index strong form of EMH, 190–1 ticker symbol, 21, 25, 29, 33, 35, style-free, 23, 50, 106, 113 71, 239 subpoena, 175, 180, 182 Tim Cook, 11 superior author (SA), 67, 158, 162 time-sequencing, 141, 145, 150 superior model, 107 time-series analysis, 81, 265 supervision, 29, 82, 259 tips, 20–1, 26, 65, 182, 187, 209, supply and demand, 58, 214 212–13, 216 Support vector machine (SVM), Tokyo Joe, 26, 43, 175, 245 94–5, 101, 107, 253–9, 262 topic-specific, 20, 33 surrogate, 29, 227–8 tout, 25, 31, 35, 44, 62, 160, 165, surveillance, 182 175–9, 186, 208, 210, 230 swindler, 179 Toyota, 215 swing traders, 50 Trade2Win, 29, 243 swing, 9, 21, 50, 165–6 Tradestation, 218 trading decisions, 13, 21, 25, 67, Taiwan, 25, 65 137, 200, 208, 211, 220, 227, takeover rumors, 63, 94, 143–4, 233, 235, 261 152, 167, 254–5, 259 trading hours, 23, 46, 114, 207, 244 tandem, 51–2 trading philosophy, 113 Target (TGT), 242 traditional media, 2–6, 225, 231 TD Ameritrade, 208–9, 227 training data (dataset), 87–95, technical analysis, 189–90, 219 97–103, 124 telecommunication, 142 transaction costs, 25, 68, 70, 146, Term frequency inverse document 162–4, 170, 205 frequency (TFIDF), 95, 102 Treasury bills, 222 term of use (TOU), 35 Treasury bonds, 222 terms of service (TOS), 39, 41, 43 Treasury Inflation Protected Tesla, 215 Securities (TIPS), 222 text classifier, 71, 87–95, 104–7, Treasury notes, 222 119, 166, 246 Treasury Security, 222 text-to-voice, 241 TripAdvisor, 5 Thelion, 2–3, 12–14, 28, 35, 38, troll, 55–6, 65 43, 49, 51, 57, 62–3, 82, 87, 124, Tumblr, 2 131, 144, 163–7, 221–8, 237–9, turnover ratio, 147, 149 241–7, 255–9, 263–5 tweets, 9, 36, 38, 171, 213–14, 228, theoretical models, 67, 118, 121, 245 159 Twitter, 2–4, 7–11, 19–21, 36–7, TheStreet.com, 38, 220 84, 94, 157, 169, 171, 192, 213– TheTradeXchange, 158, 166 17, 237, 241–7 Thinkorswim, 209 typos, 22 Thomson Reuters MarketPsych Indices (TRMIs), 10 UCLA, 177 Thumbs Down, 12, 28, 30, 68, 217 unconditional probabilities, 104 Thumbs Up, 12, 28, 30–1, 68, 217 unconstitutional, 41, 180 Index 295 underperform, 25, 28, 193, 196, 216 Warburg Dillon Read, 200 unemployment rate, 162 Washington, D.C., 181 uninformed, 159, 165 watchdog, 16, 181 unregistered securities, 40, 176, weak form of EMH, 190 182, 233 weak-fundamental, 60, 131 US Supreme Court, 41, 179–80 Web 1.0, 2–3 user-rating system, 69, 237–9, 245 Web 2.0, 2–3 user-reward, 49, 256 Web applications, 1–2 utility function, 81 web functions, 2 utterances, 107, 113 web site operator, 39 Web-based application, 27 Value Line Investment Survey web-crawler, 87 (VLIS), 8, 195 WebICE, 208 value-added services, 12, 14, 239– weighting, 48–9 40, 245 well-informed, 159 ValueLine, 8, 195 Wells Fargo Advisors, 228 ValuEngine, 8, 194–5 whisperer, 189, 191, 193, 195, 197, ValueWalk, 38 199–205 value-weighted, 240 whistleblowers, 188 vector, 95–7, 100–4, 107, 141–2, Whole Foods, 10, 44, 64, 174, 170, 253, 256 229–30 victim, 165, 182, 210 Wiki, 3 video blogging, 216 Wild Oats Markets, 10, 44, 64, 174, video conferencing, 1 230 video-sharing, 85, 216–17, 246 wisdom of crowds, 205 Vine, 217 wisdom, 125, 205, 208 virtual game world, 3 witnesses, 182 Visual Basic, 87 Wright Quality Rating, 195 VIX, 168–9 voice messages, 241 Yahoo! Finance, 8–9, 12, 33, 38–9, voice-to-text, 241 43, 51, 63, 84, 92, 115, 144–5, volatility-tracking fund, 160 147, 152–3, 162, 166–7, 174–5, volume-weighted volatility, 149, 192, 201, 219, 221, 228, 244–5 151, 168 Yahoo.com, 2, 20 voluntary basis, 89 YouTube, 3, 85, 216–17, 246 Yun Soo Oh Park, 26, 175 Wall Street Journal, 220 WallStreetPit, 43, 62, 87, 91, 113– Zacks, 196, 216 14, 118, 120–8, 130, 146, 243 zero-cost, 157, 161, 170 Wal-Mart (WMT), 242 Zillow, 2