股吧和交易:股吧中的信息内容研究
|
摘要
利用中国最大的股吧2011年1月至2014年6月的数据,研究了中国股吧与个股股票市场的关系.采用朴素贝叶斯的文本情绪分类技术,将帖子按投资意见分为"买入"、"中性"、"卖出",构造了情绪看涨指数、意见一致指数、发帖量等股吧特征变量,利用FamaMacBeth截面回归方法,研究股吧特征变量与市场特征变量包括收益率、成交量、波动性的关系,结果显示情绪看涨指数与收益率、发帖量与成交量、投资者意见分歧与波动性之间存在双向的预测作用,说明股吧包含了未反应在当前股票市场价格的信息.利用股票收益率和帖子情绪构造了衡量帖子投资意见的质量,研究股吧的信息传播机制,结果发现高质量的投资意见能够通过股吧帖子阅读量得以传播识别,但不能通过帖子的评论量被投资者识别.另外,有评论和无评论的帖子质量没有显著区别,进一步说明投资者对待不同质量帖子的关注度没有差异.
The increasing popularity of "big data" research and development on finance is the common interesting point both in industrial and academic circles. The mining and analyses of online message contained therein has been generally considered one of the most important approaches on information effciency of stock market.Online message board has become a vibrant online platform for exchanging stockrelated information. This study explores the relationship between online message board and stock market,taking advantage of data derived from the biggest stock BBS in China from January 2011, to June 2014. Using text classification algorithm based on Na(i|¨)ve Bayes, we analyze roughly 8.8 million stock-related messages on a daily basis. We find a bidirectional prediction association between stock BBS sentiment and stock returns, message volume and trading volume, as well as disagreement and volatility by Fama-Macbeth cross-section regressions. We empirically explore the mechanism behind the efficient aggregation of information in stock BBS. Specifically,we investigate the association between the quality of investment advice and the level of readers and comments. Our results demonstrate that high quality of investment advice has more readers, but not comments, which amplifies their share of voice. And there is no difference in the quality of investment advice between the comments and non-comments, which further suggest that greater weight is not given to high quality pieces of investment advice.
The increasing popularity of "big data" research and development on finance is the common interesting point both in industrial and academic circles. The mining and analyses of online message contained therein has been generally considered one of the most important approaches on information effciency of stock market.Online message board has become a vibrant online platform for exchanging stockrelated information. This study explores the relationship between online message board and stock market,taking advantage of data derived from the biggest stock BBS in China from January 2011, to June 2014. Using text classification algorithm based on Na(i|¨)ve Bayes, we analyze roughly 8.8 million stock-related messages on a daily basis. We find a bidirectional prediction association between stock BBS sentiment and stock returns, message volume and trading volume, as well as disagreement and volatility by Fama-Macbeth cross-section regressions. We empirically explore the mechanism behind the efficient aggregation of information in stock BBS. Specifically,we investigate the association between the quality of investment advice and the level of readers and comments. Our results demonstrate that high quality of investment advice has more readers, but not comments, which amplifies their share of voice. And there is no difference in the quality of investment advice between the comments and non-comments, which further suggest that greater weight is not given to high quality pieces of investment advice.
引文
[1]Wysocki P D.Cheap talk on the web:The determinants of postings on stock message boards.SSRN Electronic Journal,1998.
[2]Felton J,Kim J.Warnings from the Enron message board.The Journal of Investing,2002,11(3):29-52.
[3]Clarkson P M,Joyce D,Tutticci I.Market reaction to takeover rumour in internet discussion sites.Accounting&Finance,2006,46(1):31-52.
[4]Antweiler W,Frank M Z.Is all that talk just noise?The information content of internet stock message boards.The Journal of Finance,2004,59(3):1259-1294.
[5]Tetlock P C.Giving content to investor sentiment:The role of media in the stock market.The Journal of Finance,2007,62(3):1139-1168.
[6]Bollen J,Mao H,Zeng X.Twitter mood predicts the stock market.Journal of Computational Science,2011,2(1):1-8.
[7]Joseph K,Wintoki M B,Zhang Z.Forecasting abnormal stock returns and trading volume using investor sentiment:Evidence from online search.International Journal of Forecasting,2011,27(4):1116-1127.
[8]Da Z,Engelberg J,Gao P.The sum of all FEARS investor sentiment and asset prices.The Review of Financial Studies,2014,28(1):1-32.
[9]Siganos A,Vagenas-Nanos E,Verwijmeren P.Facebook's daily sentiment and international stock markets.Journal of Economic Behavior&Organization,2014,107:730-743.
[10]Tumarkin R,Whitelaw R F.News or noise?Internet postings and stock prices.Financial Analysts Journal,2001,57(3):41-51.
[11]Kim S H,Kim D.Investor sentiment from internet message postings and the predictability of stock returns.Journal of Economic Behavior&Organization,2014,1(7):708-729.
[12]董大勇,肖作平.交易市场与网络论坛间存在信息传递吗?管理评论,2011,23(11):3-11.(Dong D Y,Xiao Z P.Is there information transmission between market and internet forum?Management Review,2011,23(11):3-11.)
[13]张永杰,张维,金曦,等.互联网知道的更多么?——网络开源信息对资产定价的影响.系统工程理论与实践,2011,31(4):577-586.(Zhang Y J,Zhang W,Jin X,et al.Does the Internet know more?—Open source information and asset pricing.Systems Engineering—Theory&Practice,2011,31(4):577-586.)
[14]易洪波,赖娟娟,董大勇.网络论坛不同投资者情绪对交易市场的影响——基于VAR模型的实证分析.财经论丛,2015,(1):46-54.(Yi H B,Lai J J,Dong D Y.A study of the influence of BBS investor sentiments on the trading market—An empirical analysis based on VAR model.Collected Essays on Finance and Economics,2015,(1):46-54.)
[15]Fan M,Tan Y,Whinston A B.Evaluation and design of online cooperative feedback mechanisms for reputation management.IEEE Transactions on Knowledge and Data Engineering,2005,17(2):244-254.
[16]Gu B,Konana P,Chen H W M.Melting-pot or homophily?—An empirical investigation of user interactions in virtual investment-related communities.SSRN Electronic Journal,2008.
[17]Zhang Y.Determinants of poster reputation on internet stock message boards.American Journal of Economics and Business Administration,2009,1(2):114.
[18]Sprenger T O,Tumasjan A,Sandner P G,et al.Tweets and trades:The information content of stock microblogs.European Financial Management,2014,20(5):926-957.
[19]Houser D,Wooders J.Reputation in auctions:Theory,and evidence from eBay.Journal of Economics&Management Strategy,2006,15(2):353-369.
[20]Konana P,Menon N M,Balasubramanian S.The implications of online investing.Communications of the ACM,2000,43(1):34-41.
[21]Litan R E,Rivlin A M.Projecting the economic impact of the Internet.The American Economic Review,2001,91(2):313-317.
[22]Resnick P,Zeckhauser R.Trust among strangers in Internet transactions:Empirical analysis of eBay's reputation system.The Economics of the Internet and E-Commerce,Emerald Group Publishing Limited,2002,127-157.
[23]Cao H H,Coval J D,Hirshleifer D.Sidelined investors,trading-generated news,and security returns.The Review of Financial Studies,2002,15(2):615-648.
[24]Dewally M.Internet investment advice:Investing with a rock of salt.Financial Analysts Journal,2003,59(4):65-77.
[25]Danthine J P,Moresi S.Volatility,information and noise trading.European Economic Review,1993,37(5):961-982.
[26]Brown G W.Volatility,sentiment,and noise traders.Financial Analysts Journal,1999,55(2):82-90.
[27]Das S R,Chen M Y.Yahoo!for Amazon:Sentiment extraction from small talk on the web.Management Science,2007,53(9):1375-1388.
[28]Jones C M,Kaul G,Lipson M L.Transactions,volume,and volatility.The Review of Financial Studies,1994,7(4):631-651.
[29]Shalen C T.Volume,volatility,and the dispersion of beliefs.The Review of Financial Studies,1993,6(2):405-434.
[30]Li X,Wang H,Yan X.Accurate recommendation based on opinion mining.Genetic and Evolutionary Computing,2015,399-408.
[31]Parkinson M.The extreme value method for estimating the variance of the rate of return.Journal of Business,1980,53(1):61-65.
[2]Felton J,Kim J.Warnings from the Enron message board.The Journal of Investing,2002,11(3):29-52.
[3]Clarkson P M,Joyce D,Tutticci I.Market reaction to takeover rumour in internet discussion sites.Accounting&Finance,2006,46(1):31-52.
[4]Antweiler W,Frank M Z.Is all that talk just noise?The information content of internet stock message boards.The Journal of Finance,2004,59(3):1259-1294.
[5]Tetlock P C.Giving content to investor sentiment:The role of media in the stock market.The Journal of Finance,2007,62(3):1139-1168.
[6]Bollen J,Mao H,Zeng X.Twitter mood predicts the stock market.Journal of Computational Science,2011,2(1):1-8.
[7]Joseph K,Wintoki M B,Zhang Z.Forecasting abnormal stock returns and trading volume using investor sentiment:Evidence from online search.International Journal of Forecasting,2011,27(4):1116-1127.
[8]Da Z,Engelberg J,Gao P.The sum of all FEARS investor sentiment and asset prices.The Review of Financial Studies,2014,28(1):1-32.
[9]Siganos A,Vagenas-Nanos E,Verwijmeren P.Facebook's daily sentiment and international stock markets.Journal of Economic Behavior&Organization,2014,107:730-743.
[10]Tumarkin R,Whitelaw R F.News or noise?Internet postings and stock prices.Financial Analysts Journal,2001,57(3):41-51.
[11]Kim S H,Kim D.Investor sentiment from internet message postings and the predictability of stock returns.Journal of Economic Behavior&Organization,2014,1(7):708-729.
[12]董大勇,肖作平.交易市场与网络论坛间存在信息传递吗?管理评论,2011,23(11):3-11.(Dong D Y,Xiao Z P.Is there information transmission between market and internet forum?Management Review,2011,23(11):3-11.)
[13]张永杰,张维,金曦,等.互联网知道的更多么?——网络开源信息对资产定价的影响.系统工程理论与实践,2011,31(4):577-586.(Zhang Y J,Zhang W,Jin X,et al.Does the Internet know more?—Open source information and asset pricing.Systems Engineering—Theory&Practice,2011,31(4):577-586.)
[14]易洪波,赖娟娟,董大勇.网络论坛不同投资者情绪对交易市场的影响——基于VAR模型的实证分析.财经论丛,2015,(1):46-54.(Yi H B,Lai J J,Dong D Y.A study of the influence of BBS investor sentiments on the trading market—An empirical analysis based on VAR model.Collected Essays on Finance and Economics,2015,(1):46-54.)
[15]Fan M,Tan Y,Whinston A B.Evaluation and design of online cooperative feedback mechanisms for reputation management.IEEE Transactions on Knowledge and Data Engineering,2005,17(2):244-254.
[16]Gu B,Konana P,Chen H W M.Melting-pot or homophily?—An empirical investigation of user interactions in virtual investment-related communities.SSRN Electronic Journal,2008.
[17]Zhang Y.Determinants of poster reputation on internet stock message boards.American Journal of Economics and Business Administration,2009,1(2):114.
[18]Sprenger T O,Tumasjan A,Sandner P G,et al.Tweets and trades:The information content of stock microblogs.European Financial Management,2014,20(5):926-957.
[19]Houser D,Wooders J.Reputation in auctions:Theory,and evidence from eBay.Journal of Economics&Management Strategy,2006,15(2):353-369.
[20]Konana P,Menon N M,Balasubramanian S.The implications of online investing.Communications of the ACM,2000,43(1):34-41.
[21]Litan R E,Rivlin A M.Projecting the economic impact of the Internet.The American Economic Review,2001,91(2):313-317.
[22]Resnick P,Zeckhauser R.Trust among strangers in Internet transactions:Empirical analysis of eBay's reputation system.The Economics of the Internet and E-Commerce,Emerald Group Publishing Limited,2002,127-157.
[23]Cao H H,Coval J D,Hirshleifer D.Sidelined investors,trading-generated news,and security returns.The Review of Financial Studies,2002,15(2):615-648.
[24]Dewally M.Internet investment advice:Investing with a rock of salt.Financial Analysts Journal,2003,59(4):65-77.
[25]Danthine J P,Moresi S.Volatility,information and noise trading.European Economic Review,1993,37(5):961-982.
[26]Brown G W.Volatility,sentiment,and noise traders.Financial Analysts Journal,1999,55(2):82-90.
[27]Das S R,Chen M Y.Yahoo!for Amazon:Sentiment extraction from small talk on the web.Management Science,2007,53(9):1375-1388.
[28]Jones C M,Kaul G,Lipson M L.Transactions,volume,and volatility.The Review of Financial Studies,1994,7(4):631-651.
[29]Shalen C T.Volume,volatility,and the dispersion of beliefs.The Review of Financial Studies,1993,6(2):405-434.
[30]Li X,Wang H,Yan X.Accurate recommendation based on opinion mining.Genetic and Evolutionary Computing,2015,399-408.
[31]Parkinson M.The extreme value method for estimating the variance of the rate of return.Journal of Business,1980,53(1):61-65.