Twitter Sentiment Analysis: How To Hedge Your Bets In The Stock Markets

Emerging interest of trading companies and hedge funds in mining social web has created new avenues for intelligent systems that make use of public opinion in driving investment decisions. It is well accepted that at high frequency trading, investors are tracking memes rising up in microblogging forums to count for the public behavior as an important feature while making short term investment decisions. We investigate the complex relationship between tweet board literature (like bullishness, volume, agreement etc) with the financial market instruments (like volatility, trading volume and stock prices). We have analyzed Twitter sentiments for more than 4 million tweets between June 2010 and July 2011 for DJIA, NASDAQ-100 and 11 other big cap technological stocks. Our results show high correlation (upto 0.88 for returns) between stock prices and twitter sentiments. Further, using Granger’s Causality Analysis, we have validated that the movement of stock prices and indices are greatly affected in the short term by Twitter discussions. Finally, we have implemented Expert Model Mining System (EMMS) to demonstrate that our forecasted returns give a high value of R-square (0.952) with low Maximum Absolute Percentage Error (MaxAPE) of 1.76 % for Dow Jones Industrial Average (DJIA). We introduce and validate performance of market monitoring elements derived from public mood that can be exploited to retain a portfolio within limited risk state during typical market conditions.

[1]  Susan G. Watts,et al.  Whisper Forecasts of Quarterly Earnings Per Share , 1999 .

[2]  Mike Y. Chen,et al.  Yahoo! For Amazon: Sentiment Parsing from Small Talk on the Web , 2001 .

[3]  Eric Gilbert,et al.  Widespread Worry and the Stock Market , 2010, ICWSM.

[4]  Kyungjik Lee,et al.  Expert system for predicting stock market timing using a candlestick chart , 1999 .

[5]  Garth P. McCormick,et al.  Communications to the Editor--Exponential Forecasting: Some New Variations , 1969 .

[6]  DewallyMichaël.  Internet Investment Advice: Investing with a Rock of Salt , 2006 .

[7]  Asuman E. Ozdaglar,et al.  Spread of (Mis)Information in Social Networks , 2009, Games Econ. Behav..

[8]  Peter D. Wysocki.  Cheap Talk on the Web: The Determinants of Postings on Stock Message Boards , 1998 .

[9]  Werner Antweiler,et al.  Is All that Talk Just Noise? The Information Content of Internet Stock Message Boards , 2001 .

[10]  Isabell M. Welpe,et al.  Tweets and Trades: The Information Content of Stock Microblogs , 2010 .

[11]  P. Gloor,et al.  Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear” , 2011 .

[12]  Bernardo A. Huberman,et al.  Predicting the Future with Social Media , 2010, 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[13]  Andrew B. Whinston,et al.  A Twitter-Based Prediction Market: Social Network Approach , 2011, ICIS.

[14]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[15]  G. Burton.  MALKIEL, . The Efficient Market Hypothesis and Its Critics, Journal of Economic Perspectives, , . , 2003 .

[16]  Martin Szomszor,et al.  #Swineflu: Twitter Predicts Swine Flu Outbreak in 2009 , 2010, eHealth.

[17]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[18]  John Seely Brown,et al.  Book Reviews : The Social Life of Information By John Seely Brown and Paul Duguid. Boston: Harvard Business School Press, 2000. 320 pages , 2000 .

[19]  Isabell M. Welpe,et al.  Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment , 2010, ICWSM.

[20]  Johan Bollen,et al.  Predicting Financial Markets: Comparing Survey,News, Twitter and Search Engine Data , 2011, ArXiv.

[21]  Zhi Da,et al.  In Search of Attention , 2009 .

[22]  Hong Miao,et al.  S&P 500 Index‐Futures Price Jumps and Macroeconomic News , 2013 .

[23]  Khaled Rasheed,et al.  Stock market prediction with multiple classifiers , 2007, Applied Intelligence.

[24]  Hong Miao,et al.  Information Driven Price Jumps and Trading Strategy: Evidence from Stock Index Futures , 2011 .

[25]  M. Dewally,et al.  Internet Investment Advice: Investing with a Rock of Salt , 2000 .

[26]  T. Rao,et al.  Analyzing Stock Market Movements Using Twitter Sentiment Analysis , 2012, ASONAM 2012.

[27]  John M. Silvester,et al.  The Social Life of Information: Brown, J.S., & Duguid, P. (2000). Cambridge, MA: Harvard Business School Publishing. ISBN 0-87584-762-5. 320 pages , 2000, Internet High. Educ..

[28]  B. Malkiel.  The Efficient Market Hypothesis and Its Critics , 2003 .

[29]  Jeremy Piger.  Is all that talk just noise , 2006 .

[30]  Tugrul U. Daim,et al.  Using artificial neural network models in stock market index prediction , 2011, Expert Syst. Appl..

[31]  M. J. Klass,et al.  On the Estimation of Security Price Volatilities from Historical Data , 1980 .

[32]  Danah Boyd,et al.  Social Network Sites: Definition, History, and Scholarship , 2007, J. Comput. Mediat. Commun..

[33]  Susan G. Watts,et al.  Whisper Forecasts of Quarterly Earnings Per Share , 1999 .

[34]  Son Doan,et al.  An analysis of Twitter messages in the 2011 Tohoku Earthquake , 2011, eHealth.