Forecasting with Social Media: Evidence from Tweets on Soccer Matches

Social media is now used as a forecasting tool by a variety of firms and agencies. But how useful are such data in forecasting outcomes? Can social media add any information to that produced by a prediction/betting market? We source 13.8 million posts from Twitter, and combine them with contemporaneous Betfair betting prices, to forecast the outcomes of English Premier League soccer matches as they unfold. Using a microblogging dictionary to analyze the content of Tweets, we find that the aggregate tone of Tweets contains significant information not in betting prices, particularly in the immediate aftermath of goals and red cards. (JEL G14, G17)

[1]  K. Arrow,et al.  The New Palgrave Dictionary of Economics , 2020 .

[2]  Richard P. Larrick,et al.  The social psychology of the wisdom of crowds. , 2012 .

[3]  Tie Su,et al.  How the Equity Market Responds to Unanticipated Events , 2003 .

[4]  Martin Spann,et al.  Sports forecasting: a comparison of the forecast accuracy of prediction markets, betting odds and tipsters , 2009 .

[5]  David I. Harvey.  The evaluation of economic forecasts , 1997 .

[6]  Sam K. Hui,et al.  The Role of Surprise: Understanding Overreaction and Underreaction to Unanticipated Events using In-Play Soccer Betting Market , 2013 .

[7]  Tim Loughran,et al.  When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10-Ks , 2010 .

[8]  A. Shleifer,et al.  The Limits of Arbitrage , 1995 .

[9]  Petra E. Todd,et al.  Matching Estimators , 2006 .

[10]  L. V. Williams,et al.  Prediction Markets, Twitter and Bigotgate , 2014 .

[11]  Fuyu Yang,et al.  Slowing Down Fast Traders: Evidence from the Betfair Speed Bump , 2016 .

[12]  L. V. Williams,et al.  Forecasting Elections: Forecasting Elections , 2016 .

[13]  David Rothschild,et al.  Trading Strategies and Market Microstructure: Evidence from a Prediction Market , 2015 .

[14]  Karen Croxson,et al.  Information and Efficiency: Goal Arrival in Soccer Betting , 2014 .

[15]  David Paton,et al.  Market Efficiency in Person-to-Person Betting , 2006 .

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

[17]  S. Pokharel.  Wisdom of Crowds: The Value of Stock Opinions Transmitted through Social Media , 2014 .

[18]  Koleman Strumpf,et al.  Historical presidential betting markets , 2004 .

[19]  Leighton Vaughan Williams,et al.  Prediction Markets, Social Media and Information Efficiency , 2016 .

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

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

[22]  Finn Årup Nielsen,et al.  A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs , 2011, #MSM.

[23]  F. Galton.  Vox Populi , 1907, Nature.

[24]  Fuyu Yang,et al.  The Role of Speculative Trade in Market Efficiency: Evidence from a Betting Exchange , 2017 .

[25]  Business School,et al.  Forecasting Elections , 2018, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[26]  Jason Phillips.  Rumors , 2018, Oxford Scholarship Online.

[27]  Eric Zitzewitz,et al.  Corporate prediction markets: evidence from google, ford, and firm X , 2014, EC.

[28]  James Surowiecki.  The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies, and nations Doubleday Books. , 2004 .

[29]  Christopher Avery,et al.  The "Caps" Prediction System and Stock Market Returns , 2009 .

[30]  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.

[31]  Wesley S. Chan,et al.  Stock Price Reaction to News and No-news: Drift and Reversal After Headlines , 2003 .

[32]  Alex Edmans,et al.  Sports Sentiment and Stock Returns , 2006 .