A Large Scale Study to Understand the Relation between Twitter and Financial Market

Twitter has transformed from an online platform for communication to a mega content generator for all kinds of topics. The topic of posts (or tweets) generated on Twitter cover diverse topics of interests. For example, politics, public personalities, events and corporate organizations. In this study, we analysed if tweets related to corporate organizations can predict the financial market. Our analysis is performed on a Twitter dataset which spans over more than two years and is related to more than 1723 stocks. To the best of our knowledge this amount of large and big dataset, specifically in terms of time length and stocks has not been studied in the past. Our quantitative analysis shows that on an average correlation between tweets and stocks' volume being traded is 0.29 on an average. In this empirical study, we also evaluated the stocks from Yahoo's sectors' categories perspective to find which sectors are more correlated than others. We also looked at the influence of important users, that is users with a large number of followers. Our results ally with the fact that important users contribute more in influencing the market [3] rather than the wisdom of the crowd [6]. The verification of our results using statistical approaches on a large dataset can be seen as a contribution in the area of financial studies using data from online platforms.

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

[2]  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.

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

[4]  Ronen Feldman,et al.  Identifying and Following Expert Investors in Stock Microblogs , 2011, EMNLP.

[5]  Nello Cristianini,et al.  Flu Detector - Tracking Epidemics on Twitter , 2010, ECML/PKDD.

[6]  Guido Caldarelli,et al.  Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics , 2014, PloS one.

[7]  Johan Bollen,et al.  Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena , 2009, ICWSM.

[8]  Fabrizio Lillo,et al.  Modelling systemic price cojumps with Hawkes factor models , 2015 .

[9]  M. Tumminello,et al.  How News Affect the Trading Behavior of Different Categories of Investors in a Financial Market , 2012 .

[10]  Munmun De Choudhury,et al.  Can blog communication dynamics be correlated with stock market activity? , 2008, Hypertext.

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

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

[13]  Aristides Gionis,et al.  Correlating financial time series with micro-blogging activity , 2012, WSDM '12.

[14]  Guido Caldarelli,et al.  Web Search Queries Can Predict Stock Market Volumes , 2011, PloS one.

[15]  Guido Caldarelli,et al.  S 1 Appendix , 2016 .

[16]  Nada Lavrac,et al.  Predictive Sentiment Analysis of Tweets: A Stock Market Application , 2013, CHI-KDD.

[17]  Stylianos Kampakis,et al.  Using Twitter to predict football outcomes , 2014, ArXiv.

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

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

[20]  David M. Pennock,et al.  Predicting consumer behavior with Web search , 2010, Proceedings of the National Academy of Sciences.

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

[22]  Wei Wei,et al.  Correlating S&P 500 stocks with Twitter data , 2012, HotSocial '12.