【Topic】Leveraging Public Sentiment to Beat the Market: An Empirical Study of Twitter Information and Stock Market Performance

【Speaker】Ting Li, Assistant professor at Rotterdam School of Management (RSM), Erasmus University, Netherland

【Time】April 25, Thursday, 14-16pm

【Venue】Room 453, Weilun Building, Tsinghua SEM 

【Language 】English

【Organizer】Department of Management Science and Engineering

【Abstract】This paper examines whether Twitter messages are related to stock market performance, which metric of Twitter has the strongest relationship, and whether one can use them to make better investment decisions in the financial market. We gathered four-month of Twitter, Google search, financial performance data of S&P 100 companies. We developed a Twitter-specific classifier to conduct sentiment analysis. Through an elaborated process of tweet feature reduction and spam removal, we were able to improve the accuracy of the sentiment analysis classifier for 6%. Our empirical findings suggest that (1) a higher tweet volume leads to an increase in trading volume and volatility; (2) A higher tweet bullishness leads to an increase in stock returns; (3) A higher tweet agreement among Twitter users leads to an decrease in trading volume; (4) Tweet quality strengthens the positive relationship between tweet bullishness and stock returns; (5) Tweet quality strengthens the negative relationship between tweet agreement and trading volume and stock returns. Furthermore, we simulated a set of trading strategies using tweet bullishness and tweet quality, our best result outperforms the market for more than 17% including transaction cost. To our knowledge, this is the first study that leverages Twitter information and device a practical and conceivable trading strategy for individual investors.

【Biography】Dr. Ting Li is an assistant professor at Rotterdam School of Management (RSM), Erasmus University in the Netherlands. Her research interests include the strategic use of information technology, consumer decision making in online and mobile channel, electronic commerce, social media and user generated content, pricing and revenue management, and business networks. Her work has been published in the Decision Support Systems, European Journal of Information Systems, International Journal of Electronic Commerce and several edited books. She was the runner-up for Prof. Aart Bosman Dissertation Award and Accenture-PIM Marketing Science Dissertation Award. Her interdisciplinary research has been sponsored by multiple grants from the Dutch National Science Foundation (NWO). Prior to joining academic, she worked for General Electric and IBM in the area of e-business in supply chains, web services, and grid computing. She obtained her Ph.D. in Management Science at the Erasmus University and MSc in Computational Science at the University of Amsterdam.