【主讲】美国威斯康星大学密尔沃基分校教授Huimin Zhao

【题目】预测还是被预测？一个社交媒体情绪和股票回报的实证研究

【时间】2015年6月1日（周一）14.00-16.00

【地点】清华经管学院伟伦楼453

【语言】英文

【主办】管理科学与工程系

【简历】Huimin Zhao老师的简历

Huimin Zhao is a Professor of Information Technology Management in the Sheldon B. Lubar School of Business at the University ofWisconsin–Milwaukee. He received the B.E. and M.E. degrees in Automation from Tsinghua University, Beijing, China, in 1990 and 1993,respectively, and the Ph.D. degree in Management Information Systems from the University of Arizona, Tucson, Arizona, USA in 2002.His current research interests include data mining and healthcare informatics. He has published in such journals as MIS Quarterly,Communications of the ACM, ACM Transactions on MIS, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions onSystems, Man, and Cybernetics, Information Systems, Journal of Management Information Systems, Journal of the AIS, and DecisionSupport Systems. He is serving as a senior editor for Decision Support Systems and an associate editor for MIS Quarterly.

【摘要】As the largest source of public opinion, social media are believed to capture the“wisdom of the crowd”. Usinginformation extracted from social media to predict social and economic activities—for example, stock market behavior—has become animportant research topic. We study the relationship between daily stock return and social media sentiment using a dataset spanningfive years from StockTwits. Contrary to several past studies, we find no evidence—despite the large power provided by the largedataset—that StockTwits sentiment has predictive power on daily stock return, thus calling for caution in interpreting the findingsfrom past studies favoring the use of social media sentiment to predict stock returns. On the other hand, we find, for the firsttime, strong evidence that daily stock return predicts StockTwits sentiment. The effect of daily stock return on StockTwits sentimentpeaks almost immediately (i.e., on the next day) and quickly wears out. The effect of daily stock return on negative sentiment ismuch stronger than that on positive sentiment.