45 Pages Posted: 17 Jul 2015 Last revised: 19 May 2016
Date Written: December 4, 2015
Prior research examines how companies exploit Twitter in communicating with investors, how information in tweets by individuals may be used to predict the stock market as a whole, and how Twitter activity relates to earnings response coefficients (the beta from the returns/earnings regression). In this study, we investigate whether analyzing the aggregate opinion in individual tweets about a company’s prospects can predict its earnings and the stock price reaction to them. Our dataset contains 998,495 tweets (covering 34,040 firm-quarters from 3,662 distinct firms) by individuals in the nine-trading-day period leading to firms’ quarterly earnings announcements in the four-year period, January 1, 2009 to December 31, 2012. Using four alternative measures of aggregate opinion in individual tweets, we find that the aggregate opinion successfully predicts the company’s forthcoming quarterly earnings. We also document a positive association between the aggregate opinion and the immediate abnormal stock price reaction to the quarterly earnings announcement. These findings are more pronounced for firms in weaker information environments (smaller firms with lower analyst following and lower institutional ownership). Finally, we provide evidence that our results are not driven by concurrent information from sources other than Twitter, such as press articles or web portals. Overall, these findings highlight the importance for financial market participants to consider the aggregate information on Twitter when assessing the future prospects and value of companies.
Keywords: Wisdom of Crowds, Twitter, social media, earnings, analyst earnings forecast, abnormal returns
Suggested Citation: Suggested Citation
Bartov, Eli and Faurel, Lucile and Mohanram, Partha S., Can Twitter Help Predict Firm-Level Earnings and Stock Returns? (December 4, 2015). Rotman School of Management Working Paper No. 2631421; 2016 Canadian Academic Accounting Association (CAAA) Annual Conference. Available at SSRN: https://ssrn.com/abstract=2631421 or http://dx.doi.org/10.2139/ssrn.2631421