Trade the tweet: Social media text mining and sparse matrix factorization for stock market prediction |
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| Affiliation: | 1. Consultant, United States;2. University of Tokyo, Graduate School of Economics, Japan;3. EDHEC Business School, United States;1. FinStats.com, United States;2. Computer Science & Engineering Department, University of Connecticut, United States;1. Department of Empirical Finance and Econometrics, Zeppelin University, Germany;2. Department of Econometrics and Statistics, University of Hohenheim, Stuttgart, 70593, Germany;1. McCombs School of Business, University of Texas at Austin, 2100 Speedway Stop B6500, Austin, TX 78712, USA;2. W. P. Carey School of Business, Arizona State University, Main Campus PO BOX 874606, Tempe, AZ 85287, USA |
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| Abstract: | We investigate the potential use of textual information from user-generated microblogs to predict the stock market. Utilizing the latent space model proposed by Wong et al. (2014), we correlate the movements of both stock prices and social media content. This study differs from models in prior studies in two significant ways: (1) it leverages market information contained in high-volume social media data rather than news articles and (2) it does not evaluate sentiment. We test this model on data spanning from 2011 to 2015 on a majority of stocks listed in the S&P 500 Index and find that our model outperforms a baseline regression. We conclude by providing a trading strategy that produces an attractive annual return and Sharpe ratio. |
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