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  4. The Wisdom of Twitter Crowds:Predicting Stock Market Reactions to FOMC Meetings via Twitter Feeds

The Wisdom of Twitter Crowds:Predicting Stock Market Reactions to FOMC Meetings via Twitter Feeds

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Author(s)
Azar, Pablo Daniel
•
Lo, Andrew W
Date Issued
May 2016
Journal
The Journal of Portfolio Management
Publisher
Institutional Investor Journals
Citation
Azar, Pablo D. and Lo, Andrew W. “The Wisdom of Twitter Crowds:Predicting Stock Market Reactions to FOMC Meetings via Twitter Feeds.” The Journal of Portfolio Management 42, no. 5 (May 2016): 123–134.
Version
Original manuscript
Abstract
With the rise of social media, investors have a new tool for measuring sentiment in real time. However, the nature of these data sources raises serious questions about its quality. Because anyone on social media can participate in a conversation about markets—whether the individual is informed or not—these data may have very little information about future asset prices. In this article, the authors show that this is not the case. They analyze a recurring event that has a high impact on asset prices—Federal Open Market Committee (FOMC) meetings—and exploit a new dataset of tweets referencing the Federal Reserve. The authors show that the content of tweets can be used to predict future returns, even after controlling for common asset pricing factors. To gauge the economic magnitude of these predictions, the authors construct a simple hypothetical trading strategy based on this data. They find that a tweet-based asset allocation strategy outperforms several benchmarks—including a strategy that buys and holds a market index, as well as a comparable dynamic asset allocation strategy that does not use Twitter information.
MIT Department
Massachusetts Institute of Technology. Department of Economics
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
http://creativecommons.org/licenses/by/4.0/
Persistent DSpace Link
http://hdl.handle.net/1721.1/109079
DOI of Published Version
https://doi.org/10.3905/jpm.2016.42.5.123
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