PT  - JOURNAL ARTICLE
AU  - Pablo D. Azar
AU  - Andrew W. Lo
TI  - The Wisdom of Twitter Crowds: <em>Predicting Stock Market Reactions to FOMC Meetings via Twitter Feeds</em>
AID  - 10.3905/jpm.2016.42.5.123
DP  - 2016 Jul 31
TA  - The Journal of Portfolio Management
PG  - 123--134
VI  - 42
IP  - 5
4099  - https://pm-research.com/content/42/5/123.short
4100  - https://pm-research.com/content/42/5/123.full
AB  - 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.TOPICS: Theory, in markets