The Wisdom of Twitter Crowds:Predicting Stock Market Reactions to FOMC Meetings via Twitter Feeds
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 no...
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| Format: | Article |
| Language: | en_US |
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Institutional Investor Journals
2017
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| Online Access: | http://hdl.handle.net/1721.1/109079 https://orcid.org/0000-0001-9156-2428 https://orcid.org/0000-0003-2944-7773 |
| _version_ | 1826199871201214464 |
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| author | Azar, Pablo Daniel Lo, Andrew W |
| author2 | Massachusetts Institute of Technology. Department of Economics |
| author_facet | Massachusetts Institute of Technology. Department of Economics Azar, Pablo Daniel Lo, Andrew W |
| author_sort | Azar, Pablo Daniel |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:27:08Z |
| format | Article |
| id | mit-1721.1/109079 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:27:08Z |
| publishDate | 2017 |
| publisher | Institutional Investor Journals |
| record_format | dspace |
| spelling | mit-1721.1/1090792022-10-01T03:44:36Z The Wisdom of Twitter Crowds:Predicting Stock Market Reactions to FOMC Meetings via Twitter Feeds Azar, Pablo Daniel Lo, Andrew W Massachusetts Institute of Technology. Department of Economics Sloan School of Management Azar, Pablo Daniel Lo, Andrew W 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. 2017-05-15T14:18:43Z 2017-05-15T14:18:43Z 2016-05 2016-03 Article http://purl.org/eprint/type/JournalArticle 0095-4918 2168-8656 http://hdl.handle.net/1721.1/109079 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. https://orcid.org/0000-0001-9156-2428 https://orcid.org/0000-0003-2944-7773 en_US http://dx.doi.org/10.3905/jpm.2016.42.5.123 The Journal of Portfolio Management Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by/4.0/ application/pdf Institutional Investor Journals SSRN |
| spellingShingle | Azar, Pablo Daniel Lo, Andrew W The Wisdom of Twitter Crowds:Predicting Stock Market Reactions to FOMC Meetings via Twitter Feeds |
| title | The Wisdom of Twitter Crowds:Predicting Stock Market Reactions to FOMC Meetings via Twitter Feeds |
| title_full | The Wisdom of Twitter Crowds:Predicting Stock Market Reactions to FOMC Meetings via Twitter Feeds |
| title_fullStr | The Wisdom of Twitter Crowds:Predicting Stock Market Reactions to FOMC Meetings via Twitter Feeds |
| title_full_unstemmed | The Wisdom of Twitter Crowds:Predicting Stock Market Reactions to FOMC Meetings via Twitter Feeds |
| title_short | The Wisdom of Twitter Crowds:Predicting Stock Market Reactions to FOMC Meetings via Twitter Feeds |
| title_sort | wisdom of twitter crowds predicting stock market reactions to fomc meetings via twitter feeds |
| url | http://hdl.handle.net/1721.1/109079 https://orcid.org/0000-0001-9156-2428 https://orcid.org/0000-0003-2944-7773 |
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