“Prediction is very difficult, especially about the future.”
This paper evaluates the question of whether sentiment extracted from social media and options volume anticipates future asset return. The research utilized both textual based data and a particular market data derived call-put ratio, collected between July 2009 and September 2012. It shows that: (1) features derived from market data and a call-put ratio can improve model performance, (2) sentiment derived from StockTwits, a social media platform for the financial community, further enhances model performance, (3) aggregating all features together also facilitates performance, and (4) sentiment from social media and market data can be used as risk factors in an asset pricing framework.
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The authors would like to thank StockTwits for providing the messages. The authors also thank Shu-Heng Chen, Blake LeBaron, Jon Kaufman, David Starer, Hamed Ghoddusi, Khaldoun Khashanah, and three anonymous referees for suggestions and informal discussions about this research. The opinions presented are the exclusive responsibility of the authors.
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Houlihan, P., Creamer, G.G. Can Sentiment Analysis and Options Volume Anticipate Future Returns?. Comput Econ 50, 669–685 (2017). https://doi.org/10.1007/s10614-017-9694-4
- Social media
- Investor sentiment
- Behavioral finance
- Machine learning