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Can Sentiment Analysis and Options Volume Anticipate Future Returns?

Author

Listed:
  • Patrick Houlihan

    (Stevens Institute of Technology)

  • Germán G. Creamer

    (Stevens Institute of Technology)

Abstract

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.

Suggested Citation

  • Patrick Houlihan & Germán G. Creamer, 2017. "Can Sentiment Analysis and Options Volume Anticipate Future Returns?," Computational Economics, Springer;Society for Computational Economics, vol. 50(4), pages 669-685, December.
    • Handle: RePEc:kap:compec:v:50:y:2017:i:4:d:10.1007_s10614-017-9694-4
    DOI: 10.1007/s10614-017-9694-4
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    Cited by:

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    2. Meng‐Feng Yen & Yu‐Pei Huang & Liang‐Chih Yu & Yueh‐Ling Chen, 2022. "A Two-Dimensional Sentiment Analysis of Online Public Opinion and Future Financial Performance of Publicly Listed Companies," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1677-1698, April.

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