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Leveraging Social Media to Predict Continuation and Reversal in Asset Prices

Author

Listed:
  • Patrick Houlihan

    (Stevens Institute of Technology)

  • Germán G. Creamer

    (Stevens Institute of Technology)

Abstract

Using features extracted from StockTwits messages between July 2009 and September 2012, we show through simulations that: (1) message volume and sentiment can be used as a risk factor in an asset pricing model framework; (2) message volume and sentiment help explain the diffusion of price information over several days, and (3) message volume and sentiment can be used as features to predict asset price directional moves. Our findings suggest statistics derived from message volume and sentiment can improve asset price forecasts and leads to a profitable trading strategy.

Suggested Citation

  • Patrick Houlihan & Germán G. Creamer, 2021. "Leveraging Social Media to Predict Continuation and Reversal in Asset Prices," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 433-453, February.
    • Handle: RePEc:kap:compec:v:57:y:2021:i:2:d:10.1007_s10614-019-09932-9
    DOI: 10.1007/s10614-019-09932-9
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    References listed on IDEAS

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    3. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).

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