Leveraging Social Media to Predict Continuation and Reversal in Asset Prices

Patrick Houlihan, Germán G. Creamer

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.

Original languageEnglish
Pages (from-to)433-453
Number of pages21
JournalComputational Economics
Volume57
Issue number2
DOIs
StatePublished - Feb 2021

Keywords

  • Computational finance
  • Crowdsourcing
  • Machine learning
  • Sentiment analysis
  • Social media

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