Can Sentiment Analysis and Options Volume Anticipate Future Returns?

Patrick Houlihan, Germán G. Creamer

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

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.

Original languageEnglish
Pages (from-to)669-685
Number of pages17
JournalComputational Economics
Volume50
Issue number4
DOIs
StatePublished - 1 Dec 2017

Keywords

  • Behavioral finance
  • Investor sentiment
  • Machine learning
  • Social media

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