Can Sentiment Analysis and Options Volume Anticipate Future Returns?

Patrick Houlihan () and German Creamer
Additional contact information
Patrick Houlihan: Stevens Institute of Technology

Computational Economics, 2017, vol. 50, issue 4, No 7, 669-685

Abstract: 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.

Keywords: Social media; Investor sentiment; Behavioral finance; Machine learning (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s10614-017-9694-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:50:y:2017:i:4:d:10.1007_s10614-017-9694-4

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-017-9694-4

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().