Leveraging Social Media to Predict Continuation and Reversal in Asset Prices

This is a pre-print of an article published in Computational Economics, 2019. The final authenticated version is available online at DOI: 10.1007/s10614-019-09932-9

23 Pages Posted: 29 Nov 2014 Last revised: 18 Nov 2019

See all articles by Patrick Houlihan

Patrick Houlihan

Stevens Institute of Technology

Germán G. Creamer

Stevens Institute of Technology, School of Business; Columbia University - Department of Computer Science

Date Written: February 2019

Abstract

Using features extracted from StockTwits messages between July 2009 and September 2012, we show through simulations that: 1) both message volume and sentiment help explain the diffusion of price information; 2) both message volume and sentiment can be used as features to predict asset price directional moves, we show that positive and negative sentiment diffuses into an assets price over a period of days. Our findings suggest statistics derived from both message volume and message sentiment can improve asset price forecasts.

Keywords: Social Media, Crowdsourcing, Sentiment

JEL Classification: G02, G12, C32

Suggested Citation

Houlihan, Patrick and Creamer, Germán G., Leveraging Social Media to Predict Continuation and Reversal in Asset Prices (February 2019). This is a pre-print of an article published in Computational Economics, 2019. The final authenticated version is available online at DOI: 10.1007/s10614-019-09932-9, Available at SSRN: https://ssrn.com/abstract=2527968 or http://dx.doi.org/10.2139/ssrn.2527968

Patrick Houlihan (Contact Author)

Stevens Institute of Technology ( email )

Hoboken, NJ 07030
United States

Germán G. Creamer

Stevens Institute of Technology, School of Business ( email )

1 Castle Point on Hudson
Hoboken, NJ 07030
United States
2012168986 (Phone)

HOME PAGE: http://www.creamer-co.com

Columbia University - Department of Computer Science ( email )

New York, NY 10027
United States

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