17 Pages Posted: 29 Nov 2014 Last revised: 3 Feb 2017
Date Written: November 1, 2015
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: Suggested Citation
Houlihan, Patrick and Creamer, Germán G., Leveraging Social Media to Predict Continuation and Reversal in Asset Prices (November 1, 2015). Available at SSRN: https://ssrn.com/abstract=2527968 or http://dx.doi.org/10.2139/ssrn.2527968