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
Date Written: February 2019
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