Leveraging Social Media to Predict Continuation and Reversal in Asset Prices

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