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) message volume and sentiment can be used as a risk factor in an asset pricing model framework; (2) message volume and sentiment help explain the diffusion of price information over several days, and (3) message volume and sentiment can be used as features to predict asset price directional moves. Our findings suggest statistics derived from message volume and sentiment can improve asset price forecasts and leads to a profitable trading strategy.