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We examine whether a put-call ratio, derived from a unique set of market data, can be used to predict directional moves in asset prices during various market conditions between March 2005 and Decem...

This paper evaluates if sentiment extracted from social media and options volume anticipates future asset return. Using both textual based data and a particular market data derived call-put ratio, between July 2009 and September 2012, this research shows that: 1) features derived from market data and a call-put ratio improve model performance, and 2) sentiment derived from StockTwits, a social media platform for the financial community, further improves model performance.

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.

We examine whether a particular put-call ratio, derived from a unique set of market data, can be used to predict directional moves in asset prices during various market conditions between March 2005 and December 2012. Our findings show: 1) specific market participant option trading volume is shown to be a predecessor to asset price movements; 2) portfolios adjusted for risk, momentum and transaction costs exhibit abnormal excess returns. These findings suggest that short positions of a specific market participant improve the overall performance of a given portfolio.