t This paper evaluates the question of whether sentiment extracted from social
media and options volume anticipates future asset return. The research utilized both
textual based data and a particular market data derived call-put ratio, collected between
July 2009 and September 2012. It shows that: (1) features derived from market data
and a call-put ratio can improve model performance, (2) sentiment derived from StockTwits,
a social media platform for the financial community, further enhances model
performance, (3) aggregating all features together also facilitates performance, and
(4) sentiment from social media and market data can be used as risk factors in an asset
pricing framework.t This paper evaluates the question of whether sentiment extracted from social
media and options volume anticipates future asset return. The research utilized both
textual based data and a particular market data derived call-put ratio, collected between
July 2009 and September 2012. It shows that: (1) features derived from market data
and a call-put ratio can improve model performance, (2) sentiment derived from StockTwits,
a social media platform for the financial community, further enhances model
performance, (3) aggregating all features together also facilitates performance, and
(4) sentiment from social media and market data can be used as risk factors in an asset
pricing framework.

• Can Sentiment Analysis and Options Volume Anticipate Future Returns?