Sentiment, Social Reach and Deep Learning. Can Financial Microblogging Services Predict Future Stock Returns?

Microblogging platforms are playing an increasingly important role in the exchange of information and opinions among investors. Therefore, this study investigates the causal relationship between investor’s sentiment on the StockTwits platform and US stock returns. In the analysis, we also incorporate the social reach of users and tweets which has been largely neglected in previous literature. We demonstrate that our deep-learning model outperforms established dictionary and machine learning approaches in terms of sentiment classification accuracy. While past price movements strongly influence current sentiment, especially in intraday periods, the reverse direction is scarcely statistically detectable. The inclusion of social reach does not increase the explanatory power of sentiment. In more extreme market phases, tweet volume is a stronger predictor, but with mixed implications regarding the sign. An analysis of the prediction success rate of stock prices shows that only slightly less than half of the users are correct with their prediction