Exploiting social relations and sentiment for stock prediction

In this paper we first exploit cash-tags ("$" followed by stocks' ticker symbols) in Twitter to build a stock network, where nodes are stocks connected by edges when two stocks co-occur frequently in tweets. We then employ a labeled topic model to jointly model both the tweets and the network structure to assign each node and each edge a topic respectively. This Semantic Stock Network (SSN) summarizes discussion topics about stocks and stock relations. We further show that social sentiment about stock (node) topics and stock relationship (edge) topics are predictive of each stock's market. For prediction, we propose to regress the topic-sentiment time-series and the stock's price time series. Experimental results demonstrate that topic sentiments from close neighbors are able to help improve the prediction of a stock markedly.