Exploiting Social Relations and Sentiment for Stock Prediction

Jianfeng Si, Arjun Mukherjee, Bing Liu, Sinno Jialin Pan, Qing Li, Huayi Li

Abstract: In this paper we first exploit cash-tags ("" fol- lowed 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 fur- ther show that social sentiment about stock (node) topics and stock relationship (edge) topics are predictive of each stock's market. For predic- tion, we propose to regress the topic-sentiment time-series and the stock's price time series. Ex- perimental results demonstrate that topic senti- ments from close neighbors are able to help im- prove the prediction of a stock markedly.