The role of social sentiment in stock markets: a view from joint effects of multiple information sources

Social sentiment reflects grassroots views regarding stock trends and has played a leading role in stock movements. Previous studies have relied predominantly on statistical models, regression models or vector-based predictive models to analyze the influence of social sentiment without considering other information sources or their intrinsic interactions. However, stock movements are in essence driven by various types of highly interrelated information sources including firm characteristics, social sentiment, and professional opinions. This paper describes the degree to which the problem arises in understanding the role of social sentiment in financial markets and proposes a novel intelligent stock analysis system to solve it. It first captures social sentiment and professional opinions from textual information in social media and financial news, respectively, and then represents the whole market information space consisting of these two information sources along with firm characteristics via tensors. Finally, a tensor-based learning algorithm is utilized to capture the interactions of these information sources on stock movements. Experiments performed on an entire year of data of China Securities Index (CSI 100) stocks demonstrate the effectiveness of the proposed intelligent system to study the role of social sentiment from the perspective of joint effects of multiple information sources compared with traditional vector-based systems.