Affiliations: [a] Department of Management Science, City University of Hong Kong, Hong Kong. E-mail: [email protected] | [b] School of Software Engineering, South China University of Technology, Guangzhou, China. E-mail: [email protected] | [c] Department of Industrial and Manufacturing Systems Engineering, Hong Kong University, Hong Kong. E-mail: [email protected] | [d] International Business School, Shaanxi Normal University, Xian, China | [e] Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong. E-mail: [email protected]
Abstract: Over the past several years, as the development of Internet, social media websites such as Twitter and Weibo have received much attention due to their enormous users. A lot of research has been done on sentiment analysis and opinion mining in these websites. However the number of research on using the data in the social media websites to predict the stock market price movement is limited. Behavioral economics and behavioral finance believe that public mood is correlated with economic indicators and financial decisions are significantly driven by emotions. This paper first presents a Chinese emotion mining approach and discusses whether the public emotions or opinions in the Chinese social media websites could be used to predict the stock market price in China. The experimental results demonstrate that the emotions automatically extracted from the large scale Weibo posts represent the real public opinions about some special topics of the stock market in China. Some public mood states extracted such as the “Happiness” and “Disgust” states are highly correlated with the change of stock price according to the Granger causality analysis. Finally, a nonlinear autoregressive model with exogenous sentiment inputs is proposed to predict the stock price movement.