Premium
Analyzing stock market trends using social media user moods and social influence
Author(s) -
Li Daifeng,
Wang Yintian,
Madden Andrew,
Ding Ying,
Tang Jie,
Sun Gordon Guozheng,
Zhang Ning,
Zhou Enguo
Publication year - 2019
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.24173
Subject(s) - microblogging , irrational number , social media , behavioral economics , stock market , financial market , stock (firearms) , china , stock trading , financial economics , economics , computer science , finance , world wide web , political science , mechanical engineering , paleontology , geometry , mathematics , horse , biology , law , engineering
Information from microblogs is gaining increasing attention from researchers interested in analyzing fluctuations in stock markets. Behavioral financial theory draws on social psychology to explain some of the irrational behaviors associated with financial decisions to help explain some of the fluctuations. In this study we argue that social media users who demonstrate an interest in finance can offer insights into ways in which irrational behaviors may affect a stock market. To test this, we analyzed all the data collected over a 3‐month period in 2011 from Tencent Weibo (one of the largest microblogging websites in China). We designed a social influence (SI)‐based Tencent finance‐related moods model to simulate investors' irrational behaviors, and designed a Tencent Moods‐based Stock Trend Analysis (TM_STA) model to detect correlations between Tencent moods and the Hushen‐300 index (one of the most important financial indexes in China). Experimental results show that the proposed method can help explain the data fluctuation. The findings support the existing behavioral financial theory, and can help to understand short‐term rises and falls in a stock market. We use behavioral financial theory to further explain our findings, and to propose a trading model to verify the proposed model.