[Objective] This paper explores the relationship between stock market fluctuations and social media users’ interactive behaviors, aiming to predict the stock prices with social data. [Methods] Firstly, we set snapshots and constructed several social networks by crawling the quotes of Sina Finance Blogs. Then, we extracted the topological features and conducted correlation analysis between the topological features and Shanghai Composite Index. Finally, we used the Granger causality test to further examine the relationship between Shanghai Composite Index and the correlated features. [Results] There was a relationship of quadratic term between graph Density and Shanghai Composite Index, and the extreme point was 3,400. There was a positive correlation between blog Nodes’ average number of likes and Shanghai Composite Index (correlation coefficient = 0.486). Taking the first order lag, the average number of likes can be the Granger cause of Shanghai Composite Index. [Limitations] We did not caculate the emotional scores of the blogs and only extracted the basic topological features. [Conclusions] Users’ social network behaviors could help us predict the changes of stock market.
王欣瑞,何跃. 社交媒体用户交互行为与股票市场的关联分析研究: 基于新浪财经博客的实证[J]. 数据分析与知识发现, 2019, 3(11): 108-119.
Xinrui Wang,Yue He. Predicting Stock Market Fluctuations with Social Media Behaviors: Case Study of Sina Finance Blog. Data Analysis and Knowledge Discovery, 2019, 3(11): 108-119.
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