【摘要】 【目的】探究社交媒体用户交互行为的社会网络与股市之间的关系,检验社会网络属性对股市的预测能力。【方法】利用新浪财经博客的转载信息,设置时间快照构建多个网络图;提取网络属性并与上证指数做相关性分析;最后将具有相关性的网络属性与上证指数进行格兰杰因果关系检验。【结果】网络密度与上证指数呈现二次项关系,极值点为3 400;博主节点的平均点赞数与上证指数呈现正相关性,相关系数为0.486;平均点赞数取一阶滞后具有协整关系,可以作为上证指数的格兰杰因。【局限】由于长文本情感分析和算法优化的问题,未计算博文的情感且所选取的网络属性均为基本属性。【结论】本文验证了社交媒体用户的交互行为对股市的预测能力,交互行为的社会网络属性能够提高股市预测的精度。更多还原

【Abstract】 [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.更多还原