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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (11): 108-119    DOI: 10.11925/infotech.2096-3467.2019.0061
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Predicting Stock Market Fluctuations with Social Media Behaviors: Case Study of Sina Finance Blog
Xinrui Wang,Yue He()
Business School, Sichuan University, Chengdu 610064, China
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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.
Key words： Users’ Behavior;      Social Networks      Sina Finance Blog      Correlation Analysis      Granger Causality Test     
Received: 14 January 2019      Published: 18 December 2019
ZTFLH:  G353.1  
Corresponding Authors: Yue He     E-mail: 402216179@qq.com
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Cite this article:

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.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0061     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I11/108

参数名称 解释
平均节点度 网络结构基本属性
密度 用户所关注博主的多样性
平均边权重 用户对关注博主的专一程度
平均阅读量 博主的影响力
平均点赞数 受众对博客文章的认可度
时间 当月至少发表过一篇博文的
博主的数量		 博文总数量 时间 当月至少发表过一篇博文的
博主的数量		 博文总数量
2016,1 48 1 132 2017,1 71 2 415
2016,2 53 1 458 2017,2 69 2 885
2016,3 56 1 497 2017,3 74 2 641
2016,4 56 1 587 2017,4 76 2 746
2016,5 61 1 738 2017,5 77 3 068
2016,6 64 1 957 2017,6 78 2 837
2016,7 64 2 049 2017,7 80 2 886
2016,8 66 2 012 2017,8 77 2 822
2016,9 71 1 905 2017,9 76 2 236
2016,10 65 2 521 2017,10 75 2 902
2016,11 66 2 614 2017,11 76 2 919
2016,12 68 2 299 2017,12 78 2 848
控制变量 变量 MA·MA3
相关性 显著性
Average Read、Average Like、Nodes Counts、Edges Counts、Density Average Edges Weight -.442 .058
Average Like、Nodes Counts、Edges Counts、Average Edges Weight、Density Average Read -.406 .084
Edges Counts、Average Edges Weight、Density、Average Read、Average Like Nodes Counts -.434 .063
Average Edges Weight、Nodes Counts、Density、Average Read、Average Like Edges Counts .247 .307
Average Read、Average Like、Nodes Counts、Edges Counts、Average Edges Weight Density -.508 .026*
Nodes Counts、Edges Counts、Average Edges Weight、Density、Average Read Average Like .486 .035*
均方和 自由度 均方 F统计量 显著性
MA·MA3与
密度回归分析		 回归 .000 2 .000 8.071 .003
残差 .000 21 .000
总体 .000 23
未标准化系数 标准误差 标准化系数 t统计量 显著性
MA·MA3 -4.526E-5 .000 -42.849 -2.760 .012
MA·MA3 ** 2 6.637E-9 .000 42.367 . .
常数 .078 .028 2.819 .010
均方和 自由度 均方 F统计量 显著性
MA·MA3与
平均点赞数
回归分析		 回归 39138.341 1 39138.341 16.050 .001
残差 53647.400 22 2438.518
总体 92785.741 23
未标准化系数 标准误差 标准化系数 t统计量 显著性
MA·MA3 .373 .093 .649 4.006 .001
常数 -1036.985 313.440 -3.308 .003
检验统计量 1%评判值 5%评判值 10%评判值
MA·MA3 -0.66 -3.75 -3.00 -2.63
Z检验P值=0.86
Density -2.78
Z检验P值=0.06
Average Like -2.39
Z检验P值=0.15
滞后阶数 参数 对数似然函数 特征值 统计量 5%评判值
0 3 -41.42 . 37.98 29.68
1 8 -29.85 0.63 14.82* 15.41
2 11 -22.91 0.45 0.94 3.76
3 12 -22.44 0.04
方程 剔除 F统计量 自由度 P值
1 MA·MA3 Density .00 1 0.99
2 MA·MA3 Average Like 4.18 1 0.06*
3 MA·MA3 ALL 3.29 2 0.06*
4 Density MA·MA3 .00 1 0.95
5 Density Average Like 7.61 1 0.01**
6 Density ALL 4.96 2 0.02**
7 Average Like MA·MA3 1.92 1 0.18
8 Average Like Density 5.04 1 0.04**
9 Average Like ALL 3.58 2 0.05**
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