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Data Analysis and Knowledge Discovery  2023, Vol. 7 Issue (6): 134-147    DOI: 10.11925/infotech.2096-3467.2022.0482
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Associations Between Following Network of Online Investment Community and Stock Market
Li Yulu,Zhao Jichang()
School of Economics and Management, Beihang University, Beijing 100191, China
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Abstract  

[Objective] This paper explores the stock preference in the following network of Guba (a Chinese online investment community) users. It examines the correlation between the stock market performance and the social structures of the network. [Methods] First, we used statistical analysis to study users’ preferences. Then, we utilized complex network analysis to learn the structural characteristics of the users’ following network. Finally, we conducted a correlation analysis to examine the correlations between network structures and stock price fluctuations. [Results] Users with the following relationships in the network are more similar in their stock preference (K-S test~0.235, p~0). The structures of the following network affect the dissemination of information, which is significantly correlated to the fluctuation of stock prices. The structural variables of network efficiency are significantly negative (p~0.01). Our findings suggest that the stronger the ability of the following network to spread information, the more independent the fluctuation of stock price will be from the fluctuation of other stocks and the market average. Increasing the ability to disseminate information on the following network can reduce the co-oscillation of the stock price in China. [Limitations] This study lacks experimental validation and analysis comparison of data from different social platforms. [Conclusions] The research methods and results presented in this paper can provide some guidance for market regulation and stock investment.
Key words： Social Media      Following Network      Stock Price Fluctuation      Correlation Analysis      Information Dissemination Capabilities     
Received: 13 May 2022      Published: 09 August 2023
ZTFLH:  TP391  
  G35  
Fund:National Natural Science Foundation of China(71871006)
Corresponding Authors: Zhao Jichang，ORCID：0000-0002-5319-8060，E-mail： jichang@buaa.edu.cn。   
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Yulu Li
Jichang Zhao

Cite this article:

Li Yulu, Zhao Jichang. Associations Between Following Network of Online Investment Community and Stock Market. Data Analysis and Knowledge Discovery, 2023, 7(6): 134-147.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.0482     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2023/V7/I6/134

Research Framework
字段名 说明 数据结构
ID 用户ID String
post_count 用户的发帖数量 Int
stock_count 用户的自选股数量 Int
stock_list 用户的自选股列表 List
following_list 用户关注的其他用户ID列表 List
Structure of Guba Users’ Characteristics
Distribution of User Preference Similarity Before and After Random
Distribution of User Attention Subnetwork Size of the Two Stocks
Variable Outlier Filtering
变量 Coef. St.Err. t-value p-value [95% Conf Interval] Sig
efficiency -3 491.86 836.63 -4.17 0 -5 131.704 -1 852.015 ***
reciprocity 0.761 0.188 4.04 0 0.391 1.13 ***
transitivity 0.548 0.224 2.44 0.015 0.108 0.987 **
subrate -8.73e-08 0 -4.21 0 0 0 ***
avposts -0.004 0 -9.72 0 -0.005 -0.003 ***
avstocks -0.009 0.001 -9.15 0 -0.011 -0.007 ***
adjusted_profit 0 0 -14.54 0 0 0 ***
roe -0.002 0.001 -2.29 0.022 -0.004 0 **
profit_to_revenue 0 0 -3.09 0.002 0 0 ***
scale 0 0 15.29 0 0 0 ***
Constant 2.219 0.289 7.69 0 1.654 2.785 ***
Mean dependent var -1.084 SD dependent var 2.222
R-squared 0.021 Number of obs 24 726
F-test 53.831 Prob > F 0.000
Akaike crit. （AIC） 109 135.233 Bayesian crit. （BIC） 109 216.389
Regression Results on c o m v _ r _ s q u a r e of the Two Stocks
变量 VIF 1/VIF
avstocks 1.430 0.702
adjusted_profit 1.250 0.801
subrate 1.240 0.805
scale 1.180 0.849
efficiency 1.170 0.858
avposts 1.090 0.916
reciprocity 1.060 0.947
transitivity 1.020 0.976
roe 1.010 0.993
profit_to_revenue 1.000 0.996
Mean VIF 1.140
VIF Test
Schematic Diagram of Transitive Network Structure
变量 Coef. St.Err. t-value p-value [95% Conf Interval] Sig
efficiency -334.86 131.967 -2.54 0.011 -593.524 -76.195 **
reciprocity 0.186 0.03 6.25 0 0.127 0.244 ***
transitivity 0.09 0.035 2.54 0.011 0.02 0.159 **
subrate -7.18e-09 0 -2.19 0.028 0 0 **
avposts -0.001 0 -11.51 0 -0.001 -0.001 ***
avstocks -0.001 0 -4.72 0 -0.001 0 ***
adjusted_profit 0 0 -18.20 0 0 0 ***
roe -0.001 0 -4.72 0 -0.001 0 ***
profit_to_revenue 0 0 -6.36 0 0 0 ***
scale 0 0 16.08 0 0 0 ***
Constant 0.861 0.046 18.91 0 0.772 0.95 ***
Mean dependent var 0.500 SD dependent var 0.351
R-squared 0.027 Number of obs 24 726
F-test 68.039 Prob > F 0.000
Akaike crit. （AIC） 17 805.972 Bayesian crit. （BIC） 17 887.128
Regression Results on c o m v _ c o r r of the Two Stocks
变量 Coef. St.Err. t-value p-value [95% Conf Interval] Sig
efficiency -3 942.509 1 842.634 -2.14 0.032 -7 555.888 -329.13 **
reciprocity 0.931 0.796 1.17 0.242 -0.63 2.491
transitivity -0.314 0.511 -0.62 0.538 -1.316 0.687
subrate -4.501 2.082 -2.16 0.031 -8.583 -0.419 **
avposts -0.005 0.001 -3.96 0 -0.007 -0.003 ***
avstocks 0.012 0.005 2.57 0.01 0.003 0.02 **
adjusted_profit 0 0 11.07 0 0 0 ***
roe 0.002 0.001 1.40 0.163 -0.001 0.004
profit_to_revenue 0 0 -1.57 0.117 0 0
scale 0 0 3.58 0 0 0 ***
Constant 2.499 1.593 1.57 0.117 -0.626 5.623
Mean dependent var -0.420 SD dependent var 1.890
R-squared 0.071 Number of obs 2 333
F-test 17.624 Prob > F 0.000
Akaike crit. （AIC） 9 439.032 Bayesian crit. （BIC） 9 496.581
Regression Results on c o m v _ r _ s q u a r e of One Stock（Ⅰ）
变量 Coef. St.Err. t-value p-value [95% Conf Interval] Sig
efficiency -970.574 297.349 -3.26 0.001 -1 553.671 -387.477 ***
reciprocity 0.331 0.128 2.58 0.01 0.079 0.583 ***
transitivity -0.094 0.082 -1.14 0.254 -0.256 0.068
subrate -1.073 0.336 -3.19 0.001 -1.731 -0.414 ***
avposts -0.001 0 -4.32 0 -0.001 0 ***
avstocks 0.003 0.001 3.86 0 0.001 0.004 ***
adjusted_profit 0 0 13.74 0 0 0 ***
roe 0 0 1.63 0.103 0 0.001
profit_to_revenue 0 0 -2.11 0.035 0 0 **
scale 0 0 3.47 0.001 0 0 ***
Constant 1.239 0.257 4.82 0 0.735 1.743 ***
Mean dependent var 0.612 SD dependent var 0.310
R-squared 0.103 Number of obs 2 333
F-test 26.684 Prob > F 0.000
Akaike crit. （AIC） 928.037 Bayesian crit. （BIC） 985.587
Regression Results on c o m v _ c o r r of One Stock（Ⅱ）
[1] Hein O, Schwind M, Spiwoks M. Network Centrality and Stock Market Volatility: The Impact of Communication Topologies on Prices[J]. Journal of Finance and Investment Analysis, 2012, 1(1): 199-232.
[2] Ozsoylev H N, Walden J, Yavuz M D, et al. Investor Networks in the Stock Market[J]. The Review of Financial Studies, 2014, 27(5): 1323-1366.
doi: 10.1093/rfs/hht065
[3] Chen K, Luo P, Liu L B, et al. News, Search and Stock Co-Movement: Investigating Information Diffusion in the Financial Market[J]. Electronic Commerce Research and Applications, 2018, 28: 159-171.
doi: 10.1016/j.elerap.2018.01.015
[4] Liu K Y, Zhou J N, Dong D Y. Improving Stock Price Prediction Using the Long Short-Term Memory Model Combined with Online Social Networks[J]. Journal of Behavioral and Experimental Finance, 2021, 30: 100507.
doi: 10.1016/j.jbef.2021.100507
[5] Zhou Z K, Xu K, Zhao J C. Tales of Emotion and Stock in China: Volatility, Causality and Prediction[J]. World Wide Web, 2018, 21(4): 1093-1116.
doi: 10.1007/s11280-017-0495-4
[6] Debata B, Dash S R, Mahakud J. Investor Sentiment and Emerging Stock Market Liquidity[J]. Finance Research Letters, 2018, 26: 15-31.
doi: 10.1016/j.frl.2017.11.006
[7] Jing N, Wu Z, Wang H F. A Hybrid Model Integrating Deep Learning with Investor Sentiment Analysis for Stock Price Prediction[J]. Expert Systems with Applications, 2021, 178: 115019.
doi: 10.1016/j.eswa.2021.115019
[8] Zhang X, Shi J W, Wang D, et al. Exploiting Investors Social Network for Stock Prediction in China’s Market[J]. Journal of Computational Science, 2018, 28: 294-303.
doi: 10.1016/j.jocs.2017.10.013
[9] Kumar S, Saini M, Goel M, et al. Modeling Information Diffusion in Online Social Networks Using a Modified Forest-Fire Model[J]. Journal of Intelligent Information Systems, 2021, 56(2): 355-377.
doi: 10.1007/s10844-020-00623-8
[10] 张宁, 尹乐民, 何立峰. 网络股评“发布者-关注者”BSI与股票市场关联性研究[J]. 数据分析与知识发现, 2018, 2(6): 1-12.
[10] (Zhang Ning, Yin Lemin, He Lifeng. Impacts of “Poster-Follower” Sentiment on Stock Market Performance[J]. Data Analysis and Knowledge Discovery, 2018, 2(6): 1-12.)
[11] 刘海飞, 许金涛, 柏巍, 等. 社交网络、投资者关注与股价同步性[J]. 管理科学学报, 2017, 20(2): 53-62.
[11] (Liu Haifei, Xu Jintao, Bai Wei, et al. Social Networks, Investor Attention and Stock Price Synchronicity[J]. Journal of Management Sciences in China, 2017, 20(2): 53-62.)
[12] 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.
[12] (Wang Xinrui, He Yue. Predicting Stock Market Fluctuations with Social Media Behaviors: Case Study of Sina Finance Blog[J]. Data Analysis and Knowledge Discovery, 2019, 3(11): 108-119.)
[13] Latora V, Marchiori M. Efficient Behavior of Small-World Networks[J]. Physical Review Letters, 2001, 87(19): 198701.
doi: 10.1103/PhysRevLett.87.198701
[14] Wu J R, Xu K, Chen X Y, et al. Price Graphs: Utilizing the Structural Information of Financial Time Series for Stock Prediction[J]. Information Sciences, 2022, 588: 405-424.
doi: 10.1016/j.ins.2021.12.089
[15] Lu S, Zhao J C, Wang H W. The Emergence of Critical Stocks in Market Crash[J]. Frontiers in Physics, 2020, 8: 49.
doi: 10.3389/fphy.2020.00049
[16] Roll R. The International Crash of October 1987[J]. Financial Analysts Journal, 1988, 44(5): 19-35.
doi: 10.2469/faj.v44.n5.19
[17] Morck R, Yeung B, Yu W. The Information Content of Stock Markets: Why do Emerging Markets Have Synchronous Stock Price Movements?[J]. Journal of Financial Economics, 2000, 58(1-2): 215-260.
doi: 10.1016/S0304-405X(00)00071-4
[18] Drake M S, Jennings J, Roulstone D T, et al. The Comovement of Investor Attention[J]. Management Science, 2017, 63(9): 2847-2867.
doi: 10.1287/mnsc.2016.2477
[19] Su F, Wang X Y. Investor Co-Attention and Stock Return Co-Movement: Evidence from China’s A-Share Stock Market[J]. The North American Journal of Economics and Finance, 2021, 58: 101548.
doi: 10.1016/j.najef.2021.101548
[20] Prasetyo I, Aliyyah N, Rusdiyanto, et al. Impact Financial Performance to Stock Prices: Evidence from Indonesia[J]. Journal of Legal, Ethical and Regulatory Issues, 2021, 24: 1-11.
[21] 董琳. 上市公司股价与财务指标相关性分析[J]. 商业会计, 2013(19): 80-82.
[21] (Dong Lin. Correlation Analysis Between Stock Price and Financial Indicators of Listed Companies[J]. Commercial Accounting, 2013(19): 80-82.)
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