A generative model for the collective attention of the Chinese stock market investors

被引:3
作者
Liu, Jian-Guo [1 ,2 ]
Yang, Zhen-Hua [3 ,4 ,6 ]
Li, Sheng-Nan [5 ]
Yu, Chang-Rui [4 ]
机构
[1] Shanghai Univ Finance & Econ, Res Ctr Fin Tech, Shanghai 200433, Peoples R China
[2] Shanghai Univ Finance & Econ, Shanghai Key Lab Fin Inf Tech, Shanghai 200433, Peoples R China
[3] Huzhou Univ, Business Sch, Huzhou 313000, Peoples R China
[4] Shanghai Univ Finance & Econ, Sch Informat Management Engn, Shanghai 200433, Peoples R China
[5] Univ Shanghai Sci & Technol, Res Ctr Complex Syst Sci, Shanghai 200093, Peoples R China
[6] Univ Florida, Warrington Coll Business, Gainesville, FL 32611 USA
基金
中国国家自然科学基金;
关键词
Investor collective attention; Recent attention; Cumulative attention; Stock market; LIMITED ATTENTION; OVERCONFIDENCE; POPULARITY; SEARCH; NEWS;
D O I
10.1016/j.physa.2018.08.036
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Collective attention of investors maps the interests and intention of investors directly in the stock market. However, the evolution mechanism of the collective attention from the viewpoint of complex system is missing. In this paper, we empirically investigate the investor collective attention mechanism based on a best-known stock trading platform between 2014 and 2016. Taking the global and recent popularity effects into account, we introduce a generative model for the collective attention of millions of investors who are deciding their trading behavior among thousands of stocks in Chinese stock market. The experimental results show that the investor attention is more closely affected by recent attention, with the optimal case, when the memory effect parameter T = 10 and the recent popularity parameter gamma = 0.1, the model could regenerate the collective attention more accurately, say Kendall's tau = 0.92 for the Shanghai Stock Exchange(SSE) and Shenzhen Stock Exchange(SZSE) simultaneously. This work may shed some lights for deeply understanding the mechanism of the investor collective attention for the financial market. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码:1175 / 1182
页数:8
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