Herding, social network and volatility

被引:16
|
作者
Wang, Guocheng [1 ]
Wang, Yanyi [2 ]
机构
[1] Chinese Acad Social Sci, Inst Quantitat & Tech Econ, Beijing 100732, Peoples R China
[2] Renmin Univ China, Sch Finance, Beijing 100872, Peoples R China
基金
中国国家自然科学基金;
关键词
Heterogeneous beliefs; Herding; Social networks; Guru; Adaptive beliefs system; Market volatility; STOCK-MARKET PARTICIPATION; ASSET PRICING MODEL; BEHAVIOR; INFORMATION; INVESTMENT; GURUS; SPECULATORS; CASCADES; BELIEFS; IMPACT;
D O I
10.1016/j.econmod.2017.04.018
中图分类号
F [经济];
学科分类号
02 ;
摘要
Investors' expectations are highly influenced by their surroundings' opinions, especially from those who are believed as gurus. These opinion leaders (i.e., gurus) may manipulate the information when the information is disseminated to their followers. It is unclear whether herding behaviors will still emerge in this situation and if so, how these behaviors would influence the market volatility. In this paper, we model agents who choose either to follow the gurus with different precisions of information, or to be a chartist based on evolutionary considerations. Numerical simulations show that increasing the quality of gurus' private information would lead to more intensive herding behavior of followers and produce a U-shaped effect on the market volatility. Besides, increasing the proportion of gurus in the market would lead to more intensive herding but would decrease the market volatility. Interestingly, the market environment also affects investors' choices. Investors are more willing to herd on gurus in boom times or in depression. This paper sheds light on how informed gurus affect investors' behavior and market volatility through direct communication.
引用
收藏
页码:74 / 81
页数:8
相关论文
共 50 条
  • [21] The Prevalence, Sources, and Effects of Herding
    Boyd, Naomi E.
    Buyuksahin, Bahattin
    Haigh, Michael S.
    Harris, Jeffrey H.
    JOURNAL OF FUTURES MARKETS, 2016, 36 (07) : 671 - 694
  • [22] Regulatory mood-congruence and herding: Evidence from cannabis stocks
    Andrikopoulos, Panagiotis
    Gebka, Bartosz
    Kallinterakis, Vasileios
    JOURNAL OF ECONOMIC BEHAVIOR & ORGANIZATION, 2021, 185 : 842 - 864
  • [23] Herding in the Singapore stock Exchange
    Arjoon, Vaalmikki
    Bhatnagar, Chandra Shekhar
    Ramlakhan, Prakash
    JOURNAL OF ECONOMICS AND BUSINESS, 2020, 109
  • [24] OPECmeetings, oil market volatility and herding behaviour in the Saudi Arabia stock market
    Gabbori, Dina
    Awartani, Basel
    Maghyereh, Aktham
    Virk, Nader
    INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS, 2021, 26 (01) : 870 - 888
  • [25] Numerological superstitions and market-wide herding: Evidence from China
    Cui, Yueting
    Gavriilidis, Konstantinos
    Gebka, Bartosz
    Kallinterakis, Vasileios
    INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS, 2024, 93
  • [26] Sentiment, Herding and Volatility Forecasting: Evidence from GARCH-MIDAS Approach
    Cui, Yanxian
    Zheng, Hong
    Yuan, Ying
    FLUCTUATION AND NOISE LETTERS, 2023, 22 (02):
  • [27] Mobile social network and herding effect in China’s stock market
    Li J.
    Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice, 2023, 43 (09): : 2535 - 2555
  • [28] Herding by foreign investors and emerging market equity returns: Evidence from Korea
    Jeon, Jin Q.
    Moffett, Clay M.
    INTERNATIONAL REVIEW OF ECONOMICS & FINANCE, 2010, 19 (04) : 698 - 710
  • [29] Does Algorithmic Trading Induce Herding?
    Fu, Servanna Mianjun
    Alexakis, Christos
    Pappas, Vasileios
    Skarmeas, Emmanouil
    Verousis, Thanos
    INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS, 2024,
  • [30] Herding in mutual funds: A complex network approach
    D'Arcangelis, Anna Maria
    Rotundo, Giulia
    JOURNAL OF BUSINESS RESEARCH, 2021, 129 : 679 - 686