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A social network model of investment behaviour in the stock market

Author

Listed:
  • Bakker, L.
  • Hare, W.
  • Khosravi, H.
  • Ramadanovic, B.

Abstract

To consider the psychological factors that impact market valuation, a model is formulated for investment behaviour of traders whose decisions are influenced by their trusted peers’ behaviour. The model is implemented and several different “trust networks” are tested. Simulation results demonstrate that real life trust networks can significantly delay the stabilisation of a market.

Suggested Citation

  • Bakker, L. & Hare, W. & Khosravi, H. & Ramadanovic, B., 2010. "A social network model of investment behaviour in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(6), pages 1223-1229.
    • Handle: RePEc:eee:phsmap:v:389:y:2010:i:6:p:1223-1229
    DOI: 10.1016/j.physa.2009.11.013
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    References listed on IDEAS

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    2. Wei, Yi-ming & Ying, Shang-jun & Fan, Ying & Wang, Bing-Hong, 2003. "The cellular automaton model of investment behavior in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 325(3), pages 507-516.
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    Cited by:

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    2. Li, Huajiao & An, Haizhong & Huang, Jiachen & Huang, Xuan & Mou, Songtao & Shi, Yanli, 2016. "The evolutionary stability of shareholders’ co-holding behavior for China’s listed energy companies based on associated maximal connected sub-graphs of derivative holding-based networks," Applied Energy, Elsevier, vol. 162(C), pages 1601-1607.
    3. Bian, Yue-tang & Xu, Lu & Li, Jin-sheng, 2016. "Evolving dynamics of trading behavior based on coordination game in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 281-290.
    4. Zhao, Zheng & Zhang, YongJie & Feng, Xu & Zhang, Wei, 2014. "An analysis of herding behavior in security analysts’ networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 116-124.
    5. Ducha, F.A. & Atman, A.P.F. & Bosco de Magalhães, A.R., 2021. "Information flux in complex networks: Path to stylized facts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    6. Liu, Qian & Li, Huajiao & Liu, Xueyong & Jiang, Meihui, 2018. "Information networks in the stock market based on the distance of the multi-attribute dimensions between listed companies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 505-513.
    7. Cao, Guangxi & Xie, Wenhao, 2022. "Detrended multiple moving average cross-correlation analysis and its application in the correlation measurement of stock market in Shanghai, Shenzhen, and Hong Kong," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    8. Haifei Liu & Tingqiang Chen & Zuhan Hu, 2017. "Dynamic Evolution of Securities Market Network Structure under Acute Fluctuation Circumstances," Complexity, Hindawi, vol. 2017, pages 1-11, November.
    9. Chen, Kun & Luo, Peng & Sun, Bianxia & Wang, Huaiqing, 2015. "Which stocks are profitable? A network method to investigate the effects of network structure on stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 224-235.
    10. Elisa Tjondro & Saarce Elsye Hatane & Retnaningtyas Widuri & Josua Tarigan, 2023. "Rational versus Irrational Behavior of Indonesian Cryptocurrency Owners in Making Investment Decision," Risks, MDPI, vol. 11(1), pages 1-18, January.
    11. Kylie J. Gilbey & Sharon Purchase, 2023. "Segmented financial risk tolerances within the standardised initial public offering regulatory environment of the Australian Securities Exchange," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(S1), pages 1447-1475, April.

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