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Media coverage and investor scare behavior diffusion

Author

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  • Liu, Chen
  • Li, Xuefei

Abstract

From the perspective of communication science and combining with the research results of behavioral finance, this paper analyzes the diffusion mechanism of investor scare behavior, and constructs a network diffusion model of investor scare behavior with the help of complex network theory. Also, it theoretically analyzes the network topological characteristics of investor scare behavior diffusion and the evolution characteristics of it in numerical simulation. The main conclusions are as follows: (1) In local financial network, the probability of investor scare behavior diffusion shows the characteristic of monotone diminishing with the increase of media attention and media sentiment, while it shows the characteristics of monotone increasing with the increase of media authority. But it is gradually disappearing in global financial network. Besides, it presents the rising trend of marginal increasing with the increase of financial market noise and information dissemination ability of media in local and global financial networks. (2) When the financial market noise interacts with media sentiment, media attention, media authority and information dissemination ability of media respectively, the probability of investor scare behavior diffusion shows the characteristic of monotone increasing in both local and global financial networks. (3) When media sentiment interacts with media attention and media authority respectively, the probability of investor scare behavior diffusion shows the characteristic of monotone diminishing and the characteristics of monotone increasing respectively in local financial network. While it shows the characteristics of monotone increasing in local financial network when media attention interacts with media authority

Suggested Citation

  • Liu, Chen & Li, Xuefei, 2019. "Media coverage and investor scare behavior diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    • Handle: RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119308180
    DOI: 10.1016/j.physa.2019.121398
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    Citations

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    Cited by:

    1. Li, Tong & Chen, Hui & Liu, Wei & Yu, Guang & Yu, Yongtian, 2023. "Understanding the role of social media sentiment in identifying irrational herding behavior in the stock market," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 163-179.
    2. Thiago Christiano Silva & Benjamin Miranda Tabak & Idamar Magalhães Ferreira, 2019. "Modeling Investor Behavior Using Machine Learning: Mean-Reversion and Momentum Trading Strategies," Complexity, Hindawi, vol. 2019, pages 1-14, December.
    3. Zicheng Pan & Qianting Ma & Junfei Ding & Lei Wang, 2021. "Research on the stock correlation networks and network entropies in the Chinese green financial market," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(2), pages 1-11, February.

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