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Network Centrality and Stock Market Volatility: The Impact of Communication Topologies on Prices

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  • Oliver Hein
  • Michael Schwind
  • Markus Spiwoks

Abstract

We investigate the impact of agent communication networks on prices in an artificial stock market. Networks with different centralization measures are tested for their effect on the volatility of prices. Trading strategies diffuse through the different network topologies, mimetic contagion arises through the adaptive behavior of the heterogeneous agents. Short trends may trigger cascades of buy and sell orders due to increased diffusion speed within highly centralized communication networks. Simulation results suggest a correlation between the network centralization measures and the volatility of the resulting stock prices.

Suggested Citation

  • Oliver Hein & Michael Schwind & Markus Spiwoks, 2012. "Network Centrality and Stock Market Volatility: The Impact of Communication Topologies on Prices," Journal of Finance and Investment Analysis, SCIENPRESS Ltd, vol. 1(1), pages 1-9.
    • Handle: RePEc:spt:fininv:v:1:y:2012:i:1:f:1_1_9
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    Cited by:

    1. Joohyun Kim & Ohsung Kwon & Duk Hee Lee, 2019. "Observing Cascade Behavior Depending on the Network Topology and Transaction Costs," Computational Economics, Springer;Society for Computational Economics, vol. 53(1), pages 207-225, January.
    2. Ibrahim Filiz & Thomas Nahmer & Markus Spiwoks & Kilian Bizer, 2018. "Portfolio diversification: the influence of herding, status-quo bias, and the gambler’s fallacy," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(2), pages 167-205, May.
    3. Li, Bingqing & Wang, Lijia & Lu, Guoxiang, 2017. "Price dynamics, social networks and communication," Finance Research Letters, Elsevier, vol. 22(C), pages 197-201.
    4. Ibrahim Filiz & Jan René Judek & Marco Lorenz & Markus Spiwoks, 2021. "Sticky Stock Market Analysts," JRFM, MDPI, vol. 14(12), pages 1-27, December.
    5. Liu, Keyan & Zhou, Jianan & Dong, Dayong, 2021. "Improving stock price prediction using the long short-term memory model combined with online social networks," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).

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