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A Model of Synchronization for Self-Organized Crowding Behavior

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  • Jake J. Xia

Abstract

This paper proposes a general model for synchronized crowding behavior. An order parameter is introduced to quantify the level of synchronization which is shown a function of percentage of agents in reactive state. Further, synchronization is shown to be driven by the most active agents with the highest volatility. A tipping point is identified when crowd becomes self-amplifying and unstable. By applying this model, financial bubbles, market momentum and volatility patterns are simulated.

Suggested Citation

  • Jake J. Xia, 2016. "A Model of Synchronization for Self-Organized Crowding Behavior," Papers 1612.01132, arXiv.org.
    • Handle: RePEc:arx:papers:1612.01132
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    File URL: http://arxiv.org/pdf/1612.01132
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    References listed on IDEAS

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    1. Barberis, Nicholas & Greenwood, Robin & Jin, Lawrence & Shleifer, Andrei, 2018. "Extrapolation and bubbles," Journal of Financial Economics, Elsevier, vol. 129(2), pages 203-227.
    2. Shleifer, Andrei, 2000. "Inefficient Markets: An Introduction to Behavioral Finance," OUP Catalogue, Oxford University Press, number 9780198292272.
    3. J. Doyne Farmer, 2002. "Market force, ecology and evolution," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 11(5), pages 895-953, November.
    4. Scharfstein, David S & Stein, Jeremy C, 1990. "Herd Behavior and Investment," American Economic Review, American Economic Association, vol. 80(3), pages 465-479, June.
    5. Jean-Philippe Bouchaud, 2010. "The endogenous dynamics of markets: price impact and feedback loops," Papers 1009.2928, arXiv.org.
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    Cited by:

    1. Nicolás Magner & Jaime F. Lavín & Mauricio A. Valle, 2022. "Modeling Synchronization Risk among Sustainable Exchange Trade Funds: A Statistical and Network Analysis Approach," Mathematics, MDPI, vol. 10(19), pages 1-30, October.

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