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Amplified imitation in percolation model of stock market

Author

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  • Makowiec, D.
  • Gnaciński, P.
  • Miklaszewski, W.

Abstract

The herd behavior of the Cont–Bouchaud model is amplified by allowing clusters to copy decisions of some other cluster in the next time step. The results of the model are compared to data from the Warsaw Stock Exchange. It follows that the mechanism of the amplified imitation could be responsible for the sell decision on a poorly developed, emergent market.

Suggested Citation

  • Makowiec, D. & Gnaciński, P. & Miklaszewski, W., 2004. "Amplified imitation in percolation model of stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 331(1), pages 269-278.
  • Handle: RePEc:eee:phsmap:v:331:y:2004:i:1:p:269-278
    DOI: 10.1016/j.physa.2003.09.014
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    References listed on IDEAS

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    1. Thomas Lux & Michele Marchesi, 1999. "Scaling and criticality in a stochastic multi-agent model of a financial market," Nature, Nature, vol. 397(6719), pages 498-500, February.
    2. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    3. Castiglione, Filippo & Stauffer, Dietrich, 2001. "Multi-scaling in the Cont–Bouchaud microscopic stock market model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 300(3), pages 531-538.
    4. Cont, Rama & Bouchaud, Jean-Philipe, 2000. "Herd Behavior And Aggregate Fluctuations In Financial Markets," Macroeconomic Dynamics, Cambridge University Press, vol. 4(2), pages 170-196, June.
    5. Stauffer, Dietrich & Sornette, Didier, 1998. "Log-periodic oscillations for biased diffusion on random lattice," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 252(3), pages 271-277.
    6. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
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    Citations

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    Cited by:

    1. Xiao, Di & Wang, Jun, 2012. "Modeling stock price dynamics by continuum percolation system and relevant complex systems analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4827-4838.
    2. Ding, Li & Guan, Zhi-Hong, 2008. "Modeling wireless sensor networks using random graph theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(12), pages 3008-3016.
    3. Yang, ChunXia & Hu, Sen & Xia, BingYing, 2012. "The endogenous dynamics of financial markets: Interaction and information dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(12), pages 3513-3525.
    4. Lux, Thomas & Alfarano, Simone, 2016. "Financial power laws: Empirical evidence, models, and mechanisms," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 3-18.

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