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Agent-Based Stock Market Model with Endogenous Agents' Impact

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  • Jan A. Lipski
  • Ryszard Kutner

Abstract

The three-state agent-based 2D model of financial markets as proposed by Giulia Iori has been extended by introducing increasing trust in the correctly predicting agents, a more realistic consultation procedure as well as a formal validation mechanism. This paper shows that such a model correctly reproduces the three fundamental stylised facts: fat-tail log returns, power-law volatility autocorrelation decay in time and volatility clustering.

Suggested Citation

  • Jan A. Lipski & Ryszard Kutner, 2013. "Agent-Based Stock Market Model with Endogenous Agents' Impact," Papers 1310.0762, arXiv.org, revised Dec 2013.
    • Handle: RePEc:arx:papers:1310.0762
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    File URL: http://arxiv.org/pdf/1310.0762
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    References listed on IDEAS

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    1. Iori, Giulia, 2002. "A microsimulation of traders activity in the stock market: the role of heterogeneity, agents' interactions and trade frictions," Journal of Economic Behavior & Organization, Elsevier, vol. 49(2), pages 269-285, October.
    2. Jan A. Lipski & Ryszard Kutner, 2013. "Trust in foreseeing neighbours - a novel threshold model of financial market," Papers 1301.1824, arXiv.org.
    3. M. Cristelli & L. Pietronero & A. Zaccaria, 2011. "Critical Overview of Agent-Based Models for Economics," Papers 1101.1847, arXiv.org.
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    Cited by:

    1. Johann Lussange & Ivan Lazarevich & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2021. "Modelling Stock Markets by Multi-agent Reinforcement Learning," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 113-147, January.
    2. Johann Lussange & Boris Gutkin, 2023. "Order book regulatory impact on stock market quality: a multi-agent reinforcement learning perspective," Papers 2302.04184, arXiv.org.
    3. Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Modelling crypto markets by multi-agent reinforcement learning," Papers 2402.10803, arXiv.org.
    4. Johann Lussange & Stefano Vrizzi & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2023. "Stock Price Formation: Precepts from a Multi-Agent Reinforcement Learning Model," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1523-1544, April.

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