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Artificial stock markets with different maturity levels: simulation of information asymmetry and herd behavior using agent-based and network models

Author

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  • Hazem Krichene

    (Université de Tunis
    University of Hyogo)

  • Mhamed-Ali El-Aroui

    (Université de Carthage)

Abstract

This paper aims mainly at building artificial stock markets with different maturity levels by modeling information asymmetry and herd behavior. The developed artificial markets are multi-assets, order-driven and populated by agents having heterogeneous behaviors and information. Agents are defined by their information and their herd behavior levels. Agents trade multiple risky assets based on their wealth, their behaviors and their available information which spread among multiple behavioral networks. In a novel contribution to artificial stock markets literature, agents’ behaviors modeling is mixed with social network simulation to reproduce different degrees of information asymmetry and herd behavior based on several assortative topologies. Several simulations validated the proposed model since univariate and multivariate stylized facts were reproduced both for mature and immature stock markets. The proposed artificial stock market can be considered as a first step toward decision and simulation tools for optimal management, strategy analysis and predictions evolution of immature stock markets.

Suggested Citation

  • Hazem Krichene & Mhamed-Ali El-Aroui, 2018. "Artificial stock markets with different maturity levels: simulation of information asymmetry and herd behavior using agent-based and network models," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(3), pages 511-535, October.
    • Handle: RePEc:spr:jeicoo:v:13:y:2018:i:3:d:10.1007_s11403-017-0191-6
    DOI: 10.1007/s11403-017-0191-6
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    Cited by:

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    2. Gao, Zhenbin & Zhang, Jie, 2023. "The fluctuation correlation between investor sentiment and stock index using VMD-LSTM: Evidence from China stock market," The North American Journal of Economics and Finance, Elsevier, vol. 66(C).
    3. Li, Jing & Liu, XiaoWen, 2024. "An agent-based simulation model for analyzing and optimizing omni-channel retailing operation decisions," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    4. Tang, Zhenpeng & Ran, Meng & Zhao, Yongxiang, 2020. "Stock trading dynamics and pedestrian counterflows: Analogies and differences," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    5. Ibrahim Filiz & Jan René Judek & Marco Lorenz & Markus Spiwoks, 2021. "Sticky Stock Market Analysts," JRFM, MDPI, vol. 14(12), pages 1-27, December.

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    More about this item

    Keywords

    Agent-based model; Multi-assets trading; Immature stock markets; Information asymmetry; Herd behavior; Assortativity;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • O16 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Financial Markets; Saving and Capital Investment; Corporate Finance and Governance

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