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An agent-based model for designing a financial market that works well

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  • Takanobu Mizuta

Abstract

Designing a financial market that works well is very important for developing and maintaining an advanced economy, but is not easy because changing detailed rules, even ones that seem trivial, sometimes causes unexpected large impacts and side effects. A computer simulation using an agent-based model can directly treat and clearly explain such complex systems where micro processes and macro phenomena interact. Many effective agent-based models investigating human behavior have already been developed. Recently, an artificial market model, which is an agent-based model for a financial market, has started to contribute to discussions on rules and regulations of actual financial markets. I introduce an artificial market model to design financial markets that work well and describe a previous study investigating tick size reduction. I hope that more artificial market models will contribute to designing financial markets that work well to further develop and maintain advanced economies.

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  • Takanobu Mizuta, 2019. "An agent-based model for designing a financial market that works well," Papers 1906.06000, arXiv.org.
    • Handle: RePEc:arx:papers:1906.06000
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    1. Thierry Foucault & Albert J. Menkveld, 2008. "Competition for Order Flow and Smart Order Routing Systems," Journal of Finance, American Finance Association, vol. 63(1), pages 119-158, February.
    2. Lukas Menkhoff & Mark P. Taylor, 2007. "The Obstinate Passion of Foreign Exchange Professionals: Technical Analysis," Journal of Economic Literature, American Economic Association, vol. 45(4), pages 936-972, December.
    3. Braun-Munzinger, Karen & Liu, Zijun & Turrell, Arthur, 2016. "An agent-based model of dynamics in corporate bond trading," Bank of England working papers 592, Bank of England.
    4. Carl Chiarella & Giulia Iori, 2002. "A simulation analysis of the microstructure of double auction markets," Quantitative Finance, Taylor & Francis Journals, vol. 2(5), pages 346-353.
    5. J. Doyne Farmer & Duncan Foley, 2009. "The economy needs agent-based modelling," Nature, Nature, vol. 460(7256), pages 685-686, August.
    6. 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.
    7. Vince Darley & Alexander V Outkin, 2007. "A NASDAQ Market Simulation:Insights on a Major Market from the Science of Complex Adaptive Systems," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 6217.
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    Cited by:

    1. Masanori Hirano & Ryosuke Takata & Kiyoshi Izumi, 2023. "PAMS: Platform for Artificial Market Simulations," Papers 2309.10729, arXiv.org.
    2. Masanori Hirano & Kiyoshi Izumi & Hiroyasu Matsushima & Hiroki Sakaji, 2020. "Comparing Actual and Simulated HFT Traders' Behavior for Agent Design," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(3), pages 1-6.
    3. Masanori Hirano & Hiroki Sakaji & Kiyoshi Izumi, 2022. "Policy Gradient Stock GAN for Realistic Discrete Order Data Generation in Financial Markets," Papers 2204.13338, arXiv.org.
    4. Masanori Hirano & Kiyoshi Izumi & Takashi Shimada & Hiroyasu Matsushima & Hiroki Sakaji, 2020. "Impact Analysis of Financial Regulation on Multi-Asset Markets Using Artificial Market Simulations," JRFM, MDPI, vol. 13(4), pages 1-20, April.

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