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Intraday trading patterns in an intelligent autonomous agent-based stock market

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  • Kluger, Brian D.
  • McBride, Mark E.

Abstract

Market microstructure studies of intraday trading patterns have established that there is a regular pattern of high volumes near both the open and close of the trading day. O'Hara (1995) points out the many difficulties in specifying all the necessary elements of a strategic model for determining and attaining an equilibrium describing intraday patterns. We develop an autonomous agent-based market microstructure simulation with both informed agents and uninformed liquidity-motivated agents. Both types of agents can learn when to trade, but are zero-intelligence on all other behavior. We do not impose an equilibrium concept but instead look for emergent behavior. Our results demonstrate that trading patterns can arise in such a model as a result of interactions between informed and uninformed agents even though the agents are non-strategic and not fully rational. As long as there is rudimentary social or individual learning, uninformed liquidity-motivated agents can coordinate to avoid trading with informed agents and suffering adverse selection losses. The extent and pattern of coordination between uninformed agents depends on the learning specification, the percentage of informed agents and the degree of cooperation/competition among the informed agents.

Suggested Citation

  • Kluger, Brian D. & McBride, Mark E., 2011. "Intraday trading patterns in an intelligent autonomous agent-based stock market," Journal of Economic Behavior & Organization, Elsevier, vol. 79(3), pages 226-245, August.
    • Handle: RePEc:eee:jeborg:v:79:y:2011:i:3:p:226-245
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    References listed on IDEAS

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    Cited by:

    1. Pyo, Dong-Jin, 2014. "A Multi-Factor Model of Heterogeneous Traders in a Dynamic Stock Market," Staff General Research Papers Archive 37358, Iowa State University, Department of Economics.
    2. Staccioli, Jacopo & Napoletano, Mauro, 2021. "An agent-based model of intra-day financial markets dynamics," Journal of Economic Behavior & Organization, Elsevier, vol. 182(C), pages 331-348.
    3. Bogner, Kristina, 2015. "The effect of project funding on innovative performance: An agent-based simulation model," Hohenheim Discussion Papers in Business, Economics and Social Sciences 10-2015, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    4. Pyo, Dong-Jin, 2015. "Animal spirits and stock market dynamics," ISU General Staff Papers 201501010800005596, Iowa State University, Department of Economics.
    5. repec:hal:spmain:info:hdl:2441/5mqflt6amg8gab4rlqn6sbko4b is not listed on IDEAS
    6. Chen, Shu-Heng, 2012. "Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 1-25.
    7. Dong-Jin Pyo, 2017. "A multi-factor model of heterogeneous traders in a dynamic stock market," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1416902-141, January.
    8. Ya-Chi Huang & Chueh-Yung Tsao, 2018. "Discovering Traders’ Heterogeneous Behavior in High-Frequency Financial Data," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 821-846, April.
    9. Lijian Wei & Wei Zhang & Xue-Zhong He & Yongjie Zhang, 2013. "Learning and Information Dissemination in Limit Order Markets," Research Paper Series 333, Quantitative Finance Research Centre, University of Technology, Sydney.
    10. Carl Chiarella & Xue-Zhong He & Lijian Wei, 2013. "Learning and Evolution of Trading Strategies in Limit Order Markets," Research Paper Series 335, Quantitative Finance Research Centre, University of Technology, Sydney.
    11. Chiarella, Carl & He, Xue-Zhong & Wei, Lijian, 2015. "Learning, information processing and order submission in limit order markets," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 245-268.
    12. Wood, Aaron D. & Mason, Charles F. & Finnoff, David, 2016. "OPEC, the Seven Sisters, and oil market dominance: An evolutionary game theory and agent-based modeling approach," Journal of Economic Behavior & Organization, Elsevier, vol. 132(PB), pages 66-78.
    13. Doris Neuberger & Roger Rissi, 2014. "Macroprudential Banking Regulation: Does One Size Fit All?," Journal of Banking and Financial Economics, University of Warsaw, Faculty of Management, vol. 1(1), pages 5-28, May.
    14. Lijian Wei & Xiong Xiong & Wei Zhang & Xue-Zhong He & Yongjie Zhang, 2017. "The effect of genetic algorithm learning with a classifier system in limit order markets," Published Paper Series 2017-3, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    15. Philip Z. Maymin, 2010. "Schizophrenic Representative Investors," Papers 1004.4592, arXiv.org.
    16. Arifovic, Jasmina & He, Xue-zhong & Wei, Lijian, 2022. "Machine learning and speed in high-frequency trading," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).

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