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Building an artificial stock market populated by reinforcement‐learning agents

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  • Aleksandras Vytautas Rutkauskas
  • Tomas Ramanauskas
Abstract
In this paper we propose an artificial stock market model based on interaction of heterogeneous agents whose forward‐looking behaviour is driven by the reinforcement‐learning algorithm combined with some evolutionary selection mechanism. We use the model for the analysis of market self‐regulation abilities, market efficiency and determinants of emergent properties of the financial market. Distinctive and novel features of the model include strong emphasis on the economic content of individual decision‐making, application of the Q‐learning algorithm for driving individual behaviour, and rich market setup. Along with that a parallel version of the model is presented, which is mainly based on research of current changes in the market, as well as on search of newly emerged consistent patterns, and which has been repeatedly used for optimal decisions’ search experiments in various capital markets.

Suggested Citation

  • Aleksandras Vytautas Rutkauskas & Tomas Ramanauskas, 2009. "Building an artificial stock market populated by reinforcement‐learning agents," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 10(4), pages 329-341, September.
    • Handle: RePEc:taf:jbemgt:v:10:y:2009:i:4:p:329-341
    DOI: 10.3846/1611-1699.2009.10.329-341
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    References listed on IDEAS

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    1. Lettau, Martin, 1997. "Explaining the facts with adaptive agents: The case of mutual fund flows," Journal of Economic Dynamics and Control, Elsevier, vol. 21(7), pages 1117-1147, June.
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    3. Tomas Ramanauskas, 2009. "Agent-Based Financial Modelling: A Promising Alternative to the Standard Representative-Agent Approach," Bank of Lithuania Working Paper Series 3, Bank of Lithuania.
    4. Sethi, Rajiv & Franke, Reiner, 1995. "Behavioural Heterogeneity under Evolutionary Pressure: Macroeconomic Implications of Costly Optimisation," Economic Journal, Royal Economic Society, vol. 105(430), pages 583-600, May.
    5. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2007. "Agent-based Models of Financial Markets," Papers physics/0701140, arXiv.org.
    6. Lux, Thomas, 1995. "Herd Behaviour, Bubbles and Crashes," Economic Journal, Royal Economic Society, vol. 105(431), pages 881-896, July.
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    Cited by:

    1. Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Modelling crypto markets by multi-agent reinforcement learning," Papers 2402.10803, arXiv.org.

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