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Building an Artificial Stock Market Populated by Reinforcement-Learning Agents

Author

Listed:
  • Tomas Ramanauskas

    (Bank of Lithuania)

  • Aleksandras Vytautas Rutkauskas

    (Vilnius Gediminas Technical University)

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.

Suggested Citation

  • Tomas Ramanauskas & Aleksandras Vytautas Rutkauskas, 2009. "Building an Artificial Stock Market Populated by Reinforcement-Learning Agents," Bank of Lithuania Working Paper Series 6, Bank of Lithuania.
    • Handle: RePEc:lie:wpaper:6
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    File URL: https://www.lb.lt/en/publications/no-6-building-an-artificial-stock-market-populated-by-reinforcement-learning-agents
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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.

    More about this item

    Keywords

    agent-based financial modelling; artificial stock market; complex dynamical system; emergent properties; market efficiency; agent heterogeneity; reinforcement learning;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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