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Reinforcement Learning in Limit Order Markets

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Abstract

Information-based reinforcement learning is effective for trading and price discovery in limit order markets. It helps traders to learn a statistical equilibrium in which traders' expected payoffs and out-sample payoffs are highly correlated. Consistent with rational equilibrium models, the order choice between buy and sell and between market and limit orders for informed traders mainly depends on their information about fundamental value, while uninformed traders trade on a short-run momentum of the informed market orders. The learning increases liquidity supply of uninformed and liquidity consumption of informed, generating diagonal effect on order submission and hump-shaped order books, and improving traders' profitability and price discovery. The results shed a light into the market practice of using machine learning in limit order markets.

Suggested Citation

  • Xue-Zhong He & Shen Lin, 2019. "Reinforcement Learning in Limit Order Markets," Research Paper Series 403, Quantitative Finance Research Centre, University of Technology, Sydney.
    • Handle: RePEc:uts:rpaper:403
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    More about this item

    Keywords

    Reinforcement Learning; Order Book Information; Limit Orders; Momentum Trading;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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