Reinforcement Learning in Limit Order Markets

Xuezhong (Tony) He (tonyxhe@gmail.com) and Shen Lin

No 403, Research Paper Series from Quantitative Finance Research Centre, University of Technology, Sydney

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.

Keywords: Reinforcement Learning; Order Book Information; Limit Orders; Momentum Trading (search for similar items in EconPapers)
JEL-codes: C63 D82 D83 G14 (search for similar items in EconPapers)
Pages: 76 pages
Date: 2019-02-01
New Economics Papers: this item is included in nep-big, nep-cmp and nep-mst
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