Reinforcement Learning Equilibrium in Limit Order Markets

This paper introduces an information-based reinforcement learning to exploit information channels to traders’ trading behavior in an equilibrium limit order market. Anticipating that informed traders are more likely to submit market buy (sell) orders when asset is significantly under (over) valued,...

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Bibliographic Details
Published inJournal of economic dynamics & control Vol. 144; p. 104497
Main Authors He, Xue-Zhong, Lin, Shen
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.11.2022
Subjects
and price discovery
endogenous liquidity provision
Limit order market
order chasing
reinforcement learning
C63
and price discovery
endogenous liquidity provision
G14
Limit order market
order chasing
D83
reinforcement learning
D82
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Summary:This paper introduces an information-based reinforcement learning to exploit information channels to traders’ trading behavior in an equilibrium limit order market. Anticipating that informed traders are more likely to submit market buy (sell) orders when asset is significantly under (over) valued, uninformed traders tend to chase market buy (sell) orders of the informed to buy (sell). To gain from the order chasing of the uninformed, informed traders strategically submit more market buy (sell) and limit sell (buy) orders. This amplifies the order chasing behaviour of the uninformed, generating predictable trading behaviours that can improve information efficiency but reduce market liquidity. Order book information and learning can have opposite effects on order choices and endogenous liquidity provision for the informed and uninformed. Furthermore, more informed trading is beneficial, but fast trading can be harmful for market quality.
ISSN:0165-1889
1879-1743
DOI:10.1016/j.jedc.2022.104497