Reinforcement Learning Equilibrium in Limit Order Markets

Xue Zhong He, Shen Lin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number104497
JournalJournal of Economic Dynamics and Control
Volume144
DOIs
Publication statusPublished - Nov 2022

Keywords

  • Limit order market
  • and price discovery
  • endogenous liquidity provision
  • order chasing
  • reinforcement learning

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