Modelling Stock Markets by Multi-agent Reinforcement Learning

Johann Lussange, Ivan Lazarevich, Sacha Bourgeois-Gironde, Stefano Palminteri, Boris Gutkin

Research output: Contribution to journalArticlepeer-review


Quantitative finance has had a long tradition of a bottom-up approach to complex systems inference via multi-agent systems (MAS). These statistical tools are based on modelling agents trading via a centralised order book, in order to emulate complex and diverse market phenomena. These past financial models have all relied on so-called zero-intelligence agents, so that the crucial issues of agent information and learning, central to price formation and hence to all market activity, could not be properly assessed. In order to address this, we designed a next-generation MAS stock market simulator, in which each agent learns to trade autonomously via reinforcement learning. We calibrate the model to real market data from the London Stock Exchange over the years 2007 to 2018, and show that it can faithfully reproduce key market microstructure metrics, such as various price autocorrelation scalars over multiple time intervals. Agent learning thus enables accurate emulation of the market microstructure as an emergent property of the MAS.

Original languageAmerican English
Pages (from-to)113-147
Number of pages35
JournalComputational Economics
Issue number1
StatePublished - Jan 2021
Externally publishedYes


  • Agent-based
  • Multi-agent system
  • Reinforcement learning
  • Stock markets

All Science Journal Classification (ASJC) codes

  • Economics, Econometrics and Finance (miscellaneous)
  • Computer Science Applications


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