Modelling Stock Markets by Multi-agent Reinforcement Learning

Johann Lussange (), Ivan Lazarevich, Sacha Bourgeois-Gironde, Stefano Palminteri and Boris Gutkin
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Johann Lussange: École Normale Supérieure
Ivan Lazarevich: École Normale Supérieure
Stefano Palminteri: École Normale Supérieure
Boris Gutkin: École Normale Supérieure

Computational Economics, 2021, vol. 57, issue 1, No 6, 113-147

Abstract: Abstract 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.

Keywords: Agent-based; Reinforcement learning; Multi-agent system; Stock markets (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10614-020-10038-w

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