Stock Price Formation: Precepts from a Multi-Agent Reinforcement Learning Model

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

Computational Economics, 2023, vol. 61, issue 4, No 7, 1523-1544

Abstract: Abstract In the past, the bottom-up study of financial stock markets relied on first-generation multi-agent systems (MAS) , which employed zero-intelligence agents and often required the additional implementation of so-called noise traders to emulate price formation processes. Nowadays, thanks to the tools developed in cognitive science and machine learning, MAS can quantitatively gauge agent learning, a pivotal element for information and stock price estimation in finance. In our previous work, we therefore devised a new generation MAS stock market simulator , which implements two key features: firstly, each agent autonomously learns to perform price forecasting and stock trading via model-free reinforcement learning ; secondly, all agents ’ trading decisions feed a centralised double-auction limit order book, emulating price and volume microstructures. Here, we study which trading strategies (represented as reinforcement learning policies) the agents learn and the time-dependency of their heterogeneity. Our central result is that there are more ways to succeed in trading than to fail. More specifically, we find that : i- better-performing agents learn in time more diverse trading strategies than worse-performing ones, ii- they tend to employ a fundamentalist, rather than chartist, approach to asset price valuation, and iii- their transaction orders are less stringent (i.e. larger bids or lower asks).

Keywords: Agent-based; Reinforcement learning; Multi-agent system; Stock markets (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10614-022-10249-3

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