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Recent advances in the fields of machine learning and neurofinance haveyielded new exciting research perspectives in practical inference ofbehavioural economy in financial markets and microstructure study. We herepresent the latest results from a recently published stock market simulatorbuilt around a multi-agent system architecture, in which each agent is anautonomous investor trading stocks by reinforcement learning (RL) via acentralised double-auction limit order book. The RL framework allows for theimplementation of specific behavioural and cognitive traits known to traderpsychology, and thus to study the impact of these traits on the whole stockmarket at the mesoscale. More precisely, we narrowed our agent design to threesuch psychological biases known to have a direct correspondence with RL theory,namely delay discounting, greed, and fear. We compared ensuing simulated datato real stock market data over the past decade or so, and find that marketstability benefits from larger populations of agents prone to delay discountingand most astonishingly, to greed.

Mesoscale impact of trader psychology on stock markets: a multi-agent AI approach