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