7 citations · 10 across the 3 of their papers we have counts for
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q-fin.TR2019★ 1 cited
Stock market microstructure inference via multi-agent reinforcement learning
J. Lussange, I. Lazarevich, S. Bourgeois-Gironde +2
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 age…
q-fin.TR2019★ 2 cited
Stock price formation: useful insights from a multi-agent reinforcement learning model
J. Lussange, S. Bourgeois-Gironde, S. Palminteri +1
In the past, financial stock markets have been studied with previous generations of multi-agent systems (MAS) that relied on zero-intelligence agents, and often the necessity to im…