Stock price formation: useful insights from a multi-agent reinforcement learning model

10 Oct 2019J. LussangeS. Bourgeois-GirondeS. PalminteriB. Gutkin

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 implement so-called noise traders to sub-optimally emulate price formation processes. However recent advances in the fields of neuroscience and machine learning have overall brought the possibility for new tools to the bottom-up statistical inference of complex systems... (read more)

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