Learning and Cognition in Financial Markets: A Paradigm Shift for Agent-Based Models

Johann Lussange (1) , Alexis Belianin (2) , Sacha Bourgeois-Gironde (3) , Boris Gutkin (2, 1)

1 ENS-PSL - École normale supérieure - Paris
2 HSE - Vysšaja škola èkonomiki = National Research University Higher School of Economics [Moscow]
3 IJN - Institut Jean-Nicod

The history of research in finance and economics has been widely impacted by the field of Agent-based Computational Economics (ACE). While at the same time being popular among natural science researchers for its proximity to the successful methods of physics and chemistry for example, the field of ACE has also received critics by a part of the social science community for its lack of empiricism. Yet recent trends have shifted the weights of these general arguments and potentially given ACE a whole new range of realism. At the base of these trends are found two present-day major scientific breakthroughs: the steady shift of psychology towards a hard science due to the advances of neuropsychology, and the progress of reinforcement learning due to increasing computational power and big data. We outline here the main lines of a computational research study where each agent would trade by reinforcement learning.

Mots clés: Multi-agent systems; Reinforcement learning; Financial markets; Agent-based models

Domaines: Sciences cognitives

Johann Lussange, Alexis Belianin, Sacha Bourgeois-Gironde, Boris Gutkin. Learning and Cognition in Financial Markets: A Paradigm Shift for Agent-Based Models. Intelligent Systems and Applications Proceedings of the 2020 Intelligent Systems Conference, pp.241-255, 2021, ⟨10.1007/978-3-030-55190-2_19⟩. ⟨hal-03055057⟩