Financial Econometrics and Empirical Market Microstructure

Bera, Anil K.; Ivliev, Sergey; Lillo, Fabrizio

doi:10.1007/978-3-319-09946-0

Cited by 8 publications

References 179 publications

(257 reference statements)

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

Lussange

Belianin

Bourgeois‐Gironde

et al. 2020

Advances in Intelligent Systems and Computing

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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.

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

Lussange

Belianin

Bourgeois‐Gironde

et al. 2020

Advances in Intelligent Systems and Computing

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Modelling Stock Markets by Multi-agent Reinforcement Learning

et al. 2020

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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 agents trading via a centralised order book, in order to emulate complex and diverse market phenomena. These past financial models have all relied on so-called zero-intelligence agents, so that the crucial issues of agent information and learning, central to price formation and hence to all market activity, could not be properly assessed. In order to address this, we designed a next-generation MAS stock market simulator, in which each agent learns to trade autonomously via reinforcement learning. We

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