Learning and cognition in financial markets: A paradigm shift for agent-based models

Johann Lussange; Alexis Belianin; Sacha Bourgeois-Gironde; Boris Gutkin

doi:10.1007/978-3-030-55190-2_19

Learning and cognition in financial markets: A paradigm shift for agent-based models

Johann Lussange, Alexis Belianin, Sacha Bourgeois-Gironde, Boris Gutkin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Intelligent Systems and Applications - Proceedings of the 2020 Intelligent Systems Conference IntelliSys Volume 3
Editors	Kohei Arai, Supriya Kapoor, Rahul Bhatia
Publisher	Springer
Pages	241-255
Number of pages	15
ISBN (Print)	9783030551896
DOIs	https://doi.org/10.1007/978-3-030-55190-2_19
State	Published - 2021
Externally published	Yes
Event	Intelligent Systems Conference, IntelliSys 2020 - London, United Kingdom Duration: 3 Sep 2020 → 4 Sep 2020

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1252 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	Intelligent Systems Conference, IntelliSys 2020
Country/Territory	United Kingdom
City	London
Period	3/09/20 → 4/09/20

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2021.

Keywords

Agent-based models
Financial markets
Multi-agent systems
Reinforcement learning

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science

Access to Document

10.1007/978-3-030-55190-2_19

Cite this

Lussange, J., Belianin, A., Bourgeois-Gironde, S., & Gutkin, B. (2021). Learning and cognition in financial markets: A paradigm shift for agent-based models. In K. Arai, S. Kapoor, & R. Bhatia (Eds.), Intelligent Systems and Applications - Proceedings of the 2020 Intelligent Systems Conference IntelliSys Volume 3 (pp. 241-255). (Advances in Intelligent Systems and Computing; Vol. 1252 AISC). Springer. https://doi.org/10.1007/978-3-030-55190-2_19

Lussange, Johann ; Belianin, Alexis ; Bourgeois-Gironde, Sacha et al. / Learning and cognition in financial markets : A paradigm shift for agent-based models. Intelligent Systems and Applications - Proceedings of the 2020 Intelligent Systems Conference IntelliSys Volume 3. editor / Kohei Arai ; Supriya Kapoor ; Rahul Bhatia. Springer, 2021. pp. 241-255 (Advances in Intelligent Systems and Computing).

@inproceedings{eda234758b384683ad12725ce1450f98,

title = "Learning and cognition in financial markets: A paradigm shift for agent-based models",

abstract = "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.",

keywords = "Agent-based models, Financial markets, Multi-agent systems, Reinforcement learning",

author = "Johann Lussange and Alexis Belianin and Sacha Bourgeois-Gironde and Boris Gutkin",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2021.; Intelligent Systems Conference, IntelliSys 2020 ; Conference date: 03-09-2020 Through 04-09-2020",

year = "2021",

doi = "10.1007/978-3-030-55190-2_19",

language = "English",

isbn = "9783030551896",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer",

pages = "241--255",

editor = "Kohei Arai and Supriya Kapoor and Rahul Bhatia",

booktitle = "Intelligent Systems and Applications - Proceedings of the 2020 Intelligent Systems Conference IntelliSys Volume 3",

address = "Netherlands",

}

Lussange, J, Belianin, A, Bourgeois-Gironde, S & Gutkin, B 2021, Learning and cognition in financial markets: A paradigm shift for agent-based models. in K Arai, S Kapoor & R Bhatia (eds), Intelligent Systems and Applications - Proceedings of the 2020 Intelligent Systems Conference IntelliSys Volume 3. Advances in Intelligent Systems and Computing, vol. 1252 AISC, Springer, pp. 241-255, Intelligent Systems Conference, IntelliSys 2020, London, United Kingdom, 3/09/20. https://doi.org/10.1007/978-3-030-55190-2_19

Learning and cognition in financial markets: A paradigm shift for agent-based models. / Lussange, Johann; Belianin, Alexis; Bourgeois-Gironde, Sacha et al.
Intelligent Systems and Applications - Proceedings of the 2020 Intelligent Systems Conference IntelliSys Volume 3. ed. / Kohei Arai; Supriya Kapoor; Rahul Bhatia. Springer, 2021. p. 241-255 (Advances in Intelligent Systems and Computing; Vol. 1252 AISC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Learning and cognition in financial markets

T2 - Intelligent Systems Conference, IntelliSys 2020

AU - Lussange, Johann

AU - Belianin, Alexis

AU - Bourgeois-Gironde, Sacha

AU - Gutkin, Boris

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2021.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Agent-based models

KW - Financial markets

KW - Multi-agent systems

KW - Reinforcement learning

UR - http://www.scopus.com/inward/record.url?scp=85090097344&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-55190-2_19

DO - 10.1007/978-3-030-55190-2_19

M3 - Conference contribution

AN - SCOPUS:85090097344

SN - 9783030551896

T3 - Advances in Intelligent Systems and Computing

SP - 241

EP - 255

BT - Intelligent Systems and Applications - Proceedings of the 2020 Intelligent Systems Conference IntelliSys Volume 3

A2 - Arai, Kohei

A2 - Kapoor, Supriya

A2 - Bhatia, Rahul

PB - Springer

Y2 - 3 September 2020 through 4 September 2020

ER -

Lussange J, Belianin A, Bourgeois-Gironde S, Gutkin B. Learning and cognition in financial markets: A paradigm shift for agent-based models. In Arai K, Kapoor S, Bhatia R, editors, Intelligent Systems and Applications - Proceedings of the 2020 Intelligent Systems Conference IntelliSys Volume 3. Springer. 2021. p. 241-255. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-55190-2_19

Learning and cognition in financial markets: A paradigm shift for agent-based models

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this