A Model for Evolution of Investors Behavior in Stock Market Based on Reinforcement Learning in Network
Joint Authors
Zhuang, Yaming
Liu, Xiaqun
Li, Jinsheng
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-28
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
This paper builds an evolution model of investors behavior based on the reinforcement learning in multiplex networks.
Due to the heterogeneity of learning characteristics of bounded rational investors in investment decisions, we consider, respectively, the evolution mechanism of individual investors and institutional investors on the complex network theory and reinforcement learning theory.
We perform mathematical analysis and simulation to further explain the evolution characteristics of investors behavior.
The conclusions are drawn as follows: First, the intensity of returns competition among institutional investors and the forgetting effect both have an impact on the equilibrium of their evolution as to all institutional investors and individual investors.
Second, the network topology significantly affects the behavioral evolution of individual investors compared with institutional investors.
American Psychological Association (APA)
Liu, Xiaqun& Zhuang, Yaming& Li, Jinsheng. 2020. A Model for Evolution of Investors Behavior in Stock Market Based on Reinforcement Learning in Network. Complexity،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1141514
Modern Language Association (MLA)
Liu, Xiaqun…[et al.]. A Model for Evolution of Investors Behavior in Stock Market Based on Reinforcement Learning in Network. Complexity No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1141514
American Medical Association (AMA)
Liu, Xiaqun& Zhuang, Yaming& Li, Jinsheng. A Model for Evolution of Investors Behavior in Stock Market Based on Reinforcement Learning in Network. Complexity. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1141514
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141514