A Model for Evolution of Investors Behavior in Stock Market Based on Reinforcement Learning in Network

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...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2020, Vol.2020 (2020), p.1-13
Main Authors: Liu, Xiaqun, Li, Jinsheng, Zhuang, Yaming
Format: Article
Language:English
Subjects:
Behavior
Behavior evolution
Decision making
Evolution
Financial institutions
Financial markets
Heterogeneity
Information dissemination
Institutional investments
Investment
Investment policy
Investments
Investor behavior
Learning
Learning strategies
Learning theory
Mathematical analysis
Network topologies
Numerical analysis
Securities markets
Social networks
Stock exchanges
Stocks
Volatility
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Summary: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.
ISSN:1076-2787
1099-0526
DOI:10.1155/2020/3561538