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

Xiaqun Liu, Yaming Zhuang and Jinsheng Li

Complexity, 2020, vol. 2020, 1-13

Abstract:

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:3561538

DOI: 10.1155/2020/3561538

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