Purpose
The purpose of this paper is to explore the evolvement of investors’ behavior in stock market dynamically on the basis of non-cooperative strategy applied by investors in complex networks.
Design/methodology/approach
Using modeling and simulation research method, this study designs and conducts a mathematical modeling and its simulation experiment of financial market behavior according to research’s basic norms of complex system theory and methods. Thus the authors acquire needed and credible experimental data.
Findings
The conclusions drawn in this paper are as follows. The dynamical evolution of investors’ trading behavior is not only affected by the stock market network structure, but also by the risk dominance degree of certain behavior. The dynamics equilibrium of trading behavior’s evolvement is directly influenced by the risk dominance degree of certain behavior, connectivity degree and the heterogeneity of the stock market networks.
Research limitations/implications
This paper focuses on the dynamical evolvement of investors’ behavior on the basis of the hypothesis that common investors prefer to mimic their network neighbors’ behavior through different analysis by the strategy of anti-coordination game in complex network. While the investors’ preference and the beliefs among them are not easy to quantify, that is deterministic or stochastic as the environment changes, and is heterogeneous definitely. Thus, these limitations should be broken through in the future research.
Originality/value
This paper aims to address the dynamical evolvement of investors’ behavior in stock market networks on the principle of non-cooperative represented by anti-coordination game in networks for the first time, considering that investors prefer to mimic their network neighbors’ behavior through different analysis by the strategy of differential choosing in every time step. The methodology designed and used in this study is a pioneering and exploratory experiment.
Thermodynamic motif analysis for directed stock market networks
