This work concerns the modeling of evolvement of trading behavior in stock markets. Based on the assumption of the investors" limited rationality, the evolution mechanism of trading behavior is modeled according to the investment strategy of coordination game in network, that investors are prone to imitate their neighbors" activity through comprehensive analysis on the risk dominance degree of certain investment behavior, the network topology of their relationship and its heterogeneity. We investigate by mean-field analysis and extensive simulations the evolution of investors" trading behavior in various typical networks under different risk dominance degree of investment behavior. Our results indicate that the evolution of investors" behavior is affected by the network structure of stock market and the effect of risk dominance degree of investment behavior; the stability of equilibrium states of investors" behavior dynamics is directly related with the risk dominance degree of some behavior; connectivity and heterogeneity of the network plays an important role in the evolution of the investment behavior in stock market.
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.
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.
Interfirm cooperation can be seen as a significant and effective way for exploring radical innovation. In this article, a framework of interfirm cooperation, with a core manufacture and upstream counterparties in industry, and its evolving mechanism in the reverse-chain radical innovation are established from the perspective of the fundamental role played by knowledge collaboration. Then, an evolution model of interfirm cooperation is constructed on the theory of vibration mechanics, and its evolutionary dynamics is explored through numerical and simulation analysis mainly on the key factors of knowledge potential difference and knowledge rent-seeking behaviour within the firms. The findings show that, if there is no knowledge-based rent-seeking behaviour from the upstream firms, the probable innovative performance from the interfirm cooperation should vary for the knowledge potential difference between the cooperative firms, but can come to a certain equilibrium state. Meanwhile, if the knowledge rent-seeking behaviour does exist, knowledge potential difference would lead the innovative performance evolving ultimately in divergence. What’s more, the negative effect caused by the rent-seeking behaviour could be alleviated or weakened to some extent by the excitation mechanisms presented by the core firms in the cooperation system. Therefore, the drawn conclusions should be useful for the core manufactures’ implementing various strategies to maintain or enhance the cooperation for radical innovation in industry.
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