Simulation of stock market investor behavior based on bayesian learning and complex network

The increasing complexity of the financial system has increased the uncertainty of the market, which has led to the complexity of the evolution of limited rational investor behavior decisions. Moreover, it also has a negative effect on the market and affects the development of the real economy and social stability. In view of the interconnected characteristics of various elements presented in financial complexity, based on complex network theory, Bayesian learning theory and social learning theory, this study systematically describes the behavioral decision-making mechanism of individual investors and institutional investors from the perspective of network learning. In addition, this study builds an evolutionary model of investor behavior based on Bayesian learning strategies. According to the results of the horizontal and vertical bidirectional studies simulated by experiments, we can see that the method proposed in this study has a certain effect on the evaluation and decision support of stock market investment.