Study of the trading behavior on agent-based system simulation

In this paper, an artificial stock market based on Agent is built combined with system simulation technology. The heterogeneous investors will evolve and adapt to the environment through social learning in the artificial stock market. Public rule set is composed of the trading rule of each investor. And the public rule set evolves by genetic algorithm. This paper analyzes and researches the impact of social learning in the different learning speeds on financial market and micro-level investor.