The chaos analysis of long memory process in artificial stock markets consist of multi-agents

We consider realistic settings of an artificial market from the viewpoint of a long memory process. With the aim of analyzing the mechanism of stock price change, we construct an artificial stock market composed of multiple agents whose investment strategies are represented by tree-shaped programs. The market is optimized using genetic programming so that the change of its stock price resembles that of a "real" stock market statistically. In order to perform an efficient optimization and analyze agents\´ behavior easily, we use ADG - automatically defined groups proposed previously. We show experimentally that complex changes in a real market appear in the proposed artificial market.