Simulation of stock prices behavior based on cellular automata

The modeling theory and method of cellular automata are applied to the study on the price behavior in stock market. Based oil cellular automata, ail evolutionary model for stock prices behavior is formulated. The paper classifies different kinds of investors in the stock market into three groups, each group has its own strategy. The random movement in stock prices reflects people's strategy and random changes going on in the outside environment. According to the study of stock prices movements under different kinds of investor combination and influencing degree of the macro factors, the simulation results show that technical investors and the macro factors are the key factors to bring the stock price move up and down.