Data processing in the imitational agent-based model of financial market

Knowledge of dynamic properties of processes, that take place in finance is important in applications for derivatives pricing and risk control. The widely used Black and Scholes Theory assumes geometric Brownian motion of underlying asset price. However, an attempt to provide a model, which better describes price movements often leads to impossibility of studying the results analytically. Approach, which uses imitational models to study the dynamics in a complex system is a promising one. This paper presents the imitational model of the financial market, which is agent based, and provides a posibility to apply pattern recognition algorithms to build and calibrate a decision making support system in the financial market