Agent-Based Stock Market Simulation Model

In this paper we propose an artificial stock market model based on the interaction of heterogeneous agents whose forward-looking behaviour is driven by the reinforcement learning algorithm combined with an evolu¬tionary selection mechanism. We use the model for the analysis of market self-regulation abilities, market efficiency and determinants of emergent properties of the financial market. Novel features of the model include a strong empha¬sis on the economic content of individual decision-making, the application of the Q-learning algorithm for driving individual behaviour, and rich market setup. A parallel version of the model which is based on the research of current changes in the market as well as on the search for newly emerged consistent patterns and which has been repeatedly used for optimal decisions' search experiments in various capital markets is presented.