Abstract

The paper analyses the predictive power of heterogeneous agents interacting in an evolutionary agent-based model of the stock market when simulated through Altreva Adaptive Modeler multi-agent simulation software application. The paper tests the long-term prediction accuracy of an evolutionary agent-based model when simulating the S&P500 stock market index. The model incorporates 2,000 agents which trade amongst each other on an artificial stock market which uses a double auction trading mechanism. Within the evolutionary agent-based model, the population of agents is continuously adapting and evolving by using genetic programming in order to obtain new agents with better trading strategies generated from combining the trading strategies of the best performing agents and thus replacing the agents which have the worst performing trading strategies.

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