Towards modeling securities markets as a society of heterogeneous trading agents

In recent article, Farmer and Foley [3] claimed that the agent-based modeling may be a better way to help guide financial policies than traditional mathematical models. The authors argue that such models can accurately predict short periods ahead as long as the scenario remains almost the same, but fail in times of high volatility. Another real world problem that is rarely addressed in agent-based modeling is the fact that humans do not make decisions under risk strictly based on expected utility. This context inspired the goal of this work: modeling trading agents to populate an artificial market and use it to predict market price evolution in high and low volatility periods. We developed a set of simple trading agents and executed a set of simulated experiments to evaluate their performance. The simulated experiments showed that the artificial market prediction performance is better for low volatility periods than for higher volatility periods. This observation suggests that in high volatility period trading agent strategies are influenced by some other factor that is not present or is smaller in other period. These facts lead us to believe that in high volatility period human agents can be influenced by psychological biases. We also propose in this paper one simple trading agent model that includes prospect theory concepts in its decision making process. We intend to use such model in future work.