Incorporating swarm behavior into the adaptation mechanism of an order-driven artificial financial market

Agent-based artificial financial markets are an area of increasing interest in computational finance. Recent work by LeBaron and Yamamoto proposes an order-driven market model based on evolutionary algorithm based artificial agents. In this paper, we present a mechanism for incorporating elements of swarm intelligence into this model, and find that our model produces market price behavior that, in some ways, is closer to that of real financial markets.