Agent-based simulations of financial markets: zero- and positive-intelligence models

To analyze the impact of intelligent traders with differing fundamental motivations on agent-based simulations of financial markets, we construct both zero-intelligence and positive-intelligence models of those markets using the MASON agent-based modeling framework. We exploit our software implementation of multifractal detrended fluctuation analysis (MF-DFA) to analyze the price paths generated by both simulation models as well as the price paths of selected stocks traded on the New York Stock Exchange. We study the changes in the models' macrolevel price paths when altering some of the microlevel agent behaviors; and we compare and contrast the multifractal properties of the zero- and positive-intelligence price paths with those properties of the selected real price paths. For the positive-intelligence and real price paths, we generally observed long-range dependence in the small-magnitude fluctuations and short-range dependence in the large-magnitude fluctuations. On the other hand, the zero-intelligence price paths failed to exhibit the multifractal properties seen in the selected real price paths.