Multifractal time series analysis of positive-intelligence agent-based simulations of financial markets

To analyze the impact of intelligent traders with differing fundamental motivations on agent-based simulations of financial markets, we extend the classical zero-intelligence model of financial markets to a positive-intelligence model using the MASON agent-based modeling framework. We exploit multifractal detrended fluctuation analysis (MF-DFA) to analyze the series of stock prices generated by the positive-intelligence simulation. We study the changes in this output process as analyzed by MF-DFA when altering the mix of agents with competing market philosophies; and we compare and contrast the results of fitting conventional time series models to such output processes with the results of applying MF-DFA to the same processes.