How to Identify Investor's types in real financial markets by means of agent based simulation

The paper proposes a computational adaptation of the principles underlying principal component analysis with agent based simulation in order to produce a novel modeling methodology for financial time series and financial markets. Goal of the proposed methodology is to find a reduced set of investor's models (agents) which is able to approximate or explain a target financial time series. As computational testbed for the study, the learning system L-FABS was chosen which combines simulated annealing with agent based simulation for approximating financial time series. Two experimental case studies showing the efficacy of the proposed methodology are reported.