  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, we choose the learning system L
FABS which combines simulated annealing with agent based simulation for
approximating financial time series. We will also comment on how L FABS s
architecture could exploit parallel computation to scale when dealing with
massive agent simulations. Two experimental case studies showing the efficacy
of the proposed methodology are reported.
