Filippo Neri
Department of Electrical and Computer Engineering,
University of Naples, Naples
email:[email protected]
December 31, 2020
Abstract
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.
Keywords: Computing methodologies Artificial intelligence,
Computing methodologies Learning paradigms, Applied
computing Economics