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

January 2, 2021 by systems

Filippo Neri

Filippo Neri
Department of Electrical and Computer Engineering,
University of Naples, Naples

email:filippo.neri.email@gmail.com
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

pre-print available

Filed Under: Machine Learning

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