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

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  • Filippo Neri

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.

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  • Filippo Neri, 2020. "How to Identify Investor's types in real financial markets by means of agent based simulation," Papers 2101.03127, arXiv.org.
    • Handle: RePEc:arx:papers:2101.03127
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