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

• URL: http://arxiv.org/abs/2101.03127v1
• Date: Thu, 31 Dec 2020 16:22:30 GMT
• Title: How to Identify Investor's types in real financial markets by means of agent based simulation
• Authors: Filippo Neri
• Abstract summary: The paper proposes a computational adaptation of the principles underlying principal component analysis with agent based simulation. The proposed methodology is to find a reduced set of investor s models which is able to approximate or explain a target financial time series.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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