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

TL;DR

This paper introduces a novel agent-based simulation methodology inspired by principal component analysis to identify investor types in financial markets, demonstrated through two case studies.

Contribution

It develops a new modeling approach combining PCA principles with agent-based simulation for financial time series analysis.

Findings

Effective approximation of financial time series

Scalable architecture leveraging parallel computation

Successful case studies demonstrating methodology efficacy

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.