How to Identify Investor's types in real financial markets by means of agent based simulation
This research aims to provide a novel method for identifying investor types in real financial markets, which could benefit financial analysts and modelers by simplifying complex market dynamics.
This paper introduces a new modeling methodology for financial time series and markets, combining principal component analysis with agent-based simulation. The goal is to identify a reduced set of investor models that can approximate or explain a target financial time series, demonstrated through two experimental case studies.
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.