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

arXiv: https://arxiv.org/abs/2101.03127 · HTML: https://mlsanity.com/paper/2101.03127/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [Domain Specific Concept Drift Detectors for Predicting Financial Time Series](https://mlsanity.com/paper/2103.14079/) (2021)
- [Mapping Learning Algorithms on Data, a useful step for optimizing performances and their comparison](https://mlsanity.com/paper/2107.06981/) (2021)

## Abstract (arXiv)

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.