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

Filippo Neri

Papers from arXiv.org

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.

Date: 2020-12
New Economics Papers: this item is included in nep-cmp, nep-hme and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2101.03127 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2101.03127

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().