Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Matrix evolutions : synthetic correlations and explainable machine learning for constructing robust investment portfolios
Authors: Papenbrock, Jochen
Schwendner, Peter
Jaeger, Markus
Krügel, Stephan
et. al: No
DOI: 10.3905/jfds.2021.1.056
Published in: The Journal of Financial Data Science
Volume(Issue): 3
Issue: 2
Page(s): 51
Pages to: 69
Issue Date: 13-Mar-2021
Publisher / Ed. Institution: Portfolio Management Research
ISSN: 2640-3943
Language: English
Subjects: Statistical method; Big data/machine learning; Performance measurement; Portfolio construction
Subject (DDC): 006: Special computer methods
332.6: Investment
Abstract: In this article, the authors present a novel and highly flexible concept to simulate correlation matrixes of financial markets. It produces realistic outcomes regarding stylized facts of empirical correlation matrixes and requires no asset return input data. The matrix generation is based on a multiobjective evolutionary algorithm, so the authors call the approach matrix evolutions. It is suitable for parallel implementation and can be accelerated by graphics processing units and quantum-inspired algorithms. The approach is useful for backtesting, pricing, and hedging correlation-dependent investment strategies and financial products. Its potential is demonstrated in a machine learning case study for robust portfolio construction in a multi-asset universe: An explainable machine learning program links the synthetic matrixes to the portfolio volatility spread of hierarchical risk parity versus equal risk contribution.
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Management and Law
Organisational Unit: Institute of Wealth & Asset Management (IWA)
Appears in collections:Publikationen School of Management and Law

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