Matrix evolutions : synthetic correlations and explainable machine learning for constructing robust investment portfolios

Papenbrock, Jochen; Schwendner, Peter; Jaeger, Markus; Krügel, Stephan

doi:10.2139/ssrn.3663220

Publication type:	Working paper – expertise – study
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:	10.2139/ssrn.3663220
Extent:	20
Issue Date:	2020
Publisher / Ed. Institution:	SSRN
Language:	English
Subjects:	Machine learning; Portfolio optimization; Risk parity; XAI; Asset allocation; Explainable AI; Portfolia construction; Scenario analysis
Subject (DDC):	004: Computer science 332.6: Investment
Abstract:	In this paper we present a novel and highly flexible method to simulate correlation matrices of financial markets. It produces realistic outcomes regarding stylized facts of empirical correlation matrices and requires no asset return input data. The matrix generation is based on a multi-objective evolutionary algorithm so we call the approach ‘Matrix Evolutions’. It is suitable for parallel implementation and can be accelerated by graphics processing units (GPUs) and quantum-inspired algorithms. The approach can be used for pricing, hedging and trading correlation-based financial products. We demonstrate the potential of Matrix Evolutions in a machine learning case study for robust portfolio construction in a multi-asset universe. In this study we organize an explainable machine learning program to establish a link from the simulated matrices to relative investment performance. The training data consists of the synthetic matrices produced by Matrix Evolutions and an automatic labeling by Monte-Carlo simulation of the relative investment performance of the following two approaches for portfolio construction: the novel Hierarchical Risk Parity approach by Lopez de Prado (2016b) which is based on representation learning and the traditional equal risk contribution approach.
URI:	https://digitalcollection.zhaw.ch/handle/11475/22053
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

Files in This Item:

There are no files associated with this item.

Show full item record

Papenbrock, J., Schwendner, P., Jaeger, M., & Krügel, S. (2020). Matrix evolutions : synthetic correlations and explainable machine learning for constructing robust investment portfolios. SSRN. https://doi.org/10.2139/ssrn.3663220

Papenbrock, J. et al. (2020) Matrix evolutions : synthetic correlations and explainable machine learning for constructing robust investment portfolios. SSRN. Available at: https://doi.org/10.2139/ssrn.3663220.

J. Papenbrock, P. Schwendner, M. Jaeger, and S. Krügel, “Matrix evolutions : synthetic correlations and explainable machine learning for constructing robust investment portfolios,” SSRN, 2020. doi: 10.2139/ssrn.3663220.

PAPENBROCK, Jochen, Peter SCHWENDNER, Markus JAEGER und Stephan KRÜGEL, 2020. Matrix evolutions : synthetic correlations and explainable machine learning for constructing robust investment portfolios. SSRN

Papenbrock, Jochen, Peter Schwendner, Markus Jaeger, and Stephan Krügel. 2020. “Matrix Evolutions : Synthetic Correlations and Explainable Machine Learning for Constructing Robust Investment Portfolios.” SSRN. https://doi.org/10.2139/ssrn.3663220.

Papenbrock, Jochen, et al. Matrix Evolutions : Synthetic Correlations and Explainable Machine Learning for Constructing Robust Investment Portfolios. SSRN, 2020, https://doi.org/10.2139/ssrn.3663220.