Matrix Evolutions: Synthetic Correlations and Explainable Machine Learning for Constructing Robust Investment Portfolios

Jochen Papenbrock, Peter Schwendner, Markus Jaeger and Stephan Krügel The Journal of Financial Data Science Spring 2021, jfds.2021.1.056; DOI: https://doi.org/10.3905/jfds.2021.1.056

Posted: 2 Oct 2020 Last revised: 5 Apr 2021

See all articles by Jochen Papenbrock

Jochen Papenbrock

NVIDIA GmbH

Peter Schwendner

Zurich University of Applied Sciences

Markus Jaeger

Munich Reinsurance Company, Financial Solutions

Stephan Krügel

Munich Reinsurance Company, Financial Solutions

Date Written: July 29, 2020

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.

Keywords: Asset Allocation, Portfolio Construction Explainable AI, XAI, Machine Learning, Hierarchical Risk Parity, Monte Carlo, GPU, CUDA, Scenario Analysis, Simulation, Convex Optimization, Clustering, Risk-Based Optimization

JEL Classification: C15, G11, G17, G0, G1, G2, G15, G24, E44

Suggested Citation

Papenbrock, Jochen and Schwendner, Peter and Jaeger, Markus and Krügel, Stephan, Matrix Evolutions: Synthetic Correlations and Explainable Machine Learning for Constructing Robust Investment Portfolios (July 29, 2020). Jochen Papenbrock, Peter Schwendner, Markus Jaeger and Stephan Krügel The Journal of Financial Data Science Spring 2021, jfds.2021.1.056; DOI: https://doi.org/10.3905/jfds.2021.1.056, Available at SSRN: https://ssrn.com/abstract=3663220 or http://dx.doi.org/10.2139/ssrn.3663220

Jochen Papenbrock (Contact Author)

NVIDIA GmbH ( email )

Germany
+49-(0)1741435555 (Phone)

HOME PAGE: http://www.nvidia.com/en-us/industries/finance/

Peter Schwendner

Zurich University of Applied Sciences ( email )

School of Management and Law
Technoparkstrasse 2
Winterthur, CH 8401
Switzerland
8400 (Fax)

Markus Jaeger

Munich Reinsurance Company, Financial Solutions ( email )

Königinstr. 107
Munich, 80802
Germany

Stephan Krügel

Munich Reinsurance Company, Financial Solutions ( email )

Königinstr. 107
Munich, 80802
Germany

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