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
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: Suggested Citation