|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|
|Published in:||The Journal of Financial Data Science|
|Publisher / Ed. Institution:||Portfolio Management Research|
|Subjects:||Statistical method; Big data/machine learning; Performance measurement; Portfolio construction|
|Subject (DDC):||006: Special computer methods |
|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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