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dc.contributor.authorPapenbrock, Jochen-
dc.contributor.authorSchwendner, Peter-
dc.contributor.authorJaeger, Markus-
dc.contributor.authorKrügel, Stephan-
dc.date.accessioned2021-03-15T10:07:45Z-
dc.date.available2021-03-15T10:07:45Z-
dc.date.issued2020-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/22053-
dc.description.abstractIn 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.de_CH
dc.format.extent20de_CH
dc.language.isoende_CH
dc.publisherSSRNde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectMachine learningde_CH
dc.subjectPortfolio optimizationde_CH
dc.subjectRisk parityde_CH
dc.subjectXAIde_CH
dc.subjectAsset allocationde_CH
dc.subjectExplainable AIde_CH
dc.subjectPortfolia constructionde_CH
dc.subjectScenario analysisde_CH
dc.subject.ddc004: Informatikde_CH
dc.subject.ddc332.6: Investitionde_CH
dc.titleMatrix evolutions : synthetic correlations and explainable machine learning for constructing robust investment portfoliosde_CH
dc.typeWorking Paper – Gutachten – Studiede_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Management and Lawde_CH
zhaw.organisationalunitInstitut für Wealth & Asset Management (IWA)de_CH
dc.identifier.doi10.2139/ssrn.3663220de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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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.


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