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  2. School of Management and Law
  3. Publikationen
  4. Publikationen School of Management and Law
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dc.contributor.authorPapenbrock, Jochen-
dc.contributor.authorSchwendner, Peter-
dc.contributor.authorJaeger, Markus-
dc.contributor.authorKrügel, Stephan-
dc.date.accessioned2021-04-29T08:27:41Z-
dc.date.available2021-04-29T08:27:41Z-
dc.date.issued2021-03-13-
dc.identifier.issn2640-3943de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/22348-
dc.description.abstractIn 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.de_CH
dc.language.isoende_CH
dc.publisherPortfolio Management Researchde_CH
dc.relation.ispartofThe Journal of Financial Data Sciencede_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectStatistical methodde_CH
dc.subjectBig data/machine learningde_CH
dc.subjectPerformance measurementde_CH
dc.subjectPortfolio constructionde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc332.6: Investitionde_CH
dc.titleMatrix evolutions : synthetic correlations and explainable machine learning for constructing robust investment portfoliosde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Management and Lawde_CH
zhaw.organisationalunitInstitut für Wealth & Asset Management (IWA)de_CH
dc.identifier.doi10.3905/jfds.2021.1.056de_CH
zhaw.funding.euNode_CH
zhaw.issue2de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end69de_CH
zhaw.pages.start51de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume3de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Papenbrock, J., Schwendner, P., Jaeger, M., & Krügel, S. (2021). Matrix evolutions : synthetic correlations and explainable machine learning for constructing robust investment portfolios. The Journal of Financial Data Science, 3(2), 51–69. https://doi.org/10.3905/jfds.2021.1.056
Papenbrock, J. et al. (2021) ‘Matrix evolutions : synthetic correlations and explainable machine learning for constructing robust investment portfolios’, The Journal of Financial Data Science, 3(2), pp. 51–69. Available at: https://doi.org/10.3905/jfds.2021.1.056.
J. Papenbrock, P. Schwendner, M. Jaeger, and S. Krügel, “Matrix evolutions : synthetic correlations and explainable machine learning for constructing robust investment portfolios,” The Journal of Financial Data Science, vol. 3, no. 2, pp. 51–69, Mar. 2021, doi: 10.3905/jfds.2021.1.056.
PAPENBROCK, Jochen, Peter SCHWENDNER, Markus JAEGER und Stephan KRÜGEL, 2021. Matrix evolutions : synthetic correlations and explainable machine learning for constructing robust investment portfolios. The Journal of Financial Data Science. 13 März 2021. Bd. 3, Nr. 2, S. 51–69. DOI 10.3905/jfds.2021.1.056
Papenbrock, Jochen, Peter Schwendner, Markus Jaeger, and Stephan Krügel. 2021. “Matrix Evolutions : Synthetic Correlations and Explainable Machine Learning for Constructing Robust Investment Portfolios.” The Journal of Financial Data Science 3 (2): 51–69. https://doi.org/10.3905/jfds.2021.1.056.
Papenbrock, Jochen, et al. “Matrix Evolutions : Synthetic Correlations and Explainable Machine Learning for Constructing Robust Investment Portfolios.” The Journal of Financial Data Science, vol. 3, no. 2, Mar. 2021, pp. 51–69, https://doi.org/10.3905/jfds.2021.1.056.


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