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  2. School of Management and Law
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  4. Publikationen School of Management and Law
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dc.contributor.authorJaeger, Markus-
dc.contributor.authorKrügel, Stephan-
dc.contributor.authorMarinelli, Dimitri-
dc.contributor.authorPapenbrock, Jochen-
dc.contributor.authorSchwendner, Peter-
dc.date.accessioned2021-08-26T10:24:53Z-
dc.date.available2021-08-26T10:24:53Z-
dc.date.issued2021-
dc.identifier.issn2640-3943de_CH
dc.identifier.issn2640-3951de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/23004-
dc.description.abstractIn this article, the authors construct a pipeline to benchmark hierarchical risk parity (HRP) relative to equal risk contribution (ERC) as examples of diversification strategies allocating to liquid multi-asset futures markets with dynamic leverage (volatility target). The authors use interpretable machine learning concepts (explainable AI) to compare the robustness of the strategies and to back out implicit rules for decision-making. The empirical dataset consists of 17 equity index, government bond, and commodity futures markets across 20 years. The two strategies are back tested for the empirical dataset and for about 100,000 bootstrapped datasets. XGBoost is used to regress the Calmar ratio spread between the two strategies against features of the bootstrapped datasets. Compared to ERC, HRP shows higher Calmar ratios and better matches the volatility target. Using Shapley values, the Calmar ratio spread can be attributed especially to univariate drawdown measures of the asset classes.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.subjectBig datade_CH
dc.subjectMachine learningde_CH
dc.subjectPerformance measurementde_CH
dc.subjectStatistical methodde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc332.6: Investitionde_CH
dc.titleInterpretable machine learning for diversified portfolio constructionde_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.2021.1.066de_CH
zhaw.funding.euinfo:eu-repo/grantAgreement/EC/H2020/825215//A FINancial supervision and TECHnology compliance training programme/FIN-TECHde_CH
zhaw.issue3de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end51de_CH
zhaw.pages.start31de_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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Jaeger, M., Krügel, S., Marinelli, D., Papenbrock, J., & Schwendner, P. (2021). Interpretable machine learning for diversified portfolio construction. The Journal of Financial Data Science, 3(3), 31–51. https://doi.org/10.3905/jfds.2021.1.066
Jaeger, M. et al. (2021) ‘Interpretable machine learning for diversified portfolio construction’, The Journal of Financial Data Science, 3(3), pp. 31–51. Available at: https://doi.org/10.3905/jfds.2021.1.066.
M. Jaeger, S. Krügel, D. Marinelli, J. Papenbrock, and P. Schwendner, “Interpretable machine learning for diversified portfolio construction,” The Journal of Financial Data Science, vol. 3, no. 3, pp. 31–51, 2021, doi: 10.3905/jfds.2021.1.066.
JAEGER, Markus, Stephan KRÜGEL, Dimitri MARINELLI, Jochen PAPENBROCK und Peter SCHWENDNER, 2021. Interpretable machine learning for diversified portfolio construction. The Journal of Financial Data Science. 2021. Bd. 3, Nr. 3, S. 31–51. DOI 10.3905/jfds.2021.1.066
Jaeger, Markus, Stephan Krügel, Dimitri Marinelli, Jochen Papenbrock, and Peter Schwendner. 2021. “Interpretable Machine Learning for Diversified Portfolio Construction.” The Journal of Financial Data Science 3 (3): 31–51. https://doi.org/10.3905/jfds.2021.1.066.
Jaeger, Markus, et al. “Interpretable Machine Learning for Diversified Portfolio Construction.” The Journal of Financial Data Science, vol. 3, no. 3, 2021, pp. 31–51, https://doi.org/10.3905/jfds.2021.1.066.


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