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dc.contributor.authorSchwendner, Peter-
dc.contributor.authorPapenbrock, Jochen-
dc.contributor.authorJaeger, Markus-
dc.contributor.authorKrügel, Stephan-
dc.date.accessioned2021-11-29T09:06:29Z-
dc.date.available2021-11-29T09:06:29Z-
dc.date.issued2021-
dc.identifier.issn2640-3943de_CH
dc.identifier.issn2640-3951de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/23563-
dc.description.abstractIn this article, the authors present a conceptual framework named adaptive seriational risk parity (ASRP) to extend hierarchical risk parity (HRP) as an asset allocation heuristic. The first step of HRP (quasi-diagonalization), determining the hierarchy of assets, is required for the actual allocation done in the second step (recursive bisectioning). In the original HRP scheme, this hierarchy is found using single-linkage hierarchical clustering of the correlation matrix, which is a static tree-based method. The authors compare the performance of the standard HRP with other static and adaptive tree-based methods, as well as seriation-based methods that do not rely on trees. Seriation is a broader concept allowing reordering of the rows or columns of a matrix to best express similarities between the elements. Each discussed variation leads to a different time series reflecting portfolio performance using a 20-year backtest of a multi-asset futures universe. Unsupervised learningbased on these time-series creates a taxonomy that groups the strategies in high correspondence to the construction hierarchy of the various types of ASRP. Performance analysis of the variations shows that most of the static tree-based alternatives to HRP outperform the single-linkage clustering used in HRP on a risk-adjusted basis. Adaptive tree methods show mixed results, and most generic seriation-based approaches underperform.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.subjectHierarchial risk parityde_CH
dc.subjectHierarchial structurede_CH
dc.subjectPortfolio allocationde_CH
dc.subjectSeriationde_CH
dc.subject.ddc332.6: Investitionde_CH
dc.titleAdaptive seriational risk parity and other extensions for heuristic portfolio construction using machine learning and graph theoryde_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.2021.1.078de_CH
zhaw.funding.euNode_CH
zhaw.issue4de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end83de_CH
zhaw.pages.start65de_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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Schwendner, P., Papenbrock, J., Jaeger, M., & Krügel, S. (2021). Adaptive seriational risk parity and other extensions for heuristic portfolio construction using machine learning and graph theory. The Journal of Financial Data Science, 3(4), 65–83. https://doi.org/10.3905/jfds.2021.1.078
Schwendner, P. et al. (2021) ‘Adaptive seriational risk parity and other extensions for heuristic portfolio construction using machine learning and graph theory’, The Journal of Financial Data Science, 3(4), pp. 65–83. Available at: https://doi.org/10.3905/jfds.2021.1.078.
P. Schwendner, J. Papenbrock, M. Jaeger, and S. Krügel, “Adaptive seriational risk parity and other extensions for heuristic portfolio construction using machine learning and graph theory,” The Journal of Financial Data Science, vol. 3, no. 4, pp. 65–83, 2021, doi: 10.3905/jfds.2021.1.078.
SCHWENDNER, Peter, Jochen PAPENBROCK, Markus JAEGER und Stephan KRÜGEL, 2021. Adaptive seriational risk parity and other extensions for heuristic portfolio construction using machine learning and graph theory. The Journal of Financial Data Science. 2021. Bd. 3, Nr. 4, S. 65–83. DOI 10.3905/jfds.2021.1.078
Schwendner, Peter, Jochen Papenbrock, Markus Jaeger, and Stephan Krügel. 2021. “Adaptive Seriational Risk Parity and Other Extensions for Heuristic Portfolio Construction Using Machine Learning and Graph Theory.” The Journal of Financial Data Science 3 (4): 65–83. https://doi.org/10.3905/jfds.2021.1.078.
Schwendner, Peter, et al. “Adaptive Seriational Risk Parity and Other Extensions for Heuristic Portfolio Construction Using Machine Learning and Graph Theory.” The Journal of Financial Data Science, vol. 3, no. 4, 2021, pp. 65–83, https://doi.org/10.3905/jfds.2021.1.078.


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