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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.accessioned2021-03-15T10:09:07Z-
dc.date.available2021-03-15T10:09:07Z-
dc.date.issued2020-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/22054-
dc.description.abstractIn this paper, we construct a pipeline to investigate heuristic diversification strategies in asset allocation. We use machine learning concepts (“explainable AI”) to compare the robustness of different strategies and back out implicit rules for decision making. In a first step, we augment the asset universe (the empirical dataset) with a range of scenarios generated with a block bootstrap from the empirical dataset. Second, we backtest the candidate strategies over a long period of time, checking their performance variability. Third, we use XGBoost as a regression model to connect the difference between the measured performances between two strategies to a pool of statistical features of the portfolio universe tailored to the investigated strategy. Finally, we employ the concept of Shapley values to extract the relationships that the model could identify between the portfolio characteristics and the statistical properties of the asset universe. On the basis of this information, we discuss the similarity between the bootstrapped datasets characterized by their Shapley values using a network technique. We test this pipeline for studying risk-parity strategies with a volatility target, and in particular, comparing the machine learning-driven Hierarchical Risk Parity (HRP) to the classical Equal Risk Contribution (ERC) strategy. In the augmented dataset built from a multi-asset investment universe of commodities, equities and fixed income futures, we find that HRP better matches the volatility target, and shows better risk-adjusted performances. Finally, we train XGBoost to learn the difference between the realized Calmar ratios of HRP and ERC and extract explanations. The explanations provide fruitful ex-post indications of the connection between the statistical properties of the universe and the strategy performance in the training set. For example, the model confirms that features addressing the hierarchical properties of the universe are connected to the relative performance of HRP respect to ERC.de_CH
dc.format.extent19de_CH
dc.language.isoende_CH
dc.publisherSSRNde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectHRPde_CH
dc.subjectMachine learningde_CH
dc.subjectRisk parityde_CH
dc.subjectXAIde_CH
dc.subjectAsset allocationde_CH
dc.subjectExplainable AIde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc332.6: Investitionde_CH
dc.titleUnderstanding machine learning for diversified portfolio construction by explainable AIde_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.3528616de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Jaeger, M., Krügel, S., Marinelli, D., Papenbrock, J., & Schwendner, P. (2020). Understanding machine learning for diversified portfolio construction by explainable AI. SSRN. https://doi.org/10.2139/ssrn.3528616
Jaeger, M. et al. (2020) Understanding machine learning for diversified portfolio construction by explainable AI. SSRN. Available at: https://doi.org/10.2139/ssrn.3528616.
M. Jaeger, S. Krügel, D. Marinelli, J. Papenbrock, and P. Schwendner, “Understanding machine learning for diversified portfolio construction by explainable AI,” SSRN, 2020. doi: 10.2139/ssrn.3528616.
JAEGER, Markus, Stephan KRÜGEL, Dimitri MARINELLI, Jochen PAPENBROCK und Peter SCHWENDNER, 2020. Understanding machine learning for diversified portfolio construction by explainable AI. SSRN
Jaeger, Markus, Stephan Krügel, Dimitri Marinelli, Jochen Papenbrock, and Peter Schwendner. 2020. “Understanding Machine Learning for Diversified Portfolio Construction by Explainable AI.” SSRN. https://doi.org/10.2139/ssrn.3528616.
Jaeger, Markus, et al. Understanding Machine Learning for Diversified Portfolio Construction by Explainable AI. SSRN, 2020, https://doi.org/10.2139/ssrn.3528616.


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