Interpretable Machine Learning for Diversified Portfolio Construction

Markus Jaeger, Stephan Krügel, Dimitri Marinelli, Jochen Papenbrock and Peter Schwendner. The Journal of Financial Data Science Summer 2021, jfds.2021.1.066; DOI: https://doi.org/10.3905/jfds.2021.1.066

Posted: 8 Jan 2021 Last revised: 24 Jun 2021

See all articles by Markus Jaeger

Markus Jaeger

Munich Reinsurance Company, Financial Solutions

Stephan Krügel

Munich Reinsurance Company, Financial Solutions

Dimitri Marinelli

Munich Re

Jochen Papenbrock

NVIDIA GmbH

Peter Schwendner

Zurich University of Applied Sciences

Date Written: November 13, 2020

Abstract

In this paper, 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 backtested 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.

Keywords: asset allocation, portfolio construction, explainable artificial intelligence, Hierarchical Risk Parity

JEL Classification: C15, G11, G17, G0, G1, G2, G15, G24, E44

Suggested Citation

Jaeger, Markus and Krügel, Stephan and Marinelli, Dimitri and Papenbrock, Jochen and Schwendner, Peter, Interpretable Machine Learning for Diversified Portfolio Construction (November 13, 2020). Markus Jaeger, Stephan Krügel, Dimitri Marinelli, Jochen Papenbrock and Peter Schwendner. The Journal of Financial Data Science Summer 2021, jfds.2021.1.066; DOI: https://doi.org/10.3905/jfds.2021.1.066, Available at SSRN: https://ssrn.com/abstract=3730144 or http://dx.doi.org/10.2139/ssrn.3730144

Markus Jaeger

Munich Reinsurance Company, Financial Solutions ( email )

Königinstr. 107
Munich, 80802
Germany

Stephan Krügel

Munich Reinsurance Company, Financial Solutions ( email )

Königinstr. 107
Munich, 80802
Germany

Dimitri Marinelli

Munich Re ( email )

Königinstr. 107
Munich, 80802
Germany

Jochen Papenbrock

NVIDIA GmbH ( email )

Germany
+49-(0)1741435555 (Phone)

HOME PAGE: http://www.nvidia.com/en-us/industries/finance/

Peter Schwendner (Contact Author)

Zurich University of Applied Sciences ( email )

School of Management and Law
Technoparkstrasse 2
Winterthur, CH 8401
Switzerland
8400 (Fax)

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