Adaptive seriational risk parity and other extensions for heuristic portfolio construction using machine learning and graph theory

Schwendner, Peter; Papenbrock, Jochen; Jaeger, Markus; Krügel, Stephan

doi:10.3905/jfds.2021.1.078

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Adaptive seriational risk parity and other extensions for heuristic portfolio construction using machine learning and graph theory
Authors:	Schwendner, Peter Papenbrock, Jochen Jaeger, Markus Krügel, Stephan
et. al:	No
DOI:	10.3905/jfds.2021.1.078
Published in:	The Journal of Financial Data Science
Volume(Issue):	3
Issue:	4
Page(s):	65
Pages to:	83
Issue Date:	2021
Publisher / Ed. Institution:	Portfolio Management Research
ISSN:	2640-3943 2640-3951
Language:	English
Subjects:	Hierarchial risk parity; Hierarchial structure; Portfolio allocation; Seriation
Subject (DDC):	332.6: Investment
Abstract:	In 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.
URI:	https://digitalcollection.zhaw.ch/handle/11475/23563
Fulltext version:	Published version
License (according to publishing contract):	Licence according to publishing contract
Departement:	School of Management and Law
Organisational Unit:	Institute of Wealth & Asset Management (IWA)
Appears in collections:	Publikationen School of Management and Law

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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.