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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