|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|
|Published in:||The Journal of Financial Data Science|
|Publisher / Ed. Institution:||Portfolio Management Research|
|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.|
|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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