RT Journal Article SR Electronic T1 Adaptive Seriational Risk Parity and Other Extensions for Heuristic Portfolio Construction Using Machine Learning and Graph Theory JF The Journal of Financial Data Science FD Institutional Investor Journals SP jfds.2021.1.078 DO 10.3905/jfds.2021.1.078 A1 Peter Schwendner A1 Jochen Papenbrock A1 Markus Jaeger A1 Stephan Krügel YR 2021 UL https://pm-research.com/content/early/2021/10/06/jfds.2021.1.078.abstract AB 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.Key Findings▪ The authors introduce the adaptive seriational risk parity (ASRP) framework as a hierarchy of decisions to implement the quasi-diagonalization step of hierarchical risk parity (HRP) with seriation-based and tree-based variations as alternatives to single linkage. Tree-based variations are further separated in static and adaptive versions. Altogether, 57 variations are discussed and connected to the literature.▪ Backtests of the 57 different HRP-type asset allocation variations applied to a multi-asset futures universe lead to a correlation matrix of the resulting 57 portfolio return time series. This portfolio return correlation matrix can be visualized as a dendrogram using single-linkage clustering. The correlation hierarchy reflected by the dendrogram is similar to the construction hierarchy of the quasi-diagonalization step. Most seriation-based strategies seem to underperform HRP on a risk-adjusted basis. Most static tree-based variations outperform HRP, whereas adaptive tree-based methods show mixed results.▪ The presented variations fit into a triple artificial intelligence approach to connect synthetic data generation with explainable machine learning. This approach generates synthetic market data in the first step. The second step applies an HRP-type portfolio allocation approach as discussed in this article. The third step uses a model-agnostic explanation such as the SHAP framework to explain the resulting performance with features of the synthetic market data and with model selection in the second step.