@article {Raffinot89, author = {Thomas Raffinot}, title = {Hierarchical Clustering-Based Asset Allocation}, volume = {44}, number = {2}, pages = {89--99}, year = {2017}, doi = {10.3905/jpm.2018.44.2.089}, publisher = {Institutional Investor Journals Umbrella}, abstract = {This article proposes a hierarchical clustering-based asset allocation method, which uses graph theory and machine learning techniques. Hierarchical clustering refers to the formation of a recursive clustering, suggested by the data, not defined a priori. Several hierarchical clustering methods are presented and tested. Once the assets are hierarchically clustered, the authors compute a simple and efficient capital allocation within and across clusters of assets, so that many correlated assets receive the same total allocation as a single uncorrelated one. The out-of-sample performances of hierarchical clustering-based portfolios and more traditional risk-based portfolios are evaluated across three disparate datasets, which differ in term of the number of assets and the assets{\textquoteright} composition. To avoid data snooping, the authors assess the comparison of profit measures using the bootstrap-based model confidence set procedure. Their empirical results indicate that hierarchical clustering-based portfolios are robust and truly diversified and achieve statistically better risk-adjusted performances than commonly used portfolio optimization techniques.TOPICS: Big data/machine learning, portfolio management/multi-asset allocation}, issn = {0095-4918}, URL = {https://jpm.pm-research.com/content/44/2/89}, eprint = {https://jpm.pm-research.com/content/44/2/89.full.pdf}, journal = {The Journal of Portfolio Management} }