Sparse Portfolio Selection via Topological Data Analysis based Clustering. (arXiv:2401.16920v1 [q-fin.PM])

This paper uses topological data analysis (TDA) tools and introduces a
data-driven clustering-based stock selection strategy tailored for sparse
portfolio construction. Our asset selection strategy exploits the topological
features of stock price movements to select a subset of topologically similar
(different) assets for a sparse index tracking (Markowitz) portfolio. We
introduce new distance measures, which serve as an input to the clustering
algorithm, on the space of persistence diagrams and landscapes that consider
the time component of a time series. …

analysis arxiv clustering construction data data analysis data-driven exploits features fin index movements paper portfolio price q-fin.pm stock stock price strategy tools tracking via