Sparse Portfolio Selection via Topological Data Analysis based Clustering

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