Sparse Portfolio Selection via Topological Data Analysis based Clustering

Anubha Goel, Damir Filipović and Puneet Pasricha
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Anubha Goel: Tampere University - Faculty of Information Technology and Communication Sciences
Damir Filipović: École Polytechnique Fédérale de Lausanne; Swiss Finance Institute
Puneet Pasricha: Indian Institute of Technology Ropar

No 24-07, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: 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. We conduct an empirical analysis on the S&P index from 2009 to 2020, including a study on the COVID-19 data to validate the robustness of our methodology. Our strategy to integrate TDA with the clustering algorithm significantly enhanced the performance of sparse portfolios across various performance measures in diverse market scenarios.

Keywords: Topological Data Analysis; Clustering; Index Tracking; Mean-Variance Portfolio; Global Minimum Variance Portfolio; Sparse Portfolios (search for similar items in EconPapers)
Pages: 36 pages
Date: 2024-01
New Economics Papers: this item is included in nep-cmp
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2407

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