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Sparse Portfolio Selection via Topological Data Analysis based Clustering

Author

Listed:
  • 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)

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.

Suggested Citation

  • Anubha Goel & Damir Filipović & Puneet Pasricha, 2024. "Sparse Portfolio Selection via Topological Data Analysis based Clustering," Swiss Finance Institute Research Paper Series 24-07, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2407
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    Keywords

    Topological Data Analysis; Clustering; Index Tracking; Mean-Variance Portfolio; Global Minimum Variance Portfolio; Sparse Portfolios;
    All these keywords.

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