Sparse Index Tracking via Topological Learning

TL;DR

This paper presents a topological data analysis-based method for sparse index tracking that improves performance and robustness over traditional techniques by capturing complex asset movement features.

Contribution

It introduces a novel TDA-based approach for regularization parameter learning in sparse index tracking, avoiding expensive estimation procedures.

Findings

Outperforms Elastic-Net and SLOPE in risk and performance metrics

Robust in turbulent market conditions

Computationally efficient and scalable

Abstract

In this research, we introduce a novel methodology for the index tracking problem with sparse portfolios by leveraging topological data analysis (TDA). Utilizing persistence homology to measure the riskiness of assets, we introduce a topological method for data-driven learning of the parameters for regularization terms. Specifically, the Vietoris-Rips filtration method is utilized to capture the intricate topological features of asset movements, providing a robust framework for portfolio tracking. Our approach has the advantage of accommodating both $\ell_1$ and $\ell_2$ penalty terms without the requirement for expensive estimation procedures. We empirically validate the performance of our methodology against state-of-the-art sparse index tracking techniques, such as Elastic-Net and SLOPE, using a dataset that covers 23 years of S&P500 index and its constituent data. Our out-of-sample results show that this computationally efficient technique surpasses conventional methods across risk metrics, risk-adjusted performance, and trading expenses in varied market conditions. Furthermore, in turbulent markets, it not only maintains but also enhances tracking performance.