Optimizing index tracking: A Random Matrix Theory approach to portfolio selection

Francesca Grassetti

Physica A: Statistical Mechanics and its Applications, 2025, vol. 674, issue C

Abstract: This paper proposes a novel methodology for index tracking that combines Random Matrix Theory with network-based eigenvalue centrality to construct compact and representative portfolios. The approach filters out noise and systemic effects from the asset correlation structure, enabling the identification of stock communities and the selection of their most influential members. A tunable parameter λ balances the trade-off between minimizing tracking error and maximizing excess return. Extensive empirical validation across diverse market conditions—classified using a volatility-based regime framework—confirms the robustness and adaptability of the method. This framework offers a scalable and computationally efficient solution for index tracking, suitable for both institutional investors and practical portfolio management.

Keywords: Index tracking; Random matrix theory; Portfolio optimization; Eigenvalue centrality; Tracking error (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437125003991
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:674:y:2025:i:c:s0378437125003991

DOI: 10.1016/j.physa.2025.130747

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().