Index tracking in financial markets: A comprehensive review of optimization, statistical, and data-driven modeling approaches

2026-05-23

Dhingra, Vrinda

Amita Sharma

Anubha Goel *

DOI：10.1016/j.knosys.2026.116011

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Index tracking, also known as passive investing, has gained significant traction in financial markets due to its cost-effective and efficient approach to replicating the performance of a specific market index. This review paper provides a comprehensive overview of the various modeling approaches and strategies developed for index tracking, highlighting the strengths and limitations of each approach. We categorize the index tracking models into three broad frameworks: Mathematical programming and decision-analytic models, statistical and econometric models and machine-learning & data-driven models. A comprehensive empirical study conducted on the S&P 500 dataset demonstrates that the tracking error volatility model under the mathematical programming and decision-analytic framework delivers the most precise index tracking, the convex co-integration model, under the statistical and econometric framework achieves the strongest return-risk balance, and the deep neural network with fixed noise model within the machine learning and data-driven framework provides a competitive performance with notably low turnover and high computational efficiency. By combining a critical review of the existing literature with comparative empirical analysis, this paper aims to provide insights into the evolving landscape of index tracking and its practical implications for investors and fund managers.

Keywords:

Index tracking

Tracking error minimization

Tracking portfolio

Cardinality constraints

Machine learning

Deep learning

Index tracking in financial markets: A comprehensive review of optimization, statistical, and data-driven modeling approaches

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