Index Weight Prediction and Capital Liquidity Analysis Based on Data Science

Abstract

This study explores the intrinsic logical relationship between index construction methodologies and capital liquidity by leveraging advanced data science and computational technologies. We propose a multi-model hybrid framework for predicting index weight changes, incorporating diverse model sets to capture complex market dynamics. Key variables are systematically identified and screened through an integrated data platform and rigorous feature engineering, enabling the construction of a forward-looking index weight fluctuation pattern. This model serves as a foundational case study for examining the impact of index rebalancing behaviors on capital liquidity. Furthermore, we design early-warning mechanisms and clustering-based response strategies to anticipate liquidity risks, simulate stress scenarios, and develop a dynamic network transmission model that maps the propagation of market shocks. The results provide a comprehensive theoretical and practical reference for index management, risk mitigation, and the maintenance of financial market stability, offering valuable insights for both regulators and institutional investors.