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Enhancing stock market predictions with multivariate signal decomposition and dynamic feature optimization

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  • Xue, Xiaorui
  • Li, Shaofang
  • Wang, Xiaonan
  • Ren, Tingting

Abstract

Predicting stock trends is vital in financial systems, providing insights for strategies aimed at generating excess returns. The market’s intrinsically chaotic, nonlinear, and multivariate characteristics hinder the efficacy of traditional deep learning models, especially in recognizing dynamic interdependencies and temporal non-stationarity. This study introduces an innovative hybrid framework (MVMD-NT-TiF) that integrates multivariate signal decomposition, non-stationary sequence modeling, and dual-attention-based feature selection into a cohesive architecture. By concurrently tackling noise, temporal adaptability, and feature redundancy, the approach facilitates precise and resilient forecasting in intricate financial contexts. Empirical findings regarding key stock indices illustrate its enhanced accuracy and universality relative to leading baselines, underscoring its use in real-world scenarios such as quantitative investing, risk management, and trend analysis.

Suggested Citation

  • Xue, Xiaorui & Li, Shaofang & Wang, Xiaonan & Ren, Tingting, 2026. "Enhancing stock market predictions with multivariate signal decomposition and dynamic feature optimization," The North American Journal of Economics and Finance, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:ecofin:v:81:y:2026:i:c:s106294082500186x
    DOI: 10.1016/j.najef.2025.102546
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