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Statistical Modeling of Opening Price Gaps in the Shanghai Stock Exchange Composite Index Using Linear Methods

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Listed:
  • Yuancheng Si

    (Anhui Academy of Social Sciences
    Fudan University)

  • Saralees Nadarajah

    (Fudan University)

  • Zongxin Zhang

    (University of Manchester)

Abstract

This study explores the determinants of the opening price gap rate (diffrate) in the Chinese stock market using a range of statistical models, including linear models with regularization terms, generalized linear models (GLMs), generalized additive models (GAMs), and Long Short-Term Memory (LSTM) models. Emphasizing predictive accuracy, interpretability, and practical applicability, the findings reveal that GAMs with regularization terms outperform other approaches in forecasting opening price gap rate. The results identify several critical factors, including domestic market indices, global liquidity conditions, and market activity measures. By providing a comprehensive framework for understanding and modeling opening price dynamics, this work lays a robust foundation for future research and practical applications in market risk management and predictive analytics.

Suggested Citation

  • Yuancheng Si & Saralees Nadarajah & Zongxin Zhang, 2025. "Statistical Modeling of Opening Price Gaps in the Shanghai Stock Exchange Composite Index Using Linear Methods," Computational Economics, Springer;Society for Computational Economics, vol. 66(4), pages 3437-3472, October.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:4:d:10.1007_s10614-024-10817-9
    DOI: 10.1007/s10614-024-10817-9
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