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

Author

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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    References listed on IDEAS

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    1. Lixu Chi & Xintian Zhuang & Dalei Song, 2012. "Investor sentiment in the Chinese stock market: an empirical analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 19(4), pages 345-348, March.
    2. Ao Kong & Robert Azencott & Hongliang Zhu & Xindan Li, 2024. "Pattern Recognition in Microtrading Behaviors Preceding Stock Price Jumps: A Study Based on Mutual Information for Multivariate Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1401-1429, April.
    3. Cheema, Muhammad A. & Chiah, Mardy & Man, Yimei, 2022. "Overnight returns, daytime reversals, and future stock returns: Is China different?," Pacific-Basin Finance Journal, Elsevier, vol. 74(C).
    4. Zhang, Hang & Tsai, Wei-Che & Weng, Pei-Shih & Tsai, Pin-Chieh, 2023. "Overnight returns and investor sentiment: Further evidence from the Taiwan stock market," Pacific-Basin Finance Journal, Elsevier, vol. 80(C).
    5. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    6. Liu, Qingfu & Tse, Yiuman, 2017. "Overnight returns of stock indexes: Evidence from ETFs and futures," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 440-451.
    7. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    8. de Jong,Piet & Heller,Gillian Z., 2008. "Generalized Linear Models for Insurance Data," Cambridge Books, Cambridge University Press, number 9780521879149, August.
    9. Florian Weißofner & Ulrich Wessels, 2020. "Overnight Returns: An International Sentiment Measure," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 21(2), pages 205-217, April.
    10. Riordan, Ryan & Storkenmaier, Andreas & Wagener, Martin & Sarah Zhang, S., 2013. "Public information arrival: Price discovery and liquidity in electronic limit order markets," Journal of Banking & Finance, Elsevier, vol. 37(4), pages 1148-1159.
    11. Plastun, Alex & Sibande, Xolani & Gupta, Rangan & Wohar, Mark E., 2020. "Price gap anomaly in the US stock market: The whole story," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    12. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    13. Aboody, David & Even-Tov, Omri & Lehavy, Reuven & Trueman, Brett, 2018. "Overnight Returns and Firm-Specific Investor Sentiment," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(2), pages 485-505, April.
    14. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    15. Jiang, George J. & Zhu, Kevin X., 2017. "Information Shocks and Short-Term Market Underreaction," Journal of Financial Economics, Elsevier, vol. 124(1), pages 43-64.
    16. Paul C. Tetlock, 2010. "Does Public Financial News Resolve Asymmetric Information?," The Review of Financial Studies, Society for Financial Studies, vol. 23(9), pages 3520-3557.
    17. Kanas, Angelos & Yannopoulos, Andreas, 2001. "Comparing linear and nonlinear forecasts for stock returns," International Review of Economics & Finance, Elsevier, vol. 10(4), pages 383-398, December.
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