IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0299164.html
   My bibliography  Save this article

Modeling opening price spread of Shanghai Composite Index based on ARIMA-GRU/LSTM hybrid model

Author

Listed:
  • Yuancheng Si
  • Saralees Nadarajah
  • Zongxin Zhang
  • Chunmin Xu

Abstract

In the dynamic landscape of financial markets, accurate forecasting of stock indices remains a pivotal yet challenging task, essential for investors and policymakers alike. This study is motivated by the need to enhance the precision of predicting the Shanghai Composite Index’s opening price spread, a critical measure reflecting market volatility and investor sentiment. Traditional time series models like ARIMA have shown limitations in capturing the complex, nonlinear patterns inherent in stock price movements, prompting the exploration of advanced methodologies. The aim of this research is to bridge the gap in forecasting accuracy by developing a hybrid model that integrates the strengths of ARIMA with deep learning techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This novel approach leverages the ARIMA model’s proficiency in linear trend analysis and the deep learning models’ capability in modeling nonlinear dependencies, aiming to provide a comprehensive tool for market prediction. Utilizing a comprehensive dataset covering the period from December 20, 1990, to June 2, 2023, the study develops and assesses the efficacy of ARIMA, LSTM, GRU, ARIMA-LSTM, and ARIMA-GRU models in forecasting the Shanghai Composite Index’s opening price spread. The evaluation of these models is based on key statistical metrics, including Mean Squared Error (MSE) and Mean Absolute Error (MAE), to gauge their predictive accuracy. The findings indicate that the hybrid models, ARIMA-LSTM and ARIMA-GRU, perform better in forecasting the opening price spread of the Shanghai Composite Index than their standalone counterparts. This outcome suggests that combining traditional statistical methods with advanced deep learning algorithms can enhance stock market prediction. The research contributes to the field by providing evidence of the potential benefits of integrating different modeling approaches for financial forecasting, offering insights that could inform investment strategies and financial decision-making.

Suggested Citation

  • Yuancheng Si & Saralees Nadarajah & Zongxin Zhang & Chunmin Xu, 2024. "Modeling opening price spread of Shanghai Composite Index based on ARIMA-GRU/LSTM hybrid model," PLOS ONE, Public Library of Science, vol. 19(3), pages 1-19, March.
  • Handle: RePEc:plo:pone00:0299164
    DOI: 10.1371/journal.pone.0299164
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0299164
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0299164&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0299164?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Su, Zhifang & Bao, Haohua & Li, Qifang & Xu, Boyu & Cui, Xin, 2022. "The prediction of price gap anomaly in Chinese stock market: Evidence from the dependent functional logit model," Finance Research Letters, Elsevier, vol. 47(PB).
    4. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    5. 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).
    6. 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).
    7. Yuancheng Si & Saralees Nadarajah, 2023. "A Statistical Analysis of Chinese Stock Indices Returns From Approach of Parametric Distributions Fitting," Annals of Data Science, Springer, vol. 10(1), pages 73-88, February.
    8. 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.
    9. 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.
    10. Ho, Hsiao-Wei & Hsiao, Yu-Jen & Lo, Wen-Chi & Yang, Nien-Tzu, 2023. "Momentum investing and a tale of intraday and overnight returns: Evidence from Taiwan," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yuancheng Si & Saralees Nadarajah, 2025. "Price Gap Anomaly: Empirical Study of Opening Price Gaps and Price Disparities in Chinese Stock Indices," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 32(2), pages 525-561, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuancheng Si & Saralees Nadarajah, 2025. "Price Gap Anomaly: Empirical Study of Opening Price Gaps and Price Disparities in Chinese Stock Indices," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 32(2), pages 525-561, June.
    2. Cornelis A. Los, 2004. "Nonparametric Efficiency Testing of Asian Stock Markets Using Weekly Data," Finance 0409033, University Library of Munich, Germany.
    3. Zhong, Meirui & Zhang, Rui & Ren, Xiaohang, 2023. "The time-varying effects of liquidity and market efficiency of the European Union carbon market: Evidence from the TVP-SVAR-SV approach," Energy Economics, Elsevier, vol. 123(C).
    4. Massa, Massimo & Manconi, Alberto & Luo, Mancy, 2017. "Much Ado About Nothing: Is the Market Affected by Political Bias?," CEPR Discussion Papers 11991, C.E.P.R. Discussion Papers.
    5. Vasile Brătian & Ana-Maria Acu & Camelia Oprean-Stan & Emil Dinga & Gabriela-Mariana Ionescu, 2021. "Efficient or Fractal Market Hypothesis? A Stock Indexes Modelling Using Geometric Brownian Motion and Geometric Fractional Brownian Motion," Mathematics, MDPI, vol. 9(22), pages 1-20, November.
    6. Kim, Jae H. & Shamsuddin, Abul, 2008. "Are Asian stock markets efficient? Evidence from new multiple variance ratio tests," Journal of Empirical Finance, Elsevier, vol. 15(3), pages 518-532, June.
    7. David Chappel & Joanne Padmore & Julia Pidgeon, 1998. "A note on ERM membership and the efficiency of the London Stock Exchange," Applied Economics Letters, Taylor & Francis Journals, vol. 5(1), pages 19-23.
    8. F. DePenya & L. Gil-Alana, 2006. "Testing of nonstationary cycles in financial time series data," Review of Quantitative Finance and Accounting, Springer, vol. 27(1), pages 47-65, August.
    9. He, Shanshan & Wang, Yudong, 2017. "Revisiting the multifractality in stock returns and its modeling implications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 11-20.
    10. Su, Zhifang & Bao, Haohua & Li, Qifang & Xu, Boyu & Cui, Xin, 2022. "The prediction of price gap anomaly in Chinese stock market: Evidence from the dependent functional logit model," Finance Research Letters, Elsevier, vol. 47(PB).
    11. Guglielmo Maria Caporale & Luis Gil-Alana, 2011. "The weekly structure of US stock prices," Applied Financial Economics, Taylor & Francis Journals, vol. 21(23), pages 1757-1764.
    12. Anju Bala & Kapil Gupta, 2020. "Examining The Long Memory In Stock Returns And Liquidity In India," Copernican Journal of Finance & Accounting, Uniwersytet Mikolaja Kopernika, vol. 9(3), pages 25-43.
    13. Onali, Enrico & Goddard, John, 2009. "Unifractality and multifractality in the Italian stock market," International Review of Financial Analysis, Elsevier, vol. 18(4), pages 154-163, September.
    14. Goddard, John & Onali, Enrico, 2012. "Self-affinity in financial asset returns," International Review of Financial Analysis, Elsevier, vol. 24(C), pages 1-11.
    15. Corzo, Teresa & Martin-Bujack, Karin & Portela, Jose & Rodríguez-Gallego, Alejandro, 2025. "Exchange rate regime changes and market efficiency: An event study," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 100(C).
    16. Pham Dan Khanh & Pham Thanh Dat & Bui Huy Nhuong, 2020. "A Re-examination of the Holiday Effect in Stock Returns: The Case of Vietnam," Edelweiss Applied Science and Technology, Learning Gate, vol. 4(1), pages 51-54.
    17. Wu, Chen-Hui, 2022. "The informativeness of brokerage reports: Privately-circulated versus publicly-disseminated news," International Review of Financial Analysis, Elsevier, vol. 83(C).
    18. Kristoufek, Ladislav, 2019. "Are the crude oil markets really becoming more efficient over time? Some new evidence," Energy Economics, Elsevier, vol. 82(C), pages 253-263.
    19. Sung Ik Kim, 2022. "ARMA–GARCH model with fractional generalized hyperbolic innovations," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-25, December.
    20. Rocha Filho, Tareísio M. & Rocha, Paulo M.M., 2020. "Evidence of inefficiency of the Brazilian stock market: The IBOVESPA future contracts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 543(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0299164. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.