IDEAS home Printed from https://ideas.repec.org/a/gam/jforec/v7y2025i3p50-d1749318.html
   My bibliography  Save this article

SGR-Net: A Synergistic Attention Network for Robust Stock Market Forecasting

Author

Listed:
  • Rasmi Ranjan Khansama

    (Biomedical Sensors & Systems Lab, University of Memphis, Memphis, TN 38152, USA
    Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar 752054, Odisha, India)

  • Rojalina Priyadarshini

    (Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar 752054, Odisha, India)

  • Surendra Kumar Nanda

    (Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar 752054, Odisha, India)

  • Rabindra Kumar Barik

    (School of Computer Applications, KIIT Deemed to be University, Bhubaneswar 752054, Odisha, India)

  • Manob Jyoti Saikia

    (Biomedical Sensors & Systems Lab, University of Memphis, Memphis, TN 38152, USA
    Electrical and Computer Engineering Department, University of Memphis, Memphis, TN 38152, USA)

Abstract

Owing to the high volatility, non-stationarity, and complexity of financial time-series data, stock market trend prediction remains a crucial but difficult endeavor. To address this, we present a novel Multi-Perspective Fused Attention model (SGR-Net) that amalgamates Random, Global, and Sparse Attention mechanisms to improve stock trend forecasting accuracy and generalization capability. The proposed Fused Attention model (SGR-Net) is trained on a rich feature space consisting of thirteen widely used technical indicators derived from raw stock index prices to effectively classify stock index trends as either uptrends or downtrends. Across nine global stock indices—DJUS, NYSE AMEX, BSE, DAX, NASDAQ, Nikkei, S&P 500, Shanghai Stock Exchange, and NIFTY 50—we evaluated the proposed model and compared it against baseline deep learning techniques, which include LSTM, GRU, Vanilla Attention, and Self-Attention. Experimental results across nine global stock index datasets show that the Fused Attention model produces the highest accuracy of 94.36% and AUC of 0.9888. Furthermore, even at lower epochs of training, i.e., 20 epochs, the proposed Fused Attention model produces faster convergence and better generalization, yielding an AUC of 0.9265, compared with 0.9179 for Self-Attention, on the DJUS index. The proposed model also demonstrates competitive training time and noteworthy performance on all nine stock indices. This is due to the incorporation of Sparse Attention, which lowers computation time to 57.62 s, only slightly more than the 54.22 s required for the Self-Attention model on the Nikkei 225 index. Additionally, the model incorporates Global Attention, which captures long-term dependencies in time-series data, and Random Attention, which addresses the problem of overfitting. Overall, this study presents a robust and reliable model that can help individuals, research communities, and investors identify profitable stocks across diverse global markets.

Suggested Citation

  • Rasmi Ranjan Khansama & Rojalina Priyadarshini & Surendra Kumar Nanda & Rabindra Kumar Barik & Manob Jyoti Saikia, 2025. "SGR-Net: A Synergistic Attention Network for Robust Stock Market Forecasting," Forecasting, MDPI, vol. 7(3), pages 1-36, September.
    • Handle: RePEc:gam:jforec:v:7:y:2025:i:3:p:50-:d:1749318
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-9394/7/3/50/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-9394/7/3/50/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
    2. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    Full references (including those not matched with items on IDEAS)

    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. Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021. "Stock Market Analysis with Text Data: A Review," Papers 2106.12985, arXiv.org, revised Jul 2021.
    2. Frédy Pokou & Jules Sadefo Kamdem & François Benhmad, 2024. "Hybridization of ARIMA with Learning Models for Forecasting of Stock Market Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1349-1399, April.
    3. Rabeh Khalfaoui & Sami Ben Jabeur & Shawkat Hammoudeh & Wissal Ben Arfi, 2025. "The role of political risk, uncertainty, and crude oil in predicting stock markets: evidence from the UAE economy," Annals of Operations Research, Springer, vol. 345(2), pages 1105-1135, February.
    4. Werner Kristjanpoller & Kevin Michell & Cristian Llanos & Marcel C. Minutolo, 2025. "Incorporating causal notions to forecasting time series: a case study," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-22, December.
    5. Gupta, Abhijit, 2025. "Decoding Futures Price Dynamics: A Regularized Sparse Autoencoder for Interpretable Multi-Horizon Forecasting and Factor Discovery," OSF Preprints 4rzky_v1, Center for Open Science.
    6. Won Joong Kim & Gunho Jung & Sun-Yong Choi, 2020. "Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning," Complexity, Hindawi, vol. 2020, pages 1-23, July.
    7. Lukas Ryll & Sebastian Seidens, 2019. "Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey," Papers 1906.07786, arXiv.org, revised Jul 2019.
    8. Shalini Sharma & Víctor Elvira & Emilie Chouzenoux & Angshul Majumdar, 2021. "Recurrent Dictionary Learning for State-Space Models with an Application in Stock Forecasting," Post-Print hal-03184841, HAL.
    9. Dominik Stempie'n & Robert 'Slepaczuk, 2025. "Hybrid Models for Financial Forecasting: Combining Econometric, Machine Learning, and Deep Learning Models," Papers 2505.19617, arXiv.org.
    10. Wei Dai & Yuan An & Wen Long, 2021. "Price change prediction of ultra high frequency financial data based on temporal convolutional network," Papers 2107.00261, arXiv.org.
    11. Sina Montazeri & Akram Mirzaeinia & Haseebullah Jumakhan & Amir Mirzaeinia, 2024. "CNN-DRL for Scalable Actions in Finance," Papers 2401.06179, arXiv.org.
    12. Dimitrios Kartsonakis Mademlis & Nikolaos Dritsakis, 2021. "Volatility Forecasting using Hybrid GARCH Neural Network Models: The Case of the Italian Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 11(1), pages 49-60.
    13. Rad, Hossein & Low, Rand Kwong Yew & Miffre, Joëlle & Faff, Robert, 2023. "The commodity risk premium and neural networks," Journal of Empirical Finance, Elsevier, vol. 74(C).
    14. Noura Metawa & Mohamemd I. Alghamdi & Ibrahim M. El-Hasnony & Mohamed Elhoseny, 2021. "Return Rate Prediction in Blockchain Financial Products Using Deep Learning," Sustainability, MDPI, vol. 13(21), pages 1-16, October.
    15. Kentaro Imajo & Kentaro Minami & Katsuya Ito & Kei Nakagawa, 2020. "Deep Portfolio Optimization via Distributional Prediction of Residual Factors," Papers 2012.07245, arXiv.org.
    16. Nazarian, Rafik & Gandali Alikhani, Nadiya & Naderi, Esmaeil & Amiri, Ashkan, 2013. "Forecasting Stock Market Volatility: A Forecast Combination Approach," MPRA Paper 46786, University Library of Munich, Germany.
    17. James Wallbridge, 2020. "Transformers for Limit Order Books," Papers 2003.00130, arXiv.org.
    18. Zhu (Drew) Zhang & Jie Yuan & Amulya Gupta, 2024. "Let the Laser Beam Connect the Dots: Forecasting and Narrating Stock Market Volatility," INFORMS Journal on Computing, INFORMS, vol. 36(6), pages 1400-1416, December.
    19. Burka, Dávid & Puppe, Clemens & Szepesváry, László & Tasnádi, Attila, 2022. "Voting: A machine learning approach," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1003-1017.
    20. Chi Chen & Li Zhao & Wei Cao & Jiang Bian & Chunxiao Xing, 2020. "Trimming the Sail: A Second-order Learning Paradigm for Stock Prediction," Papers 2002.06878, arXiv.org.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:gam:jforec:v:7:y:2025:i:3:p:50-:d:1749318. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.