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

Rasmi Ranjan Khansama, Rojalina Priyadarshini, Surendra Kumar Nanda, Rabindra Kumar Barik and Manob Jyoti Saikia ()
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Rasmi Ranjan Khansama: Biomedical Sensors & Systems Lab, University of Memphis, Memphis, TN 38152, USA
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

Forecasting, 2025, vol. 7, issue 3, 1-36

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.

Keywords: stock market trend prediction; sparse attention; global attention; random attention (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2025
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