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Stock Market Prediction Using Deep Attention Bi-directional Long Short-Term Memory

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  • B. Prakash

    (Vellore Institute of Technology, Tamil Nadu)

  • B. Saleena

    (Vellore Institute of Technology, Tamil Nadu)

Abstract

Trustworthy predictions of future stock can promote significant profits, and it has attracted several financial analysts and investors. Accuracy suffers when more features are added and time consumption increases. To address these issues, this study proposes an Effective Stock Market Prediction with a Deep Attention BiLSTM framework optimized utilizing the COOT Birds Algorithm (DABiLSTM-COOT). Initially, the stock data are collected from the NSE stock dataset (Nifty 50), and the technical and fundamental indicators are measured for effective closing price prediction. The optimal features are chosen by adopting the Improved Binary Butterfly optimization (IBBO) algorithm. The DABiLSTM-COOT method predicts closing prices more accurately. Also, the performances are analyzed in terms of precision, recall, and F1 score, MSE, RMSE and MAPE.

Suggested Citation

  • B. Prakash & B. Saleena, 2025. "Stock Market Prediction Using Deep Attention Bi-directional Long Short-Term Memory," Computational Economics, Springer;Society for Computational Economics, vol. 66(1), pages 903-927, July.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:1:d:10.1007_s10614-024-10719-w
    DOI: 10.1007/s10614-024-10719-w
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    References listed on IDEAS

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    1. Kok-Leong Yap & Wee-Yeap Lau & Izlin Ismail, 2021. "Deep learning neural network for the prediction of Asian Tiger stock markets," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 8(04), pages 1-35, December.
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    3. Mojtaba Nabipour & Pooyan Nayyeri & Hamed Jabani & Amir Mosavi, 2020. "Deep learning for Stock Market Prediction," Papers 2004.01497, arXiv.org.
    4. Hakan Gunduz, 2021. "An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-24, December.
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