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Efficient Vietnamese Stock Price Prediction Using Deep Learning Models

In: Proceedings of the 5th International Conference on Research in Management and Technovation

Author

Listed:
  • Thuy Thi Thu Nguyen

    (ThuongMai University)

  • Trung Chi Nguyen

    (Hanoi National University of Education)

Abstract

Stock forecasting has become an important task in financial investment activities in order to build the accurate predictions of stock prices in the future. In this paper, we focus on using deep learning methods combined with the computation of stock technical indicators including Rate of Change (ROC), Stochastic Oscillator—%K, and Relative Strength Index (RSI) to increase the accuracy and reliability of stock forecasts. Our proposed model is experimentally tested on some stock data such as MBB (a military bank in Vietnam), SSI, and BID. The prediction results are calculated based on Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) demonstrating the high reliability of the proposed model and its applicability in practice to provide investors with an additional useful tool for decision-making and risk mitigation in investments.

Suggested Citation

  • Thuy Thi Thu Nguyen & Trung Chi Nguyen, 2025. "Efficient Vietnamese Stock Price Prediction Using Deep Learning Models," Springer Proceedings in Business and Economics, in: Nga Thi Hong Nguyen & José António C. Santos & Vijender Kumar Solanki & Anh Ngoc Mai (ed.), Proceedings of the 5th International Conference on Research in Management and Technovation, pages 621-633, Springer.
    • Handle: RePEc:spr:prbchp:978-981-97-9992-3_39
    DOI: 10.1007/978-981-97-9992-3_39
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