Scholarlink

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.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Efficient Vietnamese Stock Price Prediction Using Deep Learning Models

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Thuy Thi Thu Nguyen,
  • Trung Chi Nguyen

摘要

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.