<![CDATA[This study aims to construct and evaluate a forecasting model for bank stock prices in Vietnam, focusing on the TPB stock of Tien Phong Commercial Joint Stock Bank, through the integration of LSTM neural networks with moving average technical indicators (SMA, EMA, WMA) across time periods of 5, 10, and 20 days. The dataset comprises daily closing price series from August 17, 2017, to July 30, 2025, processed via cleaning, normalization, and training/testing set partitioning steps. Models were trained with 100 and 150 epochs, with performance assessed using MSE, RMSE, MAPE, and R² metrics. Results indicate that the LSTM model combined with SMA20 (100 epochs) achieves optimal performance, with MSE equal 103,685.27, RMSE equal 322, MAPE equal 1.67%, and R² equal 88.23%, excelling due to its noise filtering and long-term trend capturing capabilities. Forecasts for the next three months suggest a positive growth trend, with TPB stock prices potentially ranging from 14,500 to 18,500 VND per share. The study enhances the theoretical framework for hybrid deep learning models in financial time series forecasting, while offering practical recommendations for investors to mitigate systemic risks and optimize strategies in Vietnam's banking sector.]]>
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