Abstract
The article studies the problem of forecasting the closing price of a stock based on historical data of a previousday. The paper uses and compares algorithms based on deep learning such as LSTM, BiLSTM, and CNN. The dataset includes data on price, trading volume and some technical indicators related to VCB, MSN, and HPG shares. The results show that CNN performs better for predicting the next day’s closing price than the other architectures.
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