Applying machine learning algorithms to predict the stock price trend in the stock market – The case of Vietnam

Tran Phuoc, Pham Thi Kim Anh, Phan Huy Tam and Chien V. Nguyen ()
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Tran Phuoc: Ho Chi Minh City of University of Food Industry
Pham Thi Kim Anh: Ho Chi Minh City of University of Food Industry
Phan Huy Tam: University of Economics and Law
Chien V. Nguyen: Thu Dau Mot University

Palgrave Communications, 2024, vol. 11, issue 1, 1-18

Abstract: Abstract The aims of this study are to predict the stock price trend in the stock market in an emerging economy. Using the Long Short Term Memory (LSTM) algorithm, and the corresponding technical analysis indicators for each stock code include: simple moving average (SMA), convergence divergence moving average (MACD), and relative strength index (RSI); and the secondary data from VN-Index and VN-30 stocks, the research results showed that the forecasting model has a high accuracy of 93% for most of the stock data used, demonstrating the appropriateness of the LSTM model and the test set data is used to evaluate the model’s performance. The research results showed that the forecasting model has a high accuracy of 93% for most of the stock data used, demonstrating the appropriateness of the LSTM model in analyzing and forecasting stock price movements on the machine learning platform.

Date: 2024
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DOI: 10.1057/s41599-024-02807-x

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