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

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Listed:
  • Tran Phuoc

    (Ho Chi Minh City of University of Food Industry
    Hochiminh City University of Industry and Trade)

  • Pham Thi Kim Anh

    (Ho Chi Minh City of University of Food Industry
    Hochiminh City University of Industry and Trade)

  • Phan Huy Tam

    (University of Economics and Law
    Vietnam National University)

  • Chien V. Nguyen

    (Thu Dau Mot University)

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.

Suggested Citation

  • Tran Phuoc & Pham Thi Kim Anh & Phan Huy Tam & Chien V. Nguyen, 2024. "Applying machine learning algorithms to predict the stock price trend in the stock market – The case of Vietnam," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-18, December.
    • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-02807-x
    DOI: 10.1057/s41599-024-02807-x
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    References listed on IDEAS

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