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.

本研究的目的是预测新兴经济体股票市场的股价趋势。采用长短期记忆（LSTM）算法，每个股票代码对应的技术分析指标包括：简单移动平均线（SMA）、收敛发散移动平均线（MACD）、相对强弱指数（RSI）；以及VN-Index和VN-30股票的二手数据，研究结果表明，预测模型对于所使用的大部分股票数据具有93%的高精度，证明了LSTM模型的适当性，并且测试集数据是用于评估模型的性能。研究结果表明，该预测模型对于所使用的大部分股票数据具有高达93%的准确率，证明了LSTM模型在机器学习平台上分析和预测股票价格走势的适当性。