As one of the most popular financial market instruments, the stock has formed one of the most massive and complex financial markets in the world. It could handle millions of transactions within a short period of time and highly unpredictable. In this study, we aim to implement a famous Deep Learning method, namely the long short-term memory (LSTM) networks, for the stock price prediction. We limit the stocks to those that are included in the LQ45 financial sectors indices, i.e., BBCA, BBNI, BBRI, BBTN, BMRI, and BTPS. Rather than using too deep network architecture, we propose using a simple three-layer LSTM network architecture to predict the stocks’ closing prices. We found that the prediction results fall in the reasonable forecasting category. Moreover, it is worth noting that two of the considered stocks, namely, BBCA and BMRI, have the lowest MAPE values at 19.1020 and 18.6135, which fall in the good forecasting results. Hence, the proposed LSTM model is most recommended to be used on those two stocks.

作为最受欢迎的金融市场工具之一，股票已经形成了世界上规模最大、最复杂的金融市场之一。它可以在短时间内处理数百万笔交易，而且高度不可预测。在这项研究中，我们的目标是实现一种著名的深度学习方法，即长短期记忆 (LSTM) 网络，用于股票价格预测。我们将股票限制在 LQ45 金融板块指数中的股票，即 BBCA、BBNI、BBRI、BBTN、BMRI 和 BTPS。我们建议使用简单的三层 LSTM 网络架构来预测股票的收盘价，而不是使用太深的网络架构。我们发现预测结果属于合理的预测类别。此外，值得注意的是，其中两只被考虑的股票，即 BBCA 和 BMRI，MAPE 值最低为 19.1020 和 18.6135，属于良好的预测结果。因此，最推荐将所提出的 LSTM 模型用于这两只股票。