Harnessing Deep Learning and Technical Indicators for Enhanced Stock Predictions of Blue-Chip Stocks on the Indonesia Stock Exchange (IDX)
Received: 6 December 2024 | Revised: 23 December 2024 | Accepted: 1 January 2025 | Online: 14 January 2025
Corresponding author: Rudy Hartanto
Abstract
Given the limitations of existing models in accurately predicting stock prices, particularly in emerging markets such as Indonesia, this study aimed to evaluate the effectiveness of deep learning models in forecasting stock prices using blue-chip company shares traded on the Indonesia Stock Exchange (IDX). The main focus lies in combining historical stock data with a series of existing technical indicators, optimizing their integration to improve prediction accuracy. The accuracy of this method is reflected in a comprehensive evaluation of model performance using robust metrics, including R2, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Empirical results show the superiority of models integrating technical indicators compared to models relying only on historical data. The LSTM model showed the most significant improvement, with R2 for ASII stock jumping by 14.59% after incorporating technical indicators. The prediction accuracy of the GRU model for BBCA shares increased significantly, as shown by a decrease of 45.16% in MSE. These findings underscore the critical role of feature selection in developing prediction models. Integrating technical indicators with historical stock data increases prediction accuracy and provides additional tools for informed decision-making.
Keywords:
stock prediction, RNN, GRU, LSTM, technical indicator, Indonesia stock exchangeDownloads
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