Enhancing Stock Price Forecasting with CNN-BiGRU-Attention: A Case Study on INDY

Madilyn Louisa, Gumgum Darmawan () and Bertho Tantular
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Madilyn Louisa: Department of Statistics, Universitas Padjadjaran, Jl. Bandung Sumedang km 21 Jatinangor, Sumedang 45363, Indonesia
Gumgum Darmawan: Department of Statistics, Universitas Padjadjaran, Jl. Bandung Sumedang km 21 Jatinangor, Sumedang 45363, Indonesia
Bertho Tantular: Department of Statistics, Universitas Padjadjaran, Jl. Bandung Sumedang km 21 Jatinangor, Sumedang 45363, Indonesia

Mathematics, 2025, vol. 13, issue 13, 1-16

Abstract: The stock price of PT Indika Energy Tbk (INDY) reflects the dynamics of Indonesia’s energy sector, which is heavily influenced by global coal price fluctuations, national energy policies, and geopolitical conditions. This study aimed to develop an accurate forecasting model to predict the movement of INDY stock prices using a hybrid machine learning approach called CNN-BiGRU-AM. The objective was to generate future forecasts of INDY stock prices based on historical data from 28 August 2019 to 24 February 2025. The method applied a hybrid model combining a Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and an Attention Mechanism (AM) to address the nonlinear, volatile, and noisy characteristics of stock data. The results showed that the CNN-BiGRU-AM model achieved high accuracy with a Mean Absolute Percentage Error (MAPE) below 3%, indicating its effectiveness in capturing long-term patterns. The CNN helped extract local features and reduce noise, the BiGRU captured bidirectional temporal dependencies, and the Attention Mechanism allocated weights to the most relevant historical information. The model remained robust even when stock prices were sensitive to external factors such as global commodity trends and geopolitical events. This study contributes to providing more accurate forecasting solutions for companies, investors, and stakeholders in making strategic decisions. It also enriches the academic literature on the application of deep learning techniques in financial data analysis and stock market forecasting within a complex and dynamic environment.

Keywords: CNN-BiGRU-AM; energy market volatility; hybrid deep learning model; INDY stock prediction; stock price forecasting (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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