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All Journal International Journal Artificial Intelligent and Informatics
Moraes, Giovana
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Computer Science & IT Economics, Econometrics & Finance

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Petrobras Stock Price Prediction Using Deep Learning Approach: Performance Comparison of CNN and CNN-GRU Methods Gabrielzinho, Manuel; Moraes, Giovana
International Journal Artificial Intelligent and Informatics Vol 3, No 1 (2025)
Publisher : Research and Social Study Institute (ReSSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (647.587 KB) | DOI: 10.33292/ijarlit.v3i1.44

Abstract

This study aims to compare the effectiveness of two deep learning models, namely Convolutional Neural Network (CNN) and combined CNN with Gated Recurrent Unit (CNN-GRU), in predicting Petrobras stock prices. Using historical stock price data from Yahoo Finance for the period 2017-2023, this study evaluates the performance of both models based on several evaluation metrics. The results showed that the CNN-GRU model outperformed the pure CNN model in all evaluation metrics, with an increase in RMSE value of 4.17% and an increase in R² value of 0.47% on the test data. The CNN-GRU model achieved 96.14% accuracy on the test data, while the CNN model achieved 96.04%. These findings indicate that the integration of CNN's feature extraction capabilities with GRU's temporal dependency modeling capabilities can improve stock price prediction accuracy. This research contributes to the computational finance literature by presenting an in-depth comparative analysis of the application of hybrid deep learning architectures in stock market prediction.
Co-Authors Gabrielzinho, Manuel