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Predicting oil prices: A comparative analysis of machine learning and image recognition algorithms for trend prediction

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  • Göncü, Ahmet
  • Kuzubaş, Tolga U.
  • Saltoğlu, Burak

Abstract

This paper investigates the effectiveness of machine learning algorithms, including logistic regression, artificial neural networks, support vector machines, gradient boosting algorithms (XGBoost, ExtraTrees), random forests, and convolutional neural network (CNN) for trend prediction of daily spot oil prices across horizons of 1 to 8 days. We utilize a comprehensive set of features, including technical indicators, financial data, and volatility measures, to predict trends in closing prices. Our results reveal that the CNN model significantly outperforms other algorithms. This superior performance likely stems from CNN’s ability to capture visual patterns in price movements, potentially mimicking how traders identify trends.

Suggested Citation

  • Göncü, Ahmet & Kuzubaş, Tolga U. & Saltoğlu, Burak, 2024. "Predicting oil prices: A comparative analysis of machine learning and image recognition algorithms for trend prediction," Finance Research Letters, Elsevier, vol. 67(PB).
    • Handle: RePEc:eee:finlet:v:67:y:2024:i:pb:s1544612324009048
    DOI: 10.1016/j.frl.2024.105874
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    References listed on IDEAS

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    1. Dongyan Fan & Sicen Lai & Hai Sun & Yuqing Yang & Can Yang & Nianyang Fan & Minhui Wang, 2025. "Review of Machine Learning Methods for Steady State Capacity and Transient Production Forecasting in Oil and Gas Reservoir," Energies, MDPI, vol. 18(4), pages 1-25, February.
    2. Tianxiang Hao & Qingfu Liu & Deyu Miao & Yiuman Tse, 2025. "Predicting Commodity Returns Through Image‐Based Price Patterns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(12), pages 2434-2456, December.
    3. Liu, Jinpei & Qiu, Biyue & Du, Pengcheng & Zhao, Xiaoman & Zhu, Jiaming, 2025. "A novel probabilistic connectivity network link prediction model for natural gas price based on an improved K-shell algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 671(C).
    4. Foued Hamouda & Nadia Arfaoui & Muhammad Abubakr Naeem, 2025. "Forecasting Energy Commodity Prices Amidst Worldwide Energy Transitions Using Artificial Intelligence Models," The Energy Journal, , vol. 46(5), pages 215-244, September.

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    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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