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Forecasting Crude Oil Price Using Artificial Neural Networks: A Literature Survey

Author

Listed:
  • Manel Hamdi

    (International Finance Group Tunisia, El Manar University, Tunisia)

  • Chaker Aloui

    (College of business Administration, King Saud University, Riyadh, Saudi Arabia)

Abstract

The literature on forecasting the « black gold » price is vast. This paper provides a literature review on the various techniques that have been used to forecast crude oil price. We mainly focused on the researches that have utilized artificial neural network models in their forecasting study. Therefore, a detailed description of this model will be presented in this paper.

Suggested Citation

  • Manel Hamdi & Chaker Aloui, 2015. "Forecasting Crude Oil Price Using Artificial Neural Networks: A Literature Survey," Economics Bulletin, AccessEcon, vol. 35(2), pages 1339-1359.
    • Handle: RePEc:ebl:ecbull:eb-14-00800
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    References listed on IDEAS

    as
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    Citations

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    Cited by:

    1. Manel Hamdi & Chaker Aloui & Santosh kumar Nanda, 2016. "Comparing Functional Link Artificial Neural Network And Multilayer Feedforward Neural Network Model To Forecast Crude Oil Prices," Economics Bulletin, AccessEcon, vol. 36(4), pages 2430-2442.
    2. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
    3. Kakade, Kshitij & Jain, Ishan & Mishra, Aswini Kumar, 2022. "Value-at-Risk forecasting: A hybrid ensemble learning GARCH-LSTM based approach," Resources Policy, Elsevier, vol. 78(C).
    4. Abdollahi, Hooman & Ebrahimi, Seyed Babak, 2020. "A new hybrid model for forecasting Brent crude oil price," Energy, Elsevier, vol. 200(C).
    5. Safari, Ali & Davallou, Maryam, 2018. "Oil price forecasting using a hybrid model," Energy, Elsevier, vol. 148(C), pages 49-58.
    6. Anna Manowska & Anna Bluszcz, 2022. "Forecasting Crude Oil Consumption in Poland Based on LSTM Recurrent Neural Network," Energies, MDPI, vol. 15(13), pages 1-23, July.
    7. Gori, Fabio, 2016. "Mass and energy-capital conservation equations to forecast the oil price evolution with accumulation or depletion of the resources," Energy, Elsevier, vol. 116(P1), pages 746-760.
    8. Krzysztof Drachal, 2018. "Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices," Sustainability, MDPI, vol. 10(8), pages 1-27, August.
    9. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).

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    More about this item

    Keywords

    Crude oil price; Forecasting; Artificial neural networks;
    All these keywords.

    JEL classification:

    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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