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Forecasting Crude Oil Price Using Multiple Factors

Author

Listed:
  • Hind Aldabagh

    (Computer Science Department, Old Dominion University, Norfolk, VA 23529, USA)

  • Xianrong Zheng

    (Information Technology & Decision Sciences Department, Old Dominion University, Norfolk, VA 23529, USA)

  • Mohammad Najand

    (Finance Department, Old Dominion University, Norfolk, VA 23529, USA)

  • Ravi Mukkamala

    (Computer Science Department, Old Dominion University, Norfolk, VA 23529, USA)

Abstract

In this paper, we predict crude oil price using various factors that may influence its price. The factors considered are physical market, financial, and trading market factors, including seven key factors and the dollar index. Firstly, we select the main factors that may greatly influence the prices. Then, we develop a hybrid model based on a convolutional neural network (CNN) and long short-term memory (LSTM) network to predict the prices. Lastly, we compare the CNN–LSTM model with other models, namely gradient boosting (GB), decision trees (DTs), random forests (RFs), neural networks (NNs), CNN, LSTM, and bidirectional LSTM (Bi–LSTM). The empirical results show that the CNN–LSTM model outperforms these models.

Suggested Citation

  • Hind Aldabagh & Xianrong Zheng & Mohammad Najand & Ravi Mukkamala, 2024. "Forecasting Crude Oil Price Using Multiple Factors," JRFM, MDPI, vol. 17(9), pages 1-15, September.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:9:p:415-:d:1481091
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    References listed on IDEAS

    as
    1. Dai, Zhifeng & Kang, Jie & Hu, Yangli, 2021. "Efficient predictability of oil price: The role of number of IPOs and U.S. dollar index," Resources Policy, Elsevier, vol. 74(C).
    2. Hadi Jahanshahi & Süleyman Uzun & Sezgin Kaçar & Qijia Yao & Madini O. Alassafi, 2022. "Artificial Intelligence-Based Prediction of Crude Oil Prices Using Multiple Features under the Effect of Russia–Ukraine War and COVID-19 Pandemic," Mathematics, MDPI, vol. 10(22), pages 1-14, November.
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