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Can We Forecast Daily Oil Futures Prices? Experimental Evidence from Convolutional Neural Networks

Author

Listed:
  • Zhaojie Luo

    (Graduate School of System Informatics, Kobe University, 2-1 Rokkodai, Nada-Ku, Kobe 657-8501, Japan)

  • Xiaojing Cai

    (Graduate School of Economics, Kobe University, 2-1 Rokkodai, Nada-Ku, Kobe 657-8501, Japan)

  • Katsuyuki Tanaka

    (Graduate School of Economics, Kobe University, 2-1 Rokkodai, Nada-Ku, Kobe 657-8501, Japan)

  • Tetsuya Takiguchi

    (Graduate School of System Informatics, Kobe University, 2-1 Rokkodai, Nada-Ku, Kobe 657-8501, Japan)

  • Takuji Kinkyo

    (Graduate School of Economics, Kobe University, 2-1 Rokkodai, Nada-Ku, Kobe 657-8501, Japan)

  • Shigeyuki Hamori

    (Graduate School of Economics, Kobe University, 2-1 Rokkodai, Nada-Ku, Kobe 657-8501, Japan)

Abstract

This paper proposes a novel approach, based on convolutional neural network (CNN) models, that forecasts the short-term crude oil futures prices with good performance. In our study, we confirm that artificial intelligence (AI)-based deep-learning approaches can provide more accurate forecasts of short-term oil prices than those of the benchmark Naive Forecast (NF) model. We also provide strong evidence that CNN models with matrix inputs are better at short-term prediction than neural network (NN) models with single-vector input, which indicates that strengthening the dependence of inputs and providing more useful information can improve short-term forecasting performance.

Suggested Citation

  • Zhaojie Luo & Xiaojing Cai & Katsuyuki Tanaka & Tetsuya Takiguchi & Takuji Kinkyo & Shigeyuki Hamori, 2019. "Can We Forecast Daily Oil Futures Prices? Experimental Evidence from Convolutional Neural Networks," JRFM, MDPI, vol. 12(1), pages 1-13, January.
    • Handle: RePEc:gam:jjrfmx:v:12:y:2019:i:1:p:9-:d:195801
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    References listed on IDEAS

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    Cited by:

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    2. Li, Mingchen & Cheng, Zishu & Lin, Wencan & Wei, Yunjie & Wang, Shouyang, 2023. "What can be learned from the historical trend of crude oil prices? An ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 123(C).
    3. Li, Yuze & Jiang, Shangrong & Li, Xuerong & Wang, Shouyang, 2021. "The role of news sentiment in oil futures returns and volatility forecasting: Data-decomposition based deep learning approach," Energy Economics, Elsevier, vol. 95(C).
    4. Shigeyuki Hamori, 2020. "Empirical Finance," JRFM, MDPI, vol. 13(1), pages 1-3, January.
    5. Urolagin, Siddhaling & Sharma, Nikhil & Datta, Tapan Kumar, 2021. "A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting," Energy, Elsevier, vol. 231(C).
    6. Aniruddha Dutta & Saket Kumar & Meheli Basu, 2020. "A Gated Recurrent Unit Approach to Bitcoin Price Prediction," JRFM, MDPI, vol. 13(2), pages 1-16, February.

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