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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," Journal of Risk and Financial Management, MDPI, Open Access Journal, 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

    as
    1. Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2015. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," International Journal of Forecasting, Elsevier, vol. 31(2), pages 238-252.
    2. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    3. Drachal, Krzysztof, 2016. "Forecasting spot oil price in a dynamic model averaging framework — Have the determinants changed over time?," Energy Economics, Elsevier, vol. 60(C), pages 35-46.
    4. Nikitopoulos, Christina Sklibosios & Squires, Matthew & Thorp, Susan & Yeung, Danny, 2017. "Determinants of the crude oil futures curve: Inventory, consumption and volatility," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 53-67.
    5. Mingming, Tang & Jinliang, Zhang, 2012. "A multiple adaptive wavelet recurrent neural network model to analyze crude oil prices," Journal of Economics and Business, Elsevier, vol. 64(4), pages 275-286.
    6. Jammazi, Rania & Aloui, Chaker, 2012. "Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling," Energy Economics, Elsevier, vol. 34(3), pages 828-841.
    7. Naser, Hanan, 2016. "Estimating and forecasting the real prices of crude oil: A data rich model using a dynamic model averaging (DMA) approach," Energy Economics, Elsevier, vol. 56(C), pages 75-87.
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    More about this item

    Keywords

    crude oil futures prices forecasting; convolutional neural networks; short-term forecasting;

    JEL classification:

    • C - Mathematical and Quantitative Methods
    • E - Macroeconomics and Monetary Economics
    • F2 - International Economics - - International Factor Movements and International Business
    • F3 - International Economics - - International Finance
    • G - Financial Economics

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