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Predicting the Price of Crude Oil and its Fluctuations Using Computational Econometrics: Deep Learning, LSTM, and Convolutional Neural Networks

Author

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  • Rayan H. Assaad

    (New Jersey Institute of Technology, United States)

  • Sara Fayek

    (Missouri University of Science and Technology, United States)

Abstract

There has been a renewed interest in accurately forecasting the price of crude oil and its fluctuations. That said, this paper aims to study whether the price of crude oil in the United States (US) could be predicted using the stock prices of the top information technology companies. To this end, time-series data was collected and pre-processed as needed, and three architectures of computational neural networks were tested: deep neural networks, long-short term memory (LSTM) neural networks, and a combination of convolutional and LSTM neural networks. The findings suggest that LSTM networks are the best architectures to predict the crude oil price. The outcomes of this paper could potentially help in making the oil price prediction mechanism a more tractable task and in assisting decision-makers to improve macroeconomic policies, generate enhanced macroeconomic projections, and better assess macroeconomic risks.

Suggested Citation

  • Rayan H. Assaad & Sara Fayek, 2021. "Predicting the Price of Crude Oil and its Fluctuations Using Computational Econometrics: Deep Learning, LSTM, and Convolutional Neural Networks," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 6(2), pages 119-137.
    • Handle: RePEc:sgh:erfinj:v:6:y:2021:i:2:p:119-137
    DOI: https://doi.org/10.2478/erfin-2021-0006
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    Cited by:

    1. 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).

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