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

Rayan H. Assaad; Sara Fayek

doi:10.2478/erfin-2021-0006

Rayan H. Assaad New Jersey Institute of Technology, United States
Sara Fayek Missouri University of Science and Technology, United States

DOI: https://doi.org/10.2478/erfin-2021-0006

Keywords: Crude Oil Price, Information Technology, Deep Learning, Long-Short Term Memory (LSTM), Convolutional Neural Networks, Stock Prices

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.

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