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

• Main Authors: Rayan H. Assaad, Sara Fayek
• Format: Article
• Language: English
• Published: SGH Warsaw School of Economics, Collegium of Economic Analysis 2021-09-01
• Series: Econometric Research in Finance
• Subjects: Crude Oil Price; Information Technology; Deep Learning; Long-Short Term Memory (LSTM); Convolutional Neural Networks; Stock Prices
• Online Access: https://www.erfin.org/journal/index.php/erfin/article/view/127

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.