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

  • Rayan H. Assaad New Jersey Institute of Technology, United States
  • Sara Fayek Missouri University of Science and Technology, United States
Keywords: Crude Oil Price, Information Technology, Deep Learning, Long-Short Term Memory (LSTM), Convolutional Neural Networks, Stock Prices


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.


Alquist, R. and Kilian, L. (2010). What Do We Learn From the Price of Crude Oil Futures? Journal of Applied Econometrics, 25(4):539–573.

Alquist, R., Kilian, L., and Vigfusson, R. J. (2013). Forecasting the Price of Oil. In Handbook of Economic Forecasting, Vol. 2, pages 427–507. Elsevier.

Amin, A. and Al-Darwish, N. (2006). Structural Description to Recognizing Hand-printed Arabic Characters Using Decision Tree Learning Techniques. International Journal of Computers and Applications, 28(2):129–134.

Assaad, R., Dagli, C., and El-adaway, I. H. (2020a). A System-of-Systems Model to Simulate the Complex Emergent Behavior of Vehicle Traffic on an Urban Transportation Infrastructure Network. Procedia Computer Science, 168:139–146.

Assaad, R. and El-Adaway, I. (2020). Forecasting and Modeling Bridge Deterioration Using Data Mining Analytics. In Construction Research Congress 2020: Computer Applications, pages 125–134. Reston, VA: American Society of Civil Engineers.

Assaad, R. and El-adaway, I. H. (2020). Bridge Infrastructure Asset Management System: Comparative Computational Machine Learning Approach for Evaluating and Predicting Deck Deterioration Conditions. Journal of Infrastructure Systems, 26(3):04020032.

Assaad, R. and El-Adaway, I. H. (2020). Enhancing the Knowledge of Construction Business Failure: A Social Network Analysis Approach. Journal of Construction Engineering and Management, 146(6):04020052.

Assaad, R. and El-adaway, I. H. (2020a). Evaluation and Prediction of the Hazard Potential Level of Dam Infrastructures Using Computational Artificial Intelligence Algorithms. Journal of Management in Engineering, 36(5):04020051.

Assaad, R. and El-adaway, I. H. (2020b). Impact of Dynamic Workforce and Workplace Variables on the Productivity of the Construction Industry: New Gross Construction Productivity Indicator. Journal of Management in Engineering, 37(1):04020092.

Assaad, R. and El-Adaway, I. H. (2021). Determining Critical Combinations of Safety Fatality Causes Using Spectral Clustering and Computational Data Mining Algorithms. Journal of Construction Engineering and Management, 147(5):04021035.

Assaad, R. and El-adaway, I. H. (2021). Guidelines for Responding to COVID-19 Pandemic: Best Practices, Impacts, and Future Research Directions. Journal of Management in Engineering, 37(3):06021001.

Assaad, R., El-Adaway, I. H., and Abotaleb, I. S. (2020b). Predicting Project Performance in the Construction Industry. Journal of Construction Engineering and Management, 146(5):04020030.

Azadeh, A., Moghaddam, M., Khakzad, M., and Ebrahimipour, V. (2012). A Flexible Neural Network-Fuzzy Mathematical Programming Algorithm for Improvement of Oil Price Estimation and Forecasting. Computers and Industrial Engineering, 62(2):421–430.

Basher, S. A. and Sadorsky, P. (2006). Oil Price Risk and Emerging Stock Markets. Global Finance Journal, 17(2):224–251.

Brownlee, J. (2017). CNN Long Short-Term Memory Network.

Chen, J. (2020). Crude Oil.

Chiroma, H., Abdulkareem, S., and Herawan, T. (2015). Evolutionary Neural Network Model for West Texas Intermediate Crude Oil Price Prediction. Applied Energy, 142:266–273.

Codding, P. W., editor (2013). Structure-Based Drug Design: Experimental and Computational Approaches. Springer Science+Business Media.

Corporate Finance Institute (2020). Crude Oil Overview.

Cybersecurity and Infrastructure Security Agency (2014). Information Technology Sector.

Dawood, T., Zhu, Z., and Zayed, T. (2018). Computer Vision-Based Model for Moisture Marks Detection and Recognition in Subway Networks. Journal of Computing in Civil Engineering, 32(2):04017079.

Degiannakis, S., Filis, G., and Arora, V. (2017). Oil Prices and Stock Markets. Independent Statistics and Analysis, US Energy Information Administration, Washington, DC, 20585, 20585.

Diebold, F. X. and Mariano, R. S. (1995). Comparing Predictive Accuracy. Journal of Business and Economic Statistics, 13(3):253–264.

Edelstein, P. and Kilian, L. (2009). How Sensitive Are Consumer Expenditures to Retail Energy Prices? Journal of Monetary Economics, 56(6):766–779.

El-Sharif, I., Brown, D., Burton, B., Nixon, B., and Russell, A. (2005). Evidence on the Nature and Extent of the Relationship Between Oil Prices and Equity Values in the UK. Energy Economics, 27(6):819–830.

Ewing, B. T. and Thompson, M. A. (2007). Dynamic Cyclical Comovements of Oil Prices With Industrial Production, Consumer Prices, Unemployment and Stock Prices. Energy Policy, 35(11):5535–5540.

Fan, R. Y., Ng, S. T., and Wong, J. M. (2010). Reliability of the Box-Jenkins Model for Forecasting Construction Demand Covering Times of Economic Austerity. Construction Management and Economics, 28(3):241–254.

Fayek, S., Xia, X., Li, L., and Zhang, X. (2020a). Photogrammetry-Based Method to Determine the Absolute Volume of Soil Specimen During Triaxial Testing. Transportation Research Record, 2674(8):206–218.

Fayek, S., Xia, X., and Zhang, X. (2020b). A Least Square Optimization Approach for Determining the Soil Boundary and Absolute Volume of Unsaturated Soils. In Geo-Congress 2020: Geo-Systems, Sustainability, Geoenvironmental Engineering, and Unsaturated Soil Mechanics, pages 394–401. Reston, VA: American Society of Civil Engineers.

Fayek, S., Xia, X., and Zhang, X. (2021). Validation of Least Square Optimization Method for Determining the Absolute Volume of Unsaturated Soils. In 4th International Conference on Transportation Geotechnics (ICTG). Reston, VA: American Society of Civil Engineers.

Filis, G., Degiannakis, S., and Floros, C. (2011). Dynamic Correlation Between Stock Market and Oil Prices: The Case of Oil-Importing and Oil-Exporting Countries. International Review of Financial Analysis, 20(3):152–164.

Gers, F. A., Eck, D., and Schmidhuber, J. (2002). Applying LSTM to Time Series Predictable Through Time-Window Approaches. In Neural Nets WIRN Vietri-01, pages 193–200. Springer, London.

Gowri, S. G., Devi, R., and Sethuraman, K. (2019). Machine Learning. International Journal of Research and Analytical Reviews.

Gupta, N. and Nigam, S. (2020). Crude Oil Price Prediction Using Artificial Neural Network. Procedia Computer Science, 170:642–647.

Harvey, D., Leybourne, S., and Newbold, P. (1997). Testing the Equality of Prediction Mean Squared Errors. International Journal of Forecasting, 13(2):281–291.

Henry-Nickie, M., Frimpong, K., and Sun, H. (2019). Trends in the Information Technology Sector.

Huang, L. and Wang, J. (2018). Global Crude Oil Price Prediction and Synchronization Based Accuracy Evaluation Using Random Wavelet Neural Network. Energy, 151:875–888.

Huang, R. D., Masulis, R. W., and Stoll, H. R. (1996). Energy Shocks and Financial Markets. Journal of Futures Markets, 16(1):1–27.

Jain, A. (2015). Machine Learning Techniques for Medical Diagnosis: A Review. In Conference on Science Technology and Management.

Jammazi, R. and Aloui, C. (2012). Crude Oil Price Forecasting: Experimental Evidence From Wavelet Decomposition and Neural Network Modeling. Energy Economics, 34(3):828–841.

Jang, Y., Jeong, I., and Cho, Y. K. (2020). Business Failure Prediction of Construction Contractors Using a LSTM RNN With Accounting, Construction Market, and Macroeconomic Variables. Journal of Management in Engineering, 36(2):04019039.

Jones, C. M. and Kaul, G. (1996). Oil and the Stock Markets. The Journal of Finance, 51(2):463–491.

Kanevski, M., Parkin, R., Pozdnukhov, A., Timonin, V., Maignan, M., Demyanov, V., and Canu, S. (2004). Environmental Data Mining and Modeling Based on Machine Learning Algorithms and Geostatistics. Environmental Modelling and Software, 19(9):845–855.

Kulkarni, S. and Haidar, I. (2009). Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Futures Prices. International Journal of Computer Science and Information Security, 2(1).

Li, X., Shang, W., and Wang, S. (2019). Text-Based Crude Oil Price Forecasting: A Deep Learning Approach. International Journal of Forecasting, 35(4):1548–1560.

Liu, J., Bai, Y., and Li, B. (2007). A New Approach to Forecast Crude Oil Price Based on Fuzzy Neural Network. In Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), Vol. 3, pages 273–277. IEEE.

Mahdiani, M. R. and Khamehchi, E. (2016). A Modified Neural Network Model for Predicting the Crude Oil Price. Intellectual Economics, 10(2):71–77.

Miller, M. (2019). Information Technology Sector: Overview and Funds.

Mingming, T. and Jinliang, Z. (2012). A Multiple Adaptive Wavelet Recurrent Neural Network Model to Analyze Crude Oil Prices. Journal of Economics and Business, 64(4):275–286.

Mishra, P. N., Surendran, S., Gadi, V. K., Joseph, R. A., and Arnepalli, D. N. (2017). Generalized Approach for Determination of Thermal Conductivity of Buffer Materials. Journal of Hazardous, Toxic, and Radioactive Waste, 21(4):04017005.

Monnier, S. (2018). Cross-Validation Tools for Time Series.

Nguyen, M. (2018). Illustrated Guide to LSTM’s and GRU’s: A Step by Step Explanation.

Nicholson, C. (2019). A Beginner’s Guide to Neural Networks and Deep Learning.

Packt (2019). Cross-Validation Strategies for Time Series Forecasting.

Pardeshi, S. (2019). CNN-LSTM Architecture and Image Captioning.

Sadorsky, P. (1999). Oil Price Shocks and Stock Market Activity. Energy Economics, 21(5):449–469.

Sarzaeim, P., Bozorg-Haddad, O., Bozorgi, A., and Loaiciga, H. A. (2017). Runoff Projection Under Climate Change Conditions With Data-Mining Methods. Journal of Irrigation and Drainage Engineering, 143(8):04017026.

Scikit-learn (2020). Cross-Validation: Evaluating Estimator Performance.

Shabri, A. and Samsudin, R. (2014). Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model. Mathematical Problems in Engineering.

Wang, J., Pan, H., and Liu, F. (2012). Forecasting Crude Oil Price and Stock Price by Jump Stochastic Time Effective Neural Network Model. Journal of Applied Mathematics.

Wang, J. and Wang, J. (2016). Forecasting Energy Market Indices With Recurrent Neural Networks: Case Study of Crude Oil Price Fluctuations. Energy, 102:365–374.

Xie, W., Yu, L., Xu, S., and Wang, S. (2006). A New Method for Crude Oil Price Forecasting Based on Support Vector Machines. In International Conference on Computational Science, pages 444–451. Springer, Berlin, Heidelberg.

Yu, L., Wang, S., and Lai, K. K. (2008). Forecasting Crude Oil Price With an Emd-based Neural Network Ensemble Learning Paradigm. Energy Economics, 30(5):2623–2635.

Yun, X. and Yoon, S. M. (2019). Impact of Oil Price Change on Airline’s Stock Price and Volatility: Evidence From China and South Korea. Energy Economics, 78:668–679.

Zhao, Y., Li, J., and Yu, L. (2017). A Deep Learning Ensemble Approach for Crude Oil Price Forecasting. Energy Economics, 66:9–16.

Zou, Y., Yu, L., Tso, G. K., and He, K. (2020). Risk Forecasting in the Crude Oil Market: A Multiscale Convolutional Neural Network Approach. Physica A: Statistical Mechanics and its Applications, 541:123360.

How to Cite
Assaad, R., & Fayek, S. (2021). Predicting the Price of Crude Oil and its Fluctuations Using Computational Econometrics: Deep Learning, LSTM, and Convolutional Neural Networks. Econometric Research in Finance, 6(2), 119-137.
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