Managing risk and volatility in oil-dependent economies: the role of advanced predictive analytics
Corresponding Author(s) : Amirali Saifoddin
Future Energy,
Vol. 4 No. 4 (2025): November 2025 Issue
Abstract
The forecasting of oil production, demand, and prices holds critical significance for global economic stability and growth. Oil plays a crucial role in determining economic performance, making reliable price estimations essential for shaping public policy and guiding investment decisions. In this study, advanced neural network models were employed to enhance the accuracy of oil market forecasts, with a particular focus on their economic implications. Using Python-based implementations of Long Short-Term Memory (LSTM), Radial Basis Function (RBF), and multilayer perceptron (MLP) networks, the research compares the effectiveness of these approaches in crude oil price forecasting. The evaluation of model outputs using technical indicators revealed that the multilayer perceptron network yielded the best results. During training, it reached an average squared error of 55.28, a root mean squared error of 7.43, and a mean absolute error of 5.55; while in testing, the values were 116.01, 12.96, and 10.73, respectively. Overall, the comparative analysis indicates that the multilayer perceptron consistently surpassed both LSTM and RBF models in minimizing prediction errors. The economic relevance of these findings is underscored by the model's potential to enhance decision-making processes for investors, policymakers, and oil producers by offering more reliable forecasts. By improving accuracy by 20 to 30 percent compared to previous studies, this research provides valuable insights into optimizing resource allocation and mitigating the economic risks associated with oil price volatility.
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- H. Miao, S. Ramchander, T. Wang, and D. Yang, "Influential factors in crude oil price forecasting," Energy Economics, vol. 68, pp. 77-88, 2017.
- Z. A. Sadik, P. M. Date, and G. Mitra, "Forecasting crude oil futures prices using global macroeconomic news sentiment," IMA Journal of Management Mathematics, vol. 31, no. 2, pp. 191-215, 2020.
- M. Wang et al., "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied energy, vol. 220, pp. 480-495, 2018.
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- X. Zhang, C. Zhao, X. Zhou, X. Wu, Y. Li, and M. Wu, "Capital market and public health emergencies in Chinese sports industry based on a market model," Data Science in Finance and Economics, vol. 3, no. 2, pp. 112-132, 2023.
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- Y. Lin, Y. Xiao, and F. Li, "Forecasting crude oil price volatility via a HM-EGARCH model," Energy Economics, vol. 87, p. 104693, 2020.
- M. M. Mostafa and A. A. El-Masry, "Oil price forecasting using gene expression programming and artificial neural networks," Economic Modelling, vol. 54, pp. 40-53, 2016.
- Y. Zhao, J. Li, and L. Yu, "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, vol. 66, pp. 9-16, 2017.
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- J. Zhang, L. Xing, Z. Tan, H. Wang, and K. Wang, "Multi-head attention fusion networks for multi-modal speech emotion recognition," Computers & Industrial Engineering, vol. 168, p. 108078, 2022.
- X. Ma, Z. Dai, Z. He, J. Ma, Y. Wang, and Y. Wang, "Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction," Sensors, vol. 17, no. 4, p. 818, 2017.
- Z. Cen and J. Wang, "Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer," Energy, vol. 169, pp. 160-171, 2019.
- G. A. Busari and D. H. Lim, "Crude oil price prediction: A comparison between AdaBoost-LSTM and AdaBoost-GRU for improving forecasting performance," Computers & Chemical Engineering, vol. 155, p. 107513, 2021.
- Y. Hu, J. Ni, and L. Wen, "A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction," Physica A: Statistical Mechanics and its Applications, vol. 557, p. 124907, 2020.
- Z. Zhang, M. He, Y. Zhang, and Y. Wang, "Geopolitical risk trends and crude oil price predictability," Energy, vol. 258, p. 124824, 2022.
- Z. Niu, Y. Liu, W. Gao, and H. Zhang, "The role of coronavirus news in the volatility forecasting of crude oil futures markets: evidence from China," Resources policy, vol. 73, p. 102173, 2021.
- C.-H. Hui, C.-F. Lo, C.-H. Cheung, and A. Wong, "Crude oil price dynamics with crash risk under fundamental shocks," The North American Journal of Economics and Finance, vol. 54, p. 101238, 2020.
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References
H. Miao, S. Ramchander, T. Wang, and D. Yang, "Influential factors in crude oil price forecasting," Energy Economics, vol. 68, pp. 77-88, 2017.
Z. A. Sadik, P. M. Date, and G. Mitra, "Forecasting crude oil futures prices using global macroeconomic news sentiment," IMA Journal of Management Mathematics, vol. 31, no. 2, pp. 191-215, 2020.
M. Wang et al., "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied energy, vol. 220, pp. 480-495, 2018.
K. Zhang, M. Hong, K. Zhang, and M. Hong, "Forecasting crude oil price using LSTM neural networks," Data Sci. Financ. Econ, vol. 2, pp. 163-180, 2022.
X. Zhang, C. Zhao, X. Zhou, X. Wu, Y. Li, and M. Wu, "Capital market and public health emergencies in Chinese sports industry based on a market model," Data Science in Finance and Economics, vol. 3, no. 2, pp. 112-132, 2023.
H. Abdollahi, "A novel hybrid model for forecasting crude oil price based on time series decomposition," Applied energy, vol. 267, p. 115035, 2020.
Y. Wei, Y. Wang, and D. Huang, "Forecasting crude oil market volatility: Further evidence using GARCH-class models," Energy Economics, vol. 32, no. 6, pp. 1477-1484, 2010.
B. Wu, L. Wang, S.-X. Lv, and Y.-R. Zeng, "Forecasting oil consumption with attention-based IndRNN optimized by adaptive differential evolution," Applied Intelligence, vol. 53, no. 5, pp. 5473-5496, 2023.
Y. Lin, Y. Xiao, and F. Li, "Forecasting crude oil price volatility via a HM-EGARCH model," Energy Economics, vol. 87, p. 104693, 2020.
M. M. Mostafa and A. A. El-Masry, "Oil price forecasting using gene expression programming and artificial neural networks," Economic Modelling, vol. 54, pp. 40-53, 2016.
Y. Zhao, J. Li, and L. Yu, "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, vol. 66, pp. 9-16, 2017.
L. Yu, X. Zhang, and S. Wang, "Assessing potentiality of support vector machine method in crude oil price forecasting," EURASIA Journal of Mathematics, Science and Technology Education, vol. 13, no. 12, pp. 7893-7904, 2017.
K. He, Q. Yang, and Y. Zou, "Crude Oil Price Prediction using Embedding Convolutional Neural Network Model," Procedia Computer Science, vol. 214, pp. 959-964, 2022.
I. Méndez-Jiménez and M. Cárdenas-Montes, "Time series decomposition for improving the forecasting performance of convolutional neural networks," in Advances in Artificial Intelligence: 18th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2018, Granada, Spain, October 23–26, 2018, Proceedings 18, 2018, pp. 87-97: Springer.
C. Liu, W. Hou, and D. Liu, "Foreign exchange rates forecasting with convolutional neural network," Neural Processing Letters, vol. 46, pp. 1095-1119, 2017.
J. Zhang, L. Xing, Z. Tan, H. Wang, and K. Wang, "Multi-head attention fusion networks for multi-modal speech emotion recognition," Computers & Industrial Engineering, vol. 168, p. 108078, 2022.
X. Ma, Z. Dai, Z. He, J. Ma, Y. Wang, and Y. Wang, "Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction," Sensors, vol. 17, no. 4, p. 818, 2017.
Z. Cen and J. Wang, "Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer," Energy, vol. 169, pp. 160-171, 2019.
G. A. Busari and D. H. Lim, "Crude oil price prediction: A comparison between AdaBoost-LSTM and AdaBoost-GRU for improving forecasting performance," Computers & Chemical Engineering, vol. 155, p. 107513, 2021.
Y. Hu, J. Ni, and L. Wen, "A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction," Physica A: Statistical Mechanics and its Applications, vol. 557, p. 124907, 2020.
Z. Zhang, M. He, Y. Zhang, and Y. Wang, "Geopolitical risk trends and crude oil price predictability," Energy, vol. 258, p. 124824, 2022.
Z. Niu, Y. Liu, W. Gao, and H. Zhang, "The role of coronavirus news in the volatility forecasting of crude oil futures markets: evidence from China," Resources policy, vol. 73, p. 102173, 2021.
C.-H. Hui, C.-F. Lo, C.-H. Cheung, and A. Wong, "Crude oil price dynamics with crash risk under fundamental shocks," The North American Journal of Economics and Finance, vol. 54, p. 101238, 2020.
Y. Bai, X. Li, H. Yu, and S. Jia, "Crude oil price forecasting incorporating news text," International Journal of Forecasting, vol. 38, no. 1, pp. 367-383, 2022.
A. L. Zahouani and H. Boubaker, "Forecasting Crude Oil Price with Hybrid Approaches," Rev. Econ. Financ., vol. 21, pp. 564-576, 2023.