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Predicting the changes in the WTI crude oil price dynamics using machine learning models

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Cited by:

  1. Yuyang, Liu, 2024. "Natural resource efficiency and the road to a green economy: From scarcity to availability," Resources Policy, Elsevier, vol. 89(C).
  2. Taskin, Dilvin & Sariyer, Görkem & Acar, Ece & Cagli, Efe Caglar, 2025. "Do past ESG scores efficiently predict future ESG performance?," Research in International Business and Finance, Elsevier, vol. 74(C).
  3. Hyeon-Seok Kim & Hui-Sang Kim & Sun-Yong Choi, 2024. "Investigating the Impact of Agricultural, Financial, Economic, and Political Factors on Oil Forward Prices and Volatility: A SHAP Analysis," Energies, MDPI, vol. 17(5), pages 1-24, February.
  4. Ma, Yilin & Wang, Yudong & Wang, Weizhong & Zhang, Chong, 2023. "Portfolios with return and volatility prediction for the energy stock market," Energy, Elsevier, vol. 270(C).
  5. Gkillas, Konstantinos & Manickavasagam, Jeevananthan & Visalakshmi, S., 2022. "Effects of fundamentals, geopolitical risk and expectations factors on crude oil prices," Resources Policy, Elsevier, vol. 78(C).
  6. Zhang, Zhenya & Chang, Zheren & Gan, Yufei & Li, Jiayan, 2025. "Renewable energy, innovation, and stock markets: Machine learning perspectives on environmental sustainability," International Review of Financial Analysis, Elsevier, vol. 97(C).
  7. Xu, Kunliang & Wang, Weiqing, 2023. "Limited information limits accuracy: Whether ensemble empirical mode decomposition improves crude oil spot price prediction?," International Review of Financial Analysis, Elsevier, vol. 87(C).
  8. Emanuel Kohlscheen, 2024. "Forecasting oil prices with random forests," Empirical Economics, Springer, vol. 66(2), pages 927-943, February.
  9. Zhang, Jiahao & Chen, Xiaodan & Wei, Yu & Bai, Lan, 2023. "Does the connectedness among fossil energy returns matter for renewable energy stock returns? Fresh insights from the Cross-Quantilogram analysis," International Review of Financial Analysis, Elsevier, vol. 88(C).
  10. Chen, Fu & Tiwari, Sunil & Mohammed, Kamel Si & Huo, Weidong & Jamróz, Paweł, 2023. "Minerals resource rent responses to economic performance, greener energy, and environmental policy in China: Combination of ML and ANN outputs," Resources Policy, Elsevier, vol. 81(C).
  11. Yu, Yue & Wang, Jianzhou & Jiang, He & Lu, Haiyan, 2025. "How to manage a multifactor-driven crude oil market more effectively? A revisit based on the multiple criteria perspective," Resources Policy, Elsevier, vol. 100(C).
  12. Vicknair, David & Tansey, Michael & O'Brien, Thomas E., 2022. "Measuring fossil fuel reserves: A simulation and review of the U.S. Securities and Exchange Commission approach," Resources Policy, Elsevier, vol. 79(C).
  13. Zhang, Xiheng & Liu, Jiayu & Zhang, Kaiqi & Robert, James, 2023. "Analysis of firm performance in presence of oil price shocks: Importance of skilled management," Resources Policy, Elsevier, vol. 86(PA).
  14. Jean-Michel Sahut & Petr Hajek & Vladimir Olej & Lubica Hikkerova, 2025. "The role of news-based sentiment in forecasting crude oil price during the Covid-19 pandemic," Annals of Operations Research, Springer, vol. 345(2), pages 861-884, February.
  15. Liang, Qian & Lin, Qingyuan & Guo, Mengzhuo & Lu, Quanying & Zhang, Dayong, 2025. "Forecasting crude oil prices: A Gated Recurrent Unit-based nonlinear Granger Causality model," International Review of Financial Analysis, Elsevier, vol. 102(C).
  16. Ding, Lili & Zhao, Haoran & Zhang, Rui, 2024. "Predicting multi-frequency crude oil price dynamics: Based on MIDAS and STL methods," Energy, Elsevier, vol. 313(C).
  17. Xu, Bin & Lin, Boqiang, 2023. "Assessing the green energy development in China and its carbon reduction effect: Using a quantile approach," Energy Economics, Elsevier, vol. 126(C).
  18. repec:zib:zbjtin:v:3:y:2023:i:1:p:22-28 is not listed on IDEAS
  19. Qiang Cao & Qin Hong & Wenmei Yu, 2025. "Oil price shocks, policy uncertainty, and China’s carbon emissions trading market price," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-11, December.
  20. Zhao, Yang & Wang, Jianzhou & Wang, Shuai & Zheng, Jingwei & Lv, Mengzheng, 2025. "Using explainable deep learning to improve decision quality: Evidence from carbon trading market," Omega, Elsevier, vol. 133(C).
  21. Liang, Xuedong & Luo, Peng & Li, Xiaoyan & Wang, Xia & Shu, Lingli, 2023. "Crude oil price prediction using deep reinforcement learning," Resources Policy, Elsevier, vol. 81(C).
  22. Sen, Doruk & Hamurcuoglu, K. Irem & Ersoy, Melisa Z. & Tunç, K.M. Murat & Günay, M. Erdem, 2023. "Forecasting long-term world annual natural gas production by machine learning," Resources Policy, Elsevier, vol. 80(C).
  23. Chuandi Fang & Jinhua Cheng & Zhe You & Jiahao Chen & Jing Peng, 2023. "A Detailed Examination of China’s Clean Energy Mineral Consumption: Footprints, Trends, and Drivers," Sustainability, MDPI, vol. 15(23), pages 1-26, November.
  24. Jiang, Lan & Jiang, Hua, 2023. "Analysis of predictions considering mineral prices, residential energy, and environmental risk: Evidence from the USA in COP 26 perspective," Resources Policy, Elsevier, vol. 82(C).
  25. Shucheng Lin & Yue Wang & Haocheng Wei & Xiaoyi Wang & Zhong Wang, 2025. "Hybrid Method for Oil Price Prediction Based on Feature Selection and XGBOOST-LSTM," Energies, MDPI, vol. 18(9), pages 1-27, April.
  26. Liu, Jiexian, 2024. "Analyzing the Co-movement of FinTech market efficiency and oil Resource efficiency: An Input-Output study," Resources Policy, Elsevier, vol. 90(C).
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