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The role of news sentiment in oil futures returns and volatility forecasting: Data-decomposition based deep learning approach

Citations

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Cited by:

  1. 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.
  2. Mohammed-Khalil Ghali & Cecil Pang & Oscar Molina & Carlos Gershenson-Garcia & Daehan Won, 2025. "Forecasting Commodity Price Shocks Using Temporal and Semantic Fusion of Prices Signals and Agentic Generative AI Extracted Economic News," Papers 2508.06497, arXiv.org.
  3. Yuan, Ying & Qu, Yong & Wang, Tianyang, 2025. "Predicting risk premiums: A constraint-based model," Journal of Empirical Finance, Elsevier, vol. 83(C).
  4. Zheng, Li & Sun, Yuying & Wang, Shouyang, 2024. "A novel interval-based hybrid framework for crude oil price forecasting and trading," Energy Economics, Elsevier, vol. 130(C).
  5. Tsai, I-Chun & Chen, Han-Bo & Lin, Che-Chun, 2024. "The ability of energy commodities to hedge the dynamic risk of epidemic black swans," Resources Policy, Elsevier, vol. 89(C).
  6. 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).
  7. Nguyen, Bich Ngoc, 2025. "A sentiment-based approach to predict energy price volatility using distilRoBERTa and GARCH models," Energy Economics, Elsevier, vol. 149(C).
  8. Ouyang, Zisheng & Lu, Min & Lai, Yongzeng, 2023. "Forecasting stock index return and volatility based on GAVMD- Carbon-BiLSTM: How important is carbon emission trading?," Energy Economics, Elsevier, vol. 128(C).
  9. Hua, Xia & Dong, Dairui & Xu, Zhiwei & Huang, Wentao, 2025. "Official media sentiments toward energy and equity returns: Evidence from China," Energy, Elsevier, vol. 340(C).
  10. Zhilun Jiao & Chenrui Zhang & Wenwen Li, 2025. "Artificial Intelligence in Energy Economics Research: A Bibliometric Review," Energies, MDPI, vol. 18(2), pages 1-30, January.
  11. Xu, Zhiwei & Gan, Shiqi & Hua, Xia & Xiong, Yujie, 2024. "Can the sentiment of the official media predict the return volatility of the Chinese crude oil futures?," Energy Economics, Elsevier, vol. 140(C).
  12. Xing, Li-Min & Zhang, Yue-Jun, 2022. "Forecasting crude oil prices with shrinkage methods: Can nonconvex penalty and Huber loss help?," Energy Economics, Elsevier, vol. 110(C).
  13. Li, Mingchen & Cheng, Zishu & Lin, Wencan & Wei, Yunjie & Wang, Shouyang, 2023. "What can be learned from the historical trend of crude oil prices? An ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 123(C).
  14. Jin Shao & Lean Yu & Jingke Hong & Xianzhu Wang, 2025. "Forecasting house price index with social media sentiment: A decomposition–ensemble approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(1), pages 216-241, January.
  15. Shen, Yiran & Liu, Chang & Sun, Xiaolei & Guo, Kun, 2023. "Investor sentiment and the Chinese new energy stock market: A risk–return perspective," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 395-408.
  16. Ewald, Christian Oliver & Li, Yaoyu, 2024. "The role of news sentiment in salmon price prediction using deep learning," Journal of Commodity Markets, Elsevier, vol. 36(C).
  17. Yuze Li & Shangrong Jiang & Yunjie Wei & Shouyang Wang, 2021. "Take Bitcoin into your portfolio: a novel ensemble portfolio optimization framework for broad commodity assets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-26, December.
  18. Gaies, Brahim & Nakhli, Mohamed Sahbi & Sahut, Jean-Michel, 2024. "Unravelling the complex interactions between sentiment of uncertainty and foreign capital flows: Evidence from Brazil and South Korea," Economic Modelling, Elsevier, vol. 141(C).
  19. Shao, Jin & Hong, Jingke & Wang, Xianzhu & Yan, Xiaochen, 2023. "The relationship between social media sentiment and house prices in China: Evidence from text mining and wavelet analysis," Finance Research Letters, Elsevier, vol. 57(C).
  20. Yan, Wan-Lin & Cheung, Adrian (Wai Kong), 2025. "Quantile connectedness among climate policy uncertainty, news sentiment, oil and renewables in China," Research in International Business and Finance, Elsevier, vol. 76(C).
  21. Ouyang, Zisheng & Lu, Min & Ouyang, Zhongzhe & Zhou, Xuewei & Wang, Ren, 2024. "A novel integrated method for improving the forecasting accuracy of crude oil: ESMD-CFastICA-BiLSTM-Attention," Energy Economics, Elsevier, vol. 138(C).
  22. Fatma Hachicha & Nada Suissi & Amine Lahiani, 2025. "Bayesian Network in Machine Learning: An Empirical Investigation to Assess the Price Clustering Model During Crises," Computational Economics, Springer;Society for Computational Economics, vol. 66(6), pages 4897-4922, December.
  23. Haas, Christian & Budin, Constantin & d’Arcy, Anne, 2024. "How to select oil price prediction models — The effect of statistical and financial performance metrics and sentiment scores," Energy Economics, Elsevier, vol. 133(C).
  24. Ying Nie & Hui Wang & Huaqiang Zhang & Yuqiang Chen, 2026. "Research on flood prediction based on hybrid AI model VMD_BiLSTM: a case study in BeiJing flood, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 122(6), pages 1-28, March.
  25. Hongli Niu & Qiaoying Pan & Kunliang Xu, 2023. "Hybrid deep learning models with multi-classification investor sentiment to forecast the prices of China’s leading stocks," PLOS ONE, Public Library of Science, vol. 18(11), pages 1-24, November.
  26. Lu, Xinjie & Ma, Feng & Xu, Jin & Zhang, Zehui, 2022. "Oil futures volatility predictability: New evidence based on machine learning models11All the authors contribute to the paper equally," International Review of Financial Analysis, Elsevier, vol. 83(C).
  27. Zhao, Lu-Tao & Xing, Yue-Yue & Zhao, Qiu-Rong & Chen, Xue-Hui, 2023. "Dynamic impacts of online investor sentiment on international crude oil prices," Resources Policy, Elsevier, vol. 82(C).
  28. Tan, Jinghua & Li, Zhixi & Zhang, Chuanhui & Shi, Long & Jiang, Yuansheng, 2024. "A multiscale time-series decomposition learning for crude oil price forecasting," Energy Economics, Elsevier, vol. 136(C).
  29. Jawadi, Fredj & Bourghelle, David & Rozin, Philippe & Cheffou, Abdoulkarim Idi & Uddin, Gazi Salah, 2024. "Sentiment and energy price volatility: A nonlinear high frequency analysis," Energy Economics, Elsevier, vol. 133(C).
  30. Jiang, He & Hu, Weiqiang & Xiao, Ling & Dong, Yao, 2022. "A decomposition ensemble based deep learning approach for crude oil price forecasting," Resources Policy, Elsevier, vol. 78(C).
  31. Wang, Delu & Gan, Jun & Mao, Jinqi & Chen, Fan & Yu, Lan, 2023. "Forecasting power demand in China with a CNN-LSTM model including multimodal information," Energy, Elsevier, vol. 263(PE).
  32. Khan, Faridoon & Muhammadullah, Sara & Sharif, Arshian & Lee, Chien-Chiang, 2024. "The role of green energy stock market in forecasting China's crude oil market: An application of IIS approach and sparse regression models," Energy Economics, Elsevier, vol. 130(C).
  33. Duan, Huayou & Zhao, Chenchen & Wang, Lu & Liu, Guangqiang, 2024. "The relationship between renewable energy attention and volatility: A HAR model with markov time-varying transition probability," Research in International Business and Finance, Elsevier, vol. 71(C).
  34. Kumar, Satish & Rao, Amar & Dhochak, Monika, 2025. "Hybrid ML models for volatility prediction in financial risk management," International Review of Economics & Finance, Elsevier, vol. 98(C).
  35. Yue-Jun Zhang & Han Zhang & Rangan Gupta, 2021. "Forecasting the Artificial Intelligence Index Returns: A Hybrid Approach," Working Papers 202182, University of Pretoria, Department of Economics.
  36. Xu Gong & Keqin Guan & Qiyang Chen, 2022. "The role of textual analysis in oil futures price forecasting based on machine learning approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1987-2017, October.
  37. Ma, Chenyao & Yan, Sheng, 2022. "Deep learning in the Chinese stock market: The role of technical indicators," Finance Research Letters, Elsevier, vol. 49(C).
  38. Yue-Jun Zhang & Han Zhang & Rangan Gupta, 2023. "A new hybrid method with data-characteristic-driven analysis for artificial intelligence and robotics index return forecasting," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
  39. Kumar Kulbhaskar, Anamika & Subramaniam, Sowmya, 2023. "Breaking news headlines: Impact on trading activity in the cryptocurrency market," Economic Modelling, Elsevier, vol. 126(C).
  40. Yi Chen, 2025. "A novel method for corn futures price prediction integrating decomposition, denoising, feature selection and hybrid networks," Annals of Operations Research, Springer, vol. 353(2), pages 449-484, October.
  41. Zhao, Lu-Tao & Wang, Dai-Song & Ren, Zhong-Yuan, 2024. "The impact of joint events on oil price volatility: Evidence from a dynamic graphical news analysis model," Economic Modelling, Elsevier, vol. 130(C).
  42. Xiaohong Shen & Gaoshan Wang & Yue Wang & Alfred Peris, 2021. "The Influence of Research Reports on Stock Returns: The Mediating Effect of Machine-Learning-Based Investor Sentiment," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-14, December.
  43. Werner Kristjanpoller, 2024. "A hybrid econometrics and machine learning based modeling of realized volatility of natural gas," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-32, December.
  44. Xianfei Hui & Baiqing Sun & Hui Jiang & Yan Zhou, 2022. "Modeling dynamic volatility under uncertain environment with fuzziness and randomness," Papers 2204.12657, arXiv.org, revised Oct 2022.
  45. Pan, Zhiyuan & Fu, Ziqian & Wang, Yudong & Dong, Qingma, 2024. "Exploiting the sentiments: A simple approach for improving cross hedging effectiveness," Energy Economics, Elsevier, vol. 134(C).
  46. Wang, Xiangning & Huang, Qian & Zhang, Shuguang, 2023. "Effects of macroeconomic factors on stock prices for BRICS using the variational mode decomposition and quantile method," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
  47. Lea-Rachel Kosnik, 2023. "Additional evidence on gender and language in academic economics research," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(11), pages 5949-5968, November.
  48. Yuze Li & Shangrong Jiang & Xuerong Li & Shouyang Wang, 2022. "Hybrid data decomposition-based deep learning for Bitcoin prediction and algorithm trading," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-24, December.
  49. Chi, Yeguang & El-Jahel, Lina & Vu, Thanh, 2024. "Novel and old news sentiment in commodity futures markets," Energy Economics, Elsevier, vol. 140(C).
  50. Zhu, Bangzhu & Tian, Chao & Wang, Ping, 2024. "Exploring the relationship between Chinese crude oil futures market efficiency and market micro characteristics," Energy Economics, Elsevier, vol. 134(C).
  51. Wang, Yuejing & Ye, Wuyi & Jiang, Ying & Liu, Xiaoquan, 2024. "Volatility prediction for the energy sector with economic determinants: Evidence from a hybrid model," International Review of Financial Analysis, Elsevier, vol. 92(C).
  52. Chou, Ke-Hsin & Day, Min-Yuh & Chiu, Chien-Liang, 2023. "Do bitcoin news information flow and return volatility fit the sequential information arrival hypothesis and the mixture of distribution hypothesis?," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 365-385.
  53. Herrera, Gabriel Paes & Constantino, Michel & Su, Jen-Je & Naranpanawa, Athula, 2022. "Renewable energy stocks forecast using Twitter investor sentiment and deep learning," Energy Economics, Elsevier, vol. 114(C).
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