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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

The role of news sentiment in oil futures returns and volatility forecasting: Data-decomposition based deep learning approach
Yuze Li, Shangrong Jiang, Xuerong Li, et al.
Energy Economics (2021) Vol. 95, pp. 105140-105140
Closed Access | Times Cited: 97

Showing 1-25 of 97 citing articles:

Forecasting power demand in China with a CNN-LSTM model including multimodal information
Delu Wang, Jun Gan, Jinqi Mao, et al.
Energy (2022) Vol. 263, pp. 126012-126012
Closed Access | Times Cited: 56
Renewable energy stocks forecast using Twitter investor sentiment and deep learning
Gabriel Paes Herrera, Michel Constantino, Jen‐Je Su, et al.
Energy Economics (2022) Vol. 114, pp. 106285-106285
Closed Access | Times Cited: 43
Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid COVID-19? Mixed-data sampling approach
Jing Wu, Mingchen Li, Erlong Zhao, et al.
Tourism Management (2023) Vol. 98, pp. 104759-104759
Open Access | Times Cited: 35
What can be learned from the historical trend of crude oil prices? An ensemble approach for crude oil price forecasting
Mingchen Li, Zishu Cheng, Wencan Lin, et al.
Energy Economics (2023) Vol. 123, pp. 106736-106736
Closed Access | Times Cited: 26
The role of green energy stock market in forecasting China's crude oil market: An application of IIS approach and sparse regression models
Faridoon Khan, Sara Muhammadullah, Arshian Sharif, et al.
Energy Economics (2023) Vol. 130, pp. 107269-107269
Closed Access | Times Cited: 22
A novel hybrid model for crude oil price forecasting based on MEEMD and Mix-KELM
Jingjing Li, Zhanjiang Hong, Chengyuan Zhang, et al.
Expert Systems with Applications (2024) Vol. 246, pp. 123104-123104
Closed Access | Times Cited: 11
New energy vehicles sales forecasting using machine learning: The role of media sentiment
Jin Shao, Jingke Hong, Meiping Wang, et al.
Computers & Industrial Engineering (2025) Vol. 201, pp. 110928-110928
Closed Access | Times Cited: 1
A sentiment-enhanced hybrid model for crude oil price forecasting
Yan Fang, Wenyan Wang, Pengcheng Wu, et al.
Expert Systems with Applications (2022) Vol. 215, pp. 119329-119329
Closed Access | Times Cited: 36
Oil futures volatility predictability: New evidence based on machine learning models
Xinjie Lu, Feng Ma, Jin Xu, et al.
International Review of Financial Analysis (2022) Vol. 83, pp. 102299-102299
Closed Access | Times Cited: 33
Interval prediction approach to crude oil price based on three-way clustering and decomposition ensemble learning
Bingzhen Sun, Juncheng Bai, Xiaoli Chu, et al.
Applied Soft Computing (2022) Vol. 123, pp. 108933-108933
Closed Access | Times Cited: 32
A decomposition ensemble based deep learning approach for crude oil price forecasting
He Jiang, Weiqiang Hu, Ling Xiao, et al.
Resources Policy (2022) Vol. 78, pp. 102855-102855
Closed Access | Times Cited: 30
Analysis of financial pressure impacts on the health care industry with an explainable machine learning method: China versus the USA
Futian Weng, Jianping Zhu, Cai Yang, et al.
Expert Systems with Applications (2022) Vol. 210, pp. 118482-118482
Closed Access | Times Cited: 29
The role of textual analysis in oil futures price forecasting based on machine learning approach
Xu Gong, Keqin Guan, Qiyang Chen
Journal of Futures Markets (2022) Vol. 42, Iss. 10, pp. 1987-2017
Closed Access | Times Cited: 28
Novel deterministic and probabilistic forecasting methods for crude oil price employing optimized deep learning, statistical and hybrid models
Sourav Kumar Purohit, Sibarama Panigrahi
Information Sciences (2023) Vol. 658, pp. 120021-120021
Closed Access | Times Cited: 16
The role of news-based sentiment in forecasting crude oil price during the Covid-19 pandemic
Jean‐Michel Sahut, Petr Hájek, Vladimír Olej, et al.
Annals of Operations Research (2024)
Closed Access | Times Cited: 7
Sentiment and energy price volatility: A nonlinear high frequency analysis
Fredj Jawadi, David Bourghelle, Philippe Rozin, et al.
Energy Economics (2024) Vol. 133, pp. 107465-107465
Open Access | Times Cited: 6
Introspecting predictability of market fear in Indian context during COVID-19 pandemic: An integrated approach of applied predictive modelling and explainable AI
Indranil Ghosh, Manas Kumar Sanyal
International Journal of Information Management Data Insights (2021) Vol. 1, Iss. 2, pp. 100039-100039
Open Access | Times Cited: 33
Hybrid data decomposition-based deep learning for Bitcoin prediction and algorithm trading
Yuze Li, Shangrong Jiang, Xuerong Li, et al.
Financial Innovation (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 24
Investor sentiment and the Chinese new energy stock market: A risk–return perspective
Yiran Shen, Chang Liu, Xiaolei Sun, et al.
International Review of Economics & Finance (2022) Vol. 84, pp. 395-408
Closed Access | Times Cited: 23
Oil price volatility and new evidence from news and Twitter
Hooman Abdollahi
Energy Economics (2023) Vol. 122, pp. 106711-106711
Open Access | Times Cited: 15
Dynamic impacts of online investor sentiment on international crude oil prices
Lu‐Tao Zhao, Yue-Yue Xing, Qiu-Rong Zhao, et al.
Resources Policy (2023) Vol. 82, pp. 103506-103506
Closed Access | Times Cited: 13
The relationship between social media sentiment and house prices in China: Evidence from text mining and wavelet analysis
Jin Shao, Jingke Hong, Xianzhu Wang, et al.
Finance research letters (2023) Vol. 57, pp. 104212-104212
Closed Access | Times Cited: 13
Forecasting stock index return and volatility based on GAVMD- Carbon-BiLSTM: How important is carbon emission trading?
Zisheng Ouyang, Min Lu, Yongzeng Lai
Energy Economics (2023) Vol. 128, pp. 107134-107134
Closed Access | Times Cited: 13
Revolutionizing agricultural stock volatility forecasting: a comparative study of machine learning and HAR-RV models
Houjian Li, Xinya Huang, Fangyuan Luo, et al.
Journal of Applied Economics (2025) Vol. 28, Iss. 1
Open Access
Hybrid ML Models for Volatility Prediction in Financial Risk Management
Satish Kumar, Amar Rao, Monika Dhochak
International Review of Economics & Finance (2025), pp. 103915-103915
Open Access
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