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OpenAlex Citation Counts

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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:

Financial distress prediction: Regularized sparse-based Random Subspace with ER aggregation rule incorporating textual disclosures
Gang Wang, Jingling Ma, Gang Chen, et al.
Applied Soft Computing (2020) Vol. 90, pp. 106152-106152
Closed Access | Times Cited: 44

Showing 1-25 of 44 citing articles:

Multi-class financial distress prediction based on support vector machines integrated with the decomposition and fusion methods
Jie Sun, Hamido Fujita, Yujiao Zheng, et al.
Information Sciences (2021) Vol. 559, pp. 153-170
Closed Access | Times Cited: 102
Survey, classification and critical analysis of the literature on corporate bankruptcy and financial distress prediction
Jinxian Zhao, Jamal Ouenniche, Johannes De Smedt
Machine Learning with Applications (2024) Vol. 15, pp. 100527-100527
Open Access | Times Cited: 16
A novel ensemble feature selection method by integrating multiple ranking information combined with an SVM ensemble model for enterprise credit risk prediction in the supply chain
Gang Yao, Xiaojian Hu, Guanxiong Wang
Expert Systems with Applications (2022) Vol. 200, pp. 117002-117002
Closed Access | Times Cited: 55
Predicting financial distress using multimodal data: An attentive and regularized deep learning method
Wanliu Che, Zhao Wang, Cuiqing Jiang, et al.
Information Processing & Management (2024) Vol. 61, Iss. 4, pp. 103703-103703
Open Access | Times Cited: 14
Multi-class imbalanced enterprise credit evaluation based on asymmetric bagging combined with light gradient boosting machine
Jie Sun, Jie Li, Hamido Fujita
Applied Soft Computing (2022) Vol. 130, pp. 109637-109637
Closed Access | Times Cited: 35
Systematic Review of Financial Distress Identification using Artificial Intelligence Methods
Dovilė Kuizinienė, Tomas Krilavičius, Robertas Damaševičius, et al.
Applied Artificial Intelligence (2022) Vol. 36, Iss. 1
Open Access | Times Cited: 31
ESG performance and financial distress prediction of energy enterprises
Yang Song, Runfei Li, Zhipeng Zhang, et al.
Finance research letters (2024) Vol. 65, pp. 105546-105546
Closed Access | Times Cited: 6
Corporate financial distress prediction using the risk-related information content of annual reports
Petr Hájek, Michal Munk
Information Processing & Management (2024) Vol. 61, Iss. 5, pp. 103820-103820
Closed Access | Times Cited: 6
Interpreting the prediction results of the tree‐based gradient boosting models for financial distress prediction with an explainable machine learning approach
Jiaming Liu, Chengzhang Li, Peng Ouyang, et al.
Journal of Forecasting (2022) Vol. 42, Iss. 5, pp. 1112-1137
Closed Access | Times Cited: 27
Enterprise credit risk prediction using supply chain information: A decision tree ensemble model based on the differential sampling rate, Synthetic Minority Oversampling Technique and AdaBoost
Gang Yao, Xiaojian Hu, Taiyun Zhou, et al.
Expert Systems (2022) Vol. 39, Iss. 6
Closed Access | Times Cited: 22
Predicting financial distress using machine learning approaches: Evidence China
Md Jahidur Rahman, Hongtao Zhu
Journal of Contemporary Accounting & Economics (2024) Vol. 20, Iss. 1, pp. 100403-100403
Closed Access | Times Cited: 4
Predicting financial distress in high-dimensional imbalanced datasets: a multi-heterogeneous self-paced ensemble learning framework
Ruize Gao, Shaoze Cui, Yu Wang, et al.
Financial Innovation (2025) Vol. 11, Iss. 1
Open Access
Predicting and interpreting financial distress using a weighted boosted tree-based tree
Wanan Liu, Hong Fan, Min Xia, et al.
Engineering Applications of Artificial Intelligence (2022) Vol. 116, pp. 105466-105466
Closed Access | Times Cited: 17
Integrating the sentiments of multiple news providers for stock market index movement prediction: A deep learning approach based on evidential reasoning rule
Ruize Gao, Shaoze Cui, Hongshan Xiao, et al.
Information Sciences (2022) Vol. 615, pp. 529-556
Closed Access | Times Cited: 16
Examining the Impact of China’s New Environmental Protection Law on Enterprise Productivity and Sustainable Development
Kai Qu, Yapeng Zhang, Yinling Liu, et al.
Journal of the Knowledge Economy (2023)
Closed Access | Times Cited: 9
Multi‐class financial distress prediction based on stacking ensemble method
Xiaofang Chen, Wu Chong, Zijiao Zhang, et al.
International Journal of Finance & Economics (2024)
Closed Access | Times Cited: 3
A new interval type-2 fuzzy approach for multi-scenario project cash flow assessment based on alternative queuing method and dependency structure matrix with a case study
Seyed Ali Mirnezami, Seyed Meysam Mousavi, Vahid Mohagheghi
Engineering Applications of Artificial Intelligence (2020) Vol. 95, pp. 103815-103815
Closed Access | Times Cited: 23
Construction and analysis of the financing risk network of Chinese fisheries enterprises
Shi-Tong Zhang, Tao Li
Ocean & Coastal Management (2024) Vol. 251, pp. 107064-107064
Closed Access | Times Cited: 2
Financial risk forewarning with an interpretable ensemble learning approach: An empirical analysis based on Chinese listed companies
Shangkun Deng, Qunfang Luo, Yingke Zhu, et al.
Pacific-Basin Finance Journal (2024) Vol. 85, pp. 102393-102393
Closed Access | Times Cited: 2
Financial distress prediction using the Q&A text of online interactive platforms
Cuiqing Jiang, Lan Ma, Zhao Wang, et al.
Electronic Commerce Research and Applications (2023) Vol. 61, pp. 101292-101292
Closed Access | Times Cited: 6
Artificial Intelligence and Machine Learning in Financial Services to Improve the Business System
Komalpreet Kaur, Yogesh Kumar, Sukhpreet Kaur
Disruptive technologies and digital transformations for society 5.0 (2023), pp. 3-30
Closed Access | Times Cited: 6
Development and application of a hybrid forecasting framework based on improved extreme learning machine for enterprise financing risk
Zongguo Ma, Xu Wang, Yan Hao
Expert Systems with Applications (2022) Vol. 215, pp. 119373-119373
Closed Access | Times Cited: 10
An improved ensemble learning method for exchange rate forecasting based on complementary effect of shallow and deep features
Gang Wang, Tao Tao, Jingling Ma, et al.
Expert Systems with Applications (2021) Vol. 184, pp. 115569-115569
Closed Access | Times Cited: 13
Using social media information to predict the credit risk of listed enterprises in the supply chain
Gang Yao, Xiaojian Hu, Liangcheng Xu, et al.
Kybernetes (2022) Vol. 52, Iss. 11, pp. 4993-5016
Closed Access | Times Cited: 8
Financial distress prediction using an improved particle swarm optimization wrapper feature selection method and tree boosting ensemble
Jiaming Liu, Zihang Wang
Journal of the Operational Research Society (2024), pp. 1-24
Closed Access | Times Cited: 1
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