Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods
Xiaobo Tang, Shixuan Li, Mingliang Tan, et al.
Journal of Forecasting (2020) Vol. 39, Iss. 5, pp. 769-787
Closed Access | Times Cited: 64

Showing 1-25 of 64 citing articles:

Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis
John W. Goodell, Satish Kumar, Weng Marc Lim, et al.
Journal of Behavioral and Experimental Finance (2021) Vol. 32, pp. 100577-100577
Closed Access | Times Cited: 471
A Deep Learning-Based Approach to Constructing a Domain Sentiment Lexicon: a Case Study in Financial Distress Prediction
Shixuan Li, Wenxuan Shi, Jiancheng Wang, et al.
Information Processing & Management (2021) Vol. 58, Iss. 5, pp. 102673-102673
Closed Access | Times Cited: 78
Combining feature selection, instance selection, and ensemble classification techniques for improved financial distress prediction
Chih‐Fong Tsai, Kuen‐Liang Sue, Ya‐Han Hu, et al.
Journal of Business Research (2021) Vol. 130, pp. 200-209
Closed Access | Times Cited: 55
Speech emotion recognition and text sentiment analysis for financial distress prediction
Petr Hájek, Michal Munk
Neural Computing and Applications (2023) Vol. 35, Iss. 29, pp. 21463-21477
Open Access | Times Cited: 26
Impacts of crisis on SME bankruptcy prediction models’ performance
Mário Papík, Lenka Papíková
Expert Systems with Applications (2022) Vol. 214, pp. 119072-119072
Closed Access | Times Cited: 33
Predicting financial distress of Chinese listed companies using machine learning: To what extent does textual disclosure matter?
Qi Zhao, Weijun Xu, Yucheng Ji
International Review of Financial Analysis (2023) Vol. 89, pp. 102770-102770
Closed Access | Times Cited: 19
Predicting Construction Company Insolvent Failure: A Scientometric Analysis and Qualitative Review of Research Trends
Jun Wang, Mao Li, Martin Skitmore, et al.
Sustainability (2024) Vol. 16, Iss. 6, pp. 2290-2290
Open Access | Times Cited: 7
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
The merits of a sentiment analysis of antecedent comments for the prediction of online fundraising outcomes
Wei Wang, Lihuan Guo, Yenchun Jim Wu
Technological Forecasting and Social Change (2021) Vol. 174, pp. 121070-121070
Closed Access | Times Cited: 33
The impact of machine learning on UK financial services
Bonnie Buchanan, Danika Wright
Oxford Review of Economic Policy (2021) Vol. 37, Iss. 3, pp. 537-563
Open Access | Times Cited: 33
Detecting accounting fraud in companies reporting under US GAAP through data mining
Mário Papík, Lenka Papíková
International Journal of Accounting Information Systems (2022) Vol. 45, pp. 100559-100559
Closed Access | Times Cited: 27
Anticipating financial distress of high‐tech startups in the European Union: A machine learning approach for imbalanced samples
Yang Liu, Qingguo Zeng, Bobo Li, et al.
Journal of Forecasting (2022) Vol. 41, Iss. 6, pp. 1131-1155
Closed Access | Times Cited: 22
Diagnosis with incomplete multi-view data: A variational deep financial distress prediction method
Yating Huang, Zhao Wang, Cuiqing Jiang
Technological Forecasting and Social Change (2024) Vol. 201, pp. 123269-123269
Closed Access | Times Cited: 5
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
An Ensemble Model Minimising Misjudgment Cost: Empirical Evidence From Chinese Listed Companies
Kunpeng Yuan, Mohammad Zoynul Abedin, Petr Hájek
International Journal of Finance & Economics (2025)
Open Access
Unveiling Sentiments of the Cullen Commission: Exploring AML Compliance and Regulation Through Deep Learning Techniques
Mark Lokanan
Journal of Economic Criminology (2025), pp. 100138-100138
Open Access
Machine Learning for Identifying Risk in Financial Statements: A Survey
Elias Zavitsanos, Eirini Spyropoulou, George Giannakopoulos, et al.
ACM Computing Surveys (2025)
Closed Access
The possibilities of using AutoML in bankruptcy prediction: Case of Slovakia
Mário Papík, Lenka Papíková
Technological Forecasting and Social Change (2025) Vol. 215, pp. 124098-124098
Open Access
Data is power: the impact of data asset management on expected stock returns
Fengmin Xu, Keyun Wang, Shihao Wang, et al.
Asia-Pacific Journal of Accounting & Economics (2025), pp. 1-35
Closed Access
Predicting Financial Distress in The Textile Industry: A Comparative Analysis of Meta Models and Single Classifiers
Ahmet Akusta, Musa Gün
Uluslararası Ekonomi İşletme ve Politika Dergisi (2025) Vol. 9, Iss. 1, pp. 20-36
Open Access
Forecasting nonperforming loans using machine learning
Mohammad Abdullah, Mohammad Ashraful Ferdous Chowdhury, Ajim Uddin, et al.
Journal of Forecasting (2023) Vol. 42, Iss. 7, pp. 1664-1689
Closed Access | Times Cited: 12
Impact of sustainability on financial distress in the air transport industry: the moderating effect of Asia–Pacific
Yin Shi, Xiaoni Li, Maher Asal
Financial Innovation (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 11
Do narrative-related disclosures predict corporate failure? Evidence from UK non-financial publicly quoted firms
Mohamed Elsayed, Tamer Elshandidy
International Review of Financial Analysis (2020) Vol. 71, pp. 101555-101555
Open Access | Times Cited: 30
Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises
Lenka Papíková, Mário Papík
Intelligent Systems in Accounting Finance & Management (2022) Vol. 29, Iss. 4, pp. 254-281
Closed Access | Times Cited: 18
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
Page 1 - Next Page

Scroll to top