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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:

Default Patterns in Seven EU Countries: A Random Forest Approach
Andreas Behr, Jurij Weinblat
International Journal of the Economics of Business (2016) Vol. 24, Iss. 2, pp. 181-222
Closed Access | Times Cited: 26

Showing 1-25 of 26 citing articles:

A Bolasso based consistent feature selection enabled random forest classification algorithm: An application to credit risk assessment
Nisha Arora, Pankaj Deep Kaur
Applied Soft Computing (2019) Vol. 86, pp. 105936-105936
Closed Access | Times Cited: 156
Credit spread approximation and improvement using random forest regression
Mathieu Mercadier, Jean‐Pierre Lardy
European Journal of Operational Research (2019) Vol. 277, Iss. 1, pp. 351-365
Open Access | Times Cited: 49
Solvency Risk and Corporate Performance: A Case Study on European Retailers
Alexandra Horobeţ, Ștefania Cristina Curea, Alexandra Smedoiu Popoviciu, et al.
Journal of risk and financial management (2021) Vol. 14, Iss. 11, pp. 536-536
Open Access | Times Cited: 32
A multi-level sparse attentive fusion network integrating hard and soft information for firm-level loan default prediction
Jingling Ma, Junqiao Gong, Gang Wang, et al.
Electronic Commerce Research and Applications (2025), pp. 101479-101479
Closed Access
What makes companies zombie? Detecting the most important zombification feature using tree-based machine learning
Rayenda Khresna Brahmana
Expert Systems with Applications (2025), pp. 126538-126538
Open Access
Empirical Analysis of Ensemble Learning for Imbalanced Credit Scoring Datasets: A Systematic Review
Sudhansu R. Lenka, Sukant Kishoro Bisoy, Rojalina Priyadarshini, et al.
Wireless Communications and Mobile Computing (2022) Vol. 2022, pp. 1-18
Open Access | Times Cited: 19
Comparative analysis of methods for forecasting bankruptcies of Russian construction companies
Alexander Karminsky, Roman Burekhin
Business Informatics (2019) Vol. 13, Iss. 3, pp. 52-66
Open Access | Times Cited: 30
A multi-level classification based ensemble and feature extractor for credit risk assessment
Yuanyuan Wang, Zhuang Wu, Jing Gao, et al.
PeerJ Computer Science (2024) Vol. 10, pp. e1915-e1915
Open Access | Times Cited: 3
Supervised Learning for the Prediction of Firm Dynamics
Falco J. Bargagli-Stoffi, Jan Niederreiter, Massimo Riccaboni
Springer eBooks (2021), pp. 19-41
Closed Access | Times Cited: 20
Forecasting European high-growth Firms - A Random Forest Approach
Jurij Weinblat
Journal of Industry Competition and Trade (2017) Vol. 18, Iss. 3, pp. 253-294
Closed Access | Times Cited: 24
A combined approach based on robust PCA to improve bankruptcy forecasting
Marianna Succurro, Giuseppe Arcuri, Giuseppina Damiana Costanzo
Review of Accounting and Finance (2019) Vol. 18, Iss. 2, pp. 296-320
Closed Access | Times Cited: 22
Financial Bankruptcy across European Countries
Marianna Succurro
International Journal of Economics and Finance (2017) Vol. 9, Iss. 7, pp. 132-132
Open Access | Times Cited: 11
Foreign Versus Local Ownership and Performance in Eastern Versus Western EU: A Random Forest Application
Alexandra Horobeţ, Oana Cristina Popovici, Vlad-Cosmin Bulai, et al.
Engineering Economics (2023) Vol. 34, Iss. 2, pp. 123-138
Open Access | Times Cited: 3
Credit Scoring Using Machine Learning Algorithms and Blockchain Technology
Mariam Ossama Kotb
(2023)
Closed Access | Times Cited: 3
Machine learning for zombie hunting: predicting distress from firms’ accounts and missing values
Falco J. Bargagli-Stoffi, Fabio Incerti, Massimo Riccaboni, et al.
Industrial and Corporate Change (2023) Vol. 33, Iss. 5, pp. 1063-1097
Open Access | Times Cited: 2
Investment, default propensity score and cash flow sensitivity in six EU member states: evidence based on firm-level panel data
Andreas Behr, Christoph Schiwy, Jurij Weinblat
Applied Economics (2019) Vol. 51, Iss. 49, pp. 5345-5368
Closed Access | Times Cited: 5
Análisis combinado de factores del fracaso empresarial en el sector turístico español
Cristina Mendaña Cuervo, Nieves Remo‐Diez, Marta Toral-Heredia
Revista de estudios empresariales (2024), pp. 237-265
Open Access
Does accrual information impact municipal bond prices? Evidence from Japan using AI
Kentaro Haraguchi, Yasuhiro Tamba, Daisuke Ikeda, et al.
Public Money & Management (2024), pp. 1-12
Closed Access
Bankruptcy Risk Factors of Russian Companies
A. A. Zhukov, Egor D. Nikulin, D. A. Shchuchkin
Finance Theory and Practice (2022) Vol. 26, Iss. 6, pp. 131-155
Open Access | Times Cited: 2
Performance Dissimilarities in European Union Manufacturing: The Effect of Ownership and Technological Intensity
Lucian Belașcu, Alexandra Horobeţ, Georgiana Vrînceanu, et al.
Sustainability (2021) Vol. 13, Iss. 18, pp. 10407-10407
Open Access | Times Cited: 2
Broadening Economics in the Era of Artificial Intelligence and Experimental Evidence
Jan Niederreiter
Italian Economic Journal (2021) Vol. 9, Iss. 1, pp. 265-294
Closed Access | Times Cited: 1
Predicting Restaurant Facility Closures
Derek Snow
SSRN Electronic Journal (2018)
Closed Access


Business Informatics (2019) Vol. 13, Iss. 3
Open Access
Machine Learning for Zombie Hunting: Predicting Distress from Firms' Accounts and Missing Values
Falco Bargagli Stoffi, Massimo Riccaboni, Armando Rungi
SSRN Electronic Journal (2022)
Open Access
PREDICTING HIGH-GROWTH FIRMS IN KAZAKHSTAN WITH MACHINE LEARNING METHODS
Yelzhas KADYR, Azat Aituar, Saule KEMELBAYEVA
Public Administration and Civil Service (2022), Iss. 2, pp. 128-149
Open Access
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