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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:

Failure pattern-based ensembles applied to bankruptcy forecasting
Philippe du Jardin
Decision Support Systems (2018) Vol. 107, pp. 64-77
Closed Access | Times Cited: 55

Showing 26-50 of 55 citing articles:

Three-stage research framework to assess and predict the financial risk of SMEs based on hybrid method
Jin Xiao, Wen Zhang, Xiaoyi Jiang, et al.
Decision Support Systems (2023) Vol. 177, pp. 114090-114090
Closed Access | Times Cited: 6
A comparison of classification methods across different data complexity scenarios and datasets
Michael Scholz, Tristan Wimmer
Expert Systems with Applications (2020) Vol. 168, pp. 114217-114217
Closed Access | Times Cited: 17
Designing topological data to forecast bankruptcy using convolutional neural networks
Philippe du Jardin
Annals of Operations Research (2022) Vol. 325, Iss. 2, pp. 1291-1332
Closed Access | Times Cited: 10
Predicting bankruptcy using artificial intelligence: The case of the engineering industry
Stanislav Letkovský, Sylvia Jenčová, Petra Vašaničová, et al.
Economics & Sociology (2023) Vol. 16, Iss. 4, pp. 178-190
Open Access | Times Cited: 5
Business failure prediction using cart-based model: A case of Slovak companies
Marek Ďurica, Ivana Podhorská, Pavol Ďurana
Ekonomicko-manazerske spektrum (2019) Vol. 13, Iss. 1, pp. 51-61
Open Access | Times Cited: 15
Tax Arrears Versus Financial Ratios in Bankruptcy Prediction
Oliver Lukason, Art Andresson
Journal of risk and financial management (2019) Vol. 12, Iss. 4, pp. 187-187
Open Access | Times Cited: 15
The Future of Indonesia Islamic Banking Industry: Bankruptcy Analyzing the Second Wave of Global Financial Crisis
Patria Yunita
International Journal of Islamic Economics and Finance (IJIEF) (2020) Vol. 3, Iss. 2
Open Access | Times Cited: 14
Two‐stage hybrid learning techniques for bankruptcy prediction*
Chih‐Fong Tsai
Statistical Analysis and Data Mining The ASA Data Science Journal (2020) Vol. 13, Iss. 6, pp. 565-572
Closed Access | Times Cited: 14
Stacking-based neural network for nonlinear time series analysis
Tharindu P. De Alwis, S. Yaser Samadi
Statistical Methods & Applications (2024) Vol. 33, Iss. 3, pp. 901-924
Closed Access | Times Cited: 1
Comparison of Prediction Models Applied in Economic Recession and Expansion
Dagmar Čámská, Jiří Klečka
Journal of risk and financial management (2020) Vol. 13, Iss. 3, pp. 52-52
Open Access | Times Cited: 10
Predicting corporate defaults using machine learning with geometric-lag variables
Hyeongjun Kim, Hoon Cho, Doojin Ryu
Investment Analysts Journal (2021) Vol. 50, Iss. 3, pp. 161-175
Closed Access | Times Cited: 10
A state-of-the-art appraisal of bankruptcy prediction models focussing on the field’s core authors: 2010–2022
Ivan Soukal, Jan Mačí, Gabriela Trnková, et al.
Central European Management Journal (2023) Vol. 32, Iss. 1, pp. 3-30
Open Access | Times Cited: 3
Generalized additive model with embedded variable selection for bankruptcy prediction: Prediction versus interpretation
Carlos Valencia, Sergio Cabrales, Laura Garcia, et al.
Cogent Economics & Finance (2019) Vol. 7, Iss. 1, pp. 1597956-1597956
Open Access | Times Cited: 9
Forecasting Financial Distress With Machine Learning – A Review
Denize Lemos Duarte, Flávio Barboza
Future Studies Research Journal Trends and Strategies (2020) Vol. 12, Iss. 3, pp. 528-574
Open Access | Times Cited: 8
Combining white box models, black box machines and human interventions for interpretable decision strategies
Grégory Gadzinski, Alessio Castello
Judgment and Decision Making (2022) Vol. 17, Iss. 3, pp. 598-627
Open Access | Times Cited: 5
Financial Distress Prediction in Indonesia Stock Exchange's Listed Company Using Case Based Reasoning Concept
Dyah Sulistyowati Rahayu, Heru Suhartanto
2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA) (2020), pp. 1009-1013
Closed Access | Times Cited: 5
Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence
Dovilė Kuizinienė, Tomas Krilavičius
Electronics (2024) Vol. 13, Iss. 8, pp. 1596-1596
Open Access
Artificial Neural Networks in Forecasting the Consumer Bankruptcy Risk with Innovative Ratios
Tomasz Korol
Contemporary Economics (2024) Vol. 18, Iss. 4, pp. 391-407
Open Access
Post-pandemic performance of micro, small and medium-sized enterprises: A Self-organizing Maps application
Lisana B. Martínez, Valeria Scherger, Sofía Orazi
Cogent Business & Management (2023) Vol. 10, Iss. 3
Open Access | Times Cited: 1
Bankruptcy Prediction Using Artificial Intelligence Techniques: A Survey
Samar Aly, Marco Alfonse, Abdel-Badeeh M. Salem
Lecture notes in networks and systems (2021), pp. 335-360
Closed Access | Times Cited: 3
Corporate failure prediction using threshold‐based models
David Veganzones
Journal of Forecasting (2021) Vol. 41, Iss. 5, pp. 956-979
Closed Access | Times Cited: 3
An Early-Warning System for Financial Performance Predictions
COSTEA ADRIAN
ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH (2022) Vol. 56, Iss. 2/2022, pp. 5-20
Open Access | Times Cited: 1
Is it worth restructuring? Analysis of companies in poor financial condition in Poland
Sergiusz Herman
Wiadomości Statystyczne The Polish Statistician (2021) Vol. 66, Iss. 6, pp. 7-26
Closed Access | Times Cited: 1
Artificial intelligence in financial forecasting: Evidence from literature
Swati Sharma
AIP conference proceedings (2023) Vol. 2909, pp. 030011-030011
Closed Access
Predicting the Probability of Bankruptcy of Service Sector Enterprises Based on Ensemble Learning Methods
Dmitriy Rodionov, Aleksandra Pospelova, Evgenii Konnikov, et al.
Lecture notes in networks and systems (2023), pp. 195-208
Closed Access

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