IDEAS home Printed from https://ideas.repec.org/r/rug/rugwps/04-247.html

Bayesian Kernel-Based Classification for Financial Distress Detection

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Runchi Zhang & Zhiyi Qiu, 2020. "Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-35, June.
  2. Fabozzi, Frank J. & Recchioni, Maria Cristina & Renò, Roberto, 2025. "Fifty years at the interface between financial modeling and operations research," European Journal of Operational Research, Elsevier, vol. 327(1), pages 1-21.
  3. Martens, David & Baesens, Bart & Van Gestel, Tony & Vanthienen, Jan, 2007. "Comprehensible credit scoring models using rule extraction from support vector machines," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1466-1476, December.
  4. José Manuel Maside-Sanfiz & María-Celia López-Penabad & Ana Iglesias-Casal & Juan Torrelles Manent, 2024. "Determinants of the profitability of Sheltered Workshops: efficiency and effects of the COVID-19 crisis," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
  5. Li, Hui & Hong, Lu-Yao & He, Jia-Xun & Xu, Xuan-Guo & Sun, Jie, 2013. "Small sample-oriented case-based kernel predictive modeling and its economic forecasting applications under n-splits-k-times hold-out assessment," Economic Modelling, Elsevier, vol. 33(C), pages 747-761.
  6. Huang, Chao & Dai, Chong & Guo, Miao, 2015. "A hybrid approach using two-level DEA for financial failure prediction and integrated SE-DEA and GCA for indicators selection," Applied Mathematics and Computation, Elsevier, vol. 251(C), pages 431-441.
  7. Wei Liu & Yoshihisa Suzuki & Shuyi Du, 2025. "Ensemble learning algorithms based on easyensemble sampling for financial distress prediction," Annals of Operations Research, Springer, vol. 346(3), pages 2141-2172, March.
  8. Meng, Qingbin & Zheng, Xinxing & Wang, Solomon, 2024. "Corporate governance and financial distress in China a multi-dimensional nonlinear study based on machine learning," Pacific-Basin Finance Journal, Elsevier, vol. 88(C).
  9. Polyzos, Stathis & Samitas, Aristeidis & Katsaiti, Marina-Selini, 2020. "Who is unhappy for Brexit? A machine-learning, agent-based study on financial instability," International Review of Financial Analysis, Elsevier, vol. 72(C).
  10. Lin Zhu & Zhihua Zhang & M. James C. Crabbe, 2025. "Exploring small-scale optimization coupling learning approaches for enterprises’ financial health forecasts," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-18, December.
  11. Swati Anand & Kushendra Mishra, 2022. "Identifying potential millennial customers for financial institutions using SVM," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 27(4), pages 335-345, December.
  12. Lean Yu & Xinxie Li & Ling Tang & Zongyi Zhang & Gang Kou, 2015. "Social credit: a comprehensive literature review," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-18, December.
  13. Li, Hui & Sun, Jie, 2012. "Forecasting business failure: The use of nearest-neighbour support vectors and correcting imbalanced samples – Evidence from the Chinese hotel industry," Tourism Management, Elsevier, vol. 33(3), pages 622-634.
  14. Ding, Shusheng & Cui, Tianxiang & Bellotti, Anthony Graham & Abedin, Mohammad Zoynul & Lucey, Brian, 2023. "The role of feature importance in predicting corporate financial distress in pre and post COVID periods: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 90(C).
  15. Huaming Du & Cancan Feng & Yuqian Lei & Chenyang Zhang & Guisong Liu & Gang Kou & Carl Yang & Yu Zhao, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data and LLMs Perspective," Papers 2211.14997, arXiv.org, revised Mar 2026.
  16. Ching-Hsue Cheng & Ssu-Hsiang Wang, 2015. "A quarterly time-series classifier based on a reduced-dimension generated rules method for identifying financial distress," Quantitative Finance, Taylor & Francis Journals, vol. 15(12), pages 1979-1994, December.
  17. Agustín J. Sánchez-Medina & Félix Blázquez-Santana & Daniel L. Cerviño-Cortínez & Mónica Pellejero, 2025. "Ensemble Methods for Bankruptcy Resolution Prediction: A New Approach," Computational Economics, Springer;Society for Computational Economics, vol. 66(5), pages 3891-3926, November.
  18. Lessmann, Stefan & Voß, Stefan, 2009. "A reference model for customer-centric data mining with support vector machines," European Journal of Operational Research, Elsevier, vol. 199(2), pages 520-530, December.
  19. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, June.
  20. Geng, Ruibin & Bose, Indranil & Chen, Xi, 2015. "Prediction of financial distress: An empirical study of listed Chinese companies using data mining," European Journal of Operational Research, Elsevier, vol. 241(1), pages 236-247.
  21. Fahmida E. Moula & Chi Guotai & Mohammad Zoynul Abedin, 2017. "Credit default prediction modeling: an application of support vector machine," Risk Management, Palgrave Macmillan, vol. 19(2), pages 158-187, May.
  22. Hui Li & Ting Sun & Jinquan Zhang, 2024. "Prediction of corporate financial distress based on corporate social responsibility: New evidence from DANP, VWP and MEOWA weights methodologies," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(5), pages 4537-4565, December.
  23. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
  24. L C Thomas, 2010. "Consumer finance: challenges for operational research," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 41-52, January.
  25. Brandner, Hubertus & Lessmann, Stefan & Voß, Stefan, 2013. "A memetic approach to construct transductive discrete support vector machines," European Journal of Operational Research, Elsevier, vol. 230(3), pages 581-595.
  26. Qifeng Qiao & Peter A. Beling, 2016. "Decision analytics and machine learning in economic and financial systems," Environment Systems and Decisions, Springer, vol. 36(2), pages 109-113, June.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.