IDEAS home Printed from https://ideas.repec.org/r/pal/jorsoc/v50y1999i11d10.1057_palgrave.jors.2600818.html
   My bibliography  Save this item

Business failure prediction using the UTADIS multicriteria analysis method

Citations

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


Cited by:

  1. Michał Thor & Łukasz Postek, 2024. "Gated recurrent unit network: A promising approach to corporate default prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1131-1152, August.
  2. Roumani, Yaman & Nwankpa, Joseph K. & Roumani, Yazan F., 2016. "Examining the relationship between firm’s financial records and security vulnerabilities," International Journal of Information Management, Elsevier, vol. 36(6), pages 987-994.
  3. Füsun Küçükbay & Ebru Sürücü, 2019. "Corporate sustainability performance measurement based on a new multicriteria sorting method," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 26(3), pages 664-680, May.
  4. Shrutika Mishra & A. R. Tripathi, 2021. "AI business model: an integrative business approach," Journal of Innovation and Entrepreneurship, Springer, vol. 10(1), pages 1-21, December.
  5. Jie Sun, 2012. "Integration Of Random Sample Selection, Support Vector Machines And Ensembles For Financial Risk Forecasting With An Empirical Analysis On The Necessity Of Feature Selection," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(4), pages 229-246, October.
  6. 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.
  7. Chung-Ho Su, 2017. "A Novel Hybrid Learning Achievement Prediction Model: A Case Study in Gamification Education Applications (APPs)," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(02), pages 515-543, March.
  8. Sobrie, Olivier & Gillis, Nicolas & Mousseau, Vincent & Pirlot, Marc, 2018. "UTA-poly and UTA-splines: Additive value functions with polynomial marginals," European Journal of Operational Research, Elsevier, vol. 264(2), pages 405-418.
  9. C. Pérez‐Llera & C. Fernández‐Baizán & J. L. Fanjul & J. Feito, 2004. "A model for valuation of the branch offices of a savings bank based on rough sets," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 12(3), pages 187-213, July.
  10. Moro, Russ & Härdle, Wolfgang Karl & Aliakbari, Saeideh & Hoffmann, Linda, 2011. "Forecasting corporate distress in the Asian and Pacific region," SFB 649 Discussion Papers 2011-023, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  11. Salwa Kessioui & Michalis Doumpos & Constantin Zopounidis, 2023. "A Bibliometric Overview of the State-of-the-Art in Bankruptcy Prediction Methods and Applications," World Scientific Book Chapters, in: Emilios Galariotis & Alexandros Garefalakis & Christos Lemonakis & Marios Menexiadis & Constantin Zo (ed.), Governance and Financial Performance Current Trends and Perspectives, chapter 6, pages 123-153, World Scientific Publishing Co. Pte. Ltd..
  12. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
  13. Hussein Slim & Sylvie Nadeau, 2020. "A Mixed Rough Sets/Fuzzy Logic Approach for Modelling Systemic Performance Variability with FRAM," Sustainability, MDPI, vol. 12(5), pages 1-21, March.
  14. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
  15. Thomas E. Mckee, 2000. "Developing a bankruptcy prediction model via rough sets theory," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(3), pages 159-173, September.
  16. Boujelben, Mohamed Ayman, 2017. "A unicriterion analysis based on the PROMETHEE principles for multicriteria ordered clustering," Omega, Elsevier, vol. 69(C), pages 126-140.
  17. Chrysovalantis Gaganis, 2009. "Classification techniques for the identification of falsified financial statements: a comparative analysis," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(3), pages 207-229, July.
  18. Chrysovalantis Gaganis & Panagiota Papadimitri & Menelaos Tasiou, 2021. "A multicriteria decision support tool for modelling bank credit ratings," Annals of Operations Research, Springer, vol. 306(1), pages 27-56, November.
  19. Nikolaos Daskalakis & Nikolaos Aggelakis & John Filos, 2022. "Applying, Updating and Comparing Bankruptcy Forecasting Models. The Case of Greece," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 21(3), pages 335-354, September.
  20. Haoming Wang & Xiangdong Liu, 2021. "Undersampling bankruptcy prediction: Taiwan bankruptcy data," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-17, July.
  21. Apostolos G. Christopoulos & Ioannis G. Dokas & Iraklis Kollias & John Leventides, 2019. "An implementation of Soft Set Theory in the Variables Selection Process for Corporate Failure Prediction Models. Evidence from NASDAQ Listed Firms," Bulletin of Applied Economics, Risk Market Journals, vol. 6(1), pages 1-20.
  22. Angeliki Papana & Anastasia Spyridou, 2020. "Bankruptcy Prediction: The Case of the Greek Market," Forecasting, MDPI, vol. 2(4), pages 1-21, December.
  23. I. L. Beilin & V. V. Khomenko & N. V. Kalenskaya, 2019. "The Stability of the Regional Economic System Based on the Innovative Development of the Petrochemical Cluster," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 8, December.
  24. Michael Doumpos & Constantin Zopounidis, 1999. "A Multicriteria Discrimination Method for the Prediction of Financial Distress: The Case of Greece," Multinational Finance Journal, Multinational Finance Journal, vol. 3(2), pages 71-101, June.
  25. I Y-F Huang & W-W Wu & Y-T Lee, 2008. "Simplifying essential competencies for Taiwan civil servants using the rough set approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(2), pages 259-265, February.
  26. Fernando Zambrano Farias & María del Carmen Valls Martínez & Pedro Antonio Martín-Cervantes, 2021. "Explanatory Factors of Business Failure: Literature Review and Global Trends," Sustainability, MDPI, vol. 13(18), pages 1-26, September.
  27. Ana Paula Matias Gama & Helena Susana Amaral Geraldes, 2012. "Credit risk assessment and the impact of the New Basel Capital Accord on small and medium‐sized enterprises," Management Research Review, Emerald Group Publishing Limited, vol. 35(8), pages 727-749, July.
  28. Ramon Oehninger & Michael J. Kendzia & Felix Scherrer, 2020. "Preventing Corporate Turnarounds through an Early Warning System," International Journal of Management, Knowledge and Learning, International School for Social and Business Studies, Celje, Slovenia, vol. 9(2), pages 185-205.
  29. Demyanyk, Yuliya & Hasan, Iftekhar, 2009. "Financial crises and bank failures: a review of prediction methods," Bank of Finland Research Discussion Papers 35/2009, Bank of Finland.
  30. Sheikh Rabiul Islam & William Eberle & Sheikh K. Ghafoor & Sid C. Bundy & Douglas A. Talbert & Ambareen Siraj, 2019. "Investigating bankruptcy prediction models in the presence of extreme class imbalance and multiple stages of economy," Papers 1911.09858, arXiv.org.
  31. Ioannis Tsolas, 2015. "Firm credit risk evaluation: a series two-stage DEA modeling framework," Annals of Operations Research, Springer, vol. 233(1), pages 483-500, October.
  32. Xie, Feng & Lin, Yi & Ren, Wenwei, 2011. "Optimizing model for land use/land cover retrieval from remote sensing imagery based on variable precision rough sets," Ecological Modelling, Elsevier, vol. 222(2), pages 232-240.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.