A hybrid genetic model for the prediction of corporate failure
Author
Abstract
Suggested Citation
DOI: 10.1007/s10287-004-0017-6
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- du Jardin, Philippe, 2010. "Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy," MPRA Paper 44375, University Library of Munich, Germany.
- du Jardin, Philippe, 2008. "Bankruptcy prediction and neural networks: The contribution of variable selection methods," MPRA Paper 44384, University Library of Munich, Germany.
- Ilyes Abid & Rim Ayadi & Khaled Guesmi & Farid Mkaouar, 2022. "A new approach to deal with variable selection in neural networks: an application to bankruptcy prediction," Annals of Operations Research, Springer, vol. 313(2), pages 605-623, June.
- du Jardin, Philippe & Séverin, Eric, 2011.
"Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model,"
MPRA Paper
44262, University Library of Munich, Germany.
- P. Du Jardin & E. Séverin, 2011. "Predicting Corporate Bankruptcy Using Self-Organising map: An empirical study to Improve the Forecasting horizon of financial failure model," Post-Print hal-00801878, HAL.
- Luca Sensini, 2016. "An Empirical Analysis of Financially Distressed Italian Companies," International Business Research, Canadian Center of Science and Education, vol. 9(10), pages 75-85, October.
- du Jardin, Philippe, 2009. "Bankruptcy prediction models: How to choose the most relevant variables?," MPRA Paper 44380, University Library of Munich, Germany.
- du Jardin, Philippe, 2012. "The influence of variable selection methods on the accuracy of bankruptcy prediction models," MPRA Paper 44383, University Library of Munich, Germany.
- Der-Jang Chi & Chien-Chou Chu, 2021. "Artificial Intelligence in Corporate Sustainability: Using LSTM and GRU for Going Concern Prediction," Sustainability, MDPI, vol. 13(21), pages 1-18, October.
- Piasecki Krzysztof & Wójcicka-Wójtowicz Aleksandra, 2017. "Capacity of Neural Networks and Discriminant Analysis in Classifying Potential Debtors," Folia Oeconomica Stetinensia, Sciendo, vol. 17(2), pages 129-143, December.
- Peel Michael J., 2016. "Owner-Managed UK Corporate Start-Ups: An Exploratory Study of Financing and Failure," Entrepreneurship Research Journal, De Gruyter, vol. 6(4), pages 345-367, October.
- du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
- Amendola, Alessandra & Restaino, Marialuisa & Sensini, Luca, 2015. "An analysis of the determinants of financial distress in Italy: A competing risks approach," International Review of Economics & Finance, Elsevier, vol. 37(C), pages 33-41.
- Fayçal Mraihi & Inane Kanzari, 2019. "Predicting financial distress of companies: Comparison between multivariate discriminant analysis and multilayer perceptron for Tunisian case," Working Papers 1328, Economic Research Forum, revised 21 Aug 2019.
- Gianni Filograsso & Giacomo Tollo, 2023. "Adaptive evolutionary algorithms for portfolio selection problems," Computational Management Science, Springer, vol. 20(1), pages 1-38, December.
More about this item
Keywords
Corporate failure; Genetic algorithm; Neural network;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:comgts:v:1:y:2004:i:3:p:293-310. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.