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Financial distress prediction using the hybrid associative memory with translation
dc.contributor.author | Cleofás Sánchez, Laura | |
dc.contributor.author | García, Vicente | |
dc.contributor.author | Marqués Marzal, Ana Isabel | |
dc.contributor.author | Sánchez Garreta, Josep Salvador | |
dc.date.accessioned | 2016-05-30T14:26:09Z | |
dc.date.available | 2016-05-30T14:26:09Z | |
dc.date.issued | 2016 | |
dc.identifier.issn | 1568-4946 | |
dc.identifier.issn | 1872-9681 | |
dc.identifier.uri | http://hdl.handle.net/10234/160083 | |
dc.description.abstract | This paper presents an alternative technique for financial distress prediction systems. The method is based on a type of neural network, which is called hybrid associative memory with translation. While many different neural network architectures have successfully been used to predict credit risk and corporate failure, the power of associative memories for financial decision-making has not been explored in any depth as yet. The performance of the hybrid associative memory with translation is compared to four traditional neural networks, a support vector machine and a logistic regression model in terms of their prediction capabilities. The experimental results over nine real-life data sets show that the associative memory here proposed constitutes an appropriate solution for bankruptcy and credit risk prediction, performing significantly better than the rest of models under class imbalance and data overlapping conditions in terms of the true positive rate and the geometric mean of true positive and true negative rates. | ca_CA |
dc.description.sponsorShip | This work has partially been supported by the Mexican CONACYT through the Postdoctoral Fellowship Program [232167], the Spanish Ministry of Economy [TIN2013-46522-P], the Generalitat Valenciana [PROMETEOII/2014/062] and the Mexican PRODEP [DSA/103.5/15/7004]. We would like to thank the Reviewers for their valuable comments and suggestions, which have helped to improve the quality of this paper substantially. | ca_CA |
dc.format.extent | 24 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Elsevier | ca_CA |
dc.relation.isPartOf | Applied Soft Computing Volume 44, July 2016, Pages 144–152 | ca_CA |
dc.rights | © 2016 Elsevier B.V. All rights reserved. | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | * |
dc.subject | Associative memory | ca_CA |
dc.subject | Neural network | ca_CA |
dc.subject | Financial distress | ca_CA |
dc.subject | Bankruptcy | ca_CA |
dc.subject | Credit risk | ca_CA |
dc.title | Financial distress prediction using the hybrid associative memory with translation | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | http://dx.doi.org/10.1016/j.asoc.2016.04.005 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | http://www.sciencedirect.com/science/article/pii/S1568494616301491 | ca_CA |
dc.type.version | info:eu-repo/semantics/submittedVersion | ca_CA |
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