Dissimilarity-Based Linear Models for Corporate Bankruptcy Prediction
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Otros documentos de la autoría: García, Vicente; Marqués Marzal, Ana Isabel; Sánchez Garreta, Josep Salvador; Ochoa Domínguez, Humberto de Jesús
Metadatos
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Título
Dissimilarity-Based Linear Models for Corporate Bankruptcy PredictionAutoría
Fecha de publicación
2019-03Editor
SpringerISSN
0927-7099; 1572-9974Cita bibliográfica
GARCÍA, Vicente, et al. Dissimilarity-Based Linear Models for Corporate Bankruptcy Prediction. Computational Economics, 2019, vol. 53, no 3, p. 1019–1031.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://link.springer.com/article/10.1007%2Fs10614-017-9783-4Versión
info:eu-repo/semantics/submittedVersionPalabras clave / Materias
Resumen
Bankruptcy prediction has acquired great relevance for financial institutions due to the complexity of global economies and the growing number of corporate failures, especially since the world financial crisis of 2008. ... [+]
Bankruptcy prediction has acquired great relevance for financial institutions due to the complexity of global economies and the growing number of corporate failures, especially since the world financial crisis of 2008. In this paper, the problem of corporate bankruptcy prediction is faced by means of four linear classifiers (Fisher’s linear discriminant, linear discriminant classifier, support vector machine and logistic regression), which are designed on the dissimilarity space instead of the classical feature space. Experimental results indicate that the prediction methods implemented with the dissimilarity representation perform considerably better than the same techniques when applied onto the feature space, in terms of overall accuracy, true-positive rate and true-negative rate. [-]
Descripción
This is a pre-print of an article published in Computational Economics. The final authenticated version is available online at: https://doi.org/10.1007/s10614-017-9783-4
Publicado en
Computational Economics, 2019, vol. 53, no 3Proyecto de investigación
Mexican PRODEP: DSA/103.5/15/7004; Spanish Ministry of Economy: TIN2013-46522-P; Generalitat Valenciana: PROMETEOII/2014/062Derechos de acceso
© Springer Science+Business Media, LLC, part of Springer Nature
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info:eu-repo/semantics/openAccess
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/openAccess
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