Financial distress prediction using the hybrid associative memory with translation
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Otros documentos de la autoría: Cleofás Sánchez, Laura; García, Vicente; Marqués Marzal, Ana Isabel; Sánchez Garreta, Josep Salvador
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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Título
Financial distress prediction using the hybrid associative memory with translationAutoría
Fecha de publicación
2016Editor
ElsevierISSN
1568-4946; 1872-9681Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.sciencedirect.com/science/article/pii/S1568494616301491Versión
info:eu-repo/semantics/submittedVersionPalabras clave / Materias
Resumen
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 ... [+]
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. [-]
Publicado en
Applied Soft Computing Volume 44, July 2016, Pages 144–152Derechos de acceso
© 2016 Elsevier B.V. All rights reserved.
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