Model Selection for Financial Distress Prediction by Aggregating TOPSIS and PROMETHEE Rankings
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Otros documentos de la autoría: García, Vicente; Marqués Marzal, Ana Isabel; Cleofás Sánchez, Laura; Sánchez Garreta, Josep Salvador
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/8645
comunitat-uji-handle3:10234/146066
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Título
Model Selection for Financial Distress Prediction by Aggregating TOPSIS and PROMETHEE RankingsAutoría
Fecha de publicación
2016Editor
Springer International PublishingISBN
978-3-319-32033-5; 978-3-319-32034-2ISSN
0302-9743; 1611-3349Tipo de documento
info:eu-repo/semantics/bookPartVersión de la editorial
http://link.springer.com/chapter/10.1007%2F978-3-319-32034-2_44Palabras clave / Materias
Resumen
Many models have been explored for financial distress prediction, but no consistent conclusions have been drawn on which method shows the best behavior when different performance evaluation measures are employed. ... [+]
Many models have been explored for financial distress prediction, but no consistent conclusions have been drawn on which method shows the best behavior when different performance evaluation measures are employed. Accordingly, this paper proposes the integration of the ranking scores given by two popular multiple-criteria decision-making tools as an important step to help decision makers in selecting the model(s) properly. Selection of the most appropriate prediction method is here shaped as a multiple-criteria decision-making problem that involves a number of performance measures (criteria) and a set of techniques (alternatives). An empirical study is carried out to assess the performance of ten algorithms over six real-life bankruptcy and credit risk databases. The results reveal that the use of a unique performance measure often leads to contradictory conclusions, while the multiple-criteria decision-making techniques may yield a more reliable analysis. Besides, these allow the decision makers to weight the relevance of the individual performance metrics as a function of each particular problem. [-]
Descripción
Ponencia presentada al 11th International Conference, HAIS 2016, Seville, Spain, April 18-20, 2016
Publicado en
F. Martínez-Ávarez et al. (Eds.): Hybrid Artificial Intelligent Systems 2016, LNAI 9648, pp. 524–535, 2016.Derechos de acceso
©Springer International Publishing Switzerland 2016
© Springer International Publishing AG, Part of Springer Science+Business Media "The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-32034-2_44 "
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