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dc.contributor.authorGarcía, Vicente
dc.contributor.authorMarqués Marzal, Ana Isabel
dc.contributor.authorCleofás Sánchez, Laura
dc.contributor.authorSánchez Garreta, Josep Salvador
dc.date.accessioned2016-05-30T14:52:51Z
dc.date.available2016-05-30T14:52:51Z
dc.date.issued2016
dc.identifier.isbn978-3-319-32033-5
dc.identifier.isbn978-3-319-32034-2
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttp://hdl.handle.net/10234/160084
dc.descriptionPonencia presentada al 11th International Conference, HAIS 2016, Seville, Spain, April 18-20, 2016ca_CA
dc.description.abstractMany 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.ca_CA
dc.description.sponsorShipThis work has partially been supported by the Spanish Ministry of Economy [TIN2013-46522-P], the Generalitat Valenciana [PROMETEOII/2014/062], the Mexican PRODEP [DSA/103.5/15/7004] and the Mexican Science and Technology Council through the Postdoctoral Fellowship Program [232167].ca_CA
dc.format.extent12 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherSpringer International Publishingca_CA
dc.relation.isPartOfF. Martínez-Ávarez et al. (Eds.): Hybrid Artificial Intelligent Systems 2016, LNAI 9648, pp. 524–535, 2016.
dc.rights©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 "ca_CA
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/*
dc.subjectModel selectionca_CA
dc.subjectMulti-criteria decision-makingca_CA
dc.subjectFinancial distressca_CA
dc.subjectTOPSISca_CA
dc.subjectPROMETHEEca_CA
dc.titleModel Selection for Financial Distress Prediction by Aggregating TOPSIS and PROMETHEE Rankingsca_CA
dc.typeinfo:eu-repo/semantics/bookPartca_CA
dc.identifier.doihttp://dx.doi.org/10.1007/978-3-319-32034-2_44
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttp://link.springer.com/chapter/10.1007%2F978-3-319-32034-2_44ca_CA


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