Model Selection for Financial Distress Prediction by Aggregating TOPSIS and PROMETHEE Rankings
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Títol
Model Selection for Financial Distress Prediction by Aggregating TOPSIS and PROMETHEE RankingsAutoria
Data de publicació
2016Editor
Springer International PublishingISBN
978-3-319-32033-5; 978-3-319-32034-2ISSN
0302-9743; 1611-3349Tipus de document
info:eu-repo/semantics/bookPartVersió de l'editorial
http://link.springer.com/chapter/10.1007%2F978-3-319-32034-2_44Paraules clau / Matèries
Resum
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. [-]
Descripció
Ponencia presentada al 11th International Conference, HAIS 2016, Seville, Spain, April 18-20, 2016
Publicat a
F. Martínez-Ávarez et al. (Eds.): Hybrid Artificial Intelligent Systems 2016, LNAI 9648, pp. 524–535, 2016.Drets d'accés
©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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