Statistical merging of rating models / Figini, S. in Journal of the operational research society (JORS), Vol. 62 N° 6 (Juin 2011)  

Public ISBD [article]  inJournal of the operational research society (JORS) > Vol. 62 N° 6 (Juin 2011) . - pp. 1067–1074 Titre : Statistical merging of rating models Type de document : texte imprimé Auteurs : Figini, S., Auteur ; Giudici, P., Auteur Année de publication : 2011 Article en page(s) : pp. 1067–1074 Note générale : Recherche opérationnelle Langues : Anglais (eng) Mots-clés : In this paper we introduce and discuss statistical models aimed at predicting default probabilities of Small Medium Enterprises (SME). Such are based on two separate sources information: quantitative balance sheet ratios qualitative information derived from the opinion mining process unstructured data. We propose a novel methodology for data fusion in longitudinal survival duration using variables separately likelihood function then combining their scores linearly by weight, to obtain corresponding probability each SME. With real financial database hand, have compared results achieved terms model performance predictive capability single our own proposal. Finally, select best out-of-sample forecasts considering key indicators. Index. décimale : 001.424 Résumé : In this paper we introduce and discuss statistical models aimed at predicting default probabilities of Small and Medium Enterprises (SME). Such models are based on two separate sources of information: quantitative balance sheet ratios and qualitative information derived from the opinion mining process on unstructured data. We propose a novel methodology for data fusion in longitudinal and survival duration models using quantitative and qualitative variables separately in the likelihood function and then combining their scores linearly by a weight, to obtain the corresponding probability of default for each SME. With a real financial database at hand, we have compared the results achieved in terms of model performance and predictive capability using single models and our own proposal. Finally, we select the best model in terms of out-of-sample forecasts considering key performance indicators. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v62/n6/abs/jors201041a.html [article] Statistical merging of rating models [texte imprimé] / Figini, S., Auteur ; Giudici, P., Auteur . - 2011 . - pp. 1067–1074. Recherche opérationnelle Langues : Anglais (eng) in Journal of the operational research society (JORS) > Vol. 62 N° 6 (Juin 2011) . - pp. 1067–1074 Mots-clés : In this paper we introduce and discuss statistical models aimed at predicting default probabilities of Small Medium Enterprises (SME). Such are based on two separate sources information: quantitative balance sheet ratios qualitative information derived from the opinion mining process unstructured data. We propose a novel methodology for data fusion in longitudinal survival duration using variables separately likelihood function then combining their scores linearly by weight, to obtain corresponding probability each SME. With real financial database hand, have compared results achieved terms model performance predictive capability single our own proposal. Finally, select best out-of-sample forecasts considering key indicators. Index. décimale : 001.424 Résumé : In this paper we introduce and discuss statistical models aimed at predicting default probabilities of Small and Medium Enterprises (SME). Such models are based on two separate sources of information: quantitative balance sheet ratios and qualitative information derived from the opinion mining process on unstructured data. We propose a novel methodology for data fusion in longitudinal and survival duration models using quantitative and qualitative variables separately in the likelihood function and then combining their scores linearly by a weight, to obtain the corresponding probability of default for each SME. With a real financial database at hand, we have compared the results achieved in terms of model performance and predictive capability using single models and our own proposal. Finally, we select the best model in terms of out-of-sample forecasts considering key performance indicators. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v62/n6/abs/jors201041a.html

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