About: Probabilistic Neural Networks for Credit Rating Modelling

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	This paper presents the modelling possibilities of probabilistic neural networks to a complex real-world problem, i.e. credit rating modelling. First, current approaches in credit rating modelling are introduced. Then, probabilistic neural networks are designed to classify US companies and municipalities into rating classes. The input variables are extracted from financial statements and statistical reports in line with previous studies. These variables represent the inputs of probabilistic neural networks, while the rating classes from Standard and Poor's and Moody's rating agencies stand for the outputs. Classification accuracies, misclassification costs, and the contributions of input variables are studied for probabilistic neural networks compared to other neural networks models. The results show that the rating classes assigned to bond issuers can be classified accurately with probabilistic neural networks using a limited subset of input variables. This paper presents the modelling possibilities of probabilistic neural networks to a complex real-world problem, i.e. credit rating modelling. First, current approaches in credit rating modelling are introduced. Then, probabilistic neural networks are designed to classify US companies and municipalities into rating classes. The input variables are extracted from financial statements and statistical reports in line with previous studies. These variables represent the inputs of probabilistic neural networks, while the rating classes from Standard and Poor's and Moody's rating agencies stand for the outputs. Classification accuracies, misclassification costs, and the contributions of input variables are studied for probabilistic neural networks compared to other neural networks models. The results show that the rating classes assigned to bond issuers can be classified accurately with probabilistic neural networks using a limited subset of input variables. (en)
Title	Probabilistic Neural Networks for Credit Rating Modelling Probabilistic Neural Networks for Credit Rating Modelling (en)
skos:prefLabel	Probabilistic Neural Networks for Credit Rating Modelling Probabilistic Neural Networks for Credit Rating Modelling (en)
skos:notation	RIV/00216275:25410/10:39881988!RIV11-GA0-25410___
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(GP402/09/P090)
http://linked.open...vai/riv/dodaniDat	2011
http://linked.open...aciTvurceVysledku	Hájek, Petr
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Univerzita Pardubice / Fakulta ekonomicko-správní
http://linked.open...dnocenehoVysledku	282097
http://linked.open...ai/riv/idVysledku	RIV/00216275:25410/10:39881988
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Credit Rating; Probabilistic Neural Networks (en)
http://linked.open.../riv/klicoveSlovo	Probabilistic Neural Networks Credit Rating
http://linked.open...ontrolniKodProRIV	[D67CA122D50A]
http://linked.open...v/mistoKonaniAkce	Valencia
http://linked.open...i/riv/mistoVydani	Setúbal
http://linked.open...i/riv/nazevZdroje	Proceedings of the International Conference on Neural Computation 2010
http://linked.open...in/vavai/riv/obor	AE
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	1 (xsd:int)
http://linked.open...vavai/riv/projekt	Modelling of Municipal Finance by Computational Intelligence Methods
http://linked.open...UplatneniVysledku	2010
http://linked.open...iv/tvurceVysledku	Hájek, Petr
http://linked.open...vavai/riv/typAkce	EUR - Evropská
http://linked.open.../riv/zahajeniAkce	2010-10-24 (xsd:date)
number of pages	6 (xsd:int)
http://purl.org/ne...btex#hasPublisher	International Joint Conference on Computational Intelligence
https://schema.org/isbn	978-989-8425-32-4
http://localhost/t...ganizacniJednotka	25410

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