About: Municipal credit rating modelling by neural networks

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	The paper presents the modelling possibilities of neural networks on a complex real-world problem, i.e. municipal credit rating modelling. First, current approaches in credit rating modelling are introduced. Second, previous studies on municipal credit rating modelling are analyzed. Based on this analysis, the model is designed to classify US municipalities (located in the State of Connecticut) into rating classes. The model includes data pre-processing, the selection process of input variables, and the design of various neural networks' structures for classification. The selection of input variables is realized using genetic algorithms. The input variables are extracted from financial statements and statistical reports in line with previous studies. These variables represent the inputs of neural networks, while the rating classes from Moody's rating agency stand for the outputs. In addition to exact rating classes, data are also labelled by four basic rating classes. As a result, the classification accuracies and the contributions of input variables are studied for the different number of classes. The results show that the rating classes assigned to bond issuers can be classified with a high accuracy rate using a limited subset of input variables. The paper presents the modelling possibilities of neural networks on a complex real-world problem, i.e. municipal credit rating modelling. First, current approaches in credit rating modelling are introduced. Second, previous studies on municipal credit rating modelling are analyzed. Based on this analysis, the model is designed to classify US municipalities (located in the State of Connecticut) into rating classes. The model includes data pre-processing, the selection process of input variables, and the design of various neural networks' structures for classification. The selection of input variables is realized using genetic algorithms. The input variables are extracted from financial statements and statistical reports in line with previous studies. These variables represent the inputs of neural networks, while the rating classes from Moody's rating agency stand for the outputs. In addition to exact rating classes, data are also labelled by four basic rating classes. As a result, the classification accuracies and the contributions of input variables are studied for the different number of classes. The results show that the rating classes assigned to bond issuers can be classified with a high accuracy rate using a limited subset of input variables. (en)
Title	Municipal credit rating modelling by neural networks Municipal credit rating modelling by neural networks (en)
skos:prefLabel	Municipal credit rating modelling by neural networks Municipal credit rating modelling by neural networks (en)
skos:notation	RIV/00216275:25410/11:39892236!RIV12-GA0-25410___
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(GP402/09/P090)
http://linked.open...iv/cisloPeriodika	1
http://linked.open...vai/riv/dodaniDat	2012
http://linked.open...aciTvurceVysledku	Hájek, Petr
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
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	214361
http://linked.open...ai/riv/idVysledku	RIV/00216275:25410/11:39892236
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Support vector machines; Neural networks; Municipal credit rating; Credit rating analysis (en)
http://linked.open.../riv/klicoveSlovo	Credit rating analysis Municipal credit rating Support vector machines Neural networks
http://linked.open...odStatuVydavatele	NL - Nizozemsko
http://linked.open...ontrolniKodProRIV	[8A5F295B988C]
http://linked.open...i/riv/nazevZdroje	Decision Support Systems
http://linked.open...in/vavai/riv/obor	IN
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	2011
http://linked.open...v/svazekPeriodika	51
http://linked.open...iv/tvurceVysledku	Hájek, Petr
issn	0167-9236
number of pages	11 (xsd:int)
http://bibframe.org/vocab/doi	10.1016/j.dss.2010.11.033
http://localhost/t...ganizacniJednotka	25410

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