Municipal credit rating modelling by neural networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F11%3A39892236" target="_blank" >RIV/00216275:25410/11:39892236 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.dss.2010.11.033" target="_blank" >http://dx.doi.org/10.1016/j.dss.2010.11.033</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.dss.2010.11.033" target="_blank" >10.1016/j.dss.2010.11.033</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Municipal credit rating modelling by neural networks

Popis výsledku v původním jazyce

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 thedesign 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

Název v anglickém jazyce

Municipal credit rating modelling by neural networks

Popis výsledku anglicky

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 thedesign 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

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GP402%2F09%2FP090" target="_blank" >GP402/09/P090: Modelování místních financí metodami výpočetní inteligence</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Decision Support Systems

ISSN

0167-9236

e-ISSN

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Svazek periodika

51

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

11

Strana od-do

108-118

Kód UT WoS článku

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EID výsledku v databázi Scopus

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