Estimating bankruptcy using neural networks trained with hidden layer learning vector quantization
Autor(a) principal: | |
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Data de Publicação: | 2004 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.5/2189 |
Resumo: | The Hidden Layer Learning Vector Quantization (HLVQ), a recent algorithm for training neural networks, is used to correct the output of traditional MultiLayer Preceptrons (MLP) in estimating the probability of company bankruptcy. It is shown that this method improves the results of traditional neural networks and outperforms substantially the discriminant analysis in predicting one-year advance bankruptcy. We also studied the effect of using unbalanced samples of healthy and bankrupted firms. The database used was Diane, which contains financial accounts of French firms. The sample is composed of all 583 industrial bankruptcies found in the database with more than 35 employees, that occurred in the 1999-2000 period. For the classification models we considered 30 financial ratios published by Coface available from Diane database, and additionally the Beaver (1966) ratio of Cash Earnings to Total Debt, the 5 ratios of Altman (1968) used in his Z-model and the size of the firms measured by the logarithm of sales. Attention was given to variable selection, data pre¬processing and feature selection to reduce the dimensionality of the problem. |
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Estimating bankruptcy using neural networks trained with hidden layer learning vector quantizationBankruptcy PredictionNeural NetworksDiscriminant AnalysisRatio AnalysisThe Hidden Layer Learning Vector Quantization (HLVQ), a recent algorithm for training neural networks, is used to correct the output of traditional MultiLayer Preceptrons (MLP) in estimating the probability of company bankruptcy. It is shown that this method improves the results of traditional neural networks and outperforms substantially the discriminant analysis in predicting one-year advance bankruptcy. We also studied the effect of using unbalanced samples of healthy and bankrupted firms. The database used was Diane, which contains financial accounts of French firms. The sample is composed of all 583 industrial bankruptcies found in the database with more than 35 employees, that occurred in the 1999-2000 period. For the classification models we considered 30 financial ratios published by Coface available from Diane database, and additionally the Beaver (1966) ratio of Cash Earnings to Total Debt, the 5 ratios of Altman (1968) used in his Z-model and the size of the firms measured by the logarithm of sales. Attention was given to variable selection, data pre¬processing and feature selection to reduce the dimensionality of the problem.ISEG – Departamento de GestãoRepositório da Universidade de LisboaNeves, João Carvalho dasVieira, Armando2010-07-20T10:52:44Z20042004-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.5/2189engNeves, João Carvalho das e Armando Vieira. 2004. "Estimating bankruptcy using neural networks trained with hidden layer learning vector quantization". Instituto Superior de Economia e Gestão. Departamento de Gestão. Working papers series nº 2-04.0874-8470info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-06T14:33:25Zoai:www.repository.utl.pt:10400.5/2189Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:50:15.177059Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Estimating bankruptcy using neural networks trained with hidden layer learning vector quantization |
title |
Estimating bankruptcy using neural networks trained with hidden layer learning vector quantization |
spellingShingle |
Estimating bankruptcy using neural networks trained with hidden layer learning vector quantization Neves, João Carvalho das Bankruptcy Prediction Neural Networks Discriminant Analysis Ratio Analysis |
title_short |
Estimating bankruptcy using neural networks trained with hidden layer learning vector quantization |
title_full |
Estimating bankruptcy using neural networks trained with hidden layer learning vector quantization |
title_fullStr |
Estimating bankruptcy using neural networks trained with hidden layer learning vector quantization |
title_full_unstemmed |
Estimating bankruptcy using neural networks trained with hidden layer learning vector quantization |
title_sort |
Estimating bankruptcy using neural networks trained with hidden layer learning vector quantization |
author |
Neves, João Carvalho das |
author_facet |
Neves, João Carvalho das Vieira, Armando |
author_role |
author |
author2 |
Vieira, Armando |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Neves, João Carvalho das Vieira, Armando |
dc.subject.por.fl_str_mv |
Bankruptcy Prediction Neural Networks Discriminant Analysis Ratio Analysis |
topic |
Bankruptcy Prediction Neural Networks Discriminant Analysis Ratio Analysis |
description |
The Hidden Layer Learning Vector Quantization (HLVQ), a recent algorithm for training neural networks, is used to correct the output of traditional MultiLayer Preceptrons (MLP) in estimating the probability of company bankruptcy. It is shown that this method improves the results of traditional neural networks and outperforms substantially the discriminant analysis in predicting one-year advance bankruptcy. We also studied the effect of using unbalanced samples of healthy and bankrupted firms. The database used was Diane, which contains financial accounts of French firms. The sample is composed of all 583 industrial bankruptcies found in the database with more than 35 employees, that occurred in the 1999-2000 period. For the classification models we considered 30 financial ratios published by Coface available from Diane database, and additionally the Beaver (1966) ratio of Cash Earnings to Total Debt, the 5 ratios of Altman (1968) used in his Z-model and the size of the firms measured by the logarithm of sales. Attention was given to variable selection, data pre¬processing and feature selection to reduce the dimensionality of the problem. |
publishDate |
2004 |
dc.date.none.fl_str_mv |
2004 2004-01-01T00:00:00Z 2010-07-20T10:52:44Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.5/2189 |
url |
http://hdl.handle.net/10400.5/2189 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Neves, João Carvalho das e Armando Vieira. 2004. "Estimating bankruptcy using neural networks trained with hidden layer learning vector quantization". Instituto Superior de Economia e Gestão. Departamento de Gestão. Working papers series nº 2-04. 0874-8470 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
ISEG – Departamento de Gestão |
publisher.none.fl_str_mv |
ISEG – Departamento de Gestão |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799130977200504832 |