Applying a probabilistic neural network to hotel bankruptcy prediction
Autor(a) principal: | |
---|---|
Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://scielo.pt/scielo.php?script=sci_arttext&pid=S2182-84582016000100004 |
Resumo: | Using a probabilistic neural network and a set of financial and non-financial variables, this study seeks to improve the ability of the existing bankruptcy prediction models in the hotel industry. Our aim is to construct a hotel bankruptcy prediction model that provides high accuracy, using information sufficiently distant from the bankruptcy situation, and which is able to determine the sensitivity of the explanatory variables. Based on a sample of Spanish hotels that went bankrupt between 2005 and 2012, empirical results indicate that using information nearer to bankruptcy (one and two years prior), the most relevant variable is EBITDA to current liabilities, but using information further from bankruptcy (three years prior), return on assets is the best predictor of bankruptcy. |
id |
RCAP_6f935582ea14582e871ad79642e48f84 |
---|---|
oai_identifier_str |
oai:scielo:S2182-84582016000100004 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Applying a probabilistic neural network to hotel bankruptcy predictionHotel bankruptcy predictionprobabilistic neural networksbankruptcy variables sensitivitySpanish hotel industryUsing a probabilistic neural network and a set of financial and non-financial variables, this study seeks to improve the ability of the existing bankruptcy prediction models in the hotel industry. Our aim is to construct a hotel bankruptcy prediction model that provides high accuracy, using information sufficiently distant from the bankruptcy situation, and which is able to determine the sensitivity of the explanatory variables. Based on a sample of Spanish hotels that went bankrupt between 2005 and 2012, empirical results indicate that using information nearer to bankruptcy (one and two years prior), the most relevant variable is EBITDA to current liabilities, but using information further from bankruptcy (three years prior), return on assets is the best predictor of bankruptcy.Escola Superior de Gestão, Hotelaria e Turismo da Universidade do Algarve2016-03-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articletext/htmlhttp://scielo.pt/scielo.php?script=sci_arttext&pid=S2182-84582016000100004Tourism & Management Studies v.12 n.1 2016reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAPenghttp://scielo.pt/scielo.php?script=sci_arttext&pid=S2182-84582016000100004Fernández-Gámez,Manuel ÁngelCisneros-Ruiz,Ana JoséCallejón-Gil,Ángelainfo:eu-repo/semantics/openAccess2024-02-06T17:29:05Zoai:scielo:S2182-84582016000100004Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:33:07.950235Repositório 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 |
Applying a probabilistic neural network to hotel bankruptcy prediction |
title |
Applying a probabilistic neural network to hotel bankruptcy prediction |
spellingShingle |
Applying a probabilistic neural network to hotel bankruptcy prediction Fernández-Gámez,Manuel Ángel Hotel bankruptcy prediction probabilistic neural networks bankruptcy variables sensitivity Spanish hotel industry |
title_short |
Applying a probabilistic neural network to hotel bankruptcy prediction |
title_full |
Applying a probabilistic neural network to hotel bankruptcy prediction |
title_fullStr |
Applying a probabilistic neural network to hotel bankruptcy prediction |
title_full_unstemmed |
Applying a probabilistic neural network to hotel bankruptcy prediction |
title_sort |
Applying a probabilistic neural network to hotel bankruptcy prediction |
author |
Fernández-Gámez,Manuel Ángel |
author_facet |
Fernández-Gámez,Manuel Ángel Cisneros-Ruiz,Ana José Callejón-Gil,Ángela |
author_role |
author |
author2 |
Cisneros-Ruiz,Ana José Callejón-Gil,Ángela |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Fernández-Gámez,Manuel Ángel Cisneros-Ruiz,Ana José Callejón-Gil,Ángela |
dc.subject.por.fl_str_mv |
Hotel bankruptcy prediction probabilistic neural networks bankruptcy variables sensitivity Spanish hotel industry |
topic |
Hotel bankruptcy prediction probabilistic neural networks bankruptcy variables sensitivity Spanish hotel industry |
description |
Using a probabilistic neural network and a set of financial and non-financial variables, this study seeks to improve the ability of the existing bankruptcy prediction models in the hotel industry. Our aim is to construct a hotel bankruptcy prediction model that provides high accuracy, using information sufficiently distant from the bankruptcy situation, and which is able to determine the sensitivity of the explanatory variables. Based on a sample of Spanish hotels that went bankrupt between 2005 and 2012, empirical results indicate that using information nearer to bankruptcy (one and two years prior), the most relevant variable is EBITDA to current liabilities, but using information further from bankruptcy (three years prior), return on assets is the best predictor of bankruptcy. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-03-01 |
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://scielo.pt/scielo.php?script=sci_arttext&pid=S2182-84582016000100004 |
url |
http://scielo.pt/scielo.php?script=sci_arttext&pid=S2182-84582016000100004 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://scielo.pt/scielo.php?script=sci_arttext&pid=S2182-84582016000100004 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Escola Superior de Gestão, Hotelaria e Turismo da Universidade do Algarve |
publisher.none.fl_str_mv |
Escola Superior de Gestão, Hotelaria e Turismo da Universidade do Algarve |
dc.source.none.fl_str_mv |
Tourism & Management Studies v.12 n.1 2016 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) |
collection |
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 |
repository.mail.fl_str_mv |
|
_version_ |
1799137391290613760 |