La metodología de los Rough Sets como técnica de preprocesamiento de datos: una aplicación a las quiebras de microempresas familiares

Micro enterprises (MEs) represent over 75 % of all enterprises in the EU, accounting for over 30 % of employment. However, since the onset of the economic crisis in 2008, this business segment has suffered high rates of bankruptcies and business closures, destroying many jobs. The construction of mo...

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Detalhes bibliográficos
Autores: Vázquez Cueto, María José, Irimia Diéguez, Ana Isabel, Blanco Oliver, Antonio Jesús
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2015
País:España
Recursos:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/50739
Acesso em linha:http://hdl.handle.net/11441/50739
Access Level:acceso abierto
Palavra-chave:Rough Sets
Modelos de quiebra empresarial
Ratios financieros
Microempresa
PYME
Micro enterprises
Financial Ratios
Models of corporate bankruptcy
Descrição
Resumo:Micro enterprises (MEs) represent over 75 % of all enterprises in the EU, accounting for over 30 % of employment. However, since the onset of the economic crisis in 2008, this business segment has suffered high rates of bankruptcies and business closures, destroying many jobs. The construction of models that anticipate insolvency to allow sufficient time to take appropriate action is important to avoid bankruptcy of the MEs. However, it is difficult to obtain complete and relevant information for MEs, making it very difficult to be a good fit of the models for predicting corporate failure for this size of company. Applying Rough Sets technique as a method for pre - processing of the data, in the present study, we order the variables that best discriminate between solvent / insolvent in order to increase efficiency in predicting insolvency MEs. Additionally, we provide an application of the technique to family- MEs. Throughout this process, our results highlight the importance of considering non-financial variables to predict insolvency of MEs.