Factores determinantes de las quiebras en microempresas

• Vázquez Cueto, María José ^[1] ; Irimia-Diéguez, Ana ^[1] ; Blanco Oliver, Antonio José ^[1]
  1. [1] Universidad de Sevilla

     Universidad de Sevilla

     Sevilla, España

     
• Localización: Anales de ASEPUMA, ISSN-e 2171-892X, Nº. 22, 2014
• Idioma: español
• Enlaces
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• Dialnet Métricas: 1 Cita
• Resumen
  • español

    La pequeña y mediana empresa es uno de los principales motores de las economías europeas. De entre ellas, un alto porcentaje son microempresas las cuales generan la mayor parte del empleo. En la actualidad este segmento empresarial está sufriendo en mayor medida la situación de crisis financiera, con la consecuente elevación de la tasa de destrucción de las mismas. En este contexto, desarrollar modelos de quiebra específicos para este tamaño empresarial e identificar las variables con mayor poder explicativo constituye un reto. Aquí se aborda la cuestión llegando a ser un trabajo pionero en este campo, en tanto la metodología utilizada como en el sector al que se aplica, caracterizado por una elevada opacidad informativa. Partiendo de variables financieras y no financieras que han sido utilizadas con relativo éxito en el pronóstico de quiebra empresarial en general, tratamos de determinar cuáles de ellas están afectando en mayor medida a la microempresa. Para ello utilizamos una técnica no paramétrica de aprendizaje basada en los rough set, que aplicamos a una muestra de empresas del Reino Unido, con iguales porcentajes respecto a su situación de fallida y a su carácter familiar, por ser esta última característica un factor condicionante de los resultados.

  • English

    The small and medium enterprises are one of the main drivers of European economies. A large percentage consists of microenterprises that generate the most part of employment. Today this business segment is suffering the financial crisis, with the consequent increase in the rate of destruction of the same. So develop specific models for these bankruptcy and identify variables with greater explanatory power is challenging. So this study is becoming a pioneering work in this field in both the methodology used and the sector to which it applies, which has a higher opacity. Based on financial and non-financial variables that have been used with relative success in predicting bankruptcy in general, we try to determine which ones are affecting more to microfirms. We use a nonparametric learning technique based on the rough sets, which apply to a sample of UK firms, balanced on its failed situation and its familiar character, which determines the results.

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