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Modelos de predicción de la fragilidad empresarial: aplicación al caso colombiano para el ano 2011

Author

Listed:
  • Pérez G., Jorge Iván
  • González C., Karen Lorena
  • Lopera C., Mauricio

Abstract

Resumen: El objetivo de este trabajo consiste en la estimación de un modelo que permita medir la probabilidad de que una empresa entre en riesgo de quiebra. Para alcanzar este objetivo, se utilizaran los estados financieros reportados por las empresas a la Superintendencia de Sociedades en el ano 2011. De estos estados se construyeron indicadores financieros que fueron útiles como variables explicativas en la estimación de modelos de regresión binaria, a partir de los cuales se identificaron como relevantes las razones financieras de rentabilidad del activo, rotación del activo y nivel de endeudamiento para la medición de la probabilidad de riesgo de quiebra. Se estiman dos modelos de selección discreta: logit y probit. El primero, clasifica correctamente el 83,20% de las observaciones. Y el segundo, clasifica correctamente el 80,8%.

Suggested Citation

  • Pérez G., Jorge Iván & González C., Karen Lorena & Lopera C., Mauricio, 2013. "Modelos de predicción de la fragilidad empresarial: aplicación al caso colombiano para el ano 2011," Perfil de Coyuntura Económica, Universidad de Antioquia, CIE, issue 22, pages 205-228, August.
    • Handle: RePEc:col:000165:014696
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    More about this item

    Keywords

    Quiebra; Fragilidad; Indicadores Financieros; Modelo Probit; Modelo Logit;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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