Predicting Corporate Failure: The GRASP-LOGIT Model // Predicción de la quiebra empresarial: el modelo GRASP-LOGIT


  • Silvia Casado Yusta Universidad de Burgos
  • Laura Nuñez Letamendía IE Business School, IE University
  • Joaquín Antonio Pacheco Bonrostro Universidad de Burgos

Palabras clave:

financial distress, accounting ratios, feature selection, GRASP metaheuristic, logistic regression, dificultades financieras, ratios contables, selección de características, metaheurístico GRASP, regresión logística


Predicting corporate failure is an important problem in management science. This study tests a new method for predicting corporate failure on a sample of Spanish firms. A GRASP (Greedy Randomized Adaptive Search Procedure) strategy is proposed to use a feature selection algorithm to select a subset of available financial ratios, as a preliminary step in estimating a model of logistic regression for predicting corporate failure. Selecting only a subset of variables (financial ratios) reduces the costs of data acquisition, increases prediction accuracy by excluding irrelevant variables, and provides insight into the nature of the prediction problem allowing a better understanding of the final classification model. The proposed algorithm, that it is named GRASP-LOGIT algorithm, performs better than a simple logistic regression in that it reaches the same level of forecasting ability with fewer accounting ratios, leading to a better interpretation of the model and therefore to a better understanding of the failure process.


La predicción de la quiebra empresarial es un problema que goza de una gran relevancia en las ciencias empresariales. En este trabajo se propone un nuevo método para predecir la quiebra empresarial en una muestra de empresas españolas.  Concretamente se trata de un algoritmo de selección de variables basado en la estrategia metaheurística GRASP (procedimiento de búsqueda adaptativa aleatoria y voraz) para seleccionar un subconjunto de ratios financieros, como un paso preliminar para estimar un modelo de regresión logística que prediga la quiebra empresarial. La selección de un subconjunto de ratios financieros, de entre todos los disponibles, reduce los costes de adquisición de datos, aumenta la precisión de la predicción al excluir las variables irrelevantes y proporciona información sobre la naturaleza del problema de predicción. Todo lo anterior permite una mejor comprensión del modelo de clasificación final. Nuestro nuevo modelo, al que llamamos modelo GRASP-LOGIT, funciona mejor que una simple regresión logística en el sentido de que alcanza el mismo nivel de capacidad de predicción con menos ratios contables, lo que lleva a una mejor interpretación del modelo y, por lo tanto, a una mejor comprensión del proceso de quiebra empresarial.


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Biografía del autor/a

Silvia Casado Yusta, Universidad de Burgos

Departamento de Economía Aplicada

Profesor Titular de Universidad

Laura Nuñez Letamendía, IE Business School, IE University

Center for Insurance Research, IE

Directora Académica

Joaquín Antonio Pacheco Bonrostro, Universidad de Burgos

Departamento de Economía Aplicada

Catedrático de Universidad


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Cómo citar

Casado Yusta, S., Nuñez Letamendía, L., & Pacheco Bonrostro, J. A. (2019). Predicting Corporate Failure: The GRASP-LOGIT Model // Predicción de la quiebra empresarial: el modelo GRASP-LOGIT. Revista De Métodos Cuantitativos Para La Economía Y La Empresa, 26, Páginas 294 a 314. Recuperado a partir de