Predicting corporate failure: The GRASP-LOGIT model

Artikel

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.

Object Details

• Alternative title: Predicción de la quiebra empresarial: El modelo GRASP-LOGIT
• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Revista de Métodos Cuantitativos para la Economía y la Empresa ; ISSN: 1886-516X ; Volume: 26 ; Year: 2018 ; Pages: 294-314

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models; Multiple Variables: Other ; Operations Research; Statistical Decision Theory ; Bankruptcy; Liquidation
• Subject: Financial distress ; accounting ratios ; feature selection ; GRASP metaheuristic ; logistic regression

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Yusta, Silvia Casado ; Letamendía, Laura Nuñez ; Pacheco Bonrostro, Joaquín Antonio
• Event: Veröffentlichung
• (who): Universidad Pablo de Olavide
• (where): Sevilla
• (when): 2018

Further information

• DOI: doi:10.46661/revmetodoscuanteconempresa.2810
• Handle: http://hdl.handle.net/10419/286150
• Last update: 10.03.2025, 11:42 AM CET