Predicting corporate failurethe GRASP-LOGIT Model

  1. Silvia Casado Yusta 1
  2. Laura Nuñez Letamendía 2
  3. Joaquín Antonio Pacheco Bonrostro 1
  1. 1 Universidad de Burgos
    info

    Universidad de Burgos

    Burgos, España

    ROR https://ror.org/049da5t36

  2. 2 IE Business School, IE University
Journal:
Revista de métodos cuantitativos para la economía y la empresa

ISSN: 1886-516X

Year of publication: 2018

Volume: 26

Pages: 294-314

Type: Article

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Abstract

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.

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Funding information

This work has been partially supported by FEDER funds and the Spanish Ministry of Economy and Competitiveness (Projects ECO2013-47129-C4-3-R and ECO2016-76567-C4-2-R), the Regional Government of “Castilla y León”, Spain (Project BU329U14) and the Regional Government of “Castilla y León” and FEDER funds (Project BU062U16); all of whom are gratefully acknowledged.

Funders

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