Artikel
Predicting corporate failure: The GRASP-LOGIT model
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.
- Weitere Titel
-
Predicción de la quiebra empresarial: El modelo GRASP-LOGIT
- Sprache
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Englisch
- Erschienen in
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Journal: Revista de Métodos Cuantitativos para la Economía y la Empresa ; ISSN: 1886-516X ; Volume: 26 ; Year: 2018 ; Pages: 294-314
- Klassifikation
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Wirtschaft
Multiple or Simultaneous Equation Models; Multiple Variables: Other
Operations Research; Statistical Decision Theory
Bankruptcy; Liquidation
- Thema
-
Financial distress
accounting ratios
feature selection
GRASP metaheuristic
logistic regression
- Ereignis
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Geistige Schöpfung
- (wer)
-
Yusta, Silvia Casado
Letamendía, Laura Nuñez
Pacheco Bonrostro, Joaquín Antonio
- Ereignis
-
Veröffentlichung
- (wer)
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Universidad Pablo de Olavide
- (wo)
-
Sevilla
- (wann)
-
2018
- DOI
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doi:10.46661/revmetodoscuanteconempresa.2810
- Handle
- Letzte Aktualisierung
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10.03.2025, 11:42 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Yusta, Silvia Casado
- Letamendía, Laura Nuñez
- Pacheco Bonrostro, Joaquín Antonio
- Universidad Pablo de Olavide
Entstanden
- 2018
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