Análisis de la utilidad del algoritmo Gradient Boosting Machine (GBM) en la predicción del fracaso empresarial

  1. José Pozuelo Campillo 1
  2. Julián Martínez Vargas 1
  3. Pedro Carmona Ibáñez 1
  1. 1 Departament de Comptabilitat. Universitat de València
Zeitschrift:
Revista española de financiación y contabilidad

ISSN: 0210-2412

Datum der Publikation: 2018

Ausgabe: 47

Nummer: 4

Seiten: 507-532

Art: Artikel

DOI: 10.1080/02102412.2018.1442039 DIALNET GOOGLE SCHOLAR

Andere Publikationen in: Revista española de financiación y contabilidad

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Zusammenfassung

The aim of this study, a novel study regarding the use of the methodology based on the culture of algorithms, is to analyse ‘gradient boosting machine’ (GBM) as a powerful tool in business failure prediction of Spanish companies. Likewise, it shows its usefulness to identify the most relevant variables that anticipate business failure. We estimated GBM prediction models applying this technique to a sample of 1,506 companies in the period 2010–2013. The results show the GBM algorithm achieves better performance levels than other methodologies such as AdaBoost or logistic regression. Features linking total sales to total assets and to financial expenses are identified as key factors in business failure.

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La regresión logística también se ha empleado en la predicción del fracaso empre-sarial y conlleva la utilización de mínimos cuadrados o máxima verosimilitud para

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Fuente de los datos: Dialnet