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

• José Pozuelo Campillo ^[1] ; Julián Martínez Vargas ^[1] ; Pedro Carmona Ibáñez ^[1]
  1. [1] Universitat de València

     Universitat de València

     Valencia, España

     
• Localización: Revista española de financiación y contabilidad, ISSN 0210-2412, Vol. 47, Nº 4, 2018, págs. 507-532
• Idioma: español
• DOI: 10.1080/02102412.2018.1442039
• Títulos paralelos:
  • Analysis of the algorithm Gradient Boosting Machine (GBM) in business failure prediction
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• Dialnet Métricas: 5 Citas
• Resumen
  • español

    Este estudio, novedoso en cuanto a la utilización de la metodología basada en la cultura de los algoritmos, prueba la capacidad de la técnica ‘Gradient Boosting Machine’ (GBM) en la predicción de la quiebra de empresas españolas. Asimismo, muestra su utilidad para identificar las variables más relevantes que anticipan el fracaso empresarial.

    Aplicando esta metodología a una muestra de 1.506 empresas del periodo 2010–2013 se han estimado modelos de predicción que alcanzan porcentajes de acierto que superan los obtenidos con otras metodologías como el AdaBoost o la Regresión Logística, resultando determinantes las variables que relacionan la cifra de ventas con el activo y los gastos financieros.

  • English

    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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