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In this paper we intend to substantiate the usefulness of Deep Learning, especially feedforward neuronal networks, in the prediction of business failure. This methodology provides very good results in terms of predictive performance when large sample sizes are available. Therefore, we have developed a business failure prediction model for European companies, based on this algorithm on a sample of 61,624 companies, of which 12,128 were declared bankrupt in 2016. As independent variables were considered ratios, and economic and financial data obtained from the financial statements for the year preceding the date of failure. Deep Learning achieves a predictive performance of 94%, where companies with larger size and lower solvency are more prone to failure. The obtained results have been tested on an independent test sample, different from that used to estimate and train the model. [ABSTRACT FROM AUTHOR]

El objetivo del estudio realizado es mostrar la utilidad de la metodología Extreme Gradient Boosting (XGBoost), basada en el aprendizaje automático, en la predicción del fracaso empresarial, particularmente en el ámbito de las empresas cooperativas. Para ello, se ha estimado un modelo a partir de una muestra de cooperativas pertenecientes a diversos sectores, que ha permitido identificar que estas empresas presentan una menor propensión al fracaso cuando alcanzan mayores índices de solvencia y rentabilidad y menor endeudamiento. De acuerdo con los resultados, el modelo estimado XGBoost obtiene una capacidad predictiva del 100% en la muestra de entrenamiento y de un 86% en una muestra de comprobación independiente. Asimismo, se destaca la facilidad de la interpretación de los resultados alcanzados, siendo posible determinar el signo y el efecto de cada variable sobre la predicción final de todas y cada una de las observaciones. Este trabajo aporta a la literatura del fracaso empresarial del sector cooperativo la constatación de la utilidad de una nueva metodología (XGBoost), que proporciona un elevado nivel de aciertos y una sencilla interpretabilidad de los resultados. [ABSTRACT FROM AUTHOR]

This paper focuses on the study of cooperative societies, given their marked social nature and their strong tradition in the Valencian Community. Starting from the financial data provided by an economic and financial database for the period 2006-2007, we carried out a comparative analysis of cooperative enterprises and private capital ones, in an attempt to determine and explain, where appropriate, the differences in solvency, profitability, indebtedness, tax burden and activity, and at the same time, we offer a diagnosis that makes clear the strengths and weaknesses of this vital area of the industrial fabric of the Valencian Community. [ABSTRACT FROM AUTHOR]