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This study focuses on the search for the causes, or combination of circumstances, that lead to business failure processes. There is renewed interest in this subject due to the adverse consequences that the recent economic crisis has caused in the business world. A fuzzy set Qualitative Comparative Analysis (fsQCA) is thus carried out to identify the combination of financial ratios that points to situations of financial difficulty. The study centres on the cooperative sector, represented by a sample of 56 companies holding this legal status, belonging to various different productive sectors. The results obtained, and confirmed through a number of different robustness tests, reveal the presence of sufficient conditions comprising combinations of variables reflecting high indebtedness, low liquidity, low solvency and small firm size, representing a scenario that would be sufficient for an entity to face business continuity problems. Thanks to its ability to identify combinations of variables that warn of business failure, as well as its ease of interpretability, the fsQCA technique can be extremely useful for business management and the identification of business failure situations. [ABSTRACT FROM AUTHOR]

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]