Bayesian networks to predict financial distress in Spanish Banking

• JESSICA PAULE-VIANEZ ^[1] ; JOSE PABLO ARIAS-NICOLáS ^[2] ; JOSE LUIS COCA-PéREZ ^[2]
  1. [1] Universidad Rey Juan Carlos

     Universidad Rey Juan Carlos

     Madrid, España

     
  2. [2] Universidad de Extremadura

     Universidad de Extremadura

     Badajoz, España

     
• Localización: Rect@: Revista Electrónica de Comunicaciones y Trabajos de ASEPUMA, ISSN-e 1575-605X, Vol. 20, Nº. 2, 2019, págs. 131-152
• Idioma: inglés
• DOI: 10.24309/recta.2019.20.2.02
• Enlaces
  • Texto completo (pdf)
• Resumen
  • español

    Este trabajo desarrolla un modelo predictivo a corto plazo del financial distress en el sistema bancario español con redes bayesianas. Como las quiebras de bancos han sido escasas, este documento ha considerado también otros problemas financieros, agrupados bajo el término financial distress, como son el incumplimiento de sus obligaciones, la necesidad de intervenci´on de organismos externos, la ayuda estatal, las fusiones y adquisiciones con problemas, y las liquidaciones. Las variables utilizadas para predecir el financial distress en el sistema bancario español han sido variables financieras, clasificadas siguiendo el sistema de calificación de CAMELS, y variables económicas, cuya repercusión en la salud de estas entidades ha sido demostrada por diversos trabajos previos. Con una muestra de 148 instituciones bancarias, la alta tasa de aciertos obtenida demuestra que las redes bayesianas constituyen una metodología prometedora para predecir el financial distress a corto plazo en el sector bancario español.

  • English

    This paper develops a short-term predictive model of financial distress in Spanish banking system with Bayesian networks. As bank failures have been scarce, this document has also considered other financial problems, encompassed under the term financial distress, such as non-compliance with its obligations, the need for intervention by external agencies, state aid, mergers and acquisitions with problems, and liquidations. The variables used to predict financial distress in the Spanish banking system have been financial variables, classified according to the CAMELS rating system, and economic variables, whose impact on the health of these entities has been demonstrated by several previous studies. With a sample of 148 banking institutions, the high success rate obtained shows that the Bayesian networks constitute a promising methodology for predicting short-term financial distress in the Spanish banking sector.

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