¿Bancos con problemas?: un sistema de alerta temprana para la prevención de crisis bancarias

• Ana Fernández Sáinz ^[1] ; Felipe Llaugel ^[2]
  1. [1] Universidad del País Vasco/Euskal Herriko Unibertsitatea

     Universidad del País Vasco/Euskal Herriko Unibertsitatea

     Leioa, España

     
  2. [2] Universidad Autónoma de Santo Domingo

     Universidad Autónoma de Santo Domingo

     Santo Domingo De Guzmán, República Dominicana

     
• Localización: Management Letters / Cuadernos de Gestión, ISSN 1131-6837, Vol. 11, Nº 2, 2011, págs. 149-168
• Idioma: español
• DOI: 10.5295/cdg.100239af
• Enlaces
  • Texto completo
• Dialnet Métricas: 1 Cita
• Resumen
  • español

    Las autoridades reguladoras y supervisoras de los sistemas financieros han probado diversos métodos para intentar encontrar un procedimiento eficaz en la elaboración de un sistema de alerta temprana de las crisis bancarias. Los Modelos de Regresión Logística han sido usados aunque han mostrado algunas debilidades, por lo que se necesitan nuevos y mejores métodos. La crisis bancaria ocurrida en la República Dominicana entre los años 2002 y 2004 se ha usado para comparar la eficacia de la Regresión Logística frente al uso del método Support Vector Machines (SVM) para la detección de crisis bancarias. En el análisis se usan 30 indicadores financieros para determinar cuáles de ellos son los más apropiados en la construcción de un modelo capaz de distinguir un banco en problemas de uno solvente. En este contexto, el método de SVM generó mejores resultados que la Regresión Logística en la detección de los bancos con problemas y se contradicen las afirmaciones de otros estudios que plantean la poca efectividad de los indicadores financieros para detectar crisis bancarias en economías emergentes.

  • English

    The regulatory and supervisory financial authorities have tried various methods to find an effective procedure in developing an early warning system of banking crises. Logistic regression models have been used but have shown some weaknesses, so we need new and better methods. The banking crisis occurred in the Dominican Republic between 2002 and 2004 has been used to compare the effectiveness of the logistic regression method over the use of Support Vector Machines (SVM) for the detection of banking crisis. In the analysis 30 financial indicators are used to determine which ones are most appropriate to build a model able to classify banks. In this context, the SVM method produced better results than logistic regression, in detecting problem banks and contradict the findings of other studies that ask about the ineffectiveness of the financial indicators to identify banking crises in emerging economies.

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