Pedro Carmona Ibáñez

Banking failure prediction: a boosting classification tree approach

ABSTRACT The recent financial crisis shows that failure of some financial institutions can cause other banks to fail and ultimately cause damage to the financial system worldwide. Eurozone banks that experienced either liquidity or solvency problems during the financial markets turmoil were bailed out by their national governments with the financial support and supervision of the European Union. This paper applies the boosted classification tree methodology to predict failure in the banking sector and identifies four key scorecard variables that are worth tracking closely in order to anticipate and prevent bank financial distress. The data used in this study comprises 2006–2012 annual series of 25 financial ratios of 155 banks in the Eurozone. The findings indicate that the greater the size and the higher the income from non-operating items and net loans to deposits, the more likely is bank failure; conversely, the higher the Interbank ratio the lower the chances of bank financial distress. For the sake of their own financial soundness, banks should fund lending activities through clients’ deposits and should avoid relying excessively on non-recurring sources of income.ABSTRACTThe recent financial crisis shows that failure of some financial institutions can cause other banks to fail and ultimately cause damage to the financial system worldwide. Eurozone banks that experienced either liquidity or solvency problems during the financial markets turmoil were bailed out by their national governments with the financial support and supervision of the European Union. This paper applies the boosted classification tree methodology to predict failure in the banking sector and identifies four key scorecard variables that are worth tracking closely in order to anticipate and prevent bank financial distress. The data used in this study comprises 2006–2012 annual series of 25 financial ratios of 155 banks in the Eurozone. The findings indicate that the greater the size and the higher the income from non-operating items and net loans to deposits, the more likely is bank failure; conversely, the higher the Interbank ratio the lower the chances of bank financial distress. For the sake of...

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

RESUMEN 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.