Predicting bank insolvencies using machine learning techniques

Predicting bank insolvencies using machine learning techniques

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Predicting bank insolvencies using machine learning techniques

In this work, we employ a series of modeling techniques to predict bank insolvencies on a sample of US based financial institutions. Our empirical results indicate that the method of Random Forests (RF) has a superior out of sample and out of time predictive performance not only compared to broadly used bank failure models, such as Logistic Regression and Linear Discriminant Analysis, but also…

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