Predicting Bank Insolvency with Random Forest Classification
The aim of this paper is to evaluate a machine learning technique, Random Forest, to predict default rates for banks in the United States. This study extends the findings of a Random Forest model first introduced by Petropoulos et al. (2017) by extending their model by evaluating a longer sample period and adding macroeconomic variables to analyze how current market conditions impact the predictio