Corporate default prediction: a comparison between Merton model and random forest in an environment of data scarcity
The aim of this paper is to compare the performance of the Merton model to a machine learning technique (random forest), in a context where the number of predictors is low or the dataset is quite small. Since random forest is a data-intensive method, the main goal is to find the minimum number of explanatory variables and observations that is needed for it to perform at least as well as the Merton