This paper constructs a leading macroeconomic indicator from microeconomic data using recent machine learning techniques. Using tree-based methods, we estimate probabilities of default for publicly traded non-financial firms in the United States. We then use the cross-section of out-of-sample...

The study's objective is to check whether the predictive power of Machine Learning Techniques is better than Logistic Regression in predicting the bankruptcy of firms and that the same predictive power of ascertaining bankruptcy improves when a proxy for uncertainty is added to the model as a...

Forecasting models based on machine learning (ML) algorithms have been shown to outperform traditional models in several applications. The lack of an easily interpretable functional form, however, is a major challenge for their adoption, especially when a knowledge of the estimated relationships...