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 current paper examines the asymmetric effects of changes to monetary and fiscal variables on different types of firms in the UAE. We compute impulse responses based on local projections and select shock and switching variables using machine learning. We examine 180 firms listed in the UAE...

We are interested in forecasting bankruptcies in a probabilistic way. Specifically, we compare the classification performance of several statistical and machine-learning techniques, namely discriminant analysis (Altman's Z-score), logistic regression, least-squares support vector machines and...