This paper develops an early warning system for predicting distress for large European banks. Using a novel definition of distress derived from banks' headroom above regulatory requirements, we investigate the performance of three machine learning techniques against the traditional logistic...

This study presents novel deep neural networks (DNNs) that integrate a thorough data preparation process, including dollar bar sampling, trend scanning labeling, and piecewise aggregate approximation (PAA), to extract features for predicting the directional returns of leading technology stocks...