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...

This study analyzes the spillover effects of economic and political uncertainty from advanced, emerging and low-income economies on stock returns in Vietnam based on a firm-level approach. After controlling for firm-level and country-level factors and addressing potential endogeneity issues, we...

This paper provides a model to interpret the relative behavior of expected returns of high- and low-resilience assets from the time of the COVID-19 pandemic, including a novel definition of disaster based on COVID-19 intensity. The setup allows us to disentangle the probability of disaster and...

This study examines the out-of-sample predictability of expected skewness of oil price returns, which serves as a metric for global future risks, as we show statistically through the association with crises of different nature, for stock returns of 10 (8 advanced plus two emerging) countries...