Random forest regression (RF) is an extremely popular tool for the analysis of high-dimensional data. Nonetheless, its benefits may be lessened in sparse settings, due to weak predictors, and a pre-estimation dimension reduction (targeting) step is required. We show that proper targeting...

We present a novel approach to analyzing stock return predictability that accommodates (i) arbitrary predictor persistence, (ii) panels with common factors, (iii) multiple predictors, (iv) short- and long-horizon analysis, and relies on standard inference from least-squares estimation of a...

We show that improved hedging of bond portfolios can be achieved by matching generalized durations that are parametrized according to a parsimonious yield curve shape which is dynamically consistent with a new term structure model with stochastic level, slope, and curvature factors. Performance...