US Stock return predictability with high dimensional models

Abstract We examine the role of large information sets in the predictability of US stock using a large data set of over 400 predictors covering macro-, financial-, trade- and commodity-related variables over the period of 1960:Q1 to 2018:Q4. We consider 13 alternative models ranging from autoregressive models with no predictors to 5-factor, 60-factor and high dimensional models with over 400 predictors including assumptions of constant and time varying coefficients. We find that models that incorporate large predictors improve US stock return predictability. The outcome particularly favours models involving Dynamic Variable Selection prior with Variational Bayes (VBDV) for density forecast.