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Forecasting Stock Returns with Large Dimensional Factor Models

Research output: Working paper

Publication date1/09/2020
Place of PublicationLancaster
PublisherLancaster University, Department of Economics
<mark>Original language</mark>English

Publication series

NameEconomics Working Papers Series


We study equity premium out-of-sample predictability by extracting the information contained in a high number of macroeconomic predictors via large dimensional factor models. We compare the well known factor model with a static representation of the common components with a more general model known as the Generalized Dynamic Factor Model. Using statistical and economic evaluation criteria, we empirically show that the Generalized Dynamic Factor Model helps predicting the equity premium. Exploiting the link between business cycle and return predictability, we find more accurate predictions by combining rolling and recursive forecasts in real-time, with promising results in the aftermath of the Great Financial Crisis.