Forecasting Stock Returns with Large Dimensional Factor Models

Abstract

Motivated by the longstanding evidence that economic variables can be decomposed into common and idiosyncratic components, we study equity premium out-of-sample predictability extracting the information contained in a high number of macroeconomic predictors via large dimensional factor models. We compare the widespread 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 show that the Generalized Dynamic Factor model helps predicting the equity premium. Exploiting the well-known link between the business cycle and return predictability, we find more accurate predictions by combining rolling and recursive forecasts in real-time.

Keywords: Stock Returns Forecasting, Factor Model, Large Data Sets, Forecast Evaluation.

JEL Classification: C38, C53, C55, G11, G17.