TY - UNPB

T1 - Forecasting Stock Returns with Large Dimensional Factor Models

AU - Giovannelli, Alessandro

AU - Massacci, Daniele

AU - Soccorsi, Stefano

PY - 2020/9/1

Y1 - 2020/9/1

N2 - 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.

AB - 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.

KW - Stock Returns Forecasting

KW - Factor Model

KW - Large Data Sets

KW - Forecast Evaluation

M3 - Working paper

T3 - Economics Working Papers Series

BT - Forecasting Stock Returns with Large Dimensional Factor Models

PB - Lancaster University, Department of Economics

CY - Lancaster

ER -