Forecasting Stock Returns with Large Dimensional Factor Models

46 Pages Posted: 26 Apr 2017 Last revised: 14 Jul 2021

See all articles by Alessandro Giovannelli

Alessandro Giovannelli

University of Rome Tor Vergata

Daniele Massacci

King's College London

Stefano Soccorsi

Department of Economics, Lancaster University Management School

Date Written: June 23, 2021

Abstract

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 the Generalized Dynamic Factor Model, which accounts for time series dependence in the common components. 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 accurate predictions also 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.

Suggested Citation

Giovannelli, Alessandro and Massacci, Daniele and Soccorsi, Stefano, Forecasting Stock Returns with Large Dimensional Factor Models (June 23, 2021). Available at SSRN: https://ssrn.com/abstract=2958491 or http://dx.doi.org/10.2139/ssrn.2958491

Alessandro Giovannelli

University of Rome Tor Vergata ( email )

Via di Tor Vergata
Rome, Lazio 00133
Italy

Daniele Massacci (Contact Author)

King's College London ( email )

United Kingdom

Stefano Soccorsi

Department of Economics, Lancaster University Management School ( email )

Lancaster, LA1 4YX
United Kingdom

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