Forecasting Stock Returns with Large Dimensional Factor Models

46 Pages Posted: 26 Apr 2017 Last revised: 2 Apr 2018

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: March 28, 2018

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.

Suggested Citation

Giovannelli, Alessandro and Massacci, Daniele and Soccorsi, Stefano, Forecasting Stock Returns with Large Dimensional Factor Models (March 28, 2018). 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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