Forecasting stock returns with large dimensional factor models

Alessandro Giovannelli, Daniele Massacci and Stefano Soccorsi

Journal of Empirical Finance, 2021, vol. 63, issue C, 252-269

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 (search for similar items in EconPapers)
JEL-codes: C38 C53 C55 G11 G17 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Working Paper: Forecasting Stock Returns with Large Dimensional Factor Models (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:63:y:2021:i:c:p:252-269

DOI: 10.1016/j.jempfin.2021.07.009

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