Forecasting stock returns with large dimensional factor models

Alessandro Giovannelli, Daniele Massacci*, Stefano Soccorsi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)252-269
Number of pages18
JournalJournal of Empirical Finance
Publication statusPublished - Sept 2021


  • Factor model
  • Forecast evaluation
  • Large data sets
  • Stock returns forecasting


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