Template-Type: ReDIF-Paper 1.0 Author-Name: Alessandro Giovannelli Author-Name-First: Alessandro Author-Name-Last: Giovannelli Author-Name: Daniele Massacci Author-Name-First: Daniele Author-Name-Last: Massacci Author-Name: Stefano Soccorsi Author-Name-First: Stefano Author-Name-Last: Soccorsi Title: Forecasting Stock Returns with Large Dimensional Factor Models 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 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. Creation-Date: 2020 File-URL: http://www.lancaster.ac.uk/media/lancaster-university/content-assets/documents/lums/economics/working-papers/LancasterWP2020_019.pdf File-Format: application/pdf Number: 305661169 Classification-JEL: C38, C53, C55, G11, G17 Keywords: Stock Returns Forecasting, Factor Model, Large Data Sets, Forecast Evaluation Handle: RePEc:lan:wpaper:305661169