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