Forecasting stock returns with large dimensional factor models

Giovannelli, Alessandro and Massacci, Daniele and Soccorsi, Stefano (2021) Forecasting stock returns with large dimensional factor models. Journal of Empirical Finance, 63. pp. 252-269. ISSN 0927-5398

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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.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Empirical Finance
Additional Information:
This is the author’s version of a work that was accepted for publication in Journal of Empirical Finance. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Empirical Finance, 63, 2021 DOI: 10.1016/j.jempfin.2021.07.009
Uncontrolled Keywords:
?? stock returns forecastingfactor modellarge data setsforecast evaluationfinanceeconomics and econometrics ??
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Deposited On:
10 Aug 2021 09:05
Last Modified:
13 Nov 2023 00:27