Rights statement: 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
Accepted author manuscript, 3.52 MB, PDF document
Available under license: CC BY-NC-ND
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Forecasting stock returns with large dimensional factor models
AU - Giovannelli, Alessandro
AU - Massacci, Daniele
AU - Soccorsi, Stefano
N1 - 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
PY - 2021/9/30
Y1 - 2021/9/30
N2 - 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.
AB - 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.
KW - Stock returns forecasting
KW - Factor model
KW - Large data sets
KW - Forecast evaluation
U2 - 10.1016/j.jempfin.2021.07.009
DO - 10.1016/j.jempfin.2021.07.009
M3 - Journal article
VL - 63
SP - 252
EP - 269
JO - Journal of Empirical Finance
JF - Journal of Empirical Finance
SN - 0927-5398
ER -