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    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

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    Embargo ends: 18/01/23

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Forecasting stock returns with large dimensional factor models

Research output: Contribution to journalJournal articlepeer-review

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Forecasting stock returns with large dimensional factor models. / Giovannelli, Alessandro; Massacci, Daniele; Soccorsi, Stefano.

In: Journal of Empirical Finance, Vol. 63, 30.09.2021, p. 252-269.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Giovannelli, A, Massacci, D & Soccorsi, S 2021, 'Forecasting stock returns with large dimensional factor models', Journal of Empirical Finance, vol. 63, pp. 252-269. https://doi.org/10.1016/j.jempfin.2021.07.009

APA

Giovannelli, A., Massacci, D., & Soccorsi, S. (2021). Forecasting stock returns with large dimensional factor models. Journal of Empirical Finance, 63, 252-269. https://doi.org/10.1016/j.jempfin.2021.07.009

Vancouver

Giovannelli A, Massacci D, Soccorsi S. Forecasting stock returns with large dimensional factor models. Journal of Empirical Finance. 2021 Sep 30;63:252-269. https://doi.org/10.1016/j.jempfin.2021.07.009

Author

Giovannelli, Alessandro ; Massacci, Daniele ; Soccorsi, Stefano. / Forecasting stock returns with large dimensional factor models. In: Journal of Empirical Finance. 2021 ; Vol. 63. pp. 252-269.

Bibtex

@article{a29c8d82f1284c62b427666d14645b17,
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 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.",
keywords = "Stock returns forecasting, Factor model, Large data sets, Forecast evaluation",
author = "Alessandro Giovannelli and Daniele Massacci and Stefano Soccorsi",
note = "This is the author{\textquoteright}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",
year = "2021",
month = sep,
day = "30",
doi = "10.1016/j.jempfin.2021.07.009",
language = "English",
volume = "63",
pages = "252--269",
journal = "Journal of Empirical Finance",
issn = "0927-5398",
publisher = "Elsevier",

}

RIS

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 -