Forecasting Stock Returns with Large Dimensional Factor Models

Alessandro Giovannelli, Daniele Massacci and Stefano Soccorsi

No 305661169, Working Papers from Lancaster University Management School, Economics Department

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.

Keywords: Stock Returns Forecasting; Factor Model; Large Data Sets; Forecast Evaluation (search for similar items in EconPapers)
JEL-codes: C38 C53 C55 G11 G17 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-fmk, nep-for and nep-ore
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Citations: View citations in EconPapers (5)

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