Subsampled factor models for asset pricing: The rise of Vasa

We propose a new method, variable subsample aggregation (VASA), for equity return prediction using a large‐dimensional set of factors. To demonstrate the effectiveness, robustness, and dimension reduction power of VASA, we perform a comparative analysis between state‐of‐the‐art machine learning algo...

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Veröffentlicht in:	Journal of forecasting 2022-09, Vol.41 (6), p.1217-1247
Hauptverfasser:	De Nard, Gianluca, Hediger, Simon, Leippold, Markus
Format:	Artikel
Sprache:	eng
Schlagworte:	Asset pricing Comparative analysis Forests large‐dimensional factor models machine learning Portfolios return prediction Robustness subagging subsampling Variability
Online-Zugang:	Volltext
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