Subsampled Factor Models for Asset Pricing: The Rise of Vasa

Journal of Forecasting (2022)

64 Pages Posted: 14 Apr 2020 Last revised: 23 Feb 2023

See all articles by Gianluca De Nard

Gianluca De Nard

University of Zurich - Department of Economics; New York University - Volatility and Risk Institute

Simon Hediger

University of Zurich - Department of Economics

Markus Leippold

University of Zurich; Swiss Finance Institute

Date Written: November 1, 2020

Abstract

We propose a new method, VASA, based on variable subsample aggregation of model predictions for equity returns 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 algorithms. As a performance measure, we explore not only the global predictive but also the stock-specific R2's and their distribution. While the global R2 indicates the average forecasting accuracy, we find that high variability in the stock-specific R2's can be detrimental for the portfolio performance, due to the higher prediction risk. Since VASA shows minimal variability, portfolios formed on this method outperform the portfolios based on more complicated methods like random forests and neural nets.

Keywords: Large-dimensional factor models, machine learning, return prediction, subagging, subsampling.

JEL Classification: C13, C30, C53, C58, G12, G17

Suggested Citation

De Nard, Gianluca and Hediger, Simon and Leippold, Markus, Subsampled Factor Models for Asset Pricing: The Rise of Vasa (November 1, 2020). Journal of Forecasting (2022), Available at SSRN: https://ssrn.com/abstract=3557957 or http://dx.doi.org/10.2139/ssrn.3557957

Gianluca De Nard (Contact Author)

University of Zurich - Department of Economics ( email )

Zürichbergstrasse 14
Zürich, Zurich 8032
Switzerland

New York University - Volatility and Risk Institute ( email )

Department of Finance
New York, NY
United States

HOME PAGE: http://https://vlab.stern.nyu.edu

Simon Hediger

University of Zurich - Department of Economics ( email )

Zürichbergstrasse 14
Zurich
Switzerland

Markus Leippold

University of Zurich ( email )

Rämistrasse 71
Zürich, CH-8006
Switzerland

Swiss Finance Institute ( email )

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4
Switzerland

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