Subsampled Factor Models for Asset Pricing: The Rise of Vasa

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