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 R 2 's and their distribution. While the global R 2 indicates the average forecasting accuracy, we find that high variability in the stock-specific R 2 '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.

Heterogeneous Tail Generalized Common Factor Modeling

“…Further, their sample universe contains a wide variety of different companies, from potentially short-living micro-stocks, up to well-established large stocks. The difference in performance between clas-sical models and sophisticated machine learning methods is even smaller when using stock-specific models, and focusing only on long-living companies; see De Nard et al (2020).…”

Heterogeneous Tail Generalized Common Factor Modeling

“…Further, their sample universe contains a wide variety of different companies, from potentially short-living micro-stocks, up to well-established large stocks. The difference in performance between classical models and sophisticated machine learning methods is even smaller when using stock-specific models, and focusing only on long-living companies; see De Nard et al (2020).…”