Post-Screening Portfolio Selection

TL;DR

The paper introduces a two-step framework for high-dimensional mean--variance portfolio selection, incorporating defactoring to handle strong factors, with theoretical guarantees and empirical validation.

Contribution

It proposes PS$^2$, a novel post-screening portfolio selection method, and extends it to FPS$^2$ with defactoring for better performance in factor-rich environments.

Findings

Competitive performance in simulations and empirical data

Effective handling of strong factors through defactoring

Theoretical guarantees for the proposed methods

Abstract

We propose post-screening portfolio selection (PS$^2$), a two-step framework for high-dimensional mean--variance investing. First, assets are screened by Lasso-type regression of a constant on excess returns without an intercept. Second, portfolio weights are estimated on the selected set using standard low-dimensional methods. Because strong factors can destroy sparsity in real data, we further introduce PS$^2$ with factors (FPS$^2$), which defactors returns before screening and allows factor investing in the final step. We establish theoretical guarantees, and simulations and an empirical application show competitive performance, especially when sparse screening is appropriate or strong factors are explicitly accommodated.