Post-Screening Portfolio Selection

Fuente: arXiv
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Auteurs principaux: Uematsu, Yoshimasa, Tanaka, Shinya
Format: Preprint
Publié: 2026
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author Uematsu, Yoshimasa
Tanaka, Shinya
author_facet Uematsu, Yoshimasa
Tanaka, Shinya
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2604_17593
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Post-Screening Portfolio Selection
Uematsu, Yoshimasa
Tanaka, Shinya
Portfolio Management
Methodology
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.
title Post-Screening Portfolio Selection
topic Portfolio Management
Methodology
url https://arxiv.org/abs/2604.17593