Optimal Portfolio Size under Parameter Uncertainty

90 Pages Posted: 11 Jul 2024 Last revised: 29 Oct 2024

See all articles by Nathan Lassance

Nathan Lassance

LFIN/LIDAM, UCLouvain

Rodolphe Vanderveken

LFIN/LIDAM, UCLouvain

Frédéric D. Vrins

LFIN/LIDAM, UCLouvain

Date Written: October 29, 2024

Abstract

We introduce a method to determine the investor's optimal portfolio size that maximizes the expected out-of-sample utility under parameter uncertainty. This portfolio size trades off between accessing investment opportunities and limiting the number of estimated parameters. Unlike sparse methods such as lasso that exclude assets during the optimization step, our approach fixes the optimal number of assets before computing the portfolio weights, which improves robustness and provides greater flexibility in practical implementations. Empirically, our restricted portfolios outperform their counterparts applied to all available assets. Our methodology renders portfolio theory valuable even when the dataset dimension and sample size are comparable.

Keywords: portfolio selection, estimation risk, dimension reduction, out-of-sample performance, portfolio combination rules JEL Classification: G11

JEL Classification: G11

Suggested Citation

Lassance, Nathan and Vanderveken, Rodolphe and Vrins, Frederic Daniel, Optimal Portfolio Size under Parameter Uncertainty (October 29, 2024). Available at SSRN: https://ssrn.com/abstract=4886000 or http://dx.doi.org/10.2139/ssrn.4886000

Nathan Lassance

LFIN/LIDAM, UCLouvain ( email )

151 Chaussée de Binche
Mons, 7000
Belgium

Rodolphe Vanderveken (Contact Author)

LFIN/LIDAM, UCLouvain ( email )

34 Voie du Roman Pays - L1.03.01
Louvain-la-Neuve, 1348
Belgium

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