Optimal Portfolio Size under Parameter Uncertainty

Nathan Lassance (), Rodolphe Vanderveken () and Frédéric Vrins ()
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Nathan Lassance: Université catholique de Louvain, LIDAM/LFIN, Belgium
Rodolphe Vanderveken: Université catholique de Louvain, LIDAM/LFIN, Belgium
Frédéric Vrins: Université catholique de Louvain, LIDAM/LFIN, Belgium

No 2025011, LIDAM Reprints LFIN from Université catholique de Louvain, Louvain Finance (LFIN)

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 optimizing the portfolio weights, which improves robustness and provides greater flexibility in practical implementations. Empirically, our size-optimized 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: Dimension reduction; estimation risk; out-of-sample performance; portfolio combination rules; portfolio selection (search for similar items in EconPapers)
JEL-codes: G11 (search for similar items in EconPapers)
Pages: 105
Date: 2025-10-29
Note: In: Journal of Financial and Quantitative Analysis, 2025
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Persistent link: https://EconPapers.repec.org/RePEc:ajf:louvlr:2025011

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