Portfolio Diversification Revisited: An Out-of-Sample Comparison of Twenty-One Strategies Across Asset Universes and Rebalancing Frequencies
- 1Department of Quantitative Methods, Universidad Loyola Andalucía, 14004 Córdoba, Spain
- 2Department of Computer Languages and Computer Science, Universidad de Málaga, 29071 Málaga, Spain
Abstract
This study revisits comparative portfolio optimization by extending an established framework from eleven to twenty-one strategies and evaluating them under a common out-of-sample evaluation design. The analysis covers eight asset universes with different degrees of heterogeneity, observed over a common period (2008–2025), and two rebalancing frequencies, using rolling estimation, Ledoit–Wolf covariance shrinkage, hyperparameter calibration, Sharpe ratios in excess of the Treasury bill rate, transaction-cost adjustments, and robustness diagnostics. The expanded framework incorporates established comparators and study-specific extensions, corrects an implementation inconsistency in a previous Second-Order Stochastic Dominance procedure, and clarifies that the Yager z=2 penalty is a reparameterization of Tikhonov regularization on the unit simplex. We also examine the sensitivity of results to risk-aversion calibration and rebalancing frequency. No strategy is uniformly superior across universes. The equally weighted portfolio remains competitive in homogeneous equity universes, whereas several optimization-based strategies achieve higher nominal Sharpe ratios in the heterogeneous multi-asset and commodity universes. In a joint test based on the stationary bootstrap, however, no strategy is better than the equally weighted portfolio in a statistically robust way, and with daily rebalancing the best mean ranks correspond to the risk-parity and equally weighted portfolios. Overall, the findings support a cautious interpretation of nominal rankings and emphasize model specification, calibration, transaction costs, and multiplicity-aware evaluation in portfolio comparisons.
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BibTeX
@article{sarmientolaya2026portfolio,
title = {{Portfolio Diversification Revisited: An Out-of-Sample Comparison of Twenty-One Strategies Across Asset Universes and Rebalancing Frequencies}},
author = {Jorge Rafael Sarmiento-Laya and Francisco Fernández-Navarro},
journal = {Mathematics},
year = {2026},
month = oct,
doi = {10.3390/math14213941},
url = {https://www.mdpi.com/2227-7390/14/21/3941},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Portfolio Diversification Revisited: An Out-of-Sample Comparison of Twenty-One Strategies Across Asset Universes and Rebalancing Frequencies
AU - Jorge Rafael Sarmiento-Laya
AU - Francisco Fernández-Navarro
PY - 2026
DA - 2026/10/28
JO - Mathematics
DO - 10.3390/math14213941
UR - https://www.mdpi.com/2227-7390/14/21/3941
AB - This study revisits comparative portfolio optimization by extending an established framework from eleven to twenty-one strategies and evaluating them under a common out-of-sample evaluation design. The analysis covers eight asset universes with different degrees of heterogeneity, observed over a common period (2008–2025), and two rebalancing frequencies, using rolling estimation, Ledoit–Wolf covariance shrinkage, hyperparameter calibration, Sharpe ratios in excess of the Treasury bill rate, transaction-cost adjustments, and robustness diagnostics. The expanded framework incorporates established comparators and study-specific extensions, corrects an implementation inconsistency in a previous Second-Order Stochastic Dominance procedure, and clarifies that the Yager z=2 penalty is a reparameterization of Tikhonov regularization on the unit simplex. We also examine the sensitivity of results to risk-aversion calibration and rebalancing frequency. No strategy is uniformly superior across universes. The equally weighted portfolio remains competitive in homogeneous equity universes, whereas several optimization-based strategies achieve higher nominal Sharpe ratios in the heterogeneous multi-asset and commodity universes. In a joint test based on the stationary bootstrap, however, no strategy is better than the equally weighted portfolio in a statistically robust way, and with daily rebalancing the best mean ranks correspond to the risk-parity and equally weighted portfolios. Overall, the findings support a cautious interpretation of nominal rankings and emphasize model specification, calibration, transaction costs, and multiplicity-aware evaluation in portfolio comparisons.
SN - 2227-7390
ER -