ArticleJournal of applied statistics2026
Sparse long-only Markowitz portfolio optimization.
Article in Journal of applied statistics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This paper introduces a novel regularization framework for the Markowitz mean-variance portfolio optimization under long-only constraints. A sufficient condition that explains the sparsity of long-only optimal portfolios is derived, showing that assets with lower returns, higher volatilities, and greater co-volatilities are more likely to be excluded. Non-convex penalties, including SCAD, TLP, and MCP, are employed to enhance portfolio sparsity while preserving robust out-of-sample performance. An ADMM-type algorithm is developed for efficient portfolio weighting computation, and its effectiveness is demonstrated through both simulations and empirical studies using S&P 500 constituent stocks. The results highlight the ability of non-convex penalties to achieve sparser portfolios with superior Sharpe ratios, reduced turnover, and controlled risks compared to existing methods.
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