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Sparse long-only Markowitz portfolio optimization

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  • Tianci Qian

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.

Suggested Citation

  • Tianci Qian, 2026. "Sparse long-only Markowitz portfolio optimization," Journal of Applied Statistics, Taylor & Francis Journals, vol. 53(8), pages 1402-1426, June.
    • Handle: RePEc:taf:japsta:v:53:y:2026:i:8:p:1402-1426
    DOI: 10.1080/02664763.2025.2565597
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