What type of study is this?

This is a Experimental Study study (also classified as: Quantitative Study).

September 30, 2025

Sparse long-only Markowitz portfolio optimization

Key Points

Sparser portfolios achieved using non-convex penalties yield superior sharpe ratios compared to standard methods.
The study indicates that assets with lower returns and higher volatilities are often excluded from long-only portfolios.
An admm-type algorithm provides efficient computation for optimal portfolio weights across various simulations.
The results emphasize the framework's ability to control risks and reduce turnover while maintaining performance.

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.

Demander à l'IA

Bookmark