Decision-focused Sparse Tangent Portfolio Optimization

• URL: http://arxiv.org/abs/2607.00581v1
• Date: Wed, 01 Jul 2026 08:06:07 GMT
• Title: Decision-focused Sparse Tangent Portfolio Optimization
• Abstract summary: convex programming layer replaces discrete selection with a smooth top-$k$ operator enforcing an exact cardinality $k$.<n>This enables flow through prediction, asset selection, and re-optimization, allowing the predictive model to directly optimize portfolio performance.<n>Across four major equity markets, our method achieves competitive and often superior out-of-sample Sharpe ratios.
• Score: 32.04232046262782
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sparse tangent portfolio optimization aims to learn an interpretable, low-cardinality portfolio in the tangency direction of the mean-variance frontier. However, the associated cardinality-constrained formulation is NP-hard, and standard predict-then-optimize pipelines often misalign forecasting accuracy with downstream portfolio quality. We propose an end-to-end decision-focused learning framework that reformulates Sharpe ratio maximization as a Disciplined Parametrized Programming (DPP)-compliant convex programming layer and replaces discrete selection with a smooth top-$k$ operator enforcing an exact cardinality $k$. This enables gradient flow through prediction, asset selection, and re-optimization, allowing the predictive model to directly optimize portfolio performance. Across four major equity markets, our method achieves competitive and often superior out-of-sample Sharpe ratios compared with historical and prediction-focused baselines, with particularly strong gains in larger asset universes. Our \href{https://github.com/feuerwerksh/Diffble-card-SR}{code} is publicly available.

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