arXiv:2607. 00581v1 Announce Type: new Abstract: Sparse tangent portfolio optimization aims to learn an interpretable, low-cardinality portfolio in the tangency direction of the mean-variance frontier.
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The paper studies mean‑variance portfolio selection with an β0 penalty to encourage sparse asset allocations. It incorporates uncertainty in the mean return vector via an ellipsoidal uncertainty set, leading to a robust sparse optimization framework. The authors analyze the structure of local and global minimizers, develop a branch‑and‑bound algorithm with a novel pruning rule, and show through computational experiments that their method is effective and competitive with existing solvers.
The paper studies mean‑variance portfolio selection using an β0 penalty to encourage sparse asset allocations. It incorporates uncertainty in expected returns via an ellipsoidal set, leading to a robust sparse optimization framework. The authors analyze local and global minimizers, design a branch‑and‑bound algorithm with a novel pruning rule, and show through computational experiments that their method outperforms a mixed‑integer second‑order cone programming solver on real market data.
The paper introduces a decision‑focused learning framework for mean‑variance portfolio optimization that embeds the Karush‑Kuhn‑Tucker optimality conditions of the lower‑level optimization into a single‑level learning problem. This approach preserves budget and short‑sale constraints while remaining tractable for standard nonlinear solvers. Experiments on real‑world ETF data across two asset universes demonstrate superior performance on multiple investment metrics and highlight the benefits of the proposed regularization.
arXiv:2605.28853v2 Announce Type: replace-cross Abstract: Portfolio optimization in real-world financial markets is notoriously difficult due to non-stationarity, noisy data, and high transaction cos...
arXiv:2607. 09820v1 Announce Type: new Abstract: Predict-then-optimize systems usually compress uncertainty into a point forecast and then solve a downstream optimization problem as if the forecast were reliable.
arXiv:2606. 25808v1 Announce Type: cross Abstract: We propose a predict-optimize-explain framework that uses gradient-based sample generation to interpret various portfolio models by identifying macroeconomic conditions that induce specified portfolio outcomes.