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.
By Deniz Akkaya, Emre Can Yayla, Buse \c{S}en, Mustafa \c{C}. P{\i}nar
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.
By Haeun Jeon, Seunghoon Choi, Hyunglip Bae, Yongjae Lee, Woo Chang Kim
The paper presents a two‑level framework for portfolio management that selects window sizes in a cost‑sensitive, online manner. Candidate window sizes are treated as experts, and their aggregation weights are updated dynamically using turnover‑inclusive losses. The authors derive finite‑horizon tracking‑regret bounds that incorporate portfolio turnover, showing that with bounded losses and cost rates, a tuned Fixed Share algorithm achieves asymptotically no tracking regret for sublinear switching budgets, while Hedge handles the static case.
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.
By Kensei Nosaka, Shunnosuke Ikeda, Yuichi Takano
The paper investigates the trade‑off between sparsity and complexity in high‑dimensional asset pricing models. By separating capacity sparsity (restrictions on effective model capacity) from factor sparsity (parsimonious structure of priced risks), the authors use nonlinear feature expansions, basis pursuit, column generation, and GPU acceleration to estimate models with up to 432 million candidate factors. Their empirical results show that while sparse portfolios underperform dense ridgeless benchmarks at lower complexity, they achieve higher Sharpe ratios and lower pricing errors when the candidate set is large, indicating that capacity expansion and factor sparsity can complement each other.
By Nima Afsharhajari, Jonathan Yu-Meng Li
The paper proposes a two-level framework for portfolio management that selects window sizes in a cost-sensitive online manner. It treats candidate window sizes as experts and updates their aggregation weights using turnover-inclusive losses. The authors provide finite-horizon cost-sensitive tracking-regret bounds and show that, under bounded losses and cost rates, Fixed Share achieves asymptotically no tracking regret for sublinear switching budgets, while Hedge covers the static case.
By Yi-Chen Liu, Chung-Han Hsieh