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.
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 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 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 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
arXiv:2609.01544v1 Announce Type: cross
Abstract: Feature-based newsvendor models use observable covariates to tailor inventory decisions, aiming to balance holding and shortage costs under demand un...
By Zhaoliang Yuan, Jie Wang
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 studies high‑dimensional linear contextual bandits with knapsack constraints (CBwK), aiming to exploit sparsity for tighter regret bounds. It introduces an online hard‑thresholding estimator integrated into a primal‑dual framework, achieving sub‑linear regret that grows only logarithmically with the feature dimension. Under either a diverse‑covariate or margin condition, the regret improves to τ‑dependent rates, and when both hold simultaneously, a dual resolving scheme yields an even tighter bound. The approach also recovers optimal rates for high‑dimensional contextual bandits without knapsacks, and experiments demonstrate its practical effectiveness.
By Wanteng Ma, Dong Xia, Jiashuo Jiang
The paper introduces a distributionally robust method for learning hyperparameters of first‑order convex optimization algorithms. By minimizing a Wasserstein‑robust performance estimation problem over a dataset of problem instances, the approach interpolates between classical learning‑to‑optimize (L2O) and worst‑case PEP design. The authors solve the resulting problem with stochastic gradient descent, provide high‑probability risk bounds, and demonstrate that the learned algorithms outperform both worst‑case optimal and vanilla L2O baselines on logistic regression, LASSO, and linear programming tasks.
By Vinit Ranjan, Jisun Park, Bartolomeo Stellato
arXiv:2606. 14648v1 Announce Type: new Abstract: Robust machine learning and optimization rely on the uncertainty model choice.
By Pedro Chumpitaz-Flores, My Duong, Juan S. Borrero, Kaixun Hua
arXiv:2602. 14154v3 Announce Type: replace Abstract: Differentiating through the solution of a quadratic program (QP) is a central problem in differentiable optimization.
By Yuxuan Linghu, Zhiyuan Liu, Qi Deng
arXiv:2608.30446v1 Announce Type: cross
Abstract: Small-cap-inclusive equity universes contain recently listed and intermittently traded securities, so enforcing a common look-back discards a substan...
By Christian Bongiorno, Lorenzo Villassero