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
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.
By Kensei Nosaka, Shunnosuke Ikeda, Yuichi Takano
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...
By Rahul Fernandes, Travis Desell
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.
By Junjie Guo
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.
By Batuhan Ata\c{s}, Nur\c{s}en Ayd{\i}n, E. Mehmet K{\i}ral, \c{S}. \.Ilker Birbil
The paper explores tabular deep learning for equity signal generation, training five model classes on daily data from about 300 large‑cap US stocks over eleven years. By using Bayesian optimisation that targets trading performance across three distinct market regimes, the authors achieve regime‑robust hyperparameter selection, yielding out‑of‑sample signal precision above random and a Hybrid ensemble (XGBoost + TabNet) with an annualised return of 51.26% and a Sharpe ratio of 2.44. The study also finds that alternative data adds limited value beyond technical and fundamental features, and that the ensemble’s outperformance is driven by stock selection rather than market exposure.
arXiv:2608. 27076v1 Announce Type: new Abstract: Algorithmic trading now represents a market exceeding $20 billion, where even marginal gains in signal robustness can translate into economically significant returns.
By Joshua Le Grice
arXiv:2605. 27887v2 Announce Type: replace Abstract: Large language models (LLMs) have shown strong performance across diverse financial tasks, yet portfolio management (PM), a critical financial decision-making task, remains poorly benchmarked.
By Yuxuan Zhao, Sijia Chen, Ningxin Su
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:2607. 16194v1 Announce Type: new Abstract: In modern financial markets, decision-makers increasingly rely on quantitative methods to navigate complex trade-offs among multiple, often conflicting objectives.
By Zhiyuan Wang, Qinxu Ding, Ding Ding, Siying Zhu, Jing Ren, Yue Wang, Chong Hui Tan
arXiv:2606. 08797v1 Announce Type: cross Abstract: Decision-focused learning has shown great promise for addressing predict-then-optimize problems, particularly in the presence of under-specified models.
By St\'ephane Eilles-Chan Way, Hugo Percot, Quentin Cappart, Tias Guns, Louis-Martin Rousseau