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.
arXiv:2607. 06610v1 Announce Type: cross Abstract: Portfolio optimization under uncertainty is inherently a multi-objective decision problem involving complex interactions among return, risk, market dynamics, and practical investment constraints.
arXiv:2607. 09566v1 Announce Type: cross Abstract: Decision-making is posing an increasingly formidable challenge to investors because of the growing number of alternatives available in financial markets.
arXiv:2606. 00862v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems.
The paper proposes a Multi-Objective Reinforcement Learning framework for portfolio optimization that incorporates ratings from three ESG agencies, addressing the divergence in ESG rating methodologies. It couples this with a Preference Elicitation system using Gaussian Processes, allowing users to infer latent utility functions via pairwise comparisons of portfolios based on Sharpe ratios and ESG scores. Experiments with LLM-generated portfolio managers show that regional background influences preference weights, with European personas prioritizing ESG alignment and Texas personas favoring risk‑adjusted returns.
arXiv:2606. 15115v1 Announce Type: new Abstract: Multi-objective optimization (MOO) has emerged as a powerful approach to solving complex optimization problems involving multiple objectives.
arXiv:2509. 22047v3 Announce Type: replace Abstract: Group Relative Policy Optimization (GRPO) has been shown to be an effective algorithm when an accurate reward model is available.
arXiv:2605. 04267v2 Announce Type: replace Abstract: Interactive multi-objective optimization systems face a budget allocation dilemma: one can spend resources on expensive objective evaluations or on eliciting decision-maker preferences that identify the relevant region of the Pareto set.
arXiv:2606. 10412v1 Announce Type: new Abstract: The rapid evolution of financial technology demands sophisticated artificial intelligence systems capable of handling diverse challenges across multiple domains simultaneously.
arXiv:2409. 11535v3 Announce Type: replace Abstract: Many decision-support systems recommend actions by optimizing measurable objectives, even when a human decision-maker retains final authority and considers additional criteria that are difficult to specify in advance.
arXiv:2606. 04574v1 Announce Type: new Abstract: This study aims to determine whether the application of Deep Reinforcement Learning (DRL) as a specialized execution overlay can enhance pair trading in highly volatile cryptocurrency markets.
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.
Agentic ESOpt proposes using evolution strategies (ES) instead of reinforcement learning to fine‑tune large language‑model agents for long‑horizon tasks. ES offers model scalability, flexibility, and better long‑horizon credit assignment, enabling full‑parameter optimization with minimal GPU memory. The framework samples parameter perturbations, evaluates agents with rewards, and updates online, achieving notable performance gains on WebArena‑Lite and in test‑time prompt‑parameter co‑evolution.