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.
By Sounaq Das, Tanmay Sen, Raghu Nandan Sengupta, Aditya Gupta
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.
By Danial Ramezani, Mostafa Abouei Ardakan
arXiv:2606. 00862v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems.
By Xiao Jin, Yongxiong Wang, Haobo Liu, Yudong Du, Yukun Du
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.
By Giovanni Dispoto, Marcello Restelli, Carmine Ventre
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.
By Yiyi Zhu, Yaolin Wen, Xiang Xia, Xin An, Hanyi Si, Xiang Shu, Yangde Fu, Liang Dou, Hong Qian
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.
By Yuki Ichihara, Yuu Jinnai, Tetsuro Morimura, Mitsuki Sakamoto, Ryota Mitsuhashi, Eiji Uchibe