EvoNash-MARL: A Closed-Loop Multi-Agent Reinforcement Learning Framework for Medium-Horizon Equity Allocation

arXiv:2604.10911v1 Announce Type: new Abstract: Medium-to-long-horizon stock allocation presents significant challenges due toveak predictive structures, non-stadonary market regimes, and the degradationf signals following the application of transaction costs, capacity limits, and tail-isk constraints. Conventional approaches commonly rely on a single predictor orloosely coupled prediction-to-allocation pipeline, limiting robustness underThis work addresses a targeted design question: whetherlis