LLM-based multi-agent has gained significant attention for their potential in simulation and enhancing performance. However, existing works are limited to pure simulations or are constrained by predefined workflows, restricting their applicability and effectiveness. In this paper, we introduce the Multi-Agent Scaling Simulation (MASS) for portfolio construction. MASS achieves stable and continuous excess returns by progressively increasing the number of agents for large-scale simulations to gain a superior understanding of the market and optimizing agent distribution end-to-end through a reverse optimization process, rather than relying on a fixed workflow. We demonstrate its superiority through performance experiments, ablation studies, backtesting experiments, experiments on updated data and stock pools, scaling experiments, parameter sensitivity experiments, and visualization experiments, conducted in comparison with 6 state-of-the-art baselines on 3 challenging A-share stock pools. We expect the paradigm established by MASS to expand to other tasks with similar characteristics. The implementation of MASS has been open-sourced atthis https URL.
View on arXiv@article{guo2025_2505.10278,
title={ MASS: Multi-Agent Simulation Scaling for Portfolio Construction },
author={ Taian Guo and Haiyang Shen and Jinsheng Huang and Zhengyang Mao and Junyu Luo and Zhuoru Chen and Xuhui Liu and Bingyu Xia and Luchen Liu and Yun Ma and Ming Zhang },
journal={arXiv preprint arXiv:2505.10278},
year={ 2025 }
}