{"ID":2850471,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.21147","arxiv_id":"2510.21147","title":"Hierarchical AI Multi-Agent Fundamental Investing: Evidence from China's A-Share Market","abstract":"We present a multi-agent, AI-driven framework for fundamental investing that integrates macro indicators, industry-level and firm-specific information to construct optimized equity portfolios. The architecture comprises: (i) a Macro agent that dynamically screens and weights sectors based on evolving economic indicators and industry performance; (ii) four firm-level agents -- Fundamental, Technical, Report, and News -- that conduct in-depth analyses of individual firms to ensure both breadth and depth of coverage; (iii) a Portfolio agent that uses reinforcement learning to combine the agent outputs into a unified policy to generate the trading strategy; and (iv) a Risk Control agent that adjusts portfolio positions in response to market volatility. We evaluate the system on the constituents by the CSI 300 Index of China's A-share market and find that it consistently outperforms standard benchmarks and a state-of-the-art multi-agent trading system on risk-adjusted returns and drawdown control. Our core contribution is a hierarchical multi-agent design that links top-down macro screening with bottom-up fundamental analysis, offering a robust and extensible approach to factor-based portfolio construction.","short_abstract":"We present a multi-agent, AI-driven framework for fundamental investing that integrates macro indicators, industry-level and firm-specific information to construct optimized equity portfolios. The architecture comprises: (i) a Macro agent that dynamically screens and weights sectors based on evolving economic indicator...","url_abs":"https://arxiv.org/abs/2510.21147","url_pdf":"https://arxiv.org/pdf/2510.21147v1","authors":"[\"Chujun He\",\"Zhonghao Huang\",\"Xiangguo Li\",\"Ye Luo\",\"Kewei Ma\",\"Yuxuan Xiong\",\"Xiaowei Zhang\",\"Mingyang Zhao\"]","published":"2025-10-24T04:38:37Z","proceeding":"q-fin.PM","tasks":"[\"q-fin.PM\",\"cs.AI\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}