Hierarchical AI Multi-Agent Fundamental Investing: Evidence from China’s A-Share Market

ArXiv ID: 2510.21147 “View on arXiv”

Authors: Chujun He, Zhonghao Huang, Xiangguo Li, Ye Luo, Kewei Ma, Yuxuan Xiong, Xiaowei Zhang, Mingyang Zhao

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.

Keywords: multi-agent systems, reinforcement learning, macro indicators, fundamental analysis, hierarchical architecture, Equity

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics including reinforcement learning and complex multi-agent coordination, while also presenting rigorous backtesting on a real dataset with outperformance against benchmarks and state-of-the-art methods.

  flowchart TD
    A["Research Goal:<br>Hierarchical AI Multi-Agent<br>System for Equity Investing"] --> B["Data Inputs:<br>CSI 300 Constituents,<br>Macro, Financial, News Data"]
    B --> C["Methodology: Hierarchical Multi-Agent Architecture"]
    C --> D["Macro Agent:<br>Dynamic Sector Screening<br>& Weighting"]
    C --> E["Firm-Level Agents:<br>Fundamental, Technical,<br>Report, News Analysis"]
    D & E --> F["Portfolio Agent:<br>RL-based Integration<br>& Trading Strategy"]
    F --> G["Risk Control Agent:<br>Volatility Adjustment<br>& Drawdown Control"]
    G --> H["Outcomes:<br>Consistent Outperformance<br>Risk-Adjusted Returns,<br>Superior to Benchmarks"]