用分层智能体系统，结合宏观与公司基本面，优化A股投资组合。
Hierarchical AI Multi-Agent Fundamental Investing: Evidence from China's A-Share Market
- 分层设计：宏观与公司级智能体协同决策
- 在沪深300成分股上实现更高风险调整收益
- 适合量化投资、AI金融研究者参考
我们提出一种多智能体、人工智能驱动的基本面投资框架，整合宏观指标、行业和公司层面信息以构建优化的股票投资组合。该架构包括：(i) 宏观智能体，根据经济指标和行业表现动态筛选并加权行业；(ii) 四个公司级智能体——基本面、技术面、研报和新闻——对个股进行深度分析，确保覆盖广度与深度；(iii) 投资组合智能体，利用强化学习融合各智能体输出，生成交易策略；(iv) 风控智能体，根据市场波动调整持仓。我们在沪深300指数成分股上评估该系统，发现其在风险调整后收益和回撤控制上持续优于标准基准及当前最先进的多智能体交易系统。核心贡献在于将自上而下的宏观筛选与自下而上的基本面分析相结合，提供一种稳健且可扩展的因子化组合构建方法。
原文摘要 · Abstract (English)
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.
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