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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2510.21147 |
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| _version_ | 1866911229440688128 |
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| author | He, Chujun Huang, Zhonghao Li, Xiangguo Luo, Ye Ma, Kewei Xiong, Yuxuan Zhang, Xiaowei Zhao, Mingyang |
| author_facet | He, Chujun Huang, Zhonghao Li, Xiangguo Luo, Ye Ma, Kewei Xiong, Yuxuan Zhang, Xiaowei Zhao, Mingyang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_21147 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hierarchical AI Multi-Agent Fundamental Investing: Evidence from China's A-Share Market He, Chujun Huang, Zhonghao Li, Xiangguo Luo, Ye Ma, Kewei Xiong, Yuxuan Zhang, Xiaowei Zhao, Mingyang Portfolio Management Artificial Intelligence 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. |
| title | Hierarchical AI Multi-Agent Fundamental Investing: Evidence from China's A-Share Market |
| topic | Portfolio Management Artificial Intelligence |
| url | https://arxiv.org/abs/2510.21147 |