用多个角色智能体协作选股，提升投资组合表现。
AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions
- 设计角色分工的多智能体系统，模拟专业投研流程。
- 在不同风险偏好下表现优于传统基准模型。
- 适合量化金融、智能投顾领域研究者参考。
人工智能代理领域正迅速发展，大型语言模型（LLMs）使代理能够以类人效率和适应性自主执行并优化任务。在此背景下，多智能体协作成为解决复杂挑战的有前景方法。本研究探讨基于角色的多智能体系统在股票选择与股权研究中的应用。我们构建了一支专业化智能体团队，进行全面分析，并在不同风险容忍度下评估其选股表现，对比现有基准模型。此外，我们考察了多智能体框架在股权分析中的优势与局限，为实际有效性及实施挑战提供关键洞见。
原文摘要 · Abstract (English)
The field of artificial intelligence (AI) agents is evolving rapidly, driven by the capabilities of Large Language Models (LLMs) to autonomously perform and refine tasks with human-like efficiency and adaptability. In this context, multi-agent collaboration has emerged as a promising approach, enabling multiple AI agents to work together to solve complex challenges. This study investigates the application of role-based multi-agent systems to support stock selection in equity research and portfolio management. We present a comprehensive analysis performed by a team of specialized agents and evaluate their stock-picking performance against established benchmarks under varying levels of risk tolerance. Furthermore, we examine the advantages and limitations of employing multi-agent frameworks in equity analysis, offering critical insights into their practical efficacy and implementation challenges.
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