AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions
Tianjiao Zhao, Jingrao Lyu, Stokes Jones, Harrison Garber, Stefano Pasquali, Dhagash Mehta
人工智能（AI）智能体领域正在快速发展，这得益于大型语言模型（LLM）能够以类人的效率和适应性自主执行和完善任务。在此背景下，多智能体协作已成为一种有前景的方法，使多个AI智能体能够协同解决复杂挑战。本研究探讨了基于角色的多智能体系统在股票研究和投资组合管理中支持股票选择的应用。我们展示了一个由专业智能体团队执行的全面分析，并在不同风险承受能力水平下评估了它们的选股表现与既定基准的比较。此外，我们考察了在股票分析中采用多智能体框架的优势和局限性，为其实际效能和实施挑战提供了重要见解。
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 portfol...