Performance attribution analysis, defined as the process of explaining the drivers of the excess performance of an investment portfolio against a benchmark, stands as a significant feature of portfolio management and plays a crucial role in the investment decision-making process, particularly within the fund management industry. Rooted in a solid financial and mathematical framework, the importance and methodologies of this analytical technique are extensively documented across numerous academic research papers and books. The integration of large language models (LLMs) and AI agents marks a groundbreaking development in this field. These agents are designed to automate and enhance the performance attribution analysis by accurately calculating and analyzing portfolio performances against benchmarks. In this study, we introduce the application of an AI Agent for a variety of essential performance attribution tasks, including the analysis of performance drivers and utilizing LLMs as calculation engine for multi-level attribution analysis and question-answering (QA) tasks. Leveraging advanced prompt engineering techniques such as Chain-of-Thought (CoT) and Plan and Solve (PS), and employing a standard agent framework from LangChain, the research achieves promising results: it achieves accuracy rates exceeding 93% in analyzing performance drivers, attains 100% in multi-level attribution calculations, and surpasses 84% accuracy in QA exercises that simulate official examination standards. These findings affirm the impactful role of AI agents, prompt engineering and evaluation in advancing portfolio management processes, highlighting a significant development in the practical application and evaluation of Generative AI technologies within the domain.

翻译：绩效归因分析是指解释投资组合相对于基准的超额收益驱动因素的过程，是投资组合管理的重要功能，并在投资决策过程中发挥关键作用，尤其在基金管理行业。该分析技术植根于坚实的金融与数学框架，其重要性和方法论已在众多学术研究论文和著作中得到广泛阐述。大语言模型（LLMs）与人工智能智能体的融合标志着该领域的突破性发展。这些智能体旨在通过精确计算和分析投资组合相对于基准的业绩，实现绩效归因分析的自动化与增强。在本研究中，我们引入了一个AI智能体来执行多种关键绩效归因任务，包括分析业绩驱动因素，以及利用LLMs作为计算引擎进行多层次归因分析和问答（QA）任务。通过采用链式思维（CoT）、计划与解决（PS）等先进提示工程技术，并部署基于LangChain的标准智能体框架，本研究取得了令人鼓舞的成果：在分析业绩驱动因素方面准确率超过93%，在多层次归因计算中达到100%，在模拟官方考试标准的QA练习中准确率超过84%。这些发现证实了AI智能体、提示工程与评估在推动投资组合管理流程中的重要作用，突显了生成式AI技术在该领域实际应用与评估的重大进展。