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 aspect 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-answer (QA) exercises. 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 advancement in the practical application and evaluation of AI technologies within the domain.

翻译：绩效归因分析（Performance Attribution Analysis）定义为解释投资组合相对于基准的超额收益来源的过程，是投资组合管理的重要环节，在投资决策过程中（尤其是基金管理行业）发挥着关键作用。该分析技术基于坚实的金融与数学框架，其重要性及方法论已在大量学术论文与专著中得到详尽阐述。大语言模型（LLMs）与AI智能体的融合标志着该领域的突破性进展。这些智能体旨在通过精准计算并分析投资组合相对于基准的表现，实现绩效归因分析的自动化与增强。本研究引入AI智能体以执行多种关键绩效归因任务，包括分析业绩驱动因素、利用大语言模型作为计算引擎进行多层次归因分析以及问答（QA）练习。借助链式思维（Chain-of-Thought, CoT）、规划与求解（Plan and Solve, PS）等先进提示工程技术，并采用LangChain的标准智能体框架，本研究取得了显著成果：在业绩驱动因素分析中准确率超过93%，多层次归因计算实现100%准确率，在模拟官方考试标准的问答练习中准确率超过84%。这些发现证实了AI智能体、提示工程及评估在提升投资组合管理流程中的重要作用，突显了AI技术在该领域实际应用与评估的重大进展。