GPT4驱动的AI Agent能否成为足够优秀的业绩归因分析师？
Can a GPT4-Powered AI Agent Be a Good Enough Performance Attribution Analyst?
AI总结：
本研究利用GPT4、LangChain框架及CoT等提示工程技术构建AI Agent，实现投资组合业绩归因分析的自动化。实验表明，该Agent在业绩驱动因素分析、多层级归因计算及问答任务中准确率分别超93%、达100%及超84%，验证了生成式AI在基金管理领域的实际应用价值。
摘要：
业绩归因分析，定义为解释投资组合相对于基准超额表现驱动因素的过程，是投资组合管理的重要特征，并在投资决策过程中扮演关键角色，尤其在基金管理行业中。该分析方法根植于坚实的金融与数学框架，其重要性与方法论在众多学术研究论文与书籍中有详尽记载。大语言模型与AI Agent的整合标志着该领域的突破性进展。这些Agent旨在通过精确计算和分析投资组合相对于基准的表现，实现业绩归因分析的自动化与增强。本研究引入了AI Agent在多项核心业绩归因任务中的应用，包括业绩驱动因素分析，以及利用LLMs作为计算引擎进行多层级归因分析与问答任务。研究借助Chain-of-Thought和Plan and Solve等先进提示工程技术，并采用LangChain的标准Agent框架，取得了显著成果：在分析业绩驱动因素方面准确率超过93%，在多层级归因计算中达到100%，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 Agent、提示工程与评估在推进投资组合管理流程中的重要作用，突显了Generative AI技术在该领域实际应用与评估方面的重大进展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
