首个股票研究报告生成数据集与评估体系，支持大模型自动撰写专业报告
FinRpt: Dataset, Evaluation System and LLM-based Multi-agent Framework for Equity Research Report Generation
- 构建融合7类金融数据的自动化报告数据集生成管道
- 设计11项指标的完整评估系统，验证数据与模型有效性
- 提出多智能体框架FinRpt-Gen，适配金融报告生成任务
尽管大语言模型在股票预测和问答等金融任务中表现优异，但其在完全自动化生成股票研究报告方面仍属空白。本文首次定义了股票研究报告（ERR）生成任务，为解决数据稀缺与评估标准缺失问题，提出了开源评估基准FinRpt。我们设计了一套数据集构建流程，整合7类金融数据，自动生成高质量的ERR数据集，可用于模型训练与评估。同时，构建了包含11个指标的综合性评估体系。此外，提出专用于该任务的多智能体框架FinRpt-Gen，基于所提数据集采用监督微调与强化学习训练多个大模型智能体。实验表明，FinRpt基准的数据质量与评估指标有效性得到验证，FinRpt-Gen展现出强大性能，具有推动该领域创新的潜力。所有代码与数据集均公开可用。
原文摘要 · Abstract (English)
While LLMs have shown great success in financial tasks like stock prediction and question answering, their application in fully automating Equity Research Report generation remains uncharted territory. In this paper, we formulate the Equity Research Report (ERR) Generation task for the first time. To address the data scarcity and the evaluation metrics absence, we present an open-source evaluation benchmark for ERR generation - FinRpt. We frame a Dataset Construction Pipeline that integrates 7 financial data types and produces a high-quality ERR dataset automatically, which could be used for model training and evaluation. We also introduce a comprehensive evaluation system including 11 metrics to assess the generated ERRs. Moreover, we propose a multi-agent framework specifically tailored to address this task, named FinRpt-Gen, and train several LLM-based agents on the proposed datasets using Supervised Fine-Tuning and Reinforcement Learning. Experimental results indicate the data quality and metrics effectiveness of the benchmark FinRpt and the strong performance of FinRpt-Gen, showcasing their potential to drive innovation in the ERR generation field. All code and datasets are publicly available.
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