FinRpt: Dataset, Evaluation System and LLM-based Multi-agent Framework for Equity Research Report Generation

• URL: http://arxiv.org/abs/2511.07322v2
• Date: Wed, 12 Nov 2025 01:18:35 GMT
• Title: FinRpt: Dataset, Evaluation System and LLM-based Multi-agent Framework for Equity Research Report Generation
• Authors: Song Jin, Shuqi Li, Shukun Zhang, Rui Yan,
• Abstract summary: This paper formulates the Equity Research Report (ERR) Generation task for the first time.<n>We present an open-source evaluation benchmark for ERR generation - FinRpt.<n>We also introduce a comprehensive evaluation system including 11 metrics to assess the generated ERRs.
• Score: 14.260620842043656
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Metadata-Driven Retrieval-Augmented Generation for Financial Question Answering [0.0]
  We introduce a sophisticated indexing pipeline to create contextually rich document chunks.<n>We benchmark a spectrum of enhancements, including pre-retrieval filtering, post-retrieval reranking, and enriched embeddings.<n>Our proposed optimal architecture combines LLM-driven pre-retrieval optimizations with these contextual embeddings to achieve superior performance.
  arXiv  Detail & Related papers  (2025-10-28T13:16:36Z)
• FinSight: Towards Real-World Financial Deep Research [68.31086471310773]
  FinSight is a novel framework for producing high-quality, multimodal financial reports.<n>To ensure professional-grade visualization, we propose an Iterative Vision-Enhanced Mechanism.<n>A two-stage Writing Framework expands concise Chain-of-Analysis segments into coherent, citation-aware, and multimodal reports.
  arXiv  Detail & Related papers  (2025-10-19T14:05:35Z)
• EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements [7.259647868714988]
  We introduce EDINET-Bench, an open-source Japanese financial benchmark to evaluate the performance of large language models (LLMs)<n>Our experiments reveal that even state-of-the-art LLMs struggle, performing only slightly better than logistic regression in binary classification for fraud detection and earnings forecasting.<n>Our dataset, benchmark construction code, and evaluation code is publicly available to facilitate future research in finance with LLMs.
  arXiv  Detail & Related papers  (2025-06-10T13:03:36Z)
• FinS-Pilot: A Benchmark for Online Financial RAG System [21.64301644235658]
  FinS-Pilot is a novel benchmark for evaluating RAG systems in online financial applications.<n>Our benchmark incorporates both real-time API data and text data, organized through an intent classification framework.<n>Our work contributes both a practical evaluation framework and a dataset curated to advance research in financial NLP systems.
  arXiv  Detail & Related papers  (2025-05-31T03:50:19Z)
• LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation [50.375567142250446]
  Large language models (LLMs), trained on vast corpora, have shown strong potential in data generation.<n>We propose a novel framework, LLM-Forest, which introduces a "forest" of few-shot prompt learning LLM "trees" with their outputs aggregated via confidence-based weighted voting.<n>This framework is established on a new concept of bipartite information graphs to identify high-quality relevant neighboring entries with both feature and value granularity.
  arXiv  Detail & Related papers  (2024-10-28T20:42:46Z)
• DiscoveryBench: Towards Data-Driven Discovery with Large Language Models [50.36636396660163]
  We present DiscoveryBench, the first comprehensive benchmark that formalizes the multi-step process of data-driven discovery. Our benchmark contains 264 tasks collected across 6 diverse domains, such as sociology and engineering. Our benchmark, thus, illustrates the challenges in autonomous data-driven discovery and serves as a valuable resource for the community to make progress.
  arXiv  Detail & Related papers  (2024-07-01T18:58:22Z)
• Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs [49.57641083688934]
  We introduce a novel approach to anomaly detection in financial data using Large Language Models (LLMs) embeddings. Our experiments demonstrate that LLMs contribute valuable information to anomaly detection as our models outperform the baselines.
  arXiv  Detail & Related papers  (2024-06-05T20:19:09Z)
• NIFTY Financial News Headlines Dataset [14.622656548420073]
  The NIFTY Financial News Headlines dataset is designed to facilitate and advance research in financial market forecasting using large language models (LLMs) This dataset comprises two distinct versions tailored for different modeling approaches: (i) NIFTY-LM, which targets supervised fine-tuning (SFT) of LLMs with an auto-regressive, causal language-modeling objective, and (ii) NIFTY-RL, formatted specifically for alignment methods (like reinforcement learning from human feedback) to align LLMs via rejection sampling and reward modeling.
  arXiv  Detail & Related papers  (2024-05-16T01:09:33Z)
• FinBen: A Holistic Financial Benchmark for Large Language Models [75.09474986283394]
  FinBen is the first extensive open-source evaluation benchmark, including 36 datasets spanning 24 financial tasks. FinBen offers several key innovations: a broader range of tasks and datasets, the first evaluation of stock trading, novel agent and Retrieval-Augmented Generation (RAG) evaluation, and three novel open-source evaluation datasets for text summarization, question answering, and stock trading.
  arXiv  Detail & Related papers  (2024-02-20T02:16:16Z)
• PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance [63.51545277822702]
  PIXIU is a comprehensive framework including the first financial large language model (LLMs) based on fine-tuning LLaMA with instruction data. We propose FinMA by fine-tuning LLaMA with the constructed dataset to be able to follow instructions for various financial tasks. We conduct a detailed analysis of FinMA and several existing LLMs, uncovering their strengths and weaknesses in handling critical financial tasks.
  arXiv  Detail & Related papers  (2023-06-08T14:20:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.