FinRobot: AI Agent for Equity Research and Valuation with Large Language Models

Key points

• Problem: Equity research reports are labor-intensive work that needs both an understanding of quantitative models and industry-specific expertise. Existing automated tools such as CapitalCube and Wright Reports often fall short of the comprehensive, nuanced analysis of human analysts, and they tend to focus heavily on technical factors and simplified models, which limits their ability to incorporate discretionary judgment and qualitative assessment. The authors argue that these limitations hinder real-time adaptation to new data and accurate risk assessment.
• Input and output: Given a target company (for example Waste Management, Inc.), it gathers SEC 10-K and 10-Q filings, quarterly earnings releases and investor presentations, earnings call transcripts, other brokers' reports and alternative data such as Google Trends, and outputs a report with a financial summary, valuation, risks, competitor analysis and a buy, hold or sell call. For example, the Waste Management report in the appendix gives a BUY rating and a 12-month target price of 219.17 dollars, while stating a fair value of 144.3 to 176.6 dollars per share based essentially on EV/EBITDA of 13 to 15 times over 2019 to 2023. This is a system paper that shows a built system and one example report.
• Method: The analysis is split into three layers. The Data-CoT agent computes metrics such as revenue growth, contribution margin (revenue minus operating expense, as a share of revenue), SG&A margin and EBITDA margin from the raw data and writes mid-level summaries, and the Concept-CoT agent asks and answers its own questions, for example about revenue drivers, margins against competitors and risks, to produce revenue projections, ROIC and WACC, and a DCF-based target price. The Thesis-CoT agent assembles these results in the sell-side report format into an investment thesis with a buy, hold or sell call.
• Implementation: Data storage and processing use Oceanbase, PostgreSQL, DFS and Minio, the system handles PDF, DOC, DOCX, JPEG and PNG documents, and in the Table 1 formulas the revenue growth projection adds 1% to the previous growth while the contribution margin projection adds 0.5% to the previous value. Per the official GitHub README, the repository holds an AutoGen-based V0 library, a V1 web app (finrobot_equity) that turns a ticker into an HTML or PDF equity research report, and a PydanticAI-based V2 desktop app, and the README citation list includes this paper.
• Results: When 7 investment banking analysts scored one Waste Management report from 0 to 10, accuracy got 10 from four and 9 from three, logicality got 10 from two, 9 from four and 9.5 from one, and storytelling ranged from 7 to 10. On the same criteria GPT-4 gave accuracy 9, logicality 8 and storytelling 7. The authors report that in a density plot of GPT-4 scores for several reports generated on the same topic, FinRobot scored higher than zero-shot, few-shot and chain-of-thought prompting.
• Limits: The authors state that FinRobot meets the standards of institutions such as JPMorgan and UBS, but the experiment is one Waste Management report scored by 7 analysts and GPT-4, and the stability comparison is presented as a density plot. Reviewer comments note that the importance of the numbers is not always clear, so the report sometimes reads like a list of statistics (Reviewer 1), and that qualitative analysis of news-driven blue chip stocks is still best left to humans (Reviewer 4). As future work the authors name expansion to more industries and asset classes, including full coverage of the Dow 30, and adding reinforcement learning and sentiment analysis.

Paper links

External research summaries. These are not HDATF publications or measured product results.