FinRobot: Stock research and valuation framework based on large language model In today's increasingly complex financial markets, efficient sell-side securities research often requires the support of automated tools. However, many existing AI solutions only focus on technical indicators and lack flexible subjective analysis capabilities. They are difficult to meet the requirements of real-time adaptation to new data or accurate risk assessment, and therefore have limited value in investment practice. FinRobot introduced in this article is the first AI agent framework designed specifically for stock research. It uses the Multi-agent Chain of Thought (CoT) system to combine quantitative and qualitative analysis to simulate the comprehensive reasoning process of human analysts. The overall structure includes the following three major functional agents: 1. Data-CoT Agent: Integrate multi-source data to achieve comprehensive capture and intermediate summary of financial statements, company announcements, third-party databases and other information. 2. Concept-CoT Agent: Conduct in-depth analysis of key financial indicators and industry environment, simulate the research ideas of human analysts, and form executable analysis conclusions. 3. Thesis-CoT Agent: Finally, the analysis results are integrated into an investment recommendation report, providing comprehensive judgments such as data information, valuation indicators, and risk assessment. Unlike existing automated research platforms (such as…