RAG Workflow for Stock Earnings Report Analysis
This workflow utilizes intelligent methods to automatically analyze quarterly financial reports of publicly listed companies, extract key information, and generate structured financial analysis reports. It combines vector databases and AI technology to quickly identify financial trends and anomalies, improving analysis efficiency and reducing human errors. The final report is automatically saved to Google Docs for easy viewing and sharing, making it suitable for financial analysts, investors, and corporate finance teams, thereby supporting informed decision-making and in-depth insights.
Workflow Name
RAG Workflow for Stock Earnings Report Analysis
Key Features and Highlights
This workflow leverages advanced vector database technology (Pinecone), Google Gemini text embedding models, and AI agents to enable intelligent analysis and report generation for publicly listed companies’ quarterly earnings reports. Its core innovation lies in the use of Retrieval-Augmented Generation (RAG) technology, which automatically extracts key information from multiple PDF-format earnings documents, performs trend analysis and anomaly detection, and generates structured financial analysis reports. The final reports are saved to Google Docs for easy sharing and further editing.
Core Problems Addressed
Traditional earnings report analysis requires manually reading large volumes of lengthy and highly specialized financial documents, which is time-consuming, labor-intensive, and prone to errors. This workflow automates the processing and comprehension of multi-quarter earnings data, significantly improving analysis efficiency and accuracy. It helps users quickly obtain key financial metrics, management commentary, and trend insights, thereby supporting more informed investment decisions.
Application Scenarios
- Enabling investment researchers and financial analysts to rapidly grasp the quarterly financial status of target companies
- Allowing institutional or individual investors to compare multi-quarter financial data and analyze trends
- Assisting financial advisors and consulting firms in customizing professional financial analysis reports for clients
- Supporting corporate finance departments in automating quarterly report aggregation and insight extraction
Main Process Steps
- Retrieve the list of earnings report files to analyze from Google Sheets — Manage all target companies’ earnings report Google Drive links via Google Sheets;
- Download the corresponding quarterly earnings report PDFs from Google Drive;
- Use default data loaders and text splitters to segment PDF content into manageable text chunks;
- Generate text vector embeddings using the Google Gemini model and store them in the Pinecone vector database for efficient semantic retrieval;
- AI agents receive user queries and retrieve relevant text segments from the vector database for semantic understanding and analysis;
- Generate detailed financial analysis reports in Markdown format, including revenue, expenses, profits, key indicators, management commentary, trend comparisons, and anomaly explanations;
- Automatically save and update the analysis reports to designated Google Docs documents for convenient viewing and sharing.
Involved Systems and Services
- Google Sheets: Manages the list and links of earnings report files;
- Google Drive: Stores and provides access to earnings report PDFs;
- Google Gemini (PaLM) API: Generates text embeddings and performs natural language processing;
- Pinecone Vector Database: Stores and retrieves earnings report text embeddings to enable semantic search;
- Google Docs: Saves and displays the automatically generated financial analysis reports;
- OpenAI Chat Model (as a backup language model interface);
- n8n Automation Platform: Orchestrates all nodes to realize the automated workflow.
Target Users and Value Proposition
- Financial Analysts and Investment Researchers: Quickly generate professional, structured financial reports to enhance analysis efficiency and depth;
- Institutional Investors and Fund Managers: Support decision-making by timely uncovering financial dynamics and trends of target companies;
- Financial Advisors and Consultants: Provide clients with customized, automated financial analysis services;
- Corporate Finance Teams: Automate quarterly earnings report aggregation and analysis, saving labor costs and reducing manual errors;
- Tech Enthusiasts and Automation Practitioners: Learn and apply RAG technology and AI-driven financial analysis automation through practical use cases.
This workflow significantly lowers the barriers and workload of earnings report analysis, enabling users to gain deep, data-driven insights into corporate financial performance and underlying business conditions, achieving intelligent and automated financial intelligence.