Google Sheets Financial Data AI Analysis Market Research Automated Reporting

Generate Weekly AI Equity Research Reports

Automate professional investment analysis using financial data, market news, and AI to generate consistent, data-backed research reports.

Download Template JSON · n8n compatible · Free
AI Equity Research Report Automation Workflow Diagram

What This Workflow Does

This automation solves a critical pain point for investment professionals, analysts, and finance teams: the time-consuming, manual process of creating regular equity research reports. Traditionally, analysts spend hours each week collecting financial data, reading news, calculating ratios, and writing analysis—a repetitive process prone to human error and inconsistency.

The workflow automatically generates professional, data-driven equity research reports for selected companies on a weekly schedule. It pulls five years of financial statements from Financial Modeling Prep, gathers recent market news via NewsAPI, calculates key financial signals, uses OpenAI to generate SWOT analysis and risk assessments, compiles everything into a clean HTML report, converts it to PDF, and emails it to stakeholders—all without manual intervention.

By automating the data collection, calculation, and initial analysis phases, this workflow ensures consistent, unbiased reporting while freeing up analysts to focus on higher-value strategic decisions and portfolio management.

How It Works

The automation follows a logical pipeline that transforms raw data into actionable investment insights.

1. Weekly Trigger & Company Selection

A schedule trigger initiates the workflow weekly. It first reads a Google Sheet that acts as a control panel, identifying which companies are marked as "enabled" for analysis. This allows you to easily manage your research universe without modifying the automation itself.

2. Financial Data Collection

For each enabled company, the workflow queries the Financial Modeling Prep API to retrieve five years of income statements, balance sheets, and cash flow statements. This provides the fundamental data needed for ratio analysis and trend identification.

3. Market News Aggregation

Simultaneously, the workflow searches NewsAPI for recent headlines and articles about the company. This captures market sentiment, recent developments, and potential catalysts that might not yet be reflected in financial statements.

4. Data Processing & Signal Calculation

The raw financial data is normalized and structured. Key metrics are calculated automatically: revenue growth rates, profit margins, debt-to-equity ratios, cash flow strength, buyback activity, and other valuation indicators. These quantitative signals form the objective foundation for the analysis.

5. AI-Powered Analysis Generation

The calculated signals and aggregated news are fed to OpenAI's language model with carefully crafted prompts. The AI generates a structured SWOT analysis (Strengths, Weaknesses, Opportunities, Threats), identifies key risks, and provides a 12–24 month growth outlook based purely on the data provided.

6. Report Assembly & Distribution

The financial data, calculated signals, and AI insights are compiled into a professional HTML report template. This report is converted to PDF for easy sharing and archiving. Metadata (PDF URL, generation timestamp, file size) is logged back to Google Sheets for tracking, and the final report is emailed to designated stakeholders.

Who This Is For

This automation delivers the most value to professionals and teams who regularly produce equity research and need to scale their analysis efficiently.

Investment Analysts & Research Teams: Automate the routine data gathering and initial analysis for your coverage universe, allowing you to focus on deeper due diligence and investment recommendations.

Financial Advisors & Wealth Managers: Provide clients with consistent, data-backed updates on their holdings without increasing your team's workload. The automated reports serve as excellent client communication tools.

Corporate Finance & Strategy Teams: Monitor competitors and industry peers systematically. The workflow can be adapted to track key metrics across your competitive landscape rather than just investment targets.

Startup Founders & Business Owners: Understand public company benchmarks in your industry. While not for investment purposes, the automated analysis helps you track how public competitors are performing on key financial metrics.

Academic & Research Institutions: Generate consistent datasets for financial research projects or teaching materials about company analysis and valuation techniques.

What You'll Need

  1. n8n instance (cloud or self-hosted) with workflow execution capabilities
  2. Google Sheets API access with credentials to read/write to your research control sheet
  3. Financial Modeling Prep API key for financial statement data (free tier available)
  4. NewsAPI key for market news aggregation
  5. OpenAI API access with GPT-4 or similar model for analysis generation
  6. HTML-to-PDF service credentials (like PDFShift, DocRaptor, or similar)
  7. Gmail OAuth2 credentials or SMTP access for email distribution
  8. A Google Sheet with company tickers and an "enabled" column to control the analysis

Pro tip: Start with just 2-3 companies in your control sheet to test the workflow end-to-end before scaling to your full coverage list. This helps identify any API rate limits or formatting issues early.

Quick Setup Guide

Follow these steps to implement this automation in your n8n environment:

  1. Download and import the JSON template file into your n8n instance.
  2. Create your control Google Sheet with columns for company ticker, name, and an "enabled" flag (TRUE/FALSE).
  3. Configure all credential nodes with your API keys for FMP, NewsAPI, OpenAI, and your PDF service.
  4. Set up the Google Sheets node to point to your control sheet and map the correct columns.
  5. Customize the AI prompts in the OpenAI nodes to match your preferred analysis style and focus areas.
  6. Test with one company by setting the "enabled" flag to TRUE and running the workflow manually.
  7. Review the output and adjust any calculations or formatting in the HTML template node as needed.
  8. Configure the schedule trigger to run weekly (e.g., Monday mornings before market open).
  9. Set up the email distribution with your recipient list and preferred email template.
  10. Monitor the first few automated runs to ensure all API calls complete successfully and reports are generated correctly.

Key Benefits

Save 10-15 hours per week per analyst by automating data collection, calculation, and initial report drafting. What used to take hours for each company now happens automatically while your team focuses on higher-value analysis.

Eliminate manual data entry errors that can skew financial ratios and valuation metrics. Automated calculations ensure consistency and accuracy across all reports, removing human transcription mistakes.

Scale your coverage universe without linearly increasing headcount. Track 50 companies as easily as 5, with each receiving the same rigorous, data-backed analysis on schedule.

Create an audit trail of all analysis with timestamps, data sources, and calculation methodologies. Every report is reproducible and traceable back to the original financial statements.

React faster to market events with automated news monitoring. The system detects and incorporates relevant news immediately, rather than waiting for the next manual research cycle.

Frequently Asked Questions

Common questions about equity research automation and AI financial analysis

AI-powered equity research automation uses software to collect financial data, analyze market news, and generate investment reports automatically. Instead of analysts spending hours manually compiling spreadsheets and reading news, an automation workflow pulls data from sources like Financial Modeling Prep and NewsAPI, processes it with AI (like OpenAI), and produces structured SWOT analyses, risk assessments, and growth outlooks.

This approach ensures consistency, reduces human error, and frees up analysts for higher-value strategic work. The automation handles the quantitative heavy lifting while humans focus on qualitative judgment and investment decisions.

Automation transforms investment research from a reactive, manual task into a proactive, data-driven system. It ensures you never miss a key earnings report or news event, provides unbiased analysis based purely on numbers, and creates a repeatable process that scales across dozens of companies.

The biggest benefits are time savings (reducing report generation from hours to minutes), improved accuracy by eliminating manual data entry errors, and the ability to track more companies with the same resources. Automation also creates consistency in methodology across all your research outputs.

For comprehensive equity research, you need multiple data layers: fundamental data (Financial Modeling Prep for income statements, balance sheets, cash flows), market sentiment (NewsAPI for recent headlines and sentiment), and macroeconomic indicators.

The most effective automation combines these sources with AI interpretation to identify patterns humans might miss. Google Sheets serves as an excellent control panel to manage which companies to analyze and store results, while specialized APIs provide the raw financial data. Always verify data quality and update frequency when selecting sources.

AI-generated analysis excels at processing vast amounts of structured data quickly and without cognitive biases, making it excellent for quantitative analysis, pattern recognition, and consistency. However, it lacks human judgment for qualitative factors like management quality or industry relationships.

The optimal approach uses AI to handle data collection, calculation of financial ratios, and initial insights, while human analysts focus on strategic interpretation, market context, and investment decisions based on those insights. Think of AI as a super-powered research assistant rather than a replacement for human expertise.

When automating financial research, security is paramount. Always use encrypted connections (HTTPS) for API calls, store API keys in secure environment variables (never in plain text), implement access controls to restrict who can trigger or modify the automation, and ensure compliance with data protection regulations.

For sensitive proprietary models, consider running automation on-premises rather than cloud-based services, and regularly audit access logs to detect any unauthorized activity. Also establish data retention policies for generated reports and implement encryption for stored financial data.

Absolutely. This workflow template is designed as a foundation that can be adapted for value investing, growth investing, dividend strategies, or sector-specific analysis. You can modify the financial metrics calculated, adjust the AI prompts to focus on different aspects, or add technical analysis data.

The modular nature of automation makes it ideal for creating personalized research pipelines. For example, value investors might emphasize free cash flow and margin stability, while growth investors might focus on revenue growth and market share expansion. Each strategy requires different data points and analytical lenses.

Build regular review cycles into your automation. Schedule weekly runs to capture new financial data and news, but also create monthly 'health checks' where you verify API connections are working, update any changed data sources, and refine your AI prompts based on recent market performance.

Set up alerting for when key metrics change beyond certain thresholds, and maintain a feedback loop where human analysts can flag when automated insights need adjustment based on market conditions. This creates a continuous improvement cycle for your research automation.

Yes, GrowwStacks specializes in building custom automation solutions for investment firms, financial advisors, and corporate finance teams. We can create tailored workflows that integrate with your existing data sources, incorporate your proprietary valuation models, generate reports in your specific format, and distribute insights through your preferred channels.

Our team understands both the technical requirements of automation and the strategic needs of financial analysis, ensuring the solution delivers actionable intelligence. We work with you to identify the right data sources, design appropriate analysis methodologies, and implement robust security measures for your sensitive financial data.

  • Integration with proprietary databases and internal systems
  • Custom valuation models and scoring methodologies
  • White-labeled reporting matching your brand guidelines
  • Advanced alerting for unusual market movements

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