Important Disclaimer: EDGAR® and SEC® are trademarks of the U.S. Securities and Exchange Commission. This open-source project is created by Stefano Amorelli and built by the community is not affiliated with or approved by the U.S. Securities and Exchange Commission.
SEC EDGAR MCP provides researchers with powerful tools for conducting academic studies, financial research, and empirical analysis using comprehensive SEC filing data.

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Research Applications

Financial Studies

Analyze financial performance and market behavior

Corporate Governance

Study board composition and governance practices

Market Research

Investigate market trends and anomalies

ESG Research

Analyze environmental, social, and governance factors

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Research Methodologies

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Large-Scale Data Collection

You: "Collect financial and governance data for all S&P 500 companies for my academic study"

Assistant: I'll help you collect comprehensive data for S&P 500 companies for your research.

🔧 Tools used: get_cik_by_ticker, get_company_concepts, get_submissions, get_filing_txt_sections

📊 Data Collection Progress:
• Processing 500 companies across 11 sectors
• Financial metrics: Revenue, Net Income, Assets, R&D Spend
• Governance data: Board composition, executive compensation
• Time period: 2019-2024 (5-year dataset)

💾 Results:
• Successfully collected data for 497 companies
• 3 companies excluded due to recent IPOs
• Dataset includes 150+ financial metrics per company
• Governance data extracted from 2,485 proxy statements

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Cross-Sectional Analysis

You: "Compare revenue growth across different industries for 2024"

Assistant: I'll analyze revenue growth patterns across industries using XBRL data.

🔧 Tools used: get_xbrl_frames, get_company_concepts

📊 Industry Revenue Growth (2024 vs 2023):
• Technology (SIC 7370-7379): +18.3% avg
• Healthcare (SIC 8000-8099): +12.7% avg
• Financial Services (SIC 6000-6799): +8.4% avg
• Retail (SIC 5200-5999): +5.2% avg
• Manufacturing (SIC 3000-3999): +3.8% avg

🔍 Key Findings:
• Tech sector leads growth driven by AI investments
• Healthcare shows consistent double-digit growth
• Traditional retail lagging behind e-commerce
• 82% of companies showed positive YoY growth

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Data Analysis Examples

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Event Study Analysis

You: "Extract earnings announcement dates for my event study on market reactions"

Assistant: I'll gather earnings announcement dates for your event study analysis.

🔧 Tools used: get_submissions, get_filing_txt_sections

📅 Earnings Event Data Collected:
• Companies analyzed: 100 (from your study sample)
• Time period: Q1 2023 - Q4 2024 (8 quarters)
• Total earnings events: 800 announcements
• Filing types: 10-Q (quarterly) and 10-K (annual)

📊 Event Window Data:
• Pre-announcement period: t-5 to t-1
• Announcement date: t=0 (filing date)
• Post-announcement: t+1 to t+5
• Average filing time: 4:02 PM ET (after market close)

💡 Ready for abnormal return calculations

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Textual Analysis

You: "Analyze sentiment and tone in MD&A sections for my research on disclosure quality"

Assistant: I'll analyze the Management Discussion & Analysis sections for sentiment patterns.

🔧 Tools used: get_filing_txt_sections, stream_filing_txt_chunks

📝 Textual Analysis Results:
• Documents analyzed: 500 MD&A sections
• Average word count: 12,847 words
• Sentiment distribution:
  - Positive tone: 42%
  - Neutral tone: 35%
  - Cautious/Negative: 23%

🔍 Key Linguistic Patterns:
• Forward-looking statements: 2.3% of text
• Uncertainty words: 156 avg per document
• Positive keywords: "growth", "improvement", "strong"
• Negative keywords: "challenges", "risks", "decline"

💡 Correlation with performance: Companies with positive MD&A tone showed 3.2% higher returns

XBRL Frames

Cross-company data for empirical studies

Company Facts

Comprehensive financial data for research
Created and maintained by Stefano Amorelli. Built together with the community.