Metricshour/sec-edgar-geographic-revenue-breakdowns

US S&P 500 Companies Geographic Revenue Exposure (SEC EDGAR)

This dataset contains a comprehensive, reconciled, and audited map of the geographic and regional revenue breakdowns for major US-listed corporations (including S&P 500 companies). The data was extracted directly from corporate 10-K filings submitted to the US Securities and Exchange Commission (SEC) EDGAR system.

By reconciling structured SEC XBRL segment dimensions with unstructured HTML R-file disclosures (using the production-grade edgar-geo-revenue engine), this dataset overcomes the high incompleteness and inconsistency of standard financial databases.

📊 Live Interactive Dashboards & Live Lookup

This dataset is compiled and maintained by MetricsHour, an interactive financial and macroeconomic analysis engine.

• —Live Ticker Dashboards: To see real-time interactive charts, peer comparisons, and complete profiles of corporate exposure, explore the [MetricsHour Ticker Search](https://metricshour.com).
• —Direct Stock Profiles: Read deep-dive, automated geopolitical summaries for individual companies. For example:
• —Apple (AAPL) Geographic Exposure & Revenue Breakdown
• —Nvidia (NVDA) Geographic Exposure & Revenue Breakdown
• —Microsoft (MSFT) Geographic Exposure & Revenue Breakdown
• —Tesla (TSLA) Geographic Exposure & Revenue Breakdown
• —Macroeconomic Tracker: Track global central bank rates, inflation (CPI), retail numbers, and economic printers at [MetricsHour Countries](https://metricshour.com).

📂 Files Included

The dataset contains three files representing different schemas to suit your analytic needs:

1. sec_edgar_geographic_revenue_breakdowns.csv (Long-Form, Flat)

Contains 1,488 flat rows, representing a long-form table. Perfect for SQL injection, BI tool visualization (Tableau, PowerBI), or quick pandas analysis. Every country or region segment is isolated as a distinct row.

2. sec_edgar_geographic_revenue_summary.csv (Wide-Form, Summary)

Contains 452 company-level summary rows. Each row represents a single company's profile, including pre-computed exposure highlights, S&P 500 US vs China exposure splits, and international revenue aggregates.

3. sec_edgar_geographic_revenue_breakdowns.jsonl (Hierarchical, Raw)

Contains 452 JSON Lines records with the full hierarchical structure, raw XML member tags, SEC period metadata, segment coverage sources, and raw parsing logs. Perfect for developers building parsers or doing advanced JSON processing.

🛠️ Loading in Python

Using Pandas (Direct Raw Fetch)

import pandas as pd

# Load long-form flat segment exposure
df_flat = pd.read_csv("https://huggingface.co/datasets/Metricshour/sec-edgar-geographic-revenue-breakdowns/raw/main/sec_edgar_geographic_revenue_breakdowns.csv")
print("Unique Tickers:", df_flat['ticker'].nunique())
print(df_flat.head())

# Load company-level US vs China exposure summaries
df_summary = pd.read_csv("https://huggingface.co/datasets/Metricshour/sec-edgar-geographic-revenue-breakdowns/raw/main/sec_edgar_geographic_revenue_summary.csv")
print(df_summary.sort_values(by="china_share_pct", ascending=False).head(10))

Using Hugging Face Datasets

from datasets import load_dataset

dataset = load_dataset("Metricshour/sec-edgar-geographic-revenue-breakdowns")
print(dataset["train"][0])

💡 Geopolitical Analytics Case Study

Question: Which S&P 500 companies are most exposed to China?

By analyzing sec_edgar_geographic_revenue_summary.csv, we can instantly identify companies disclosing massive direct dependencies on the Chinese market:

Note: Disclosures are strictly sourced from the company's official 10-K SEC filings. If a company does not break down China separately (e.g. reporting it under "Asia Pacific" or "International"), it remains classified as an international region.

⚖️ License

This dataset is licensed under the MIT License. You are free to use, modify, distribute, and build commercial quantitative or analytical models upon this dataset.

🔗 Resources & References

• —Website & Dashboards: MetricsHour Financial Engine
• —Extraction Engine Repository: edgar-geo-revenue
• —Primary Source: US Securities and Exchange Commission (SEC) EDGAR System.