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Datasets:
Metricshour
/
sec-edgar-geographic-revenue-breakdowns

Tasks:
Text Classification
Tabular Classification
Modalities:
Tabular
Text
Formats:
csv
Size:
1K - 10K
Tags:
financial-data
macroeconomics
sec-edgar
geopolitical-risk
quantitative-finance
stocks
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
 Community
1
Dataset Viewer
Auto-converted to Parquet Duplicate
ticker
stringlengths
1
5
cik
int64
1.8k
2.04M
fiscal_year
int64
2.02k
2.03k
total_revenue_usd
float64
0
50,685B
segment_label
stringlengths
7
70
⌀
segment_type
stringclasses
4 values
segment_code
stringclasses
67 values
segment_revenue_usd
float64
0
21,205B
segment_share_pct
float64
0
99.9
coverage_pct
float64
0
105
verdict
stringclasses
5 values
accession
int64
249B
204,161B
A
1,090,872
2,025
6,948,000,000
a:RestOfWorldMember
region
ROW
3,382,000,000
48.68
100
REGION
109,087,225,000,087
A
1,090,872
2,025
6,948,000,000
srt:AmericasMember
region
AMERICAS
2,806,000,000
40.39
100
REGION
109,087,225,000,087
A
1,090,872
2,025
6,948,000,000
country:US
country
US
2,342,000,000
33.71
100
REGION
109,087,225,000,087
A
1,090,872
2,025
6,948,000,000
srt:AsiaPacificMember
region
APAC
2,219,000,000
31.94
100
REGION
109,087,225,000,087
A
1,090,872
2,025
6,948,000,000
srt:EuropeMember
region
EUROPE
1,923,000,000
27.68
100
REGION
109,087,225,000,087
A
1,090,872
2,025
6,948,000,000
country:CN
country
CN
1,224,000,000
17.62
100
REGION
109,087,225,000,087
AA
1,675,149
2,025
12,831,000,000
country:US
country
US
6,115,000,000
47.66
100
COUNTRY
119,312,526,077,167
AA
1,675,149
2,025
12,831,000,000
country:AU
country
AU
3,011,000,000
23.47
100
COUNTRY
119,312,526,077,167
AA
1,675,149
2,025
12,831,000,000
country:NL
country
NL
2,342,000,000
18.25
100
COUNTRY
119,312,526,077,167
AA
1,675,149
2,025
12,831,000,000
country:BR
country
BR
1,020,000,000
7.95
100
COUNTRY
119,312,526,077,167
AA
1,675,149
2,025
12,831,000,000
country:ES
country
ES
318,000,000
2.48
100
COUNTRY
119,312,526,077,167
AA
1,675,149
2,025
12,831,000,000
aa:OtherGeographicalRegionsMember
unknown
UNKNOWN
25,000,000
0.19
100
COUNTRY
119,312,526,077,167
AAL
6,201
2,025
54,633,000,000
null
none
NONE
0
0
0
NONE
620,126,000,014
AAPL
320,193
2,025
416,161,000,000
aapl:OtherCountriesMember
region
INTL
199,994,000,000
48.06
100
MIXED
32,019,325,000,079
AAPL
320,193
2,025
416,161,000,000
country:US
country
US
151,790,000,000
36.47
100
MIXED
32,019,325,000,079
AAPL
320,193
2,025
416,161,000,000
country:CN
country
CN
64,377,000,000
15.47
100
MIXED
32,019,325,000,079
ABBV
1,551,152
2,025
61,160,000,000
country:US
country
US
46,603,000,000
76.2
100
MIXED
155,115,226,000,008
ABBV
1,551,152
2,025
61,160,000,000
abbv:OtherCountriesMember
region
INTL
5,759,000,000
9.42
100
MIXED
155,115,226,000,008
ABBV
1,551,152
2,025
61,160,000,000
country:DE
country
DE
1,738,000,000
2.84
100
MIXED
155,115,226,000,008
ABBV
1,551,152
2,025
61,160,000,000
country:JP
country
JP
1,274,000,000
2.08
100
MIXED
155,115,226,000,008
ABBV
1,551,152
2,025
61,160,000,000
country:CA
country
CA
1,222,000,000
2
100
MIXED
155,115,226,000,008
ABBV
1,551,152
2,025
61,160,000,000
country:CN
country
CN
1,006,000,000
1.64
100
MIXED
155,115,226,000,008
ABBV
1,551,152
2,025
61,160,000,000
country:FR
country
FR
806,000,000
1.32
100
MIXED
155,115,226,000,008
ABBV
1,551,152
2,025
61,160,000,000
country:GB
country
GB
626,000,000
1.02
100
MIXED
155,115,226,000,008
ABBV
1,551,152
2,025
61,160,000,000
country:ES
country
ES
609,000,000
1
100
MIXED
155,115,226,000,008
ABBV
1,551,152
2,025
61,160,000,000
country:IT
country
IT
580,000,000
0.95
100
MIXED
155,115,226,000,008
ABBV
1,551,152
2,025
61,160,000,000
country:BR
country
BR
478,000,000
0.78
100
MIXED
155,115,226,000,008
ABBV
1,551,152
2,025
61,160,000,000
country:AU
country
AU
459,000,000
0.75
100
MIXED
155,115,226,000,008
ABNB
1,559,720
2,025
12,241,000,000
us-gaap:NonUsMember
region
INTL
7,427,000,000
60.67
100
REGION
155,972,026,000,004
ABNB
1,559,720
2,025
12,241,000,000
srt:NorthAmericaMember
region
NORTHAM
5,196,000,000
42.45
100
REGION
155,972,026,000,004
ABNB
1,559,720
2,025
12,241,000,000
country:US
country
US
4,814,000,000
39.33
100
REGION
155,972,026,000,004
ABNB
1,559,720
2,025
12,241,000,000
us-gaap:EMEAMember
region
EMEA
4,729,000,000
38.63
100
REGION
155,972,026,000,004
ABNB
1,559,720
2,025
12,241,000,000
srt:LatinAmericaMember
region
LATAM
1,160,000,000
9.48
100
REGION
155,972,026,000,004
ABNB
1,559,720
2,025
12,241,000,000
srt:AsiaPacificMember
region
APAC
1,156,000,000
9.44
100
REGION
155,972,026,000,004
ABT
1,800
2,025
44,328,000,000
us-gaap:NonUsMember
region
INTL
27,202,000,000
61.37
100
MIXED
162,828,026,010,185
ABT
1,800
2,025
44,328,000,000
country:US
country
US
17,126,000,000
38.63
100
MIXED
162,828,026,010,185
ABT
1,800
2,025
44,328,000,000
abt:AllOtherCountriesMember
region
INTL
15,979,000,000
36.05
100
MIXED
162,828,026,010,185
ABT
1,800
2,025
44,328,000,000
country:DE
country
DE
2,759,000,000
6.22
100
MIXED
162,828,026,010,185
ABT
1,800
2,025
44,328,000,000
country:CN
country
CN
1,907,000,000
4.3
100
MIXED
162,828,026,010,185
ABT
1,800
2,025
44,328,000,000
country:CH
country
CH
1,871,000,000
4.22
100
MIXED
162,828,026,010,185
ABT
1,800
2,025
44,328,000,000
country:IN
country
IN
1,871,000,000
4.22
100
MIXED
162,828,026,010,185
ABT
1,800
2,025
44,328,000,000
country:JP
country
JP
1,475,000,000
3.33
100
MIXED
162,828,026,010,185
ABT
1,800
2,025
44,328,000,000
country:GB
country
GB
1,340,000,000
3.02
100
MIXED
162,828,026,010,185
ACN
1,467,373
2,025
69,672,977,000
acn:AmericasSegmentMember
region
AMERICAS
35,056,715,000
50.32
100
REGION
146,737,325,000,217
ACN
1,467,373
2,025
69,672,977,000
acn:EMEASegmentMember
region
EMEA
24,643,957,000
35.37
100
REGION
146,737,325,000,217
ACN
1,467,373
2,025
69,672,977,000
acn:AsiaPacificSegmentMember
region
APAC
9,972,305,000
14.31
100
REGION
146,737,325,000,217
ADBE
796,343
2,025
23,769,000,000
srt:AmericasMember
region
AMERICAS
14,120,000,000
59.41
100
MIXED
79,634,326,000,003
ADBE
796,343
2,025
23,769,000,000
country:US
country
US
12,529,000,000
52.71
100
MIXED
79,634,326,000,003
ADBE
796,343
2,025
23,769,000,000
us-gaap:EMEAMember
region
EMEA
6,289,000,000
26.46
100
MIXED
79,634,326,000,003
ADBE
796,343
2,025
23,769,000,000
srt:AsiaPacificMember
region
APAC
3,360,000,000
14.14
100
MIXED
79,634,326,000,003
ADBE
796,343
2,025
23,769,000,000
adbe:OtherAmericasMember
region
AMERICAS
1,591,000,000
6.69
100
MIXED
79,634,326,000,003
ADC
917,251
2,025
718,398,000
null
none
NONE
0
0
0
NONE
91,725,126,000,013
ADM
7,084
2,025
80,269,000,000
country:US
country
US
31,175,000,000
38.84
100
COUNTRY
708,426,000,011
ADM
7,084
2,025
80,269,000,000
country:CH
country
CH
17,793,000,000
22.17
100
COUNTRY
708,426,000,011
ADM
7,084
2,025
80,269,000,000
adm:OtherForeignMember
unknown
UNKNOWN
15,227,000,000
18.97
100
COUNTRY
708,426,000,011
ADM
7,084
2,025
80,269,000,000
country:KY
country
KY
6,093,000,000
7.59
100
COUNTRY
708,426,000,011
ADM
7,084
2,025
80,269,000,000
country:BR
country
BR
3,358,000,000
4.18
100
COUNTRY
708,426,000,011
ADM
7,084
2,025
80,269,000,000
country:MX
country
MX
2,741,000,000
3.41
100
COUNTRY
708,426,000,011
ADM
7,084
2,025
80,269,000,000
country:GB
country
GB
2,115,000,000
2.63
100
COUNTRY
708,426,000,011
ADM
7,084
2,025
80,269,000,000
country:CA
country
CA
1,767,000,000
2.2
100
COUNTRY
708,426,000,011
ADP
8,670
2,025
20,560,900,000
country:US
country
US
18,179,200,000
88.42
100
MIXED
867,025,000,037
ADP
8,670
2,025
20,560,900,000
srt:EuropeMember
region
EUROPE
1,533,500,000
7.46
100
MIXED
867,025,000,037
ADP
8,670
2,025
20,560,900,000
country:CA
country
CA
489,300,000
2.38
100
MIXED
867,025,000,037
ADP
8,670
2,025
20,560,900,000
adp:OtherGeographicalPlacesMember
unknown
UNKNOWN
358,900,000
1.75
100
MIXED
867,025,000,037
AEE
1,002,910
2,025
8,799,000,000
null
none
NONE
0
0
0
NONE
100,291,026,000,009
AEP
4,904
2,025
21,876,000,000
null
none
NONE
0
0
0
NONE
490,426,000,013
AES
874,761
2,025
12,233,000,000
aes:TotalNonUsMember
country
US
7,177,000,000
58.67
100
COUNTRY
87,476,126,000,063
AES
874,761
2,025
12,233,000,000
country:US
country
US
5,056,000,000
41.33
100
COUNTRY
87,476,126,000,063
AES
874,761
2,025
12,233,000,000
country:CL
country
CL
1,516,000,000
12.39
100
COUNTRY
87,476,126,000,063
AES
874,761
2,025
12,233,000,000
country:DO
country
DO
1,363,000,000
11.14
100
COUNTRY
87,476,126,000,063
AES
874,761
2,025
12,233,000,000
country:SV
country
SV
1,086,000,000
8.88
100
COUNTRY
87,476,126,000,063
AES
874,761
2,025
12,233,000,000
country:MX
country
MX
760,000,000
6.21
100
COUNTRY
87,476,126,000,063
AES
874,761
2,025
12,233,000,000
country:BG
country
BG
687,000,000
5.62
100
COUNTRY
87,476,126,000,063
AES
874,761
2,025
12,233,000,000
country:PA
country
PA
649,000,000
5.31
100
COUNTRY
87,476,126,000,063
AES
874,761
2,025
12,233,000,000
country:CO
country
CO
422,000,000
3.45
100
COUNTRY
87,476,126,000,063
AES
874,761
2,025
12,233,000,000
country:PR
country
PR
404,000,000
3.3
100
COUNTRY
87,476,126,000,063
AES
874,761
2,025
12,233,000,000
country:AR
country
AR
366,000,000
2.99
100
COUNTRY
87,476,126,000,063
AES
874,761
2,025
12,233,000,000
country:VN
country
VN
321,000,000
2.62
100
COUNTRY
87,476,126,000,063
AES
874,761
2,025
12,233,000,000
country:JO
country
JO
6,000,000
0.05
100
COUNTRY
87,476,126,000,063
AES
874,761
2,025
12,233,000,000
aes:OtherNonUsMember
unknown
UNKNOWN
1,000,000
0.01
100
COUNTRY
87,476,126,000,063
AES
874,761
2,025
12,233,000,000
country:BR
country
BR
0
0
100
COUNTRY
87,476,126,000,063
AFL
4,977
2,025
17,164,000,000
null
none
NONE
0
0
0
NONE
162,828,026,011,402
AIG
5,272
2,025
26,775,000,000
aig:InternationalMember
region
INTL
14,076,000,000
52.57
100
REGION
527,226,000,023
AIG
5,272
2,025
26,775,000,000
srt:NorthAmericaMember
region
NORTHAM
12,699,000,000
47.43
100
REGION
527,226,000,023
AIZ
1,267,238
2,025
12,814,300,000
country:US
country
US
10,549,000,000
82.32
100
MIXED
126,723,826,000,010
AIZ
1,267,238
2,025
12,814,300,000
us-gaap:NonUsMember
region
INTL
2,265,300,000
17.68
100
MIXED
126,723,826,000,010
AJG
354,190
2,025
13,942,000,000
country:US
country
US
9,391,000,000
67.36
100
COUNTRY
162,828,026,008,662
AJG
354,190
2,025
13,942,000,000
country:GB
country
GB
2,477,000,000
17.77
100
COUNTRY
162,828,026,008,662
AJG
354,190
2,025
13,942,000,000
ajg:OtherForeignMember
unknown
UNKNOWN
886,000,000
6.35
100
COUNTRY
162,828,026,008,662
AJG
354,190
2,025
13,942,000,000
country:AU
country
AU
586,000,000
4.2
100
COUNTRY
162,828,026,008,662
AJG
354,190
2,025
13,942,000,000
country:CA
country
CA
395,000,000
2.83
100
COUNTRY
162,828,026,008,662
AJG
354,190
2,025
13,942,000,000
country:NZ
country
NZ
207,000,000
1.48
100
COUNTRY
162,828,026,008,662
AKAM
1,086,222
2,025
4,208,175,000
country:US
country
US
2,139,173,000
50.83
100
MIXED
108,622,226,000,022
AKAM
1,086,222
2,025
4,208,175,000
us-gaap:NonUsMember
region
INTL
2,069,002,000
49.17
100
MIXED
108,622,226,000,022
ALB
915,913
2,025
5,142,733,000
country:CN
country
CN
2,026,293,000
39.4
100
COUNTRY
91,591,326,000,018
ALB
915,913
2,025
5,142,733,000
alb:OtherForeignCountriesMember
unknown
UNKNOWN
1,076,764,000
20.94
100
COUNTRY
91,591,326,000,018
ALB
915,913
2,025
5,142,733,000
country:US
country
US
890,498,000
17.32
100
COUNTRY
91,591,326,000,018
ALB
915,913
2,025
5,142,733,000
country:KR
country
KR
789,547,000
15.35
100
COUNTRY
91,591,326,000,018
ALB
915,913
2,025
5,142,733,000
country:JP
country
JP
359,631,000
6.99
100
COUNTRY
91,591,326,000,018
ALGN
1,097,149
2,025
4,035,000,000
country:US
country
US
1,661,185,000
41.17
100
MIXED
109,714,926,000,014
End of preview. Expand in Data Studio

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.

📂 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.

Column Type Description
ticker string Stock Ticker (e.g., AAPL, NVDA)
cik string SEC Central Index Key (10-digit ID)
fiscal_year integer Fiscal year of the financial disclosure
total_revenue_usd float Total corporate revenue in USD
segment_label string Original SEC filing member label (e.g., country:US, srt:EuropeMember)
segment_type string Classified segment category (country, region, or unknown)
segment_code string Resolved ISO 3166-1 alpha-2 code (e.g., US, CN) or region acronym (INTL, APAC, EMEA)
segment_revenue_usd float Attributed segment revenue in USD
segment_share_pct float Share of corporate total revenue represented by this segment (%)
coverage_pct float Total percentage coverage accounted for by the returned segments
verdict string The classification of company's overall disclosure structure (COUNTRY, REGION, MIXED, NONE)
accession string SEC 10-K filing Accession Number

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.

Column Type Description
ticker string Stock Ticker
cik string SEC CIK
fiscal_year integer Fiscal year of disclosure
total_revenue_usd float Total corporate revenue in USD
verdict string Disclosure format verdict (COUNTRY, REGION, MIXED, NONE)
coverage_pct float Percentage of revenue successfully matched to geographical segments
top_segment_code string Segment code with the single highest revenue share
top_segment_share_pct float Percentage share of the top segment
us_revenue_usd float Disclosed corporate revenue originating from the United States (USD)
us_share_pct float United States revenue share (%)
china_revenue_usd float Disclosed corporate revenue originating from China (USD)
china_share_pct float China revenue share (%)
intl_revenue_usd float Aggregated non-US, regional, or international segment revenues (USD)
intl_share_pct float Aggregated international revenue share (%)

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:

Ticker Total Revenue (USD) China Share (%) China Revenue (USD) Verdict
NVDA $60.9B 22.0% $13.4B MIXED
AAPL $416.1B 15.5% $64.4B MIXED
QCOM $35.8B 62.5% $22.4B COUNTRY
ALB $5.1B 31.4% $1.6B COUNTRY

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

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