README.md

metadata

dataset_info:
  features:
    - name: cik
      dtype: string
    - name: company
      dtype: string
    - name: year
      dtype: int64
    - name: filing_date
      dtype: string
    - name: sic
      dtype: string
    - name: state_of_inc
      dtype: string
    - name: filing_html_index
      dtype: string
    - name: accession_number
      dtype: string
    - name: table_image
      dtype: image
    - name: table_body
      dtype: string
    - name: executives
      dtype: string
  splits:
    - name: train
      num_bytes: 210732897
      num_examples: 1849
  download_size: 191339714
  dataset_size: 210732897
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: mit
task_categories:
  - table-to-text
language:
  - en
tags:
  - finance
  - sec
  - executive-compensation
  - def14a
  - proxy-statements
pretty_name: SEC Executive Compensation
size_categories:
  - 1K<n<10K

SEC Executive Compensation Dataset

🔗 Pipeline: github.com/pierpierpy/Execcomp-AI

Structured executive compensation data extracted from SEC DEF 14A proxy statements using AI.

📊 Dataset Statistics

Metric	Value
Total documents processed	2,000
Funds (no exec comp)	376 (18.8%)
Documents with SCT	1,528
Documents without SCT	96
Total SCT tables	1,860
Multi-table documents	332
Total executive records	19,288
Unique companies	1,380
Year range	2005 - 2022

Document Breakdown

Tables by Year

---

💰 Compensation Statistics

Metric	Value
Mean total compensation	$2.12M
Median total compensation	$0.96M
Max total compensation	$147.7M
Mean salary	$368K
Mean stock awards	$0.67M

Detailed Breakdown

Component	Mean	Median	Max
Salary	$368K	$306K	$20.0M
Bonus	$125K	$0	$10.1M
Stock Awards	$675K	$22K	$146.1M
Option Awards	$242K	$0	$44.5M
Non-Equity Incentive	$290K	$0	$44.1M
Change in Pension	$57K	$0	$14.2M
Other Compensation	$83K	$16K	$15.7M
Total	$2.12M	$962K	$147.7M

🏆 Top 10 Highest Paid Executives

Name	Title	Company	Year	Total
George Kurtz	CEO	CrowdStrike Holdings	2022	$147.7M
Satya Nadella	CEO	Microsoft Corp	2014	$84.3M
Steve Mollenkopf	CEO	Qualcomm Inc	2016	$60.7M
Lisa T. Su	CEO	AMD	2020	$58.5M
Paul E. Jacobs	Executive Chairman	Qualcomm Inc	2016	$56.9M
Douglas Ingram	CEO	Sarepta Therapeutics	2019	$56.9M
Donald R. Horton	Executive Chairman	D.R. Horton	2022	$50.6M
James Heppelmann	CEO	PTC Inc	2019	$50.0M
David N. Weidman	CEO	Celanese Corp	2010	$48.9M
David N. Weidman	CEO	Celanese Corp	2009	$48.3M

Compensation Distribution

Trends Over Time

By Industry (SIC Code)

---

📋 Dataset Description

This dataset contains Summary Compensation Tables extracted from SEC filings, with:

• Original table images
• HTML table structure
• Structured JSON with executive compensation details

Fields

Field	Type	Description
cik	string	SEC Central Index Key
company	string	Company name
year	int	Filing year
filing_date	string	SEC filing date
sic	string	Standard Industrial Classification code
state_of_inc	string	State of incorporation
filing_html_index	string	Link to SEC filing
accession_number	string	SEC accession number
table_image	image	Extracted table image
table_body	string	HTML table content
executives	string	JSON array of executive compensation

Executive Schema

{
  "name": "John Smith",
  "title": "CEO",
  "fiscal_year": 2023,
  "salary": 500000,
  "bonus": 100000,
  "stock_awards": 2000000,
  "option_awards": 500000,
  "non_equity_incentive": 300000,
  "change_in_pension": 50000,
  "other_compensation": 25000,
  "total": 3475000
}

---

🚀 Quick Start

from datasets import load_dataset
import json

# Load dataset
ds = load_dataset("pierjoe/execcomp-ai")

# View first record
print(ds["train"][0])

# Parse executives JSON
record = ds["train"][0]
executives = json.loads(record["executives"])
for exec in executives:
    print(f"{exec['name']} ({exec['title']}): ${exec['total']:,}")

Analyze with Pandas

import pandas as pd
import json

# Convert to DataFrame
df = ds["train"].to_pandas()

# Parse all executives
all_execs = []
for _, row in df.iterrows():
    for exec_data in json.loads(row['executives']):
        exec_data['company'] = row['company']
        exec_data['year'] = row['year']
        all_execs.append(exec_data)

exec_df = pd.DataFrame(all_execs)

# Average compensation by year
print(exec_df.groupby('year')['total'].mean())

# Top 10 highest paid
print(exec_df.nlargest(10, 'total')[['name', 'company', 'year', 'total']])

View Table Image

# Display table image
record = ds["train"][0]
record["table_image"]  # PIL Image object

---

🔧 Source & Methodology

Data extracted from SEC EDGAR DEF 14A filings using:

• MinerU for PDF table extraction
• Qwen3-VL-32B for classification and extraction

Pipeline Steps

1. Download DEF 14A PDFs from SEC EDGAR
2. Extract tables with MinerU (VLM-based)
3. Classify tables to identify Summary Compensation Tables
4. Merge tables split across pages
5. Extract structured compensation data with VLM

---

📜 License

MIT License

🔗 Links

• GitHub: github.com/pierpierpy/Execcomp-AI
• SEC EDGAR: sec.gov/edgar