---
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](https://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

![Pipeline Stats](docs/pipeline_stats.png)

### Tables by Year

![Tables by Year](docs/tables_by_year.png)

---

## 💰 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

![Compensation Distribution](docs/compensation_distribution.png)

### Trends Over Time

![Compensation Trends](docs/compensation_trends.png)

### By Industry (SIC Code)

![By Industry](docs/compensation_by_industry.png)

---

## 📋 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

```json
{
  "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

```python
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

```python
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

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

---

## 🔧 Source & Methodology

Data extracted from [SEC EDGAR](https://www.sec.gov/edgar) DEF 14A filings using:
- **[MinerU](https://github.com/opendatalab/MinerU)** for PDF table extraction
- **[Qwen3-VL-32B](https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct)** 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](https://github.com/pierpierpy/Execcomp-AI)
- **SEC EDGAR**: [sec.gov/edgar](https://www.sec.gov/edgar)