claritystorm/sec-edgar-exec-comp

SEC EDGAR Executive Compensation 2015–Present

500K+ structured executive compensation records for S&P 500 + Russell 2000 companies — parsed from SEC EDGAR DEF 14A proxy filings, 2015–present. CEO/CFO pay, stock awards, bonuses, non-equity incentives, and total compensation, linked by CIK, ticker, and fiscal year. The most comprehensive open dataset for executive pay research, ESG governance scoring, and quant finance.

📊 Records	📅 Coverage	🏷️ License	🔄 Updated
500K+ compensation records	2015–present	Public Domain	Annual

This repo contains a free 1,000-row sample. Full dataset (CSV + Parquet) → claritystorm.com/datasets/sec-edgar-exec-comp

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Quick Start

from datasets import load_dataset
import pandas as pd

# Load the 1,000-row sample
ds = load_dataset("claritystorm/sec-edgar-exec-comp")
df = ds["train"].to_pandas()

# CEO compensation trend by fiscal year
ceo = df[df["is_ceo"] == True]
print(ceo.groupby("fiscal_year")["total_compensation"].mean().round(0))

# Top 10 highest-paid CEOs in latest year
latest_year = ceo["fiscal_year"].max()
top_ceos = (ceo[ceo["fiscal_year"] == latest_year]
            [["company_name", "executive_name", "total_compensation"]]
            .sort_values("total_compensation", ascending=False)
            .head(10))
print(top_ceos)

# Pay mix: stock awards vs. salary
df["stock_pct"] = df["stock_awards"] / df["total_compensation"].replace(0, pd.NA)
print(df.groupby("fiscal_year")["stock_pct"].mean().round(3))

Use Cases

• ESG governance scoring — CEO-to-median-worker pay ratios, pay-for-performance alignment, and board compensation governance
• Quant factor research — executive pay as an alpha signal; overpaid vs. underpaid CEO portfolios
• Corporate governance AI — train models to flag anomalous compensation structures or governance red flags
• Pay equity research — compare total compensation across sectors, company sizes, and fiscal years
• Proxy advisor analytics — replicate and extend institutional proxy voting research at scale
• Financial NLP — link to DEF 14A proxy text for compensation discussion and analysis (CD&A) NLP

Schema (selected fields)

Field	Type	Description
cik	string	SEC Central Index Key (unique company identifier)
ticker	string	Stock ticker symbol
company_name	string	Company name as filed with SEC
fiscal_year	int	Fiscal year of the compensation disclosure
date_filed	string	Proxy filing date (YYYY-MM-DD)
executive_name	string	Executive's full name (normalized)
title	string	Executive's title/position
is_ceo	bool	True if title indicates Chief Executive Officer
is_cfo	bool	True if title indicates Chief Financial Officer
salary	float	Base salary ($)
bonus	float	Discretionary bonus ($)
stock_awards	float	Grant-date fair value of stock awards ($)
option_awards	float	Grant-date fair value of option awards ($)
non_equity_incentive	float	Non-equity incentive plan compensation ($)
total_compensation	float	Total reported compensation as filed ($)
total_comp_computed	float	ClarityStorm computed total (sum of components)
sector	string	Industry sector (where available)

⬇️ Get the Full Dataset

Tier	Price	Includes
Sample	Free	1,000 rows, Public Domain (this repo)
Complete	$149	Full 500K+ rows, CSV + Parquet, commercial license
Annual	$299/yr	Complete + annual updates (new proxy season each year)

👉 Purchase at claritystorm.com/datasets/sec-edgar-exec-comp

Source

U.S. Securities and Exchange Commission (SEC), EDGAR — DEF 14A Proxy Statements. SEC EDGAR data is a US federal government work in the public domain (17 U.S.C. 105). Executive compensation for Named Executive Officers (NEOs) is required by SEC Regulation S-K Item 402.

Processed and structured by ClarityStorm Data.