---
license: cc-by-4.0
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
language:
  - en
tags:
  - finance
  - stocks
  - financial-ratios
  - fundamentals
  - valuation
  - quant
pretty_name: US Stock Financial Ratios (10 Years)
size_categories:
  - 1K<n<10K
---

# US Stock Financial Ratios Dataset

A comprehensive dataset of **44 financial ratios and metrics** for **237 major US companies** over **10+ years**, perfect for quantitative analysis and factor investing research.

## Dataset Description

This dataset contains annual financial ratios covering valuation, profitability, growth, liquidity, and leverage metrics. Ideal for building factor models, screening stocks, or training ML models on fundamental data.

### Key Statistics

| Metric | Value |
|--------|-------|
| Total Records | 2,364 |
| Companies | 237 |
| Metrics per Record | 44 |
| Years Covered | 2012-2025 |
| Sectors | All 11 GICS sectors |

## Metrics Included (44 total)

### Valuation Ratios
- `price_to_earnings_ratio` - P/E ratio
- `price_to_book_ratio` - P/B ratio
- `price_to_sales_ratio` - P/S ratio
- `enterprise_value_to_ebitda_ratio` - EV/EBITDA
- `enterprise_value_to_revenue_ratio` - EV/Revenue
- `peg_ratio` - Price/Earnings to Growth
- `free_cash_flow_yield` - FCF Yield

### Profitability Metrics
- `gross_margin` - Gross profit margin
- `operating_margin` - Operating margin
- `net_margin` - Net profit margin
- `return_on_equity` - ROE
- `return_on_assets` - ROA
- `return_on_invested_capital` - ROIC

### Growth Metrics
- `revenue_growth` - YoY revenue growth
- `earnings_growth` - YoY earnings growth
- `earnings_per_share_growth` - EPS growth
- `ebitda_growth` - EBITDA growth
- `book_value_growth` - Book value growth
- `free_cash_flow_growth` - FCF growth
- `operating_income_growth` - Operating income growth

### Liquidity Ratios
- `current_ratio` - Current assets / Current liabilities
- `quick_ratio` - (Current assets - Inventory) / Current liabilities
- `cash_ratio` - Cash / Current liabilities
- `operating_cash_flow_ratio` - Operating CF / Current liabilities

### Leverage Ratios
- `debt_to_equity` - Total debt / Equity
- `debt_to_assets` - Total debt / Assets
- `interest_coverage` - EBIT / Interest expense

### Efficiency Metrics
- `asset_turnover` - Revenue / Assets
- `inventory_turnover` - COGS / Inventory
- `receivables_turnover` - Revenue / Receivables
- `working_capital_turnover` - Revenue / Working capital
- `days_sales_outstanding` - DSO
- `operating_cycle` - Operating cycle days

### Per Share Data
- `earnings_per_share` - EPS
- `book_value_per_share` - BVPS
- `free_cash_flow_per_share` - FCF per share
- `payout_ratio` - Dividends / Earnings

### Other
- `market_cap` - Market capitalization
- `enterprise_value` - Enterprise value

## Column Descriptions

| Column | Type | Description |
|--------|------|-------------|
| `ticker` | string | Stock ticker symbol |
| `report_period` | date | End date of fiscal period |
| `fiscal_period` | string | Fiscal year/quarter (e.g., "2024-FY") |
| `period` | string | "annual" or "quarterly" |
| `currency` | string | Reporting currency (mostly USD) |
| + 39 metric columns | float | Financial ratios and metrics |

## Usage

### Load with Hugging Face Datasets

```python
from datasets import load_dataset

dataset = load_dataset("mdnh/us-stock-financial-ratios")
df = dataset['train'].to_pandas()

print(f"Records: {len(df):,}")
print(f"Companies: {df['ticker'].nunique()}")
```

### Example: Value Screening

```python
# Get latest data per company
latest = df.sort_values('report_period').groupby('ticker').last().reset_index()

# Find undervalued stocks (low P/E, high ROE)
value_stocks = latest[
    (latest['price_to_earnings_ratio'] < 15) &
    (latest['price_to_earnings_ratio'] > 0) &
    (latest['return_on_equity'] > 0.15)
][['ticker', 'price_to_earnings_ratio', 'return_on_equity']]

print(value_stocks.sort_values('return_on_equity', ascending=False))
```

### Example: Factor Analysis

```python
import pandas as pd

# Calculate factor exposures
latest = df.sort_values('report_period').groupby('ticker').last().reset_index()

# Value factor: inverse of P/E
latest['value_factor'] = 1 / latest['price_to_earnings_ratio']

# Quality factor: ROE
latest['quality_factor'] = latest['return_on_equity']

# Momentum proxy: earnings growth
latest['momentum_factor'] = latest['earnings_growth']

# Rank stocks by combined factor score
latest['combined_score'] = (
    latest['value_factor'].rank(pct=True) +
    latest['quality_factor'].rank(pct=True) +
    latest['momentum_factor'].rank(pct=True)
) / 3

print(latest.nlargest(10, 'combined_score')[['ticker', 'combined_score']])
```

### Example: Historical Trend Analysis

```python
# Track Apple's profitability over time
aapl = df[df['ticker'] == 'AAPL'].sort_values('report_period')

print(aapl[['report_period', 'gross_margin', 'net_margin', 'return_on_equity']])
```

## Use Cases

- **Factor Investing**: Build and backtest factor models (value, quality, momentum)
- **Stock Screening**: Filter stocks by fundamental criteria
- **ML Features**: Use ratios as features for price prediction models
- **Company Analysis**: Track financial health over time
- **Sector Comparison**: Compare metrics across industries
- **Research**: Study relationships between ratios and returns

## Companies Included

Top companies by market cap including:
AAPL, MSFT, GOOGL, AMZN, NVDA, META, TSLA, BRK.B, JPM, V, MA, UNH, JNJ, WMT, PG, HD, and 220+ more S&P 500 constituents.

## License

This dataset is released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).

## Citation

```bibtex
@dataset{financial_ratios_2026,
  author = {mdnh},
  title = {US Stock Financial Ratios Dataset},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/mdnh/us-stock-financial-ratios}
}
```

## Related Datasets

- [US Stock Insider Trades](https://huggingface.co/datasets/mdnh/insider-trades-us-stocks) - Insider trading transactions
- [US Company Facts](https://huggingface.co/datasets/mdnh/us-company-facts) - Company metadata
- [Hourly Stock Data 2023](https://huggingface.co/datasets/mdnh/hourly-stock-data-2023) - OHLCV + technical indicators