Free Fama-French Five-Factor API
Get academic-grade factor exposure for any US stock. Our API runs the full Fama-French five-factor regression and returns loadings, alpha, and R² — the same model used by institutional quant funds.
Endpoint
GET https://securitiesdb.com/api/v1/stocks/{ticker}/fama-frenchWhat Is the Fama-French Five-Factor Model?
The Fama-French Five-Factor Model (Fama & French, 2015) explains stock returns through five systematic risk factors:
MKT (β): Market risk premium — exposure to broad market returns
SMB: Small Minus Big — size factor (small-cap vs large-cap)
HML: High Minus Low — value factor (high vs low book-to-market)
RMW: Robust Minus Weak — profitability factor
CMA: Conservative Minus Aggressive — investment factor
α (Alpha): Excess return not explained by the five factors
Reference: Fama, E. F., & French, K. R. (2015). "A five-factor asset pricing model." Journal of Financial Economics, 116(1), 1-22.
Response Fields
| Field | Description |
|---|---|
| factors.market_beta | Market beta (systematic risk) |
| factors.size_smb | Size factor loading (+ = small-cap tilt) |
| factors.value_hml | Value factor loading (+ = value tilt) |
| factors.profitability_rmw | Profitability factor loading |
| factors.investment_cma | Investment factor loading |
| alpha_annualized | Jensen's alpha (annualized excess return) |
| r_squared | R² — how well the model fits the stock's returns |
Python Example — Factor Exposure Heatmap
import requests
tickers = ["AAPL", "TSLA", "BRK-B", "JPM", "NVDA"]
print(f"{'Ticker':>6} {'Beta':>6} {'SMB':>6} {'HML':>6} {'RMW':>6} {'CMA':>6} {'Alpha':>7} {'R²':>5}")
print("-" * 55)
for ticker in tickers:
r = requests.get(f"https://securitiesdb.com/api/v1/stocks/{ticker}/fama-french")
if r.status_code != 200:
continue
d = r.json()["data"]
f = d["factors"]
print(f"{ticker:>6} {f['market_beta']:>6.2f} {f['size_smb']:>6.2f} "
f"{f['value_hml']:>6.2f} {f['profitability_rmw']:>6.2f} "
f"{f['investment_cma']:>6.2f} {d['alpha_annualized']:>6.1%} "
f"{d['r_squared']:>5.2f}")Use Cases
- Portfolio risk attribution: Decompose returns into factor exposures to understand what's really driving performance
- Factor-neutral hedging: Identify and hedge unwanted factor tilts
- Smart beta ETF analysis: Verify if a "value ETF" actually loads on HML
- Alpha generation: Find stocks with persistent positive alpha after controlling for all five factors