Fama-French 3-Factor Model

The Fama-French 3-factor model (Eugene Fama and Kenneth French, 1992-1993) extends the CAPM by adding two factors that empirically explain a large share of the cross-sectional variation in equity returns: the size premium (SMB, "Small Minus Big") and the value premium (HML, "High Minus Low" book-to-market). The result is a factor decomposition that explains roughly 90% of the variation in diversified US equity portfolio returns.

Regression

R_i − R_f = α + β · (R_M − R_f) + s · SMB + h · HML + ε

• ·R_M − R_f — the market premium.
• ·SMB — return of a long small-cap, short large-cap portfolio.
• ·HML — return of a long high book-to-market (value), short low book-to-market (growth) portfolio.
• ·α — the residual not explained by the three factors. Anything significant here is the "true" alpha.

Why FF3 matters

Many strategies that look like they generate alpha against the S&P 500 are simply long small-caps and long value. FF3 disentangles the factor exposure from the residual. Survivorship-corrected studies (Carhart 1997, Fama-French 2010) consistently find that the median active US equity mutual fund has zero or negative FF3 alpha.

Carhart extension

Mark Carhart (1997) added a fourth factor — momentum (UMD, "Up Minus Down") — to capture the empirical fact that recent winners tend to keep winning over short horizons. Carhart 4-factor is now the de-facto standard for equity attribution.

How MEDGE Capital uses factor models

Factor regression (Fama-French / Carhart) is on the MEDGE roadmap but currently not in scope — our focus is risk decomposition, crisis stress tests and macro / regulatory data. For factor decomposition today, dedicated tools like Portfolio Visualizer remain the better fit for that specific job. See the dedicated comparison at /vs/portfolio-visualizer.