When a client asks “what's your investment style?”, most PMS managers answer qualitatively: “we're value-oriented with a quality tilt.” Fama-French factor analysis lets you answer with data.
What is the Fama-French Factor Model?
Eugene Fama and Kenneth French proposed (1993) that stock returns are driven by three systematic factors beyond the overall market. Mark Carhart added a fourth in 1997. The four factors are:
- Market (MKT) — The excess return of the broad market over the risk-free rate. This is the classic CAPM beta.
- Size (SMB: Small Minus Big) — The return premium of small-cap stocks over large-cap stocks. A positive loading means your portfolio tilts toward smaller companies.
- Value (HML: High Minus Low) — The return premium of high book-to-market (value) stocks over low book-to-market (growth) stocks. A positive loading means your portfolio tilts toward value stocks.
- Momentum (UMD: Up Minus Down) — The return premium of recent winners over recent losers. A positive loading means your portfolio holds stocks with recent price momentum.
How It Works
The model runs an OLS (Ordinary Least Squares) regression of your portfolio's excess returns against the four factor returns:
R_portfolio - R_f = alpha + beta_MKT(R_m - R_f) + beta_SMB(SMB) + beta_HML(HML) + beta_UMD(UMD) + epsilon
The output tells you:
- Alpha — Return not explained by any factor. Genuine skill (or luck).
- Factor loadings (betas) — Your exposure to each factor, with t-statistics showing statistical significance.
- R-squared — How much of your return variation is explained by the four factors.
Adapting Fama-French for Indian Markets
The original Fama-French factors are computed for US markets. For India, Capital Advantage constructs proxies from Indian market data:
- Market factor: NIFTY 50 total return minus the RBI 91-day T-bill rate
- SMB: NIFTY Smallcap 100 return minus NIFTY 50 return (captures the small-cap premium in Indian markets)
- HML: NIFTY PSU Bank return minus NIFTY IT return (PSU banks are deep value; IT stocks are growth — a practical value/growth proxy for India)
- UMD: Computed cross-sectionally from the portfolio's own constituent returns
What the Results Tell You
Example: A Typical Multi-Cap PMS
Suppose the regression produces:
- Alpha: +0.8% (annualized), t-stat: 1.4 (not statistically significant)
- MKT beta: 1.05 (significant) — slightly more aggressive than market
- SMB beta: +0.35 (significant) — clear small-cap tilt
- HML beta: +0.20 (not significant) — slight value lean, but not meaningful
- UMD beta: -0.10 (not significant) — no momentum exposure
- R-squared: 0.87 — 87% of return variation explained by factors
Interpretation: This PMS's returns are primarily driven by market exposure and a small-cap tilt. The 0.8% alpha is not statistically significant — meaning the manager's apparent outperformance is likely explained by factor exposures rather than genuine stock-picking skill. This is valuable self-knowledge.
Why This Matters for Client Conversations
If your R-squared is 0.95 and your alpha is zero, you're effectively running a factor portfolio. Your clients could replicate your returns with index ETFs at lower cost. Knowing this lets you either differentiate your strategy or price accordingly.
Conversely, if your R-squared is 0.60 and your alpha is significant at 3%+, you're genuinely adding value through idiosyncratic stock selection. That's a powerful story to tell prospective clients.
How to Run Factor Analysis on Capital Advantage
- Go to Equity Portfolio and enter your holdings
- Click the Factor tab
- Select the analysis period (1Y, 3Y, or 5Y)
- View factor loadings, t-statistics, p-values, R-squared, and interpretive labels
The regression runs in seconds against Indian market data. No need to source factor return series, build your own regression, or interpret raw statsmodels output.