The Fama-French three-factor model, introduced in 1992, revolutionised asset pricing by showing that market beta alone cannot explain cross-sectional differences in equity returns. Two additional factors — size (SMB, small-minus-big) and value (HML, high-minus-low book-to-market) — added substantial explanatory power.
Adapting the Model to Indian Markets
Indian equity markets present unique challenges for factor models. First, the NSE/BSE universe is heavily skewed towards large-cap stocks with institutional ownership. The SMB premium — so robust in US data — is significantly more volatile in India, partly because small-cap liquidity is thin and subject to sharp sentiment-driven swings.
The HML factor, however, shows strong persistence on NSE. PSU banks, FMCG incumbents, and traditional infrastructure companies have traded at persistent value discounts relative to technology and new-economy growth stocks — creating a consistent value spread.
The Five-Factor Extension
Fama and French extended the model in 2015 with two additional factors: profitability (RMW, robust-minus-weak) and investment (CMA, conservative-minus-aggressive). In the Indian context:
- RMW is particularly powerful — Nifty 50 companies with high return on equity (TCS, HDFC Bank, Asian Paints) have outperformed low-profitability peers by 4–6% annually over the past decade.
- CMA is more nuanced. Indian conglomerates often pursue aggressive capital expenditure cycles (Reliance, Adani Group) that temporarily compress CMA-based factor returns before paying off.
Limitations in the Indian Context
Factor models assume liquid, frictionless markets. India's circuit-breaker system (20% daily limit for most stocks), F&O restrictions on smaller stocks, and SEBI's categorisation rules all affect factor construction. Practitioners must adjust factor portfolios for these constraints — otherwise backtests overstate factor alpha.
Spectrum's Factor Analysis module runs Fama-French regressions against your actual portfolio weights, decomposing your returns into attributable factor exposures rather than reporting generic factor indices. This gives you a precise read on your implicit bets — not the market average.