India Six-Factor Premia, Attribution & Regime Analysis

Indian equities are not a scaled copy of US markets. Large, mid, and small caps can diverge sharply around credit events; quality-oriented segments often hold up better in stress; momentum can deliver strong long-run premia but also violent drawdowns; and two funds marketed with similar labels can post very different paths once factor exposures are unpacked.

This project applies a Fama–French-style six-factor framework to India: market (MF), size (SMB5), value (HML), profitability (RMW), investment (CMA), and momentum (WML). We use monthly data over a multi-year window, align factors with Nifty style benchmarks, and run the same models on a small set of Indian equity mutual funds so you can see both index-level and fund-level results in one place.

The work is aimed at investors and researchers who want evidence, not anecdotes: Which factors earned positive long-run premia in India? How correlated are they? How much of a Nifty 200 Value or Smallcap 250 return is explained by market beta alone versus a full factor model? Do funds earn alpha after controlling for known risks? Does momentum interact with value or the market factor? And does the quality factor (RMW) still pay when the market factor is negative?

Interactive exhibits in the factor-model and what-you-will-see sections organize results by theme — market puzzles, factor premia, index and fund attribution, momentum risk, and a formal hypothesis test.

Single-index benchmarks (e.g. Nifty 50) hide large dispersion across cap tiers and style tilts. A mid-cap value fund and a large-cap growth fund may both be “equity” but carry different factor bets. Multi-factor models separate market beta from size, value, profitability, investment, and momentum effects so performance can be compared on a level field.

India-specific research (including work on four- and five-factor models in Indian equities) shows that classical Fama–French structures are informative but not identical to US calibrations — premia, correlations, and crash episodes differ. This study documents those differences with transparent regressions rather than relying on US factor histories as a proxy.

For asset or index $i$ at month $t$, excess return is modelled as:

$r_{i,t}-r_{f,t}={\alpha }_i+{\sum }_k{\beta }_{i,k}{f}_{k,t}+{\epsilon }_{i,t}$

The six factors are: MF (market), SMB5 (size), HML (value), RMW (profitability), CMA (investment), and WML (momentum). Simpler models nest inside this structure: CAPM uses MF only; a four-factor variant adds size, value, and momentum; the full six-factor specification adds profitability and investment.

Reported alphas are annualised from monthly regressions. Rolling 36-month windows show how factor exposures shift — useful for spotting style drift in indices and funds.

Market puzzles — cumulative paths for large, mid, and small caps, quality vs benchmark, momentum, fund dispersion, and volatility through major macro events (demonetisation, IL&FS, COVID).

Factor premia — summary statistics, correlations, and long-run cumulative factor returns for the Indian FF6 panel.

Index regressions — how much of each Nifty style index’s return is explained by CAPM, four-factor, and six-factor models; rolling factor exposures on a highlight index.

Momentum — WML profile, drawdowns, interaction with value and market factors, and long-only vs long–short momentum behaviour.

Funds — FF6 attribution for sample Indian equity funds, model fit across specifications, rolling factor exposures, and comparison of total return vs factor-explained performance.

Hypothesis test — whether profitability (quality) earns positive returns when the market factor is negative, with robustness checks.