Factor exposure
Decomposition of a fund's returns into systematic risk factors — size, value, momentum, quality, and low-volatility — to explain the source of alpha.
Factor exposure answers a critical question about any fund claiming positive alpha: "Is this outperformance because the manager is genuinely skilled, or because they are systematically tilted toward well-known return premiums like small-cap, value, or momentum?" Factor analysis separates skill from systematic factor harvesting.
What it measures
Academic research (Fama-French, Carhart, and subsequent work) has identified persistent return premiums associated with specific stock characteristics. In India, the most relevant factors are:
- Size (SMB): Small-cap stocks outperform large-cap over long periods, on average.
- Value (HML): Cheap stocks (low P/B) outperform expensive stocks.
- Momentum (MOM): Recent winners tend to keep winning in the short term.
- Quality (QMJ): Profitable, low-leverage companies outperform.
- Low-Volatility: Low-vol stocks outperform on a risk-adjusted basis over cycles.
A fund with a large small-cap tilt will show positive returns in a small-cap rally, but this isn't "skill" — it's factor loading.
How it is computed
MintByte runs a multi-factor OLS regression of monthly fund excess returns against Indian-market factor returns:
R_p(t) − R_f(t) = α + β_mkt × MKT(t) + β_smb × SMB(t) + β_hml × HML(t)
+ β_mom × MOM(t) + β_qmj × QMJ(t) + ε(t)
Factor return series are constructed from NSE/BSE universe using CMIE Prowess data and updated quarterly. The regression uses 36 months of monthly returns.
Output: five factor loadings (betas) + a residual alpha after controlling for factor tilts. Positive residual alpha after factor adjustment is more convincing evidence of skill.
Example: A small-cap fund shows raw alpha +3.8%. After controlling for size factor (SMB loading = +0.45), the factor-adjusted alpha drops to +1.2%. Most of the apparent "alpha" was factor harvest, not stock selection.
How to interpret
- High size loading (β_smb > 0.3): Fund is behaving like a small-cap strategy. Factor risk, not skill.
- High momentum loading (β_mom > 0.3): Performance may reverse sharply in momentum reversals.
- Positive residual alpha after all factors: Genuine manager skill in stock selection or timing.
- High quality loading: Often found in defensive or large-cap tilt funds; tends to be resilient.
Factor exposure is displayed as a bar chart on fund detail pages. Users can compare factor loadings across peer funds to understand what they are actually buying.
Limitations + caveats
Indian factor return series are shorter and noisier than US series (30+ years) — the Indian data goes back reliably only to ~2000. Factor definitions vary across providers; MintByte's construction may differ from commercial factor providers. Factor premiums are not guaranteed to persist; value underperformed globally for much of 2010–2020. This analysis is most useful for evaluating active funds, less so for index funds where factor exposure is by construction.
Related metrics
- Alpha — factor exposure explains what drives (or inflates) reported alpha.
- Beta — market factor loading; factor analysis extends this to five dimensions.
- Capture Ratios — practical expression of how factor tilts interact with market cycles.
Sources
Monthly fund returns: AdvisorKhoj API. Indian factor return series: constructed from BSE 500 universe using Prowess data, updated quarterly. Regression computed quarterly; displayed on fund detail pages for schemes with 36+ months history.