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Groww Nifty Non-Cyclical Consumer Index Fund - Direct Plan Growth is an index scheme managed by Groww Mutual Fund. It has ranked in the top half of its category for 0 of the last 1 reported years. The total expense ratio is 0.47% on assets of ₹42Cr. The fund is currently managed by Mr. Nikhil Satam, appointed within the last year.
Lower is better.
This scheme classifies as Large-Growth on the 3x3 equity style box, with 98% of its portfolio classified as of 2026-05-29.
| Holding | Sector |
|---|
| Window | Min | P25 | Median |
|---|
Point-in-time CAGRs cherry-pick a single start date. The chart below shows the distribution of every possible rolling start over the fund's history, so you see the range of investor outcomes — not just one date's number.
Backtested SIP outcomes across both rolling-window scenarios and named historical stress events (COVID, Election uncertainty, Russia/Ukraine, etc.), plus per-manager alpha during their tenure on this scheme.
Same fund, monthly SIPs over rolling 1/3/5-year windows.
Mutual fund investments are subject to market risks. Read all scheme-related documents carefully. Past performance is not indicative of future results. MintByte is an AMFI-registered Mutual Fund Distributor (ARN-314872) and APMI member (APRN-01658). MintByte does not issue buy/sell recommendations on specific securities — the site is an educational data and analytics platform. Star ratings on this page reflect a 3-year category-quartile position computed in-house and are educational only.
Mutual fund schemes are subject to market risk. Read all scheme-related documents carefully before investing. Past performance is not indicative of future results. MintByte is an AMFI-registered mutual fund distributor (ARN-314872). MintByte does not issue buy/sell recommendations on specific securities — the site is an educational data and analytics platform. Not investment advice. Methodology · How we earn.
ETF-specific data. Tracking error is the standard-deviation of (ETF return − index return) over the trailing year.
In-house derivations using 3-year daily NAV vs benchmark. See methodology.
| Weight |
|---|
| ITC LIMITED | Consumer Defensive | 9.94% |
| BHARTI AIRTEL LIMITED | Communication Services | 9.63% |
| HINDUSTAN UNILEVER LIMITED | Consumer Defensive | 8.97% |
| TITAN COMPANY LIMITED | Consumer Cyclical | 8.12% |
| ASIAN PAINTS LIMITED | Basic Materials | 4.96% |
| INTERGLOBE AVIATION LIMITED | Industrials | 4.36% |
| TRENT LIMITED | Consumer Cyclical | 4.13% |
| TATA CONSUMER PRODUCTS LIMITED | Consumer Defensive | 3.35% |
| Varun Beverages Ltd | Consumer Defensive | 3.16% |
| AVENUE SUPERMARTS LIMITED | Consumer Defensive | 3.03% |
| BRITANNIA INDUSTRIES LIMITED | Consumer Defensive | 3.03% |
| THE INDIAN HOTELS COMPANY LIMITED | Consumer Cyclical | 2.51% |
| DIXON TECHNOLOGIES (INDIA) LIMITED | Technology | 2.07% |
| GODREJ CONSUMER PRODUCTS LIMITED | Consumer Defensive | 1.93% |
| MARICO LIMITED | Consumer Defensive | 1.84% |
| UNITED SPIRITS LIMITED | Consumer Defensive | 1.76% |
| Swiggy Pvt Ltd | Consumer Cyclical | 1.67% |
| FSN E-COMMERCE VENTURES LIMITED | Consumer Cyclical | 1.63% |
| VOLTAS LIMITED | Consumer Cyclical | 1.46% |
| HAVELLS INDIA LIMITED | Industrials | 1.40% |
| COLGATE-PALMOLIVE (INDIA) LIMITED | Consumer Defensive | 1.24% |
| VISHAL MEGA MART PRIVATE LIMITED | Consumer Cyclical | 1.17% |
| DABUR INDIA LIMITED | Consumer Defensive | 1.17% |
| PAGE INDUSTRIES LIMITED | Consumer Cyclical | 1.03% |
| JUBILANT FOODWORKS LIMITED | Consumer Cyclical | 0.84% |
| Sector | Holdings | Weight |
|---|---|---|
| Fast Moving Consumer Goods | — | 41.09% |
| Consumer Services | — | 25.37% |
| Consumer Durables | — | 18.01% |
| Telecommunication | — | 10.46% |
| Services | — | 4.36% |
| Textiles | — | 1.03% |
Active bets vs the average Index fund. Biggest deviations shown first.
Accent bar = fund's actual sector weight. Vertical black tick = category average for the same sector. Green overlay = overweight, dashed red = underweight. The biggest active bets show first.
How crowded into the same stocks is this fund vs the largest fund in its category?
Category leader = highest-AUM scheme in the same SEBI category. A high overlap-of-weight number means the fund is concentrated into the same names as the leader (crowded); a low one means it's genuinely differentiated.
| P75 |
|---|
| Max |
|---|
| Positive % |
|---|
| 1Y | -11.53% | -5.27% | -1.16% | 2.18% | 10.08% | 41.9% |
Each cell is one year. Q1 = top quartile within the AMFI category for that period. Cell label is the last two digits of the year.
Top-10 weight 59.7% means concentration is in line with most actively-managed Indian equity funds. Effective-N is the inverse Herfindahl index — a measure of "how many positions effectively drive the fund" after weighting. Category: Index.
Compounding maths on a notional ₹10 lakh lumpsum at 12% gross annual return. Green bar is what you'd have without the fee; red overlay is the fee drag. Fee is constant in this scenario — actual outcomes depend on real returns and any future TER changes.
What an investor SIPping into this fund actually got during named market shocks.
Each row is a back-tested SIP — monthly contribution over the regime's duration, no fees adjustment beyond NAV-baked TER. XIRR is the annualised IRR of those cashflows; Abs return is the absolute cash-on-cash; Max DD is the deepest drawdown experienced mid-investment. Past performance is not indicative of future results.
Alpha is the annualised excess return vs benchmark over the manager's tenure on this scheme. Beat-benchmark = total return beat the index over the same window.
Does the fund get worse as it gets bigger? Each dot is one historical manager-tenure: AUM at tenure-end vs alpha delivered during that tenure.
Correlation is too weak to confirm or rule out capacity-driven alpha decay. Re-evaluate as more manager-tenure data accumulates.
Each dot is one manager-tenure: X = AUM at tenure end, Y = alpha during that tenure. Connecting line in chronological order. Pearson r measures the linear relationship between AUM and alpha across the historical record. n = 3 data points.