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ICICI Prudential Nifty SDL Dec 2028 Index Fund - Direct Plan - Growth is an index scheme managed by ICICI Prudential Mutual Fund. Over rolling three-year windows since inception, investors earned a median compounded return of 8.20%, with the bottom and top quartiles at 7.63% and 8.30% respectively. It has ranked in the top half of its category for 0 of the last 1 reported years. The total expense ratio is 0.20% on assets of ₹828Cr. The fund is currently managed by Darshil Dedhia, appointed within the last year.
Lower is better.
| Holding | Sector | Weight |
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| Window | Min | P25 | Median |
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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.
| State Government of Andhra Pradesh | Others | 1.22% |
| State Government of Maharashtra | Others | 0.60% |
| P75 |
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| Max |
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| Positive % |
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| 1Y | 3.10% | 7.43% | 8.32% | 8.90% | 11.53% | 100.0% |
| 3Y | 7.01% | 7.63% | 8.20% | 8.30% | 8.65% | 100.0% |
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 1.8% means the portfolio is broad — even the top names don't dominate. 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 = 2 data points.