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ICICI Prudential Exports & Services Fund - Direct Plan - Growth is an equity scheme managed by ICICI Prudential Mutual Fund. Over rolling three-year windows since inception, investors earned a median compounded return of 22.03%, with the bottom and top quartiles at 20.08% and 24.25% respectively. It has ranked in the top half of its category for 2 of the last 2 reported years. The total expense ratio is 1.60% on assets of ₹1,366Cr. The fund is currently managed by Sri Sharma, appointed within the last year.
Lower is better.
This scheme classifies as Large-Value on the 3x3 equity style box, with 85% 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.
In-house derivations using 3-year daily NAV vs benchmark. See methodology.
| Weight |
|---|
| INFOSYS LIMITED | Technology | 7.25% |
| ICICI BANK LIMITED | Financial Services | 5.77% |
| HDFC BANK LIMITED | Financial Services | 5.39% |
| NTPC LIMITED | Utilities | 4.43% |
| RELIANCE INDUSTRIES LIMITED | Energy | 4.13% |
| LARSEN AND TOUBRO LIMITED | Industrials | 3.45% |
| STATE BANK OF INDIA | Financial Services | 2.46% |
| PRUDENT CORPORATE ADVISORY SERVICES LIMITED | Financial Services | 2.19% |
| BHARTI AIRTEL LIMITED | Communication Services | 2.15% |
| 360 ONE WAM LIMITED | Financial Services | 1.98% |
| LIFE INSURANCE CORPORATION OF INDIA | Financial Services | 1.86% |
| INTERGLOBE AVIATION LIMITED | Industrials | 1.83% |
| SUN PHARMACEUTICAL INDUSTRIES LTD. | Healthcare | 1.82% |
| AVENUE SUPERMARTS LIMITED | Consumer Defensive | 1.79% |
| SHADOWFAX TECHNOLOGIES PRIVATE LIMITED | Industrials | 1.73% |
| NETWEB TECHNOLOGIES INDIA LIMITED | Technology | 1.73% |
| BAJAJ FINSERV LIMITED | Financial Services | 1.63% |
| AXIS BANK LIMITED | Financial Services | 1.63% |
| HCL TECHNOLOGIES LIMITED | Technology | 1.46% |
| BAJAJ AUTO LIMITED | Consumer Cyclical | 1.42% |
| TECH MAHINDRA LIMITED | Technology | 1.36% |
| SBI LIFE INSURANCE COMPANY LIMITED | Financial Services | 1.36% |
| THE PHOENIX MILLS LIMITED | Real Estate | 1.14% |
| TVS MOTOR COMPANY LIMITED | Consumer Cyclical | 1.13% |
| HDFC LIFE INSURANCE COMPANY LIMITED | Financial Services | 1.08% |
| Sector | Holdings | Weight |
|---|---|---|
| Financial Services | 14 | 34.11% |
| Information Technology | — | 13.07% |
| Healthcare | 7 | 8.29% |
| Consumer Services | — | 6.89% |
| Services | — | 5.31% |
| Power | — | 4.57% |
| Automobile and Auto Components | — | 4.28% |
| Oil, Gas & Consumable Fuels | — | 4.13% |
| Construction | — | 3.93% |
| Telecommunication | — | 2.15% |
Active bets vs the average Equity 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.
| P75 |
|---|
| Max |
|---|
| Positive % |
|---|
| 1Y | -3.29% | 8.52% | 17.19% | 38.66% | 75.36% | 98.2% |
| 3Y | 16.28% | 20.08% | 22.03% | 24.25% | 29.38% | 100.0% |
| 5Y | 16.01% | 18.04% | 22.49% | 24.78% | 28.01% | 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 39.2% 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: Equity.
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.