Loading mutual fund details…
Union Retirement Fund - Regular Plan - Growth Option is a solution scheme managed by Union Mutual Fund. Over rolling three-year windows since inception, investors earned a median compounded return of 16.88%, with the bottom and top quartiles at 16.22% and 17.46% respectively. It has ranked in the top half of its category for 3 of the last 3 reported years. Risk-adjusted return (Sharpe ratio) is 0.00. The total expense ratio is 2.42% on assets of ₹196Cr. The fund is currently managed by Pratit Vajani, appointed within the last year.
Lower is better.
This scheme classifies as Large-Growth on the 3x3 equity style box, with 91% of its portfolio classified as of 2026-04-30.
| 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 |
|---|
| Multi Commodity Exchange of India Ltd | Financial Services | 3.03% |
| Azad Engineering Limited | Industrials | 1.44% |
| KSB Limited | Industrials | 1.00% |
| LG Electronics India Limited | Technology | 0.98% |
| Shriram Finance Limited | Financial Services | 0.96% |
| Jyoti CNC Automation Ltd | Industrials | 0.94% |
| Persistent Systems Ltd | Technology | 0.86% |
| Tata Motors Limited | Consumer Cyclical | 0.84% |
| Computer Age Management Services Limited | Technology | 0.57% |
| Sector | Holdings | Weight |
|---|---|---|
| Banks | 6 | 17.33% |
| Capital Markets | 2 | 7.58% |
| Consumer Durables | 1 | 5.76% |
| Pharmaceuticals & Biotechnology | 4 | 5.38% |
| Finance | 1 | 5.30% |
| Electrical Equipment | 1 | 5.01% |
| IT - Software | 1 | 4.98% |
| Auto Components | 4 | 4.89% |
| Chemicals & Petrochemicals | 3 | 4.35% |
| Industrial Products | 1 | 3.97% |
| Retailing | 3 | 3.25% |
| Automobiles | 3 | 3.05% |
| Petroleum Products | 1 | 2.67% |
| Cement & Cement Products | 2 | 2.59% |
| Power | 1 | 2.34% |
| Aerospace & Defense | 2 | 2.23% |
| Agricultural Food & other Products | 1 | 2.16% |
| Construction | 1 | 2.05% |
| Ferrous Metals | 2 | 2.04% |
| Telecom - Services | 1 | 1.64% |
| Realty | 1 | 1.44% |
| Insurance | 1 | 1.42% |
| Healthcare Services | 1 | 1.20% |
| Diversified FMCG | 1 | 1.03% |
| Industrial Manufacturing | 1 | 0.94% |
| Non - Ferrous Metals | 1 | 0.90% |
| Oil | 1 | 0.88% |
| Personal Products | 1 | 0.84% |
| Agricultural Commercial & Construction Vehicles | 1 | 0.84% |
| Household Products | 1 | 0.84% |
Active bets vs the average Solution 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 | -5.10% | 4.78% | 13.66% | 33.67% | 43.74% | 95.0% |
| 3Y | 13.99% | 16.22% | 16.88% | 17.46% | 18.78% | 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 10.6% 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: Solution.
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.
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.