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Mirae Asset Nifty PSU Bank ETF

Mirae Asset Mutual Fund · AMFI: 152929 · ISIN: INF769K01LZ4

Latest NAV

₹83.7338

as of Nov. 4, 2026

1Y: +0.12%

3Y: +11.46%

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Fund Information

05 Nov 2026 · captnemo

Fund House

Mirae Asset Mutual Fund

Period	Fund	Benchmark	Cat Min	Cat Avg	Cat Max	Excess vs BM	Excess vs Cat
1W	191.35%	84.76%	—	—	—	106.59%	—
1M	53.27%	4.44%	—	—	—	48.83%	—
3M	-10.95%	-18.27%	—	—	—	7.32%	—
6M	-7.60%	-3.80%	—	—	—	-3.80%	—
1Y	0.12%	-5.05%	-18.68%	14.57%	101.40%	5.17%	-14.45%
2Y	11.32%	4.03%	—	—	—	7.29%	—
3Y	11.46%	4.03%	-47.28%	13.63%	42.13%	7.43%	-2.17%
5Y	11.46%	4.03%	-31.90%	9.80%	30.53%	7.43%	1.66%
7Y	11.46%	4.03%	—	—	—	7.43%	—
10Y	11.46%	4.03%	—	—	—	7.43%	—
SI	11.46%	4.03%	—	—	—	7.43%	—

Period	Fund	Benchmark	Cat Min	Cat Avg	Cat Max	Excess vs BM	Excess vs Cat
1W	191.35%	84.76%	—	—	—	106.59%	—
1M	53.27%	4.44%	—	—	—	48.83%	—
3M	-10.95%	-18.27%	—	—	—	7.32%	—
6M	-7.60%	-3.80%	—	—	—	-3.80%	—
1Y	0.12%	-5.05%	-18.68%	14.57%	101.40%	5.17%	-14.45%
2Y	11.32%	4.03%	—	—	—	7.29%	—
3Y	11.46%	4.03%	-47.28%	13.63%	42.13%	7.43%	-2.17%
5Y	11.46%	4.03%	-31.90%	9.80%	30.53%	7.43%	1.66%
7Y	11.46%	4.03%	—	—	—	7.43%	—
10Y	11.46%	4.03%	—	—	—	7.43%	—
SI	11.46%	4.03%	—	—	—	7.43%	—

Window	Min	Max	Median	Mean	Win Rate (>0%)	Win Rate (>8%)	BM Median	BM Mean	Outperform %	Cat Avg
1Y Rolling	-2.66%	71.75%	22.82%	26.02%	96.6%	90.4%	2.71%	6.15%	100.0%	—
2Y Rolling	8.49%	13.98%	12.16%	11.59%	100.0%	100.0%	—	—	—	—

📈 Upside Quarters (Top 5)

Quarter	Fund	Benchmark
Q1 2025	15.55%	11.15%
Q3 2025	13.34%	9.05%
Q1 2026	8.47%	14.46%
Q2 2025	4.50%	-4.67%
Q3 2026	3.55%	0.51%

Total 5 upside quarter(s)

📉 Downside Quarters (Worst 5)

Quarter	Fund	Benchmark
Q4 2025	-7.72%	-15.62%
Q2 2026	-5.65%	-5.06%
Q4 2024	-4.28%	—

Total 3 downside quarter(s)

Year-wise Risk Metrics

Year	Fund CAGR	BM CAGR	Alpha	Beta	Sharpe	Sortino	Max DD	Upside Cap	Downside Cap
2026	-3.14%	-9.67%	8.20%	1.12	-0.39	-0.54	-19.26%	118.3%	108.9%
2025	31.00%	16.75%	13.08%	1.19	1.13	1.76	-14.63%	135.3%	124.1%
2024^*	-8.42%	—	—	—	-0.56	-0.85	-8.60%	—	—

Drawdown Chart

Crisis Period Behaviour

"Pre-inception" = fund didn't exist during that period. Hover the ⓘ on each card for how to interpret it.

Pre-inception

2018 IL&FS

—

Aug 2018 → Oct 2018

Pre-inception

COVID-19

—

Jan 2020 → Mar 2020

Pre-inception

2008 GFC

—

Jan 2008 → Mar 2009

Pre-inception

2022 Rate Hike

—

Jan 2022 → Jun 2022

Pre-inception

2015-16 China

—

Mar 2015 → Feb 2016

Partial

2024-25 Tariff

-8.9%

Sep 2024 → Apr 2025

Recovery TBD

📖 How to interpret these numbers

0% or positive — Held up during crash (exceptional)

0% to -15% — Mild damage, manageable drawdown

Below -15% — Significant hit; check recovery speed

Recovery label — Months from trough back to pre-crash peak

Market Regime Analysis

Reading the cards: ≥15% Excellent 5–15% Good 0–5% Muted <0% Lost money | Click ▼ Per-window on any card to see each individual time window

📈 Bull Market

Strong sustained uptrend (>20% from lows)

No data — fund not active in this regime

📉 Bear Market

Sustained decline >20% from recent highs

-16.7% avg CAGR

6 months covered · 1 window

↔️ Sideways / Consolidation

Range-bound market, low trend conviction

No data — fund not active in this regime

🔥 High Inflation Period

CPI/WPI elevated >6%; RBI tightening

No data — fund not active in this regime

✂️ Rate Cut Cycle

RBI cutting repo rate; liquidity supportive

54.4% avg CAGR

4 months covered · 1 window

ℹ️ Regime windows are approximations based on Nifty 50 price action and RBI policy dates. Returns shown are fund NAV CAGR within covered dates only.

⚠️ Benchmark Note: Index data shown is Price Index, not TRI (Total Return Index). TRI includes dividends and is typically 1-2% higher annually. SEBI mandates TRI for official benchmark comparison.

Sector Allocation

Financial Services

100.0%

Weightage by Market Cap

Large Cap 33.33%

Mid Cap 0.00%

Small Cap 66.68%

Asset Allocation

Debt 0.00%

Cash -0.01%

Portfolio Summary

Total Holdings

Top 10 Weight

98.97%

Portfolio Turnover

—

Top Holdings

As of Nov 2026

#	Security	Sector	Weight %	Fwd P/E
1	State Bank of India INE062A01020	Financial Services	33.33%	—
2	Bank of Baroda INE028A01039	Financial Services	12.48%	—
3	Canara Bank INE476A01022	Financial Services	11.71%	—
4	Punjab National Bank INE160A01022	Financial Services	11.36%	—
5	Union Bank of India INE692A01016	Financial Services	9.64%	—
6	Indian Bank INE562A01011	Financial Services	8.31%	—
7	Bank of Maharashtra INE457A01014	Financial Services	4.71%	—
8	Bank of India INE084A01016	Financial Services	4.60%	—
9	Central Bank of India INE483A01010	Financial Services	1.52%	—
10	Indian Overseas Bank INE565A01014	Financial Services	1.32%	—
11	UCO Bank INE691A01018	Financial Services	0.77%	—
12	Punjab & Sind Bank INE608A01012	Financial Services	0.27%	—
13	Treps	cashAndEquivalents	0.01%	—
14	Net Receivables / (Payables)	cashAndEquivalents	-0.02%	—

Costs & Fees

Category Avg (Other ETFs) · Expense Ratio: 0.21% (median: 0.11%)

Expense Ratio

— (cat avg: 0.21%)

Portfolio Turnover

—

CRISIL Rating

—

Kuvera Rating

—

Investment Rules

Min Lumpsum

—

Min Additional

—

Min SIP

—

SIP Available

✅ Yes

Lock-in Period

None

Redemption Allowed

✅ Yes

Tax Treatment

Equity Fund Tax Rules
• STCG (held <12 months): 20% flat
• LTCG (held >12 months): 12.5% on gains above ₹1.25 lakh
• ELSS: 3-year lock-in applies; LTCG rules apply

Benchmark

NIFTY BANK

SEBI-assigned category benchmark for this fund type.

Source: database

⚠️ Data shown is Price Index, not TRI. TRI returns are typically 1-2% higher annually.

Named manager data is not exposed by the available on-demand sources for this fund.

Management Context

Fund House	Mirae Asset Mutual Fund
Inception / First NAV	Oct. 3, 2024
AUM	-
Expense Ratio	-
Portfolio Turnover	-
Rating	-
Benchmark	NIFTY BANK
Data Source	captnemo

Portfolio Managed

Holdings Used

database

Sectors

Portfolio view

3Y Sharpe

0.21

NAV computed

3Y Drawdown

-21.0%

NAV computed

Investment objective is not exposed by the current metadata provider for this fund.

Computing fund scorecard & factor breakdown…

🏅 Score & Category Rank Trend

Historical tracking of model quantitative score and category standing over time.

🎛️

Personalized Ranking Scorecard

Dynamic Multi-Factor

Tailor the scoring model to your investment style. Choose an investor archetype or customize weights across Stability, Consistency, Recency, and Cost.

Personalized Score

Baseline (Default Model)

Category Rank

—

📊 Factor Breakdown (Stability · Consistency · Recency · Cost)

🛡️ Stability

—

Weight: 25% Contribution: +0.0 pts

🎯 Consistency

—

Weight: 25% Contribution: +0.0 pts

⚡ Recency

—

Weight: 25% Contribution: +0.0 pts

💰 Cost

—

Weight: 25% Contribution: +0.0 pts

🕸️ Multi-Factor Radar vs Category Benchmark

SEBI Category: Other ETFs

Factor	Fund Score	Cat. Avg	Gap vs Cat	Your Weight

🧮 Score Composition & Mathematical Transparency

Visual Point Contribution to Final Score:

Loading calculation formula…

Category Score Distribution — Other ETFs

315 funds · Avg score: 42/100

12%

14%

29%

46%

Strong (≥75) Good (55–74) Fair (40–54) Weak/Poor (<40)

🚩 Red Flags

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📖 How We Score — Methodology & Model Notes

Show ▼

⚠️ Important Disclaimer: This scorecard is generated automatically using a quantitative model based on historical data. It is not investment advice. Past performance does not guarantee future results. Always consult a SEBI-registered financial advisor before making investment decisions. This tool is for educational and informational purposes only.

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Loading NAV history for analysis…

🔍 Real-Time Technical Pattern & Divergence Signals

⚡ Real-Time Scanner Active

SMA 50 / 200 Cross

Analyzing…

50D vs 200D Simple Moving Average

RSI (14D) Divergence

Scanning…

Price vs Momentum Oscillator

MACD (12, 26, 9) Momentum

Evaluating…

Fast vs Signal Line Momentum

ADX Trend Strength

Calculating…

Directional Movement System

🚀 All-Time & Multi-Year Range Barometer

Analyzing Price Channel…

All-Time High (ATH) Peak

₹—

Peak Date: —

—

All-Time Low (ATL) Inception/Base

₹—

Trough Date: —

—

52-Week (1Y) Channel —%

₹— – ₹—

Position in 52W Channel

—

3-Year Channel —%

₹— – ₹—

3-Year Low & High Bounds

—

5-Year Channel —%

₹— – ₹—

5-Year Low & High Bounds

—

📍 Visual NAV Position Across Timeframe Channels Current NAV: ₹—

52-Week Low: ₹— (—) 52W Channel (—% of range) 52-Week High: ₹— (—)

+—% from Low —% from High

3-Year Low: ₹— (—) 3Y Channel (—% of range) 3-Year High: ₹— (—)

+—% from Low —% from High

5-Year Low: ₹— (—) 5Y Channel (—% of range) 5-Year High: ₹— (—)

+—% from Low —% from High

💡 Compounding Asset vs Order-Book Mechanics

A mutual fund's NAV is not a stock price driven by order-book bid/ask resistance. Rather, it represents the net book value of a diversified portfolio. As underlying companies generate corporate earnings, pay dividends, and compound cash flows, NAV naturally reaches new All-Time Highs year after year. Being at ATH is a healthy hallmark of secular compounding.

🛑 The "Waiting for a Dip" Timing Fallacy

Retail investors frequently pause investing when a fund reaches an ATH, hoping for a correction. Historical empirical data proves that in compounding equity funds, waiting on the sidelines causes severe cash drag and missed dividend reinvestment. Entering at an ATH historically delivers virtually identical 5-year rolling returns compared to trying to time 5–10% dips.

🧭 Historical Deployment Framework (SIP vs Lumpsum)

Systematic Allocation: Regular monthly SIP schedules utilize rupee-cost averaging to acquire units across market cycles without market timing.
Phased Capital Staging: When NAV trades near historical peaks, institutional literature often favors staggered deployment (e.g. 3–6 month STP). During severe drawdowns (>15% from ATH), margin of safety increases historically.

Signal Summary

Timeframe:

Oscillators

—

— Bear · — Neu · — Bull

Overall Summary

—

— Bear · — Neu · — Bull

Moving Averages

—

— Bear · — Neu · — Bull

Oscillators

Name	Value	Signal

Moving Averages

Indicator	Value	Signal

Pivot Points (30-Day High/Low)

Level	Classic	Fibonacci	Camarilla	Woodie	DeMark

Interactive NAV Chart & Overlays

Overlays: SMA 20 SMA 50 SMA 200 EMA 12 EMA 26 Bollinger Bands Support/Resistance

BB: period, σ RSI: period

Sub-chart Panels: MACD (12,26,9) RSI (14D)

MACD Oscillator (12, 26, 9)

Relative Strength Index (RSI 14D)

Quantitative Risk Metrics

Period-Wise Value at Risk (VaR) & Expected Shortfall (CVaR)

Underwater (Drawdown) Chart

💡 How Monte Carlo Simulation Works & How to Interpret

⚙️ Simulation Methodology

Monte Carlo simulation projects future NAV paths without assuming a rigid normal distribution:

Historical Bootstrap Resampling: Randomly samples from actual daily historical returns, preserving fat tails, skewness, and market crash jumps.
500 Simulated Trajectories: Generates 500 parallel future NAV paths over 1-Year (252D), 3-Year (756D), or 5-Year (1260D) horizons.
Percentile Distribution Analysis: Sorts simulated NAVs at each future day to compute the 95th, 75th, 50th (median), 25th, and 5th percentile bands.

🎯 Percentile Fan & Goal Planning Guide

95th Percentile 🟢 : Bull Case Scenario — Top 5% optimistic outcome during strong bull market rallies.

75th Percentile 🔵 : Above-Average Path — Favourable growth trajectory assuming positive market conditions.

50th Percentile ⚪ : Median Expected NAV — Central 50/50 baseline expectation for financial goal calculations.

25th Percentile 🟧 : Below-Average Path — Mild correction scenario. Good benchmark for conservative SIP planning.

5th Percentile 🔴 : Bear Case Scenario — Bottom 5% severe crash outcome. Essential for stress-testing emergency capital.

💡 Actionable Tip: For critical financial goals (e.g., child education or home down payment), ensure your required target corpus is met by at least the 25th percentile path, not just the 50th median.

Monte Carlo Simulation

95th (Optimistic) 75th 50th (Median) 25th 5th (Pessimistic)

Multi-Factor Composite Quant Signal Score

💡 How the Quant Signal Engine Works & How to Interpret

⚙️ Mathematical Methodology

The Signal Engine processes daily NAV history through 5 weighted quantitative factor pillars:

Trend & Momentum (20%): SMA20/50/200 alignment + 1M/3M/6M price momentum.
Technical Oscillators (20%): RSI(14), MACD histogram, and Bollinger %B mean-reversion.
Forecast Model Consensus (25%): Ensemble, Holt, ARIMA, XGBoost, & Direction Classifiers (% Bullish).
Volatility & Tail Risk (20%): ATR volatility regime, GARCH(1,1) forecast, and 95% CVaR tail risk.
Return Consistency (15%): Fraction of positive monthly returns over past 12 months.

🎯 Score Scale & Decision Guide

85 – 100 : Strong Bullish Conviction 🟢 — Multi-model alignment, strong trend & low tail risk. Favourable for lumpsum/SIP.

65 – 84 : Favourable Outlook 🔵 — Positive momentum and bullish forecast consensus with manageable volatility.

45 – 64 : Neutral / Mixed 🟡 — Sideways trend or model divergence. Ideal for steady SIP accumulation.

25 – 44 : Cautious 🟧 — Negative momentum or rising GARCH volatility. Defer large lumpsum entry.

0 – 24 : Bearish / High Tail Risk 🔴 — Downtrend alignment and high 95% CVaR crash vulnerability. Risk-off caution.

ℹ️ Note: The Composite Quant Score is strictly data-driven based on historical price patterns and statistical models. It does not account for qualitative macroeconomic news, manager changes, or portfolio sector holdings.

🧪 StrategyLab™ Strategy Validation & Reality Engine

Discover what trading, timing, and holding strategies actually work best for Mirae Asset Nifty PSU Bank ETF.

Horizon: Capital: ₹

📈 Strategy Equity Growth Trajectory (Simulated Wealth Creation)

🏆 StrategyLab Leaderboard & Empirical Accuracy Matrix

Rank & Strategy	Category	Final Value (₹)	Total Return	CAGR	Win Rate	Max Drawdown	Sharpe	Alpha vs B&H	StrategyLab Score	Backtest Profile

💡 How StrategyLab™ Backtesting Works & Strategy Insights

🧠 Machine Learning & AI Strategies

🏆 Multi-Model Ensemble: Requires consensus from at least 3 algorithmic models to trigger an investment position, filtering false breakouts.
XGBoost / LightGBM ML: Uses multi-lag return gradients and volatility features to detect non-linear momentum regimes.
LSTM & Deep Neural Nets: Evaluates long-range temporal sequence dependencies over past market cycles.

📊 Technical & Benchmark Strategies

SMA Golden/Death Cross: Trend-following filter. Switches to 6% p.a. liquid debt cash during death cross bear markets.
RSI & Bollinger Mean-Reversion: Accumulates aggressively during panic dips and secures gains at overbought bounds.
Buy & Hold Baseline: Standard continuous investment benchmark for measuring net alpha generated.

🛡️ Note: Simulated strategy cash balances earn a realistic 6.0% p.a. liquid/overnight rate when out of the market. Past backtest alpha does not guarantee future results.

Mirae Asset Nifty PSU Bank ETF

📈 Upside Quarters (Top 5)

📉 Downside Quarters (Worst 5)

Personalized Ranking Scorecard

Scoring Framework

Normalization & Fallbacks