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PGIM India Money Market Fund - Segregated Portfolio 1 - Direct Plan - Growth Option

PGIM India Mutual Fund · AMFI: 152114 · ISIN: -

Latest NAV

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Fund Information

04 Nov 2026 · captnemo

Fund House

PGIM India Mutual Fund

Category

Debt Scheme - Money Market Fund

AUM

—

Expense Ratio

—

Min SIP

—

Min Lumpsum

—

Fund Start Date

—

Lock-in Period

None

💰 Fund AUM Trend

Current: —

Trailing Returns

Full Analysis →

Returns are unavailable from the on-demand NAV source right now.

NAV History

Trailing Returns (CAGR)

No returns data yet.

Trailing Returns Chart

Calendar Year Returns

Rolling Return Statistics

All rolling windows vs benchmark — full history

Rolling returns not computed yet.

Rolling Return Calculator

Inception: — · Latest NAV: —

Rolling Window

Start Date

End Date

Rolling Returns Distribution (All Windows)

Bar = mean, error bar = min/max. BM shown beside. Right axis = % of periods beating BM.

🧮 SIP Simulator

Monthly SIP Amount (₹)

Investment Period (Years)

10 yrs

Enter values and click Calculate.

💰 Lumpsum Simulator

One-time Investment (₹)

Investment Period (Years)

10 yrs

Enter values and click Calculate.

No 3Y risk metrics available from the on-demand NAV source.

No 5Y risk metrics. Need 5+ years of NAV data.

Market Volatility Measures (3Y)

Upside Capture

—%

Downside Capture

—%

Max Drawdown

—%

Year-wise Risk Metrics

Yearly risk data unavailable.

Drawdown Chart

⚠️ 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

Weightage by Market Cap

Cap data not available — run python manage.py ingest_holdings

Asset Allocation

Asset allocation is not available from the on-demand sources for this plan.

Portfolio Summary

Category Avg (Money Market Fund) · Turnover: 2.2% (median: 2.2%)

Total Holdings

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Top 10 Weight

—

Portfolio Turnover

— (cat avg: 2.2%)

Top Holdings

No holdings were returned by the on-demand portfolio sources for this fund.

Costs & Fees

Category Avg (Money Market Fund) · Expense Ratio: 0.16% (median: 0.16%) · Turnover: 2.2%

Expense Ratio

— (cat avg: 0.16%)

Portfolio Turnover

— (cat avg: 2.2%)

CRISIL Rating

—

Kuvera Rating

—

Investment Rules

Min Lumpsum

—

Min Additional

—

Min SIP

—

SIP Available

✅ Yes

Lock-in Period

None

Redemption Allowed

✅ Yes

Tax Treatment

Debt/Hybrid Fund Tax Rules
• All gains taxed at income slab rate (no indexation benefit after April 2023)
• Applicable to funds with <65% equity allocation

Benchmark

NIFTY MONEY MARKET INDEX

SEBI-assigned category benchmark for this fund type.

⚠️ 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	PGIM India Mutual Fund
Inception / First NAV	-
AUM	-
Expense Ratio	-
Portfolio Turnover	-
Rating	-
Benchmark	NIFTY MONEY MARKET INDEX
Data Source	captnemo

Portfolio Managed

Holdings Used

unavailable

Sectors

Portfolio view

3Y Sharpe

NAV computed

3Y Drawdown

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

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

🛡️ Stability

—

Weight: 25% Contribution: +0.0 pts

🎯 Consistency

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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: Money Market Fund

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 — Money Market Fund

28 funds · Avg score: 81/100

71%

29%

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.

Loading peer funds in the same category…

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: —

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All-Time Low (ATL) Inception/Base

₹—

Trough Date: —

—

52-Week (1Y) Channel —%

₹— – ₹—

Position in 52W Channel

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3-Year Channel —%

₹— – ₹—

3-Year Low & High Bounds

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5-Year Channel —%

₹— – ₹—

5-Year Low & High Bounds

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📍 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

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— Bear · — Neu · — Bull

Overall Summary

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— Bear · — Neu · — Bull

Moving Averages

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— 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 PGIM India Money Market Fund - Segregated Po….

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.

PGIM India Money Market Fund - Segregated Portfolio 1 - Direct Plan - Growth Option

Personalized Ranking Scorecard

Scoring Framework

Normalization & Fallbacks