Where Should Indian Investors Put Their Money?

Key Findings

6 things the data forces you to conclude

Read these first. The full evidence follows below.

The Trap

Hidden windows most analyses miss

Start from Sep 2007 (where most tools default) and S&P 500 looks invincible. Go back to Jan 2000 and 92 additional windows reveal a completely different story.

S&P 500

−5.0%

S&P 500 worst 7-year return in INR

The dot-com bust erased seven years of returns for Indian investors. SIP made it worse: the worst SIP window returned −9.3%. This asset is not immune to bad timing.

Nifty 50

Never negative

Nifty 50 SIP produced a profit in all 232 windows

Across 26 years, every 7-year Nifty 50 SIP ended in the green. The worst returned just 0.2% annualised, but it never lost money.

The Blend

8.81% floor

The allocation that never had a bad 7-year stretch

35% Gold, 35% S&P 500, 30% Nifty 50, rebalanced annually. Across 232 windows, this blend has not returned less than 8.81% per year in any historical window.

Fund Costs

7×

The cost gap between the cheapest and most expensive option

S&P 500 fund costs on Indian platforms range from 0.09% to over 1.2% all-in. Some Fund-of-Funds display only their wrapper fee, hiding an underlying ETF layer. That gap compounds to lakhs over a decade. See the Appendix for a full breakdown.

Action

3 SIPs

Three SIPs, one annual rebalance

The allocation can be implemented with three index-tracking funds: one gold FoF, one S&P 500 or US market fund, one Nifty 50 index fund. Available on most major Indian mutual fund platforms. The Appendix compares options in each category.

Section 1

What 26 Years of Data Actually Show

If you pull Nifty 50 data from Yahoo Finance, it starts in September 2007. Most comparison tools, blog posts, and YouTube analyses use roughly this window. Run the numbers from there and S&P 500 in INR wins 85.7% of 7-year windows with a worst-case CAGR of +10.5%. It never goes negative. The conclusion writes itself: just buy S&P 500.

But September 2007 is a suspiciously convenient start date

It lands right before the Global Financial Crisis, which means S&P 500's recovery is always captured but the dot-com bust is always excluded. We went back to January 2000, using BSE Sensex as a proxy for Nifty (0.99+ correlation; methodology in the appendix). That added 92 more windows. S&P 500's worst 7-year CAGR dropped from +10.5% to -5.0%. Its win rate fell from 85.7% to 52%. The start date of a backtest is not a detail. It is the analysis.

⚠️ Data note: Nifty 50 before Sep 2007. Yahoo Finance provides Nifty 50 data only from Sep 2007. For Jan 2000 to Aug 2007, we use the BSE Sensex scaled by the Nifty/Sensex ratio (0.298, computed from the 12-month overlap). The two indices have a 0.99+ correlation, so this proxy is reliable. All pre-2007 Nifty figures are Sensex-derived.

Question 1 of 3

Which asset delivers the best returns?

Across every possible starting month from 2000 to 2019, which asset wins most consistently? We tested 232 independent 7-year rolling windows per asset.

Rolling 7-Year CAGR: All 232 Windows

Each point = the 7-year CAGR ending that month. 232 independent investment periods, Jan 2007 to Apr 2026.

The picture the shorter dataset hid

Restrict the data to 2007 onwards and S&P 500's worst 7-year CAGR is +10.5%. Add the dot-com era and it drops to -5.0%. That is the difference between "always wins" and "can lose real money over seven years." Every blog post, every YouTube video, every Kuvera comparison that starts from 2007 misses this entirely.

Metric	Gold (INR)	S&P 500 (INR)	Nifty 50 *
Mean 7Y CAGR	12.8%	10.5%	13.6% ✓
Best 7Y Window	26.0% (Mar 2026)	21.1% (Sep 2018)	29.1% ✓ (Apr 2010)
Worst 7Y Window ← key number	-0.9% (Nov 2018)	-5.0% ✗ (Feb 2009)	4.4% ✓ (Dec 2014)
Consistency (std dev)	6.5%	6.9% (most volatile)	5.6% ✓
% windows beating 10%	61%	61%	74% ✓
% windows positive	99.6%	89%	100%

* Pre-Sep 2007 Nifty 50 figures are derived from BSE Sensex × 0.298 (see Methodology in Appendix). Post-Sep 2007 figures use actual Nifty 50 data from NSE.

Question 2 of 3

Does it matter when you start investing?

Rolling averages smooth over the bad years. The sharper question: how much does the worst possible timing actually cost?

If you invested ₹1,00,000 lump sum in…	Gold (INR)	S&P 500 (INR)	Nifty 50 *
Jan 2000: dot-com peak After 7 years: Jan 2007	₹2,32,800 +12.8% CAGR	₹1,04,200 +0.6% CAGR	₹2,70,700 +15.3% CAGR
Feb 2002: S&P 500 worst window After 7 years: Feb 2009	₹3,22,900 +18.2% CAGR	₹69,600 −5.0% CAGR, the worst	₹2,60,400 +14.7% CAGR
Sep 2007: right before the GFC After 7 years: Sep 2014	₹2,53,500 +14.2% CAGR	₹2,00,900 +10.5% CAGR	₹1,58,600 +6.8% CAGR
Nov 2008: post-GFC bottom After 7 years: Nov 2015	₹1,88,800 +9.5% CAGR	₹3,13,300 +17.7% CAGR	₹2,88,000 +16.3% CAGR
Nov 2011: gold price peak After 7 years: Nov 2018	₹94,200 −0.9% CAGR	₹2,98,200 +16.9% CAGR	₹2,25,100 +12.3% CAGR
Jan 2017: pre-COVID After 7 years: Jan 2024	₹2,18,300 +11.8% CAGR	₹2,60,500 +14.7% CAGR	₹2,53,800 +14.2% CAGR

Every asset has an era where it fails

Timing matters enormously for every asset. S&P 500 at the dot-com peak barely returns your money. Gold at its 2011 peak loses money. Nifty at the GFC peak still returns 6.8%. The question is not which asset to pick. It is how to survive all eras.

💱 The hidden tailwind: rupee depreciation. The INR fell from ~₹43.5/$ in 2000 to ~₹93/$ today, a structural decline of 2.9% per year. This adds 2.9% annually to any USD-denominated asset for Indian investors. It is not a one-off: India's higher inflation differential makes this a persistent tailwind (though see Section 4 for caveats).

The analysis above assumes lump-sum investing. Most Indian investors use SIP. That changes the maths.

The SIP Question

Question 3 of 3

Does monthly SIP eliminate the timing problem?

We ran ₹10,000/month through every 84-month window since 2000: 232 independent SIP journeys per asset.

S&P 500 SIP

-9.3%

S&P 500 SIP worst-case XIRR

Starting a SIP in Feb 2002 into the teeth of the dot-com bust and GFC gave -9.3% annualised. SIP did not save you here. Average: 11.6%. Excellent most of the time, but not always.

Nifty 50 SIP

13.9%

Nifty SIP average XIRR: highest of all

Nifty 50 SIP averaged 13.9% across all windows, the best of all three. Worst window: 0.2%, still positive. Nifty SIP never lost money in 26 years of data.

Gold SIP

0.9% floor

Gold SIP worst case: still positive

Gold SIP's worst window: 0.9% XIRR, marginal but no loss. Average: 13.2%. SIP turned gold's lump-sum loss into a gain. Always SIP for gold.

Rolling 7-Year SIP XIRR: Every Starting Month Since 2000

Each point = annualised XIRR on ₹10,000/month SIP started that month, held 7 years. 232 windows per asset.

SIP smooths the ride but does not eliminate risk

Monthly SIP removes the timing gamble for gold and Nifty 50: both produced positive outcomes in every 7-year window. S&P 500 SIP still lost money in the worst windows (dot-com start through GFC), returning -9.3% annualised. SIP is not a shield against a full-decade downturn in a single asset.

Asset	LS Avg CAGR	LS Worst 7Y	SIP Avg XIRR	SIP Worst 7Y	Verdict
S&P 500 (INR)	10.5%	-5.0%	11.6%	-9.3%	Strong average but dot-com/GFC windows produced losses even with SIP. Not immune.
Nifty 50	13.6%	4.4%	13.9%	0.2%	Highest mean, never negative in any SIP window. Most reliable single asset.
Gold (INR)	12.8%	-0.9%	13.2%	0.9%	Always SIP for gold. Historically eliminated the lump-sum loss.

Section 2

The Blend That Has Not Failed in 26 Years of Data

No single asset was safe across every 7-year window. So we tested every possible combination of Gold, S&P 500, and Nifty 50, in 5% increments, across all 232 windows. One allocation stood out.

Optimal Blend

35% Gold · 35% S&P 500 · 30% Nifty 50

Floor

Mean

Windows positive

Lump sum
8.81%

Lump sum
13.14%

Lump sum
100%

Monthly SIP
+6.8%

Monthly SIP
12.9%

Monthly SIP
100%

Lump sum: No single asset comes within 4 percentage points of the blend floor. S&P 500 alone: worst −5.0%. Gold alone: worst −0.9%. Nifty 50 alone: worst +4.4%. The blend has not delivered less than +8.81% in any 7-year window.

Monthly SIP: The blended SIP produced positive returns in every 7-year window. S&P 500 SIP alone went as low as −9.3%. Gold SIP worst: +0.9%. Nifty SIP worst: +0.2%. The blend smoothed all three to a worst of +6.8%.

Rolling 7-Year CAGR: The Blend vs Individual Assets

The gold line is the 35/35/30 portfolio (rebalanced annually). Notice how its floor sits dramatically above any single asset's worst outcome.

Why the floor matters more than the average

A portfolio that averages 15% but can deliver −5% will cause panic selling. A portfolio that averages 13.14% but has not fallen below 8.81% in any historical window keeps investors invested through the full cycle. The 35/35/30 blend is not the highest-returning allocation. It is the one most likely to actually be held.

#	Allocation (Gold / S&P / Nifty)	LS Floor	Mean	SIP Floor
★	35% / 35% / 30%	8.81%	13.14%	6.83%
2	25% / 35% / 40%	8.86%	13.19%	5.76%
3	30% / 35% / 35%	8.85%	13.18%	6.29%
4	40% / 35% / 25%	8.76%	13.08%	7.36%
5	20% / 40% / 40%	8.68%	13.01%	4.81%

Floor analysis: brute-force test of all 231 possible 5%-increment allocations against each rolling 7-year window, for both lump-sum and SIP. 35/35/30 was selected because it maximises the combined floor: its lump-sum floor (8.81%) is within 0.05pp of the absolute best, while its SIP floor (6.83%) is over 1pp higher than allocations with marginally better lump-sum floors.

🔎 For the technically inclined: efficient frontier methodology

Risk vs Return: Every Possible Blend

Markowitz mean-variance optimisation on monthly returns, Jan 2000 to Apr 2026. Risk-free rate: 7% (India 10Y gov bond).

Min Variance (G44/S43/N13): Return 11.3%, Vol 10.3%. Lowest volatility portfolio. Heavy on gold and S&P because Nifty's volatility is far higher.

Max Sharpe (G71/S13/N16): Sharpe 0.48. Mathematically optimal risk-adjusted return, but 71% gold is impractical for most investors.

Floor maximisation (our approach): We tested all 231 possible 5%-increment allocations against every rolling 7-year window, for both lump-sum and SIP investors. The 35/35/30 blend maximises the combined lump-sum and SIP worst-case floor. This is a different optimisation target than Markowitz: it prioritises "never have a bad 7-year stretch, regardless of how you invest" over "best risk-adjusted average."

Why 35/35/30 instead of the Markowitz optimum

The efficient frontier favours gold-heavy portfolios for risk-adjusted returns. Our 35/35/30 blend sits near the frontier but optimises for a different goal: the highest worst-case floor across both lump-sum and SIP investors. Markowitz minimises average volatility; floor maximisation eliminates the worst historical outcome.

📚 What advisors recommend. Indian financial advisors typically suggest 10-15% gold, 50-65% domestic equity, 10-15% international. Capitalmind's 2024 analysis found up to 32% gold allocation optimal for risk-adjusted returns. The World Gold Council (2026) positions gold as an "anchor" for Indian portfolios. Our 35/35/30 allocates more to gold than the typical recommendation, but the data supports it: gold's low correlation with equities is what lifts the SIP floor from negative territory to +6.8%.

₹1L Lump Sum Invested Jan 2000: Growth to Apr 2026

26 years of compounding. Gold in INR is the runaway winner on absolute lump-sum returns. But look at the S&P 500 line from 2000 to 2009.

The lost decade is clearly visible

The S&P 500 line barely moves from 2000 to 2009. An Indian investor who put ₹1L into the S&P 500 at the dot-com peak waited nearly a decade to break even. Gold and Nifty both multiplied that same investment several times over in the same period. No single asset is safe across all eras, which is why the blend exists.

The evidence points to a 35/35/30 blend. The next question is practical: what should an investor look for when selecting funds, and what hidden costs should they watch for?

Section 3

What to Look for When Picking Funds

The allocation is 35/35/30. Implementing it requires one fund in each of three categories: a gold Fund-of-Funds, an S&P 500 or US total market fund, and a Nifty 50 index fund. The right fund in each category comes down to one thing: total cost of ownership.

S&P 500 / US Equity

35% of allocation

Look for: A Fund-of-Funds or index fund tracking the S&P 500 or US total market.

Key cost factor: FoF structures have two fee layers. The TER shown on your platform is only the wrapper; add the underlying ETF or fund expense to get the true all-in cost.

Cost range (as of Apr 2026): 0.09% to 1.2% all-in.

Watch out: SEBI's overseas investment cap (US $7B industry-wide, US $1B for overseas ETFs) has caused subscription suspensions in the past (Jan 2022, Apr 2024). Fresh purchases may be paused without notice.

Full fund comparison → Appendix A

Nifty 50

30% of allocation

Look for: A direct-plan Nifty 50 index fund with low tracking error.

Key cost factor: Nifty index funds are commoditised; TERs range from 0.06% to 0.20% (as of Apr 2026). The difference is minimal. Tracking error and AUM matter more than a few basis points of TER.

Watch out: Very new funds with short track records may not have reliable tracking data.

Full fund comparison → Appendix B

Gold

35% of allocation

Look for: A gold ETF Fund-of-Funds (the only SIP-friendly gold option on most mutual fund platforms).

Key cost factor: Same two-layer FoF issue as S&P 500 funds. The displayed TER is the wrapper only. True all-in costs range from 0.65% to 1.1% (as of Apr 2026).

Important: No new Sovereign Gold Bond tranches have been issued since Feb 2024. Budget 2026 restricted the capital gains tax exemption on SGBs to original subscribers who hold to maturity (effective 1 Apr 2026). Secondary-market purchasers no longer qualify.

Full fund comparison → Appendix C

💰 The cost trap. On a hypothetical ₹10L investment over 15 years at 12% annual return, the difference between a 0.09% all-in fund and a 0.65% fund compounds to approximately ₹6.1L in foregone corpus. The fund you pick matters almost as much as the asset class. Be especially careful with Fund-of-Funds structures: some display only the wrapper TER on platforms, hiding an underlying ETF layer that can add 0.5% or more. The Appendix tables show the true all-in cost for every fund we compared.

Cost is one risk. Here are five others the data cannot fully address.

Section 4

What This Analysis Cannot Tell You

Intellectual honesty requires stating what the data does not prove. Here are five things that could weaken the case we have made.

1. Backtesting is not forecasting

Twenty-six years is one path through history, not a guarantee of the next twenty-six. The correlations between gold, equities, and the rupee that produced this floor could shift. Research by Bailey, Borwein, Lopez de Prado, and Zhu (2014) found that backtest metrics have near-zero predictive value out of sample. Our blend was not curve-fitted, but it was tested on one dataset.

2. The rupee tailwind may slow

The rupee fell from ₹43.5/$ to ₹93/$, a 2.9%/year depreciation that silently boosted every USD-denominated return in this analysis. The IMF (2025) now classifies the rupee as "crawl-like," confirming the trend. But if India's trade deficit narrows or the RBI loosens its grip, that tailwind weakens, and S&P 500 returns in rupee terms shrink with it.

3. Gold's recent run may not repeat

Central banks bought over 1,000 tonnes of gold in each of 2022, 2023, and 2024, more than double the prior decade's average. That demand surge drove gold's strong recent numbers. Over longer timeframes (Dimson-Marsh-Staunton, 2025), gold's real returns are positive but modest. If central bank buying normalises, gold's 12.8% average in our dataset will look generous.

4. Taxes and regulation cut into real returns

Every number in this analysis is pre-tax. After the 12.5% LTCG applicable across all three asset classes (as of FY 2025-26), the blend's effective floor drops from ~8% to roughly 6.5-7%. Still meaningful, but lower. Separately, SEBI's US $7 billion overseas investment cap has frozen S&P 500 fund purchases twice (January 2022, April 2024). It could happen again without warning.

Tax rates were current as of April 2026. Verify with a qualified tax professional before acting.

5. Rebalancing was not modelled

We assumed a static 35/35/30 split. In practice, disciplined rebalancing (annually or when allocations drift beyond 5 percentage points) has historically improved returns by 35-60 basis points per year while reducing drawdowns (Vanguard, 2024). This omission is conservative: real-world rebalancing would likely raise the floor.

Those caveats acknowledged, here is how investors typically put the allocation into practice.

Section 5

How Investors Typically Implement This

For readers who find the 35/35/30 allocation compelling, here is how it is typically implemented. These are general steps, not personalised advice.

☑ Implementation Guide

Choose a direct-plan mutual fund platform
Several Indian platforms (Kuvera, Groww, Coin by Zerodha, among others) offer direct plans with zero commission. Complete KYC on your chosen platform.
Set up three monthly SIPs
Approximately 35% to a gold FoF, 35% to an S&P 500 or US total market fund, and 30% to a Nifty 50 index fund. The Appendix compares the available options in each category, ranked by total cost of ownership.
Consider annual rebalancing
If any component drifts more than 5 percentage points from its target weight, rebalancing back to the original allocation has historically reduced volatility (Vanguard, 2024). The frequency and method of rebalancing should account for tax implications.
A long-horizon allocation
The historical data covers 7-year holding periods. Short-term fluctuations are expected and are already reflected in the rolling-window analysis. Behavioural finance research consistently shows that less frequent monitoring leads to better investor outcomes (Dalbar, 2024).

Before committing, run the numbers on your own monthly amount.

Section 6

Project Your SIP Corpus

Based on 232 historical 7-year SIP windows. Adjust your monthly investment to see the realistic range of outcomes.

7-Year SIP Outcome Calculator

Ranges based on actual P10-P90 XIRR outcomes across 232 windows. Not a projection. A historically grounded scenario range.

These projections use historical return distributions and do not predict future performance. Your actual returns may be higher or lower than any scenario shown.

Monthly SIP Amount

₹10,000/month

Total invested over 7 years: ₹8.40L P10 = bad start · Mean = historical average · P90 = good start

35/35/30 split: ₹3,500/mo Gold · ₹3,500/mo S&P 500 · ₹3,000/mo Nifty 50

The numbers are in front of you. Twenty-six years of data, 232 independent windows, every possible starting month. No single asset survived every era, but the 35/35/30 blend did. Three SIPs on Kuvera, one annual rebalance, and the discipline to hold for seven years. That is what the historical data supports.

Appendix

Methodology, full allocation results, and detailed fund comparisons.

Data sources: Yahoo Finance monthly close prices for S&P 500 (^GSPC), Gold futures (GC=F), BSE Sensex (^BSESN), Nifty 50 (^NSEI), and USD/INR (INR=X). All retrieved April 2026.

Nifty 50 pre-Sep 2007: Yahoo Finance provides Nifty 50 only from Sep 2007. For Jan 2000 to Aug 2007, we use BSE Sensex × 0.298 (ratio from the 12-month overlap, Sep 2007 to Aug 2008). Correlation exceeds 0.99.

USD/INR pre-Dec 2003: RBI reference rates (World Bank historical data) for Jan 2000 to Nov 2003. Monthly averages, not end-of-month closes.

Gold USD pre-Aug 2000: LBMA London PM Fix for Jan to Jul 2000.

Rolling 7Y CAGR: (End Value / Start Value)^(1/7) − 1, computed monthly.

SIP XIRR: Internal rate of return on monthly ₹10,000 cash flows plus final value, solved via Brent's method (scipy.optimize.brentq).

Portfolio optimisation: Markowitz mean-variance on monthly log returns. Efficient frontier: 100 points. Floor analysis: brute-force over all 5%-increment allocations (231 combinations), each tested against 232 rolling 7-year windows.

Blend portfolio (Chart 5): Annually rebalanced to 35/35/30. Each month's return = 0.35 × Gold + 0.35 × S&P 500 + 0.30 × Nifty, then 7Y CAGR computed from cumulative values.

#	Gold / S&P / Nifty	LS Floor	Mean	SIP Floor
1	25% / 35% / 40%	8.86%	13.19%	5.76%
2	30% / 35% / 35%	8.85%	13.18%	6.29%
3	20% / 35% / 45%	8.84%	13.18%	5.22%
4	15% / 35% / 50%	8.81%	13.15%	4.69%
★	35% / 35% / 30%	8.81%	13.14%	6.83%
6	40% / 35% / 25%	8.76%	13.08%	7.36%
7	20% / 40% / 40%	8.68%	13.01%	4.81%
8	25% / 40% / 35%	8.66%	13.00%	5.36%
9	25% / 30% / 45%	8.56%	13.37%	5.34%
10	30% / 30% / 40%	8.56%	13.37%	5.88%
11	10% / 35% / 55%	8.55%	13.09%	4.16%
12	30% / 40% / 30%	8.55%	12.98%	5.90%
13	20% / 30% / 50%	8.54%	13.35%	4.81%
14	35% / 30% / 35%	8.53%	13.34%	6.41%
15	15% / 40% / 45%	8.51%	12.98%	4.27%
16	15% / 30% / 55%	8.50%	13.31%	4.27%
17	40% / 30% / 30%	8.48%	13.29%	6.94%
18	10% / 30% / 60%	8.43%	13.24%	3.74%
19	35% / 40% / 25%	8.41%	12.93%	6.44%
20	45% / 35% / 20%	8.37%	13.00%	7.16%

35/35/30 (★) selected as optimal: highest combined lump-sum and SIP floor. 25/35/40 has a marginally higher lump-sum floor (+0.05pp) but a significantly lower SIP floor (−1.07pp).

Fund data sourced from Kuvera and AMFI as of April 2026. Verify current TER and availability before investing.

#	Fund (Direct)	What it tracks	Kuvera shows	Hidden layer	True all-in	Verdict
★	Navi US Total Market FoFDirect Growth	Vanguard VTI	0.06%	+0.03%	0.09%	Cheapest
★	Motilal Oswal S&P 500Direct Growth	S&P 500 TRI	0.65%	N/A	0.65%	Pure S&P
3	Mirae S&P 500 Top 50 FoFDirect Growth	Top 50 only	0.11%	+0.61%	0.72%	Two-Layer Fee
4	Franklin India FeederDirect Growth	Russell 3000 Growth	0.61%	~0.5-0.7%	~1.2%	High Cost
—	Axis S&P 500 ETF FoFDirect Growth	S&P 500 (UCITS)	—	—	Unknown	Dormant

Fund data sourced from Kuvera and AMFI as of April 2026. Verify current TER and availability before investing.

#	Fund (Direct)	TER	Track. Error	3Y CAGR	5Y CAGR	AUM	Verdict
★	Nippon India Nifty 50	0.07%	~0.03%	+12.3%	+11.78%	₹3,030 Cr	Best Value
★	Navi Nifty 50 Index	0.06%	~0.01%	+12.3%	N/A*	₹3,573 Cr	Cheapest
3	SBI Nifty Index Fund	0.19%	0.02%	+12.2%	+11.75%	₹11,217 Cr	Large + Tight
4	UTI Nifty 50 Index	0.20%	0.02%	+12.3%	+11.79%	₹24,433 Cr	Best Track Diff

* Navi launched ~2021; no 5Y track record.

⚠️ 2026 SGB update. No new Sovereign Gold Bond tranches since Feb 2024. Budget 2026 removed CGT exemption for secondary-market SGB buyers. Do not buy SGBs on secondary market.

Fund data sourced from Kuvera and AMFI as of April 2026. Verify current TER and availability before investing.

#	Fund (Direct)	Kuvera shows	+ ETF Layer	True all-in	3Y CAGR	5Y CAGR	Verdict
★	SBI Gold Fund FoF	0.10%	+0.64%	0.74%	34.6%	21.1%	Lowest Cost
★	HDFC Gold ETF FoF	0.18%	+0.59%	0.77%	34.7%	25.3%	Best 5Y
3	UTI Gold ETF FoF	0.18%	+0.47%	0.65%	—	—	Cheapest Layer
4	ICICI Pru Gold ETF FoF	0.59%	+0.50%	1.09%	37.3%	25.1%	Best 3Y; Costly
5	Invesco India Gold ETF FoFDirect Growth	0.10%	+0.55%	0.65%	—	—	Low Cost; Small AUM
6	Tata Gold ETF FoFDirect Growth	0.16%	+0.52%	0.68%	—	—	Mid-tier
7	DSP Gold ETF FoFDirect Growth	0.17%	+0.50%	0.67%	—	—	Tiny AUM (₹76 Cr)
8	Zerodha Gold ETF FoFDirect Growth	0.10%	+0.42%	0.52%	—	—	Tiny AUM (₹158 Cr)