Investors comparing precious metals mining equities often evaluate Sprott Gold Miners ETF (SGDM) and Global X Silver Miners ETF (SIL) as specialized vehicles within the same broad sector. These ETFs do not compete directly but provide differentiated exposure to gold-focused versus silver-focused mining companies, allowing investors to align portfolios with specific commodity outlooks or risk tolerances. Both follow passive indexing strategies and target the basic materials sector, making them relevant alternatives for thematic allocation in environments shaped by inflation hedging, monetary policy shifts, and industrial demand for precious metals.
Sprott Gold Miners ETF (SGDM) seeks to track the performance of the Solactive Gold Miners Custom Factors Index before fees and expenses. The fund maintains a passive structure with a factor-based selection methodology that emphasizes gold mining companies exhibiting characteristics such as revenue growth, profitability, and balance sheet strength. It typically holds 48-52 securities, with top positions including Agnico Eagle Mines, Barrick Gold, Newmont, Wheaton Precious Metals, and Franco-Nevada. Sector allocation is concentrated at 100% in basic materials. The expense ratio stands at 0.46%. The ETF rebalances according to the underlying index rules, providing systematic exposure without active management intervention.
Global X Silver Miners ETF (SIL) aims to replicate the Solactive Global Silver Miners Total Return Index before fees and expenses. This passive, market-capitalization-weighted fund focuses on companies primarily engaged in silver mining and related activities. It generally comprises 39-40 holdings, with notable positions in Wheaton Precious Metals, Pan American Silver, Coeur Mining, and other silver producers and streamers. Allocation remains fully within the basic materials sector. The expense ratio is 0.65%. Index-driven rebalancing maintains the fund's exposure profile in line with changes in constituent market values.
The precious metals mining sector operates within a macroeconomic environment influenced by gold and silver price movements, interest rate expectations, inflation trends, and geopolitical developments. Capital flows into mining equities often respond to broader risk sentiment and central bank policies, while regulatory considerations around environmental standards and permitting affect operational timelines for producers. Silver mining faces additional demand drivers from industrial applications such as solar energy and electronics, introducing distinct volatility compared to gold-focused operations. Both ETFs position investors for equity participation in these dynamics without direct commodity ownership, highlighting the role of mining company margins, production costs, and reserve management in performance outcomes.
Over recent market cycles, SGDM and SIL have exhibited differentiated behavior tied to gold versus silver price trajectories and sector-specific rotations. SGDM's factor-tilted approach and larger-cap gold producer bias may contribute to relatively steadier performance during periods of gold price stability, while SIL's concentration in silver miners and streamers can amplify movements linked to industrial silver demand and supply constraints. Relative positioning reflects volatility differences stemming from commodity correlations, with gold equities often serving defensive hedging roles and silver equities showing greater sensitivity to economic growth indicators. Both funds demonstrate the impact of top holdings' earnings cycles and broader commodity trends on ETF-level returns across varying market regimes.
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Based on structural characteristics, Tickeron’s AI would currently assign a higher probability of favorability to Sprott Gold Miners ETF (SGDM) due to its lower expense ratio, factor-enhanced diversification across gold mining equities, and broader holdings profile relative to the more concentrated silver-focused approach of Global X Silver Miners ETF (SIL). These elements suggest potential advantages in cost efficiency and risk distribution within the precious metals equity space.
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| SGDM | SIL | SGDM / SIL | |
| Gain YTD | 6.018 | 6.325 | 95% |
| Net Assets | 667M | 4.93B | 14% |
| Total Expense Ratio | 0.46 | 0.65 | 71% |
| Turnover | 59.00 | 27.57 | 214% |
| Yield | 1.18 | 1.38 | 86% |
| Fund Existence | 12 years | 16 years | - |
| SGDM | SIL | |
|---|---|---|
| RSI ODDS (%) | 1 day ago 90% | 1 day ago 90% |
| Stochastic ODDS (%) | 1 day ago 90% | 1 day ago 90% |
| Momentum ODDS (%) | 1 day ago 90% | 1 day ago 87% |
| MACD ODDS (%) | 1 day ago 90% | 1 day ago 90% |
| TrendWeek ODDS (%) | 1 day ago 90% | 1 day ago 90% |
| TrendMonth ODDS (%) | 1 day ago 90% | 1 day ago 90% |
| Advances ODDS (%) | 3 days ago 90% | 3 days ago 90% |
| Declines ODDS (%) | 17 days ago 88% | 17 days ago 87% |
| BollingerBands ODDS (%) | 1 day ago 87% | 1 day ago 90% |
| Aroon ODDS (%) | 1 day ago 90% | 1 day ago 86% |
A.I.dvisor indicates that over the last year, SGDM has been closely correlated with AEM. These tickers have moved in lockstep 96% of the time. This A.I.-generated data suggests there is a high statistical probability that if SGDM jumps, then AEM could also see price increases.
| Ticker / NAME | Correlation To SGDM | 1D Price Change % | ||
|---|---|---|---|---|
| SGDM | 100% | -2.16% | ||
| AEM - SGDM | 96% Closely correlated | -2.60% | ||
| WPM - SGDM | 95% Closely correlated | -2.24% | ||
| NEM - SGDM | 92% Closely correlated | -3.10% | ||
| IAG - SGDM | 92% Closely correlated | -1.77% | ||
| PAAS - SGDM | 92% Closely correlated | -9.70% | ||
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