The silver mining sector attracts investor attention amid fluctuating precious metals prices, industrial demand, and macroeconomic factors such as interest rates and inflation hedging. SIL and SILJ both deliver targeted equity exposure to silver-related companies but pursue distinct strategies within the same thematic universe. They do not compete head-to-head; instead, they offer alternative entry points for investors seeking silver sector participation, with SIL emphasizing larger, more established miners and SILJ concentrating on junior operators that carry higher operational and financial risk profiles.
The Global X Silver Miners ETF (SIL) tracks the Solactive Global Silver Miners Total Return Index, which measures the performance of global companies primarily engaged in silver mining activities. The fund holds approximately 40 securities and employs a passive, market-capitalization-weighted approach with periodic rebalancing. Top holdings include Wheaton Precious Metals (WPM) at roughly 22%, Pan American Silver (PAAS) near 12%, and Coeur Mining (CDE) around 11%, reflecting a tilt toward larger producers and royalty/streaming companies. Sector allocation is overwhelmingly concentrated in basic materials, exceeding 99%. The expense ratio stands at 0.65%, and the ETF structure is fully transparent and physically replicated without leverage or derivatives overlays.
The Amplify Junior Silver Miners ETF (SILJ) seeks to replicate the performance of an index composed of small- and mid-cap companies actively involved in silver exploration, development, and production. It maintains approximately 70 holdings and follows a passive indexing methodology with regular rebalancing. Leading positions feature Hecla Mining (HL) near 9.5%, First Majestic Silver (AG) at about 9.3%, and KGHM Polska Miedz (KGH) around 5%, underscoring exposure to smaller, development-stage operators. Allocation remains almost entirely within basic materials. The expense ratio is 0.69%, and the fund operates as a straightforward equity ETF without active management, leverage, or inverse strategies.
The silver mining industry operates at the intersection of precious metals investment demand and industrial applications, including electronics, solar energy, and medical uses. Macroeconomic drivers such as real interest rate expectations, U.S. dollar strength, and global economic growth influence capital flows into the sector. Recent market cycles have highlighted silver’s dual role as both a monetary asset and an industrial commodity, creating volatility tied to supply constraints from primary silver mines and byproduct output from base metals operations. Regulatory developments around mining permits and environmental standards, along with geopolitical factors affecting major producing regions, continue to shape sector risks and opportunities for both established and junior producers.
Over recent market cycles, SIL has generally exhibited more moderate volatility compared with SILJ, reflecting its allocation to larger, more financially stable mining firms with established production profiles. SILJ tends to amplify silver price movements due to its emphasis on junior companies that often carry higher leverage to commodity prices and greater sensitivity to financing conditions. In environments of rising silver prices and favorable sector rotation, SILJ has historically delivered stronger upside participation, while SIL has provided relatively steadier exposure during periods of consolidation or macroeconomic uncertainty. Relative positioning highlights SIL as a core holding for diversified sector exposure and SILJ as a satellite allocation for investors seeking higher-beta participation in silver mining equities.
Tickeron’s AI Screener is an AI-powered stock and ETF discovery tool that helps traders and investors filter the market based on technical patterns, fundamentals, trends, volatility, and AI-driven signals. Users can scan thousands of stocks and ETFs using customizable filters such as industry, market capitalization, technical indicators, price patterns, and performance metrics. The screener helps identify trade ideas, trending stocks, breakout candidates, and market opportunities more efficiently than manual screening. Investors interested in silver mining exposure or sector-specific ideas may find the tool useful for refining their research process.
Based on observable structural factors including lower expense ratio, broader representation of established producers, and favorable cost-efficiency profile, Tickeron’s AI would currently assign a modest probabilistic preference to the Global X Silver Miners ETF (SIL) for most investors seeking silver mining exposure. The fund’s combination of competitive fees and diversified holdings within the senior segment supports more consistent positioning across varying market regimes, though individual results depend on specific risk parameters and portfolio objectives.
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| SIL | SILJ | SIL / SILJ | |
| Gain YTD | 6.109 | 5.313 | 115% |
| Net Assets | 4.74B | 3.86B | 123% |
| Total Expense Ratio | 0.65 | 0.69 | 94% |
| Turnover | 27.57 | 47.00 | 59% |
| Yield | 1.38 | 2.30 | 60% |
| Fund Existence | 16 years | 14 years | - |
| SIL | SILJ | |
|---|---|---|
| RSI ODDS (%) | 4 days ago 89% | 4 days ago 90% |
| Stochastic ODDS (%) | 4 days ago 89% | 4 days ago 90% |
| Momentum ODDS (%) | 4 days ago 90% | 4 days ago 90% |
| MACD ODDS (%) | 4 days ago 90% | 4 days ago 90% |
| TrendWeek ODDS (%) | 4 days ago 90% | 4 days ago 90% |
| TrendMonth ODDS (%) | 4 days ago 90% | 4 days ago 90% |
| Advances ODDS (%) | 6 days ago 90% | 6 days ago 90% |
| Declines ODDS (%) | 13 days ago 87% | 13 days ago 89% |
| BollingerBands ODDS (%) | 4 days ago 90% | 4 days ago 90% |
| Aroon ODDS (%) | 4 days ago 87% | 4 days ago 90% |
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A.I.dvisor indicates that over the last year, SIL has been closely correlated with PAAS. These tickers have moved in lockstep 95% of the time. This A.I.-generated data suggests there is a high statistical probability that if SIL jumps, then PAAS could also see price increases.
| Ticker / NAME | Correlation To SIL | 1D Price Change % | ||
|---|---|---|---|---|
| SIL | 100% | +6.29% | ||
| PAAS - SIL | 95% Closely correlated | +6.60% | ||
| WPM - SIL | 94% Closely correlated | +7.10% | ||
| CDE - SIL | 90% Closely correlated | +11.12% | ||
| OR - SIL | 88% Closely correlated | +2.89% | ||
| VZLA - SIL | 81% Closely correlated | +5.95% | ||
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A.I.dvisor indicates that over the last year, SILJ has been closely correlated with PAAS. These tickers have moved in lockstep 94% of the time. This A.I.-generated data suggests there is a high statistical probability that if SILJ jumps, then PAAS could also see price increases.
| Ticker / NAME | Correlation To SILJ | 1D Price Change % | ||
|---|---|---|---|---|
| SILJ | 100% | +6.04% | ||
| PAAS - SILJ | 94% Closely correlated | +6.60% | ||
| CDE - SILJ | 91% Closely correlated | +11.12% | ||
| WPM - SILJ | 91% Closely correlated | +7.10% | ||
| SKE - SILJ | 87% Closely correlated | +5.44% | ||
| OR - SILJ | 87% Closely correlated | +2.89% | ||
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