SGDM and SILJ represent distinct approaches to precious metals equity exposure, both targeting mining companies but with different size, commodity, and factor emphases. SGDM and SILJ do not compete directly as identical products; instead, they offer alternative strategies within the broader gold and silver mining sector. Investors comparing these exchange-traded funds (ETFs) often seek to understand trade-offs between established gold producers and higher-risk junior silver names amid fluctuating commodity prices and economic cycles.
SGDM is a passively managed ETF that seeks to track the Solactive Gold Miners Custom Factors Index. The index applies a transparent, rules-based methodology to select larger-sized gold companies listed on Canadian and major U.S. exchanges, prioritizing those with the highest scores in revenue growth, free cash flow yield, and lowest long-term debt-to-equity ratios. The fund typically holds around 48 securities and rebalances quarterly. Top holdings often include names such as Agnico Eagle Mines Ltd., Barrick Mining Corp., Newmont Corp., Wheaton Precious Metals Corp., and Franco-Nevada Corp. Sector allocation centers predominantly on gold mining (approximately 85%), with smaller allocations to other precious metals. SGDM carries an expense ratio of 0.46%. The ETF structure provides straightforward market exposure without leverage or active management overlays.
SILJ is a passively managed ETF designed to track the Nasdaq Junior Silver Miners Index. The index focuses on smaller companies engaged in silver mining, exploration, and development that derive the majority of revenues from silver-related activities. The fund typically holds around 70 securities, with an emphasis on pure-play junior silver miners. Holdings are concentrated in smaller market-capitalization names globally. SILJ features an expense ratio of 0.69%. The strategy remains rules-based and passive, with no use of leverage or derivatives for enhanced returns. This structure delivers targeted exposure to the junior segment of the silver mining industry.
The precious metals mining sector responds to gold and silver price dynamics, global economic growth prospects, inflation expectations, and monetary policy shifts. Capital flows into the sector often increase during periods of heightened geopolitical uncertainty or when real yields decline. Regulatory developments around mining permits and environmental standards can influence operational costs for producers. Both gold and silver mining equities face risks from commodity price volatility, rising input costs, and labor or supply-chain disruptions. Silver mining, in particular, carries additional sensitivity to industrial demand drivers such as electronics and solar energy, alongside its monetary role.
In recent market cycles, SGDM has generally exhibited lower volatility due to its focus on larger, more established gold producers with stronger balance sheets. SILJ, by contrast, has tended toward higher price swings consistent with its junior silver focus, amplifying movements in silver prices. Relative positioning reflects differing commodity tilts: SGDM benefits more directly from gold price stability and operational efficiency among senior miners, while SILJ captures amplified upside (and downside) from silver price rallies and exploration success. Sector rotation tied to interest rate expectations and broader risk sentiment influences both, though SGDM’s factor emphasis on free cash flow and debt levels provides a buffer in challenging operating environments compared with SILJ’s smaller-cap orientation.
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 seeking data-driven insights into ETFs like SGDM and SILJ may find the tool useful for ongoing market monitoring.
Based on observable structural factors including lower expense ratio, broader diversification across established gold producers, and a quality-oriented factor methodology, Tickeron’s AI would currently assign a higher probability of preference to SGDM over SILJ for investors prioritizing cost efficiency and relative stability within the precious metals sector. SILJ offers distinct value for those seeking concentrated junior silver exposure, though its higher costs and volatility profile introduce additional considerations.
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| SGDM | SILJ | SGDM / SILJ | |
| Gain YTD | 6.018 | 6.397 | 94% |
| Net Assets | 667M | 3.99B | 17% |
| Total Expense Ratio | 0.46 | 0.69 | 67% |
| Turnover | 59.00 | 47.00 | 126% |
| Yield | 1.18 | 2.30 | 51% |
| Fund Existence | 12 years | 14 years | - |
| SGDM | SILJ | |
|---|---|---|
| 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 90% |
| 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 89% |
| BollingerBands ODDS (%) | 1 day ago 87% | 1 day ago 90% |
| Aroon ODDS (%) | 1 day ago 90% | 1 day ago 90% |
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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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% | -2.39% | ||
| PAAS - SILJ | 94% Closely correlated | -9.70% | ||
| CDE - SILJ | 91% Closely correlated | -1.86% | ||
| WPM - SILJ | 91% Closely correlated | -2.24% | ||
| SKE - SILJ | 87% Closely correlated | -1.23% | ||
| OR - SILJ | 86% Closely correlated | -2.67% | ||
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