Gold mining equities provide investors with leveraged exposure to gold prices alongside operational and geopolitical considerations unique to the sector. Themes Gold Miners ETF (AUMI) and Sprott Gold Miners ETF (SGDM) both deliver targeted access to this space but pursue different strategies within the same broad theme. Rather than direct competitors, the funds represent alternative implementations of gold miner exposure—one emphasizing global pure-play market-cap weighting and the other incorporating custom factors within a North American focus. This comparison highlights structural distinctions that may influence suitability for varying investor objectives in the current environment of fluctuating commodity prices and central bank activity.
Themes Gold Miners ETF (AUMI) seeks to track the Solactive Global Pure Gold Miners Index, which selects the largest 30 companies by market capitalization deriving revenues primarily from gold mining. The fund holds approximately 30 securities and allocates nearly all assets to the Materials sector, with primary country exposures in Canada and Australia. Top holdings typically include companies such as Equinox Gold Corp., DPM Metals Inc., and AngloGold Ashanti Plc. AUMI employs a passive, market-capitalization-weighted approach with semi-annual rebalancing and carries an expense ratio of 0.35%. Launched in December 2023, the ETF is non-diversified and provides global exposure through a rules-based methodology focused on pure-play miners.
Sprott Gold Miners ETF (SGDM) tracks the Solactive Gold Miners Custom Factors Index, which targets larger-sized gold companies listed on Canadian and major U.S. exchanges while incorporating factors such as revenue growth, free cash flow yield, and low long-term debt-to-equity ratios. The fund maintains around 49 holdings, with the majority concentrated in North America. Prominent positions often feature Agnico Eagle Mines Limited, Barrick Mining Corporation, and Newmont Corporation. SGDM follows a passive, factor-enhanced strategy reconstituted quarterly and maintains an expense ratio of 0.46%. Established in 2014, the ETF offers greater scale and a longer track record within the Equity Precious Metals category.
The gold mining sector operates within a macroeconomic environment shaped by central bank gold purchases, interest rate expectations, and geopolitical developments. Persistent inflation concerns and diversification efforts by monetary authorities have supported demand for physical gold, indirectly influencing miner equities. Regulatory considerations around mining permits and environmental standards continue to affect operational costs, while capital expenditure cycles among producers influence free cash flow generation. Both ETFs remain sensitive to broader commodity trends and equity market sentiment toward resource sectors, with exposure concentrated in a cyclical industry subject to input cost pressures and reserve depletion dynamics.
In recent market cycles, gold miner ETFs have exhibited sensitivity to gold price movements and sector-specific factors such as production guidance and merger activity among holdings. AUMI’s global pure-play focus may introduce additional volatility from emerging-market or Australian operations, while SGDM’s factor emphasis and North American tilt could moderate exposure to certain regional risks. Over broader timeframes, relative performance has reflected differences in holdings concentration and rebalancing methodology, with SGDM benefiting from larger average market capitalizations among constituents. Investors evaluating positioning often consider liquidity metrics and expense differentials alongside the thematic tailwinds of sustained central bank buying and potential shifts in monetary policy.
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Based on observable structural characteristics, Tickeron’s AI would currently assign a modest probabilistic edge to Themes Gold Miners ETF (AUMI) owing to its lower expense ratio and broader global diversification across pure-play miners. SGDM’s larger scale, longer history, and factor-based selection offer compensating advantages in liquidity and established methodology. Final selection depends on individual preferences for cost efficiency versus proven operational scale within the gold mining theme.
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| AUMI | SGDM | AUMI / SGDM | |
| Gain YTD | 12.886 | 19.058 | 68% |
| Net Assets | 28.1M | 766M | 4% |
| Total Expense Ratio | 0.35 | 0.46 | 76% |
| Turnover | 29.00 | 59.00 | 49% |
| Yield | 1.03 | 1.18 | 88% |
| Fund Existence | 3 years | 12 years | - |
| AUMI | SGDM | |
|---|---|---|
| RSI ODDS (%) | 2 days ago 90% | 2 days ago 90% |
| Stochastic ODDS (%) | 2 days ago 85% | 2 days ago 86% |
| Momentum ODDS (%) | 4 days ago 90% | 2 days ago 90% |
| MACD ODDS (%) | N/A | N/A |
| TrendWeek ODDS (%) | 2 days ago 82% | 2 days ago 87% |
| TrendMonth ODDS (%) | 2 days ago 90% | 2 days ago 90% |
| Advances ODDS (%) | 5 days ago 90% | 5 days ago 90% |
| Declines ODDS (%) | N/A | N/A |
| BollingerBands ODDS (%) | 2 days ago 81% | 2 days ago 87% |
| Aroon ODDS (%) | 2 days ago 90% | 2 days ago 90% |
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| CRYPTO / NAME | Price $ | Chg $ | Chg % |
| AERGO.X | 0.010092 | 0.000518 | +5.41% |
| Aergo cryptocurrency | |||
| GNO.X | 118.602120 | -2.059845 | -1.71% |
| Gnosis cryptocurrency | |||
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| Shentu cryptocurrency | |||
| ACH.X | 0.004937 | -0.000143 | -2.82% |
| Alchemy Pay cryptocurrency | |||
| MC.X | 0.009600 | -0.000402 | -4.02% |
| MixMax cryptocurrency | |||
A.I.dvisor indicates that over the last year, AUMI has been loosely correlated with TXG. These tickers have moved in lockstep 36% of the time. This A.I.-generated data suggests there is some statistical probability that if AUMI jumps, then TXG could also see price increases.
| Ticker / NAME | Correlation To AUMI | 1D Price Change % | ||
|---|---|---|---|---|
| AUMI | 100% | -3.89% | ||
| TXG - AUMI | 36% Loosely correlated | -5.10% | ||
| EQX - AUMI | 2% Poorly correlated | -3.42% | ||
| PRU - AUMI | 2% Poorly correlated | -0.48% | ||
| AGI - AUMI | 0% Poorly correlated | -2.98% | ||
| AU - AUMI | -0% Poorly correlated | -4.40% | ||
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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% | -3.74% | ||
| AEM - SGDM | 96% Closely correlated | -4.27% | ||
| WPM - SGDM | 95% Closely correlated | -2.95% | ||
| NEM - SGDM | 92% Closely correlated | -3.26% | ||
| IAG - SGDM | 92% Closely correlated | -4.41% | ||
| PAAS - SGDM | 91% Closely correlated | -3.68% | ||
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