ICOP and XME both target the metals and mining space, yet they address distinct investor objectives within the sector. ICOP delivers concentrated exposure to global copper and metals mining equities, appealing to those seeking thematic bets on copper demand. XME provides broader U.S. metals and mining coverage across multiple sub-industries. These ETFs do not compete directly but serve as complementary or alternative tools for investors pursuing sector exposure, with differences in geographic reach, concentration, and weighting methodologies shaping their risk and return profiles in varying market environments.
The iShares Copper and Metals Mining ETF (ICOP) is a passively managed fund that seeks to track the STOXX Global Copper and Metals Mining Index (Net). It holds approximately 45 securities, primarily companies deriving significant revenue from copper and metal ore mining. Top holdings typically include BHP Group Ltd, Anglo American PLC, Grupo Mexico B, Freeport-McMoRan Inc, and Teck Resources, accounting for a substantial portion of assets. Sector allocation is overwhelmingly concentrated in materials at over 99%. The expense ratio stands at 0.47%. The fund employs physical replication and features global geographic diversification, with notable weightings in Canada, the United Kingdom, and Australia. It launched in June 2023 and maintains a semi-annual distribution schedule.
The SPDR S&P Metals & Mining ETF (XME) is a passively managed fund designed to track the S&P Metals and Mining Select Industry Index. It typically holds 37 to 39 securities representing the metals and mining segment of the S&P Total Market Index, spanning sub-industries including copper, gold, silver, steel, aluminum, and coal. The index uses a modified equal-weighted methodology with quarterly rebalancing to maintain balance and liquidity. The expense ratio is 0.35%. Geographic focus remains on U.S. companies, resulting in broader diversification across market capitalizations compared to concentrated thematic peers. The fund launched in June 2006 and distributes dividends quarterly.
The metals and mining sector continues to respond to long-term structural demand drivers such as electrification, renewable energy infrastructure, and data center expansion, which support copper and other industrial metals. Macroeconomic factors including interest rate expectations, global industrial production trends, and supply constraints from major producing regions influence capital flows into mining equities. Regulatory developments around permitting, environmental standards, and trade policies add layers of complexity. Both ETFs operate within this environment, where commodity price cycles and sector rotation between precious and industrial metals can affect relative performance across different market regimes.
In recent market cycles, ICOP’s concentrated copper focus has aligned it closely with copper price movements and global industrial demand shifts, potentially leading to higher volatility tied to specific commodity trends. XME’s equal-weighted, multi-subsector U.S. approach has provided exposure to a wider range of metals prices and company fundamentals, resulting in different sensitivity to gold, steel, and coal cycles versus pure copper plays. Over broader timeframes, relative positioning has reflected sector rotation patterns, with ICOP exhibiting greater thematic purity and XME demonstrating more balanced participation across the mining value chain. Liquidity profiles differ due to asset size and trading volumes, though both remain accessible for most investors.
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 seeking to compare or monitor funds like ICOP and XME may find the platform useful for ongoing analysis.
Based on observable structural factors, Tickeron’s AI would currently assign a modest probabilistic preference to XME. The lower expense ratio, equal-weighted diversification across multiple sub-sectors, and established track record since 2006 contribute to a more balanced risk profile and cost efficiency. ICOP’s thematic concentration offers distinct advantages for investors with specific copper views, yet XME’s broader positioning and lower fees align with durable characteristics that support relative resilience across varying market conditions.
The information on this webpage is provided for general informational and educational purposes only and is not intended as investment advice, a recommendation to purchase or sell any security, or an offer or solicitation related to investments. It does not consider your personal financial situation, goals, or risk profile, and all investing carries inherent risks, including the possibility of losing your entire investment. For more details, please review our full disclaimer.
| ICOP | XME | ICOP / XME | |
| Gain YTD | 29.989 | 14.636 | 205% |
| Net Assets | 499M | 4.87B | 10% |
| Total Expense Ratio | 0.47 | 0.35 | 134% |
| Turnover | 24.00 | 48.00 | 50% |
| Yield | 1.54 | 0.32 | 487% |
| Fund Existence | 3 years | 20 years | - |
| ICOP | XME | |
|---|---|---|
| RSI ODDS (%) | 4 days ago 83% | 4 days ago 81% |
| Stochastic ODDS (%) | 4 days ago 81% | 4 days ago 85% |
| Momentum ODDS (%) | 4 days ago 90% | 4 days ago 84% |
| MACD ODDS (%) | 4 days ago 89% | 4 days ago 83% |
| TrendWeek ODDS (%) | 4 days ago 81% | 4 days ago 86% |
| TrendMonth ODDS (%) | 4 days ago 87% | 4 days ago 90% |
| Advances ODDS (%) | 5 days ago 90% | 28 days ago 90% |
| Declines ODDS (%) | 7 days ago 84% | 7 days ago 87% |
| BollingerBands ODDS (%) | 4 days ago 81% | 4 days ago 90% |
| Aroon ODDS (%) | 4 days ago 88% | 4 days ago 90% |
A.I.dvisor indicates that over the last year, ICOP has been closely correlated with RIO. These tickers have moved in lockstep 85% of the time. This A.I.-generated data suggests there is a high statistical probability that if ICOP jumps, then RIO could also see price increases.
| Ticker / NAME | Correlation To ICOP | 1D Price Change % | ||
|---|---|---|---|---|
| ICOP | 100% | -0.98% | ||
| RIO - ICOP | 85% Closely correlated | +0.42% | ||
| MTAL - ICOP | 61% Loosely correlated | N/A | ||
| AAL - ICOP | 29% Poorly correlated | +1.23% | ||
| TKO - ICOP | 15% Poorly correlated | -1.80% | ||
| BHP - ICOP | -1% Poorly correlated | -0.34% | ||
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A.I.dvisor indicates that over the last year, XME has been closely correlated with CDE. These tickers have moved in lockstep 77% of the time. This A.I.-generated data suggests there is a high statistical probability that if XME jumps, then CDE could also see price increases.