The semiconductor sector remains central to technological advancement, including artificial intelligence, data centers, and advanced computing. Invesco Semiconductors ETF (PSI) and iShares Semiconductor ETF (SOXX) both deliver focused exposure to this industry but employ different strategies. They do not compete directly with broad-market funds; instead, they serve as complementary or alternative options for investors seeking semiconductor-specific allocation within technology portfolios. The comparison highlights structural distinctions that influence diversification, costs, and positioning amid evolving industry dynamics.
PSI seeks to track the Dynamic Semiconductor Intellidex Index, which applies a proprietary methodology to select approximately 30 U.S. semiconductor companies based on factors including price momentum, earnings momentum, quality, management action, and value. The fund typically holds around 31 securities. Top holdings often include companies such as Applied Materials (AMAT), KLA (KLAC), NVIDIA (NVDA), and Lam Research (LRCX). Sector allocation is concentrated in information technology, with roughly 48% in semiconductors and 49% in semiconductor materials and equipment. The expense ratio stands at 0.56%. As a passively managed ETF with an active index selection process, PSI distinguishes itself through its factor-driven approach rather than pure market-cap weighting.
SOXX aims to track the PHLX Semiconductor Sector Index (or equivalent ICE Semiconductor Sector Index), a modified market-capitalization-weighted benchmark of U.S.-listed equities in the semiconductor sector. The fund generally holds 31 to 34 securities. Prominent holdings include NVIDIA (NVDA), Micron Technology (MU), Advanced Micro Devices (AMD), Broadcom (AVGO), and Intel (INTC). Nearly all assets are allocated to information technology, with approximately 78% in semiconductors and 22% in semiconductor equipment. The expense ratio is 0.35%. SOXX operates as a standard passive ETF, providing straightforward exposure to leading semiconductor firms through its cap-weighted methodology.
The semiconductor industry benefits from sustained demand driven by artificial intelligence infrastructure buildout, cloud computing expansion, and electrification trends. Capital expenditures by major technology firms and government initiatives supporting domestic chip manufacturing represent ongoing catalysts. Macroeconomic factors such as interest rate environments and global supply chain developments influence capital flows into the sector. Risks include cyclical demand fluctuations, geopolitical tensions affecting supply chains, and potential regulatory scrutiny on technology exports or competition. Both ETFs operate within this environment, where innovation cycles and capital investment patterns shape relative performance across market cycles.
In recent market cycles, semiconductor ETFs have exhibited sensitivity to earnings reports from key players and broader technology spending patterns. PSI’s factor-based selection may emphasize companies demonstrating momentum or quality characteristics, potentially leading to differentiated returns during periods of sector rotation. SOXX’s market-cap weighting typically results in greater influence from the largest firms, which can amplify exposure to high-growth leaders but also increase volatility tied to those names. Relative positioning often reflects differences in top holdings concentration and rebalancing frequency inherent to each index. Investors evaluate these characteristics against their views on earnings cycles, interest rate expectations, and technological adoption trends.
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.
Based on structural factors including lower expense ratio, established market-cap methodology, and strong liquidity profile, Tickeron’s AI would currently assign a higher probability of favor to SOXX for investors prioritizing cost efficiency and broad semiconductor leader exposure. PSI’s factor-driven approach offers potential differentiation but at a higher cost, making the choice dependent on specific investor preferences for selection methodology versus efficiency in the semiconductor thematic space.
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| PSI | SOXX | PSI / SOXX | |
| Gain YTD | 67.160 | 69.077 | 97% |
| Net Assets | 2.38B | 41.7B | 6% |
| Total Expense Ratio | 0.56 | 0.34 | 165% |
| Turnover | 105.00 | 27.00 | 389% |
| Yield | 0.04 | 0.29 | 12% |
| Fund Existence | 21 years | 25 years | - |
| PSI | SOXX | |
|---|---|---|
| RSI ODDS (%) | 3 days ago 85% | 3 days ago 90% |
| Stochastic ODDS (%) | 3 days ago 90% | 3 days ago 90% |
| Momentum ODDS (%) | 3 days ago 85% | 3 days ago 87% |
| MACD ODDS (%) | 3 days ago 87% | 3 days ago 90% |
| TrendWeek ODDS (%) | 3 days ago 85% | 3 days ago 87% |
| TrendMonth ODDS (%) | 3 days ago 90% | 3 days ago 90% |
| Advances ODDS (%) | 4 days ago 88% | 4 days ago 88% |
| Declines ODDS (%) | 7 days ago 83% | 7 days ago 85% |
| BollingerBands ODDS (%) | 4 days ago 90% | 4 days ago 90% |
| Aroon ODDS (%) | 3 days ago 90% | 3 days ago 90% |
| 1 Day | |||
|---|---|---|---|
| ETFs / NAME | Price $ | Chg $ | Chg % |
| MSD | 7.29 | 0.01 | +0.14% |
| Morgan Stanley Emerging Markets Debt Fund | |||
| CGMS | 27.17 | -0.06 | -0.22% |
| Capital Group U.S. Multi-Sector Inc ETF | |||
| XTR | 29.06 | -0.07 | -0.23% |
| Global X S&P 500® Tail Risk ETF | |||
| ITDH | 43.25 | -0.16 | -0.37% |
| iShares LifePath Target Date 2060 ETF | |||
| EZA | 70.72 | -0.84 | -1.17% |
| iShares MSCI South Africa ETF | |||
A.I.dvisor indicates that over the last year, PSI has been closely correlated with ONTO. These tickers have moved in lockstep 86% of the time. This A.I.-generated data suggests there is a high statistical probability that if PSI jumps, then ONTO could also see price increases.
| Ticker / NAME | Correlation To PSI | 1D Price Change % | ||
|---|---|---|---|---|
| PSI | 100% | -4.87% | ||
| ONTO - PSI | 86% Closely correlated | -7.46% | ||
| TER - PSI | 84% Closely correlated | -4.59% | ||
| SYNA - PSI | 83% Closely correlated | -2.57% | ||
| ACLS - PSI | 81% Closely correlated | -5.58% | ||
| GFS - PSI | 77% Closely correlated | -3.41% | ||
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