Investors evaluating large-cap U.S. equity strategies often compare factor-focused exchange-traded funds (ETFs) that slice the market by style or characteristic. The iShares S&P 500 Growth ETF (IVW) and the Invesco S&P 500® Momentum ETF (SPMO) both draw from the S&P 500 universe but apply distinct selection criteria—one centered on growth attributes and the other on price momentum. These ETFs do not compete directly as identical products; instead, they offer complementary or alternative exposures for investors pursuing growth-oriented outcomes through different quantitative lenses. The comparison highlights structural distinctions that influence risk, cost, and positioning amid evolving market conditions.
The iShares S&P 500 Growth ETF (IVW) is a passively managed fund that seeks to track the S&P 500 Growth Index. This index selects large-capitalization U.S. stocks exhibiting strong growth characteristics, measured by metrics such as earnings and revenue growth. The ETF typically holds approximately 150 securities, with notable concentration in the top holdings that frequently include leading technology companies. Sector allocations place the heaviest weight in Information Technology (around 52–53%), followed by Communication Services (approximately 15%) and Financials (near 9%). The expense ratio stands at 0.18%. As an open-end passive ETF, IVW employs full replication or optimized sampling and rebalances in line with index methodology, providing straightforward exposure to the growth segment of the S&P 500 without leverage or active management overlays.
The Invesco S&P 500® Momentum ETF (SPMO) is a passively managed fund designed to track the S&P 500 Momentum Index. This index identifies approximately 100 stocks from the S&P 500 with the strongest recent price momentum. The ETF maintains a more concentrated portfolio of roughly 100 holdings and applies quarterly rebalancing to reflect changes in momentum rankings. Sector weights emphasize Information Technology (around 51–52%), with elevated allocations to Industrials (near 13%) and Communication Services (approximately 8–9%). SPMO carries an expense ratio of 0.13%. Structured as an open-end passive ETF, it uses a rules-based approach without leverage or derivatives overlays, delivering targeted exposure to momentum factors within large-cap U.S. equities.
Both ETFs operate within the large-cap U.S. equity market, where Information Technology and Communication Services sectors have driven much of the index performance in recent cycles. Macroeconomic factors such as interest rate expectations, corporate earnings growth in technology leaders, and shifts in capital flows toward high-growth or trending names influence positioning. Regulatory developments around antitrust scrutiny of large technology firms and evolving monetary policy remain relevant considerations. Sector risks include valuation compression during periods of rising rates or economic slowdowns, while catalysts encompass continued innovation in artificial intelligence, cloud computing, and semiconductor demand. These dynamics affect growth and momentum strategies differently depending on the persistence of trends versus fundamental earnings trajectories.
In recent weeks and months, relative performance between the two ETFs has reflected broader sector rotation and momentum persistence. IVW’s growth orientation has aligned with sustained leadership in mega-cap technology names during periods of favorable earnings outlooks. SPMO’s momentum filter has produced varying tilts, occasionally boosting exposure to names showing accelerating price trends across Industrials and other cyclical areas. Volatility differences arise from SPMO’s narrower holdings universe and higher sensitivity to short-term trend reversals compared with IVW’s broader growth screen. Both have participated in large-cap rallies driven by technology earnings cycles, yet SPMO’s methodology introduces potential for quicker adjustments during market regime shifts. Investors monitoring interest rate paths and earnings momentum can observe how these structural features translate into relative positioning over market cycles.
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. AI Screener
Based on observable structural factors, Tickeron’s AI would currently assign a modest probabilistic preference to SPMO. The lower expense ratio, concentrated momentum methodology, and adaptability to prevailing price trends provide potential efficiency advantages within the current large-cap environment. IVW remains a strong alternative for investors prioritizing broader growth exposure and slightly greater diversification across growth characteristics. Selection ultimately depends on an investor’s specific risk tolerance, time horizon, and views on momentum persistence versus sustained earnings growth.
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.
| IVW | SPMO | IVW / SPMO | |
| Gain YTD | 13.428 | 27.019 | 50% |
| Net Assets | 77.4B | 21.7B | 357% |
| Total Expense Ratio | 0.18 | 0.13 | 138% |
| Turnover | 22.00 | 44.00 | 50% |
| Yield | 0.37 | 0.73 | 50% |
| Fund Existence | 26 years | 11 years | - |
| IVW | SPMO | |
|---|---|---|
| RSI ODDS (%) | 1 day ago 81% | 1 day ago 90% |
| Stochastic ODDS (%) | 1 day ago 76% | 1 day ago 80% |
| Momentum ODDS (%) | 1 day ago 81% | 1 day ago 82% |
| MACD ODDS (%) | 1 day ago 88% | 1 day ago 85% |
| TrendWeek ODDS (%) | 1 day ago 79% | 1 day ago 83% |
| TrendMonth ODDS (%) | 1 day ago 88% | 1 day ago 84% |
| Advances ODDS (%) | 6 days ago 84% | 5 days ago 82% |
| Declines ODDS (%) | 1 day ago 78% | 8 days ago 76% |
| BollingerBands ODDS (%) | 1 day ago 88% | 1 day ago 85% |
| Aroon ODDS (%) | N/A | 1 day ago 72% |
| 1 Day | |||
|---|---|---|---|
| ETFs / NAME | Price $ | Chg $ | Chg % |
| JHAC | 15.77 | N/A | N/A |
| JHancock Fundamental All Cap Core ETF | |||
| QIDX | 11.98 | N/A | N/A |
| Indexperts Quality Earnings Focused ETF | |||
| MPA | 11.08 | -0.01 | -0.09% |
| Blackrock Muniyield Pennsylvania Quality Fund | |||
| CSRE | 28.93 | -0.05 | -0.17% |
| Cohen & Steers Real Estate Active ETF | |||
| DON | 58.22 | -0.33 | -0.56% |
| WisdomTree US MidCap Dividend ETF | |||
A.I.dvisor indicates that over the last year, IVW has been closely correlated with RVTY. These tickers have moved in lockstep 68% of the time. This A.I.-generated data suggests there is a high statistical probability that if IVW jumps, then RVTY could also see price increases.
| Ticker / NAME | Correlation To IVW | 1D Price Change % | ||
|---|---|---|---|---|
| IVW | 100% | -1.28% | ||
| RVTY - IVW | 68% Closely correlated | -2.08% | ||
| TER - IVW | 63% Loosely correlated | -8.77% | ||
| ETN - IVW | 61% Loosely correlated | -5.29% | ||
| MS - IVW | 59% Loosely correlated | -0.30% | ||
| SWKS - IVW | 56% Loosely correlated | -0.44% | ||
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A.I.dvisor indicates that over the last year, SPMO has been closely correlated with ETN. These tickers have moved in lockstep 71% of the time. This A.I.-generated data suggests there is a high statistical probability that if SPMO jumps, then ETN could also see price increases.
| Ticker / NAME | Correlation To SPMO | 1D Price Change % | ||
|---|---|---|---|---|
| SPMO | 100% | -2.75% | ||
| ETN - SPMO | 71% Closely correlated | -5.29% | ||
| GLW - SPMO | 71% Closely correlated | -7.68% | ||
| PWR - SPMO | 66% Loosely correlated | -3.62% | ||
| CMI - SPMO | 65% Loosely correlated | -3.17% | ||
| GEV - SPMO | 62% Loosely correlated | -6.90% | ||
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