SCHG vs VONG: Comparing Two Leading Large-Cap Growth ETFs

Key Takeaways

• Both SCHG and VONG are passively managed funds that target large-capitalization U.S. growth stocks, but they track different indexes with meaningfully different portfolio breadth.
• SCHG holds roughly 190 to 197 stocks, while VONG holds roughly 387 to 394 — making VONG the broader, less concentrated vehicle.
• SCHG carries a slightly lower expense ratio (0.04%) versus VONG (0.06%), a small but meaningful long-term cost edge.
• The two funds share nearly identical top holdings — led by NVIDIA, Apple, and Microsoft — resulting in a high degree of return correlation.
• Both are heavily weighted to information technology, so their risk profiles are dominated by mega-cap tech and semiconductor exposure.

Why This Comparison Matters

SCHG and VONG are two of the most widely used, low-cost tools for accessing U.S. large-cap growth exposure. While they compete directly for the same investor dollar, they do so through different underlying indexes and slightly different construction philosophies. This ETF comparison matters now because growth equities — led by a narrow band of mega-cap technology and artificial-intelligence beneficiaries — have dominated recent market cycles, concentrating both opportunity and risk. Understanding the structural differences between SCHG and VONG helps investors evaluate sector exposure, fund performance, and relative positioning before committing capital. I also checked this using Tickeron’s AI Screener to see how the two line up against peers.

A Closer Look at SCHG

SCHG is a passively managed exchange-traded fund that seeks to track the total return of the Dow Jones U.S. Large-Cap Growth Total Stock Market Index. That index selects stocks ranked 1–750 by full market capitalization that are classified as "growth" based on multiple fundamental factors, then weights constituents by float-adjusted market capitalization. The fund typically holds between roughly 190 and 197 positions, giving it a relatively concentrated large-cap profile.

Its top holdings are dominated by mega-cap technology and communication names, including NVDA, AAPL, MSFT, AMZN, GOOGL, AVGO, META, LLY, and TSLA. By sector, information technology represents roughly 47% to 48% of assets, followed by communication services (about 13%), consumer discretionary (about 11%), and healthcare (about 9–10%). SCHG charges an expense ratio of 0.04%, among the lowest in its category, and provides full portfolio transparency.

A Closer Look at VONG

VONG is a passively managed fund that seeks to track the performance of the Russell 1000 Growth Index, which measures the growth segment of the large-cap Russell 1000 universe. Vanguard employs a full-replication approach, holding each index constituent in approximately its index weight. With roughly 387 to 394 holdings, VONG offers substantially broader diversification than SCHG, spanning large- and some mid-cap growth names.

The portfolio is led by NVDA, AAPL, MSFT, GOOGL, AVGO, AMZN, TSLA, META, and LLY. Information technology is VONG's largest sector weight at roughly 51% to 54% of assets, followed by consumer discretionary and communication services. VONG charges an expense ratio of 0.06%. Notably, its single-largest position — NVIDIA — can exceed 12% of assets, a higher concentration than in SCHG.

The Current Market Environment

Both funds are shaped by the same macro and thematic forces. The artificial-intelligence buildout has concentrated investor capital in semiconductor, cloud-computing, and software leaders, amplifying the influence of a handful of mega-cap stocks across growth benchmarks. Interest-rate expectations remain a key swing factor: growth equities are sensitive to the discount-rate environment, and shifts in Federal Reserve policy can drive rapid repricing across the entire growth complex. Sector-specific risks include semiconductor cyclicality, regulatory scrutiny of large technology platforms, and valuation sensitivity when earnings momentum softens. Persistent market concentration in a few names raises both upside potential and drawdown risk for both SCHG and VONG.

How Performance and Positioning Compare

Because their top holdings overlap heavily, SCHG and VONG have exhibited very high return correlation across recent weeks and months, with relative performance differences driven mainly by index construction rather than fundamental divergence. VONG's broader, roughly 390-stock base spreads company-specific risk more widely, while SCHG's leaner portfolio and marginally lower fee have supported slightly stronger cost-adjusted efficiency over time. Both funds carry elevated beta (a measure of volatility relative to the broader market), meaning they tend to amplify both market rallies and declines. In recent market cycles, technology-led leadership has favored both funds, though SCHG's slightly lower concentration in a single mega-cap name can moderate single-stock drawdown risk relative to VONG. From what I see, the overlap makes relative outperformance narrow and often cost-driven.

My Take on the Choice

Based on observable structural factors, Tickeron's AI would likely lean toward SCHG as the more cost-efficient choice. Its lower 0.04% expense ratio compounds into a measurable long-term advantage, while its slightly more balanced sector distribution and less extreme single-name concentration in NVIDIA offer marginally better diversification across the mega-cap growth complex. That said, VONG's broader 390-stock base provides meaningful company-level diversification and remains a compelling option for investors seeking wider large-cap growth coverage. The distinction is narrow and probabilistic; the appropriate choice ultimately depends on whether an investor prioritizes cost and concentration management or maximum breadth.

Using AI Tools in My Research

In my own workflow, I turn to Tickeron’s AI Screener when comparing ETFs like these. The tool lets me quickly scan holdings, sector weights, and performance metrics side by side, which helps surface the small but important differences in cost and concentration that matter over time. It keeps the research process efficient without replacing careful analysis of the underlying indexes.

Disclaimer

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.

Disclaimers and Limitations

Contributor

Scott Bell|Expert

I’m a trader and independent researcher. My interest lies at the intersection of financial markets, algorithms, and capital management. I develop data-driven tools and strategies and study algorithmic approaches to market analysis. I help turn complex market data into clear insights and practical systems. I believe technology should support, not replace, investment thinking

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