These two ETFs do not compete directly. SPY is the world's most widely traded benchmark vehicle for broad U.S. large-cap equity exposure, while NANC offers a niche, politically themed strategy that packages the disclosed transactions of sitting lawmakers into a concentrated, technology-heavy portfolio. They appeal to different investors, yet both ultimately derive their performance from U.S. equities and share several of the same mega-cap technology holdings. Comparing them is useful because it illustrates the trade-off between inexpensive, diversified, market-wide exposure and a higher-cost, concentrated strategy built around a distinctive data source and rebalancing methodology. I also checked this using Tickeron’s AI Screener to see how the two line up on key metrics.
NANC is an actively managed ETF launched in February 2023 and sponsored by Subversive Capital. It seeks long-term capital appreciation by investing primarily in publicly traded companies in which Democratic members of Congress and their families have reported positions through filings required by the Stop Trading on Congressional Knowledge Act (STOCK Act). The fund currently holds roughly 90–100 equities, with assets on the order of $240–290 million.
NANC is heavily concentrated in information technology, which accounts for roughly 44% of the portfolio, followed by communication services (about 13–15%), health care (about 10%), financials (about 8%), and consumer discretionary (about 8–9%). Its largest positions include NVDA (NVIDIA), MSFT (Microsoft), GOOGL/GOOG (Alphabet), AMZN (Amazon), and AAPL (Apple), with additional weight in AMAT (Applied Materials), CRWD (CrowdStrike), PM (Philip Morris International), and AXP (American Express). The fund carries a 0.72% expense ratio and a low portfolio turnover, reflecting a relatively patient, disclosure-driven rebalancing approach.
SPY is a passively managed fund that seeks to replicate the price and yield performance of the S&P 500 Index before expenses. Launched in January 1993, it is structured as a unit investment trust (UIT), a form that requires the fund to hold dividend cash between distributions rather than reinvesting it. SPY holds approximately 500 large-cap U.S. equities spanning all 11 sectors, with total assets well above $800 billion and among the deepest trading liquidity of any exchange-traded product.
Technology is the largest sector allocation at roughly 40%, followed by financials (about 11–13%), consumer discretionary (about 9–11%), communication services (about 9.7%), and health care (about 9%). Its top holdings include NVDA, AAPL, MSFT, AMZN, GOOGL, AVGO (Broadcom), META (Meta Platforms), MU (Micron Technology), and TSLA (Tesla). SPY's expense ratio is just 0.0945%, making it one of the lowest-cost vehicles for broad U.S. large-cap exposure.
Both funds are shaped by the same dominant macro narrative of recent market cycles: the concentration of market leadership in large-cap technology and the influence of artificial intelligence and semiconductor demand on index returns. Interest-rate expectations and Federal Reserve policy remain central catalysts, influencing the valuation of the long-duration growth stocks that anchor both portfolios. For NANC, an additional thematic layer is political and regulatory risk, as its holdings are tied to congressional disclosure activity and can shift with changes in lawmakers' reported positions. For SPY, the broader drivers are earnings cycles across its diversified sector mix, including financials, industrials, energy, and health care, which moderate its technology concentration relative to NANC.
In recent weeks and months, both ETFs have been driven by the same mega-cap technology leadership, though their relative positioning has diverged in important ways. NANC's heavier technology and communication-services weight has amplified its sensitivity to AI-related momentum and semiconductor earnings, while its low energy exposure and concentrated holdings can increase volatility during sector rotations. SPY's broader diversification across financials, health care, and other cyclical sectors has tended to smooth single-stock shocks and provide ballast when growth leadership narrows. Over recent market cycles, NANC's concentrated, disclosure-driven portfolio has shown higher single-name risk, whereas SPY has exhibited a more market-neutral, benchmark-hugging profile with a beta of approximately 1.0. The cost differential also compounds over time, favoring SPY for long-horizon investors. One thing that stands out here is how NANC's approach adds a unique layer of thematic exposure that SPY simply does not attempt to capture.
Based on observable structural factors, Tickeron's AI would most likely favor SPY for the majority of investors. SPY offers superior cost efficiency (0.0945% versus 0.72%), materially broader diversification across roughly 500 holdings, deep liquidity, and a lower-concentration risk profile that reduces exposure to any single stock or sector rotation. NANC's concentrated technology positioning and disclosure-driven strategy can generate strong momentum during AI-led rallies, but it carries higher idiosyncratic and thematic risk. While NANC may appeal to investors seeking a differentiated, politically themed tilt, SPY's consistent, low-cost, broadly diversified exposure represents the more structurally resilient choice for relative positioning across full market cycles.
I frequently use Tickeron’s AI Screener when evaluating ETFs side by side. It helps me apply customizable filters for sectors, performance metrics, and technical signals to quickly compare vehicles like NANC and SPY. This approach has sharpened my view on how concentration and costs play out in real portfolios.
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.
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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The average fundamental analysis ratings, where 1 is best and 100 is worst, are as follows
Category LargeBlend