GET DAILY SIGNALSCATH vs SPY: Weighing a Catholic Values ETF Against the Classic S&P 500 Tracker
Key Takeaways
- CATH and SPY share a common large-cap U.S. equity core, but CATH layers a faith-based exclusion screen over the S&P 500, while SPY replicates the full, unconstrained index.
- SPY carries a materially lower expense ratio (0.09% versus 0.29%) and offers deeper trading liquidity, giving it a structural cost and execution advantage.
- CATH holds roughly 445–450 names versus SPY's approximately 505, and its exclusions produce a lighter technology tilt (about 34% versus 40%) and relatively heavier healthcare, financials, and industrials exposure.
- Both funds are dominated by the same mega-cap leaders, including NVDA, AAPL, and MSFT, so their performance tends to move together over full market cycles.
- Tickeron AI Verdict: SPY — the AI would currently favor SPY for its lower cost, broader diversification, and superior structural efficiency.
Why These Two ETFs Matter for Core Holdings
When building a U.S. large-cap equity allocation, CATH and SPY offer two closely related yet distinct routes. Both draw from the same pool of large American companies, but SPY serves as the standard benchmark for broad S&P 500 exposure while CATH applies values-based exclusions aimed at investors seeking similar core holdings without companies that conflict with Catholic social teaching. Their heavy overlap in top positions means the comparison centers on cost, breadth, and the impact of those ethical screens in the current environment.
Global X S&P 500 Catholic Values ETF (CATH) at a Glance
CATH tracks the S&P 500 Catholic Values Index. It begins with the S&P 500 and removes companies involved in activities inconsistent with Catholic values under the Socially Responsible Investment Guidelines of the United States Conference of Catholic Bishops. The fund typically holds 445 to 450 securities and carries a net expense ratio of 0.29%.
Its largest holdings align closely with the broader market: NVDA, AAPL, MSFT, AMZN, GOOGL, AVGO, GOOG, META, TSLA, and JPM. Technology accounts for roughly 34% of assets, followed by financial services at about 12%, communication services at 10%, consumer cyclical at 10%, healthcare at 9%, and industrials at 8%. The screening process reduces exposure to certain energy, defense, and consumer names, which modestly shifts sector weights compared with the parent index. I also checked sector breakdowns using Tickeron’s AI Screener to confirm how these tilts compare across peers.
SPDR S&P 500 ETF Trust (SPY) at a Glance
SPY is the oldest and largest U.S.-listed ETF, launched in 1993. It operates as a unit investment trust that fully replicates the S&P 500 Index and does not engage in securities lending or intra-portfolio dividend reinvestment. The fund holds approximately 505 securities and charges a net expense ratio of just 0.09%, among the lowest for large-cap blend products.
Its top holdings mirror those of CATH closely: NVDA, AAPL, MSFT, AMZN, GOOGL, AVGO, GOOG, META, MU, and TSLA. Information technology represents about 40% of assets, with financials near 11%, communication services at 10%, healthcare at 9%, and consumer cyclical at 9%. Without any ethical screen, SPY maintains full exposure across all 11 GICS sectors.
Market Environment and Thematic Drivers
Both ETFs operate in a U.S. equity market shaped by a concentrated group of mega-cap technology and artificial intelligence leaders. Names such as NVDA and MSFT have driven recent returns, concentrating performance at the top of each portfolio. Interest-rate expectations, earnings strength in large caps, and capital flows into technology continue to influence positioning. For CATH, the values screen introduces an additional dynamic: when excluded sectors like energy or defense outperform, tracking relative to the parent index can diverge. SPY captures the complete benchmark, including any cyclical or geopolitical tailwinds in those areas.
Performance Patterns and Structural Differences
Given roughly 90% or more overlap in top-weighted holdings, the funds have historically tracked each other closely over full cycles. In recent periods, large-cap technology performance has been the primary driver for both. SPY’s higher technology allocation (around 40% versus CATH’s roughly 34%) has made it slightly more responsive to AI-related momentum, while CATH’s relative emphasis on healthcare, financials, and industrials offers a modest buffer when technology leadership narrows. Volatility profiles remain similar, with both showing comparable beta to the large-cap market. The clearer distinctions appear in costs and liquidity: SPY’s 0.09% expense ratio versus CATH’s 0.29% creates a lasting structural edge, and its trading volume supports tighter spreads.
Integrating Tickeron’s AI Tools Into Portfolio Reviews
When evaluating ETFs like these, I often turn to Tickeron’s AI-powered tools to cross-check sector weights, liquidity metrics, and relative positioning against similar funds. The AI Screener helps surface comparable holdings and technical signals efficiently, while the AI Trend Prediction Engine adds context on momentum factors that can influence near-term behavior. These resources fit naturally into a disciplined research process without replacing fundamental judgment.
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.
Contributor
Sergey Savastiouk, Ph.D. has a degree in Applied Mathematics from Moscow University and has extensive experience as an entrepreneur, investor, manager, and mathematician. His professional expertise is in applied mathematics, mathematical modeling, system and pattern analysis, and software and hardware system integration. He has served as the CEO of several hi-tech start-up companies and nonprofit organizations, which has given him proven capabilities in business strategy for high-tech start-up companies, market assessment, company formation, team building, product development, marketing, and sales. He has published numerous articles in journals and magazines on related fields. As a retail investor, he spent 15 years developing his proprietary trading and quantitative algorithms (now Tickeron’s A.I.), which brought him significant returns in trading the stock market. His current work and goal in founding Tickeron is to bring professional, sophisticated stock market analysis capabilities to retail investors via an easy-to-use interface.
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