US Small-Cap Core
Glenmede Quantitative US Small Cap Equity Portfolio
Last updated: 2026-09-03
The Verdict
Glenmede Quantitative US Small Cap Equity Portfolio charges an expense ratio of 0.85%. On a $100,000 portfolio, that's $850 per year in fees, and it scales with the amount invested. Supreme.PM's decoded model tracked it at 98% correlation, for a flat monthly subscription that does not change with portfolio size.
Expense Ratio
0.85%
Star Rating
4/ 5
Market Beta
0.98
Model Correlation
98%
Sharpe Ratio
0.60
Max Drawdown
26.54%
Supreme.PM Cost
Flat monthly subscription
Not a percentage of your assets
Performance
Performance comparison — 1Y: Fund 38.6% vs Model 36.0%; 3Y: Fund 80.8% vs Model 84.3%; 5Y: Fund 81.6% vs Model 83.8%.
Alpha After Fees
Supreme.PM's model achieves 98% correlation with Glenmede Quantitative US Small Cap Equity Portfolio, providing similar exposure at a flat subscription cost.
Fee Analysis
Glenmede Quantitative US Small Cap Equity Portfolio charges 0.85% of assets annually. Supreme.PM charges a flat monthly subscription instead, so its cost does not rise as a portfolio grows.
Data sources: Refinitiv Lipper, SEC EDGAR Filings, Supreme.PM Analytics
Past performance does not guarantee future results. Model returns are backtested and may not reflect actual trading conditions.
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Frequently Asked Questions
What is the expense ratio of Glenmede Quantitative US Small Cap Equity Portfolio?
Glenmede Quantitative US Small Cap Equity Portfolio has an expense ratio of 0.85%, charged as a percentage of the amount invested. Supreme.PM's decoded model is offered for a flat monthly subscription instead.
How does Supreme.PM replicate Glenmede Quantitative US Small Cap Equity Portfolio?
Supreme.PM builds a transparent, rules-based model of listed securities designed to track this fund's return profile. Over the measured period it tracked the fund closely, at 98% correlation. The model is published in full, so its positions are visible rather than disclosed with a delay.
What are the risks of fund replication?
While our models aim for high correlation, they may not perfectly match the fund's returns due to timing differences in rebalancing, transaction costs, and disclosure delays. Backtested results may not reflect future performance.
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