Global Equity
Qblue Global Sustainable Leaders Fund Class
Last updated: 2026-09-08
The Verdict
Qblue Global Sustainable Leaders Fund Class charges an expense ratio of 0.64%. On a $100,000 portfolio, that's $640 per year in fees, and it scales with the amount invested. Supreme.PM's decoded model tracked it at 91% correlation, for a flat monthly subscription that does not change with portfolio size.
Expense Ratio
0.64%
Market Beta
1.00
Model Correlation
91%
Max Drawdown
8.56%
Supreme.PM Cost
Flat monthly subscription
Not a percentage of your assets
Performance
Performance comparison — 1Y: Fund 12.8% vs Model 14.0%.
Alpha After Fees
Supreme.PM's model achieves 91% correlation with Qblue Global Sustainable Leaders Fund Class, providing similar exposure at a flat subscription cost.
Fee Analysis
Qblue Global Sustainable Leaders Fund Class charges 0.64% 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.
Related
Frequently Asked Questions
What is the expense ratio of Qblue Global Sustainable Leaders Fund Class?
Qblue Global Sustainable Leaders Fund Class has an expense ratio of 0.64%, 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 Qblue Global Sustainable Leaders Fund Class?
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 91% 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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