China Region
Matthews China Small Companies Fund
Last updated: 2026-09-09
The Verdict
Matthews China Small Companies Fund charges an expense ratio of 1.40%. On a $100,000 portfolio, that's $1,400 per year in fees, and it scales with the amount invested. Supreme.PM's decoded model tracked it at 86% correlation, for a flat monthly subscription that does not change with portfolio size.
Expense Ratio
1.40%
Star Rating
4/ 5
Market Beta
1.01
Model Correlation
86%
Sharpe Ratio
-0.24
Max Drawdown
57.22%
Supreme.PM Cost
Flat monthly subscription
Not a percentage of your assets
Performance
Performance comparison — 1Y: Fund 16.8% vs Model 6.4%; 3Y: Fund 42.1% vs Model 39.0%; 5Y: Fund -25.4% vs Model -27.0%.
Alpha After Fees
Supreme.PM's model achieves 86% correlation with Matthews China Small Companies Fund, providing similar exposure at a flat subscription cost.
Fee Analysis
Matthews China Small Companies Fund charges 1.40% 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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Our Methodology
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4-star rated
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3-star rated
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Fidelity China Region Fund
5-star rated
Columbia Greater China Fund
2-star rated
Frequently Asked Questions
What is the expense ratio of Matthews China Small Companies Fund?
Matthews China Small Companies Fund has an expense ratio of 1.40%, 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 Matthews China Small Companies Fund?
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 86% 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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