US Small-Cap Value
Sterling Capital Behavioral Small Cap Value Equity
Last updated: 2026-09-18
The Verdict
Sterling Capital Behavioral Small Cap Value Equity charges an expense ratio of 0.84%. On a $100,000 portfolio, that's $840 per year in fees, and it scales with the amount invested. Supreme.PM's decoded model tracked it at 100% correlation, for a flat monthly subscription that does not change with portfolio size.
Expense Ratio
0.84%
Star Rating
4/ 5
Market Beta
0.94
Model Correlation
100%
Sharpe Ratio
0.57
Max Drawdown
24.04%
Supreme.PM Cost
Flat monthly subscription
Not a percentage of your assets
Performance
Performance comparison — 1Y: Fund 22.0% vs Model 23.8%; 3Y: Fund 62.2% vs Model 73.0%; 5Y: Fund 59.9% vs Model 76.8%.
Alpha After Fees
Supreme.PM's model achieves 100% correlation with Sterling Capital Behavioral Small Cap Value Equity, providing similar exposure at a flat subscription cost.
Fee Analysis
Sterling Capital Behavioral Small Cap Value Equity charges 0.84% 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 Sterling Capital Behavioral Small Cap Value Equity?
Sterling Capital Behavioral Small Cap Value Equity has an expense ratio of 0.84%, 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 Sterling Capital Behavioral Small Cap Value Equity?
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 100% 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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