India Equity
Eaton Vance Greater India Fund
Last updated: 2026-09-03
The Verdict
Eaton Vance Greater India Fund charges an expense ratio of 1.57%. On a $100,000 portfolio, that's $1,570 per year in fees, and it scales with the amount invested. Supreme.PM's decoded model tracked it at 82% correlation, for a flat monthly subscription that does not change with portfolio size.
Expense Ratio
1.57%
Star Rating
5/ 5
Market Beta
0.85
Model Correlation
82%
Sharpe Ratio
0.49
Max Drawdown
26.32%
Supreme.PM Cost
Flat monthly subscription
Not a percentage of your assets
Performance
Performance comparison — 1Y: Fund -5.3% vs Model -0.4%; 3Y: Fund 20.3% vs Model 37.2%; 5Y: Fund 22.1% vs Model 47.8%.
Alpha After Fees
Supreme.PM's model achieves 82% correlation with Eaton Vance Greater India Fund, providing similar exposure at a flat subscription cost.
Fee Analysis
Eaton Vance Greater India Fund charges 1.57% 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 Eaton Vance Greater India Fund?
Eaton Vance Greater India Fund has an expense ratio of 1.57%, 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 Eaton Vance Greater India 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 82% 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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