Monthly

Best & Worst of the Month

Every fund ranked by its total return for the month: price change from the last close of the previous month to the last close available, with each distribution reinvested on its ex-date. Gross, in the fund's trading currency.

‹ April 2026 › This month

Median fund, April

+5.0%

36 up · 0 down · 36 funds

Best

+18.1%

FEME · Fidelity Emerging Markets Quality Income UCITS ETF USD Inc ETF

Worst

+0.5%

MMLP · Alerian Midstream Energy Dividend UCITS ETF Dist ETF

Priced to

30 Apr

month closed

Pro

Past months are part of Pro

This month's ranking is free. The archive — every month back to 2018, each fund's total return with the distributions it paid — is for Pro members.

Sign in See the plans

All Covered Call Classic Dividend Core Index Fixed Income

#	Fund	Category	Total return, April	Yield TTM
01	FEME ↗ Fidelity Fidelity Emerging Markets Quality Income UCITS ETF USD Inc ETF	Classic Dividend	+18.1%	3.1%
02	WQDV ↗ iShares iShares MSCI World Quality Dividend Advanced UCITS ETF USD Dist ETF	Classic Dividend	+9.2%	1.4%
03	FUSD ↗ Fidelity Fidelity US Quality Income UCITS ETF Inc ETF	Classic Dividend	+8.8%	1.4%
04	IDWP ↗ iShares iShares Developed Markets Property Yield UCITS ETF USD Dist ETF	Classic Dividend	+8.3%	3.3%
05	FGQI ↗ Fidelity Fidelity Global Quality Income ETF Inc ETF	Classic Dividend	+8.3%	1.7%
06	GLRE ↗ State Street SPDR SPDR Dow Jones Global Real Estate UCITS ETF	Classic Dividend	+8.2%	2.7%

No fund matches this search.

Important information. Master of Yield is an independent research tool. All content on this site, including fund data, screeners, scores, model portfolios and backtests, is provided for information and education only. Nothing here is investment, legal, tax or accounting advice, nor a recommendation or solicitation to buy, sell or hold any financial instrument. We are not a broker, a bank or an authorised investment adviser, and we have no knowledge of your personal circumstances. Any investment decision you make is yours alone: consult a qualified adviser before acting.

Our approach. Our model portfolios follow a purely quantitative, rules-based method focused on income. Funds are selected, ranked and rotated by statistical criteria applied mechanically: price erosion since inception, sustainability of distributions, dividend growth, yield and total return. No discretionary judgement or view on individual companies or markets is involved, and the same rules apply to every fund.

About the backtests. Portfolio results are hypothetical simulations run on historical data, not records of real trades or of any real account. Among other simplifications, they assume every fund could be bought and sold at its month-end price with no spread, commission or market impact; they show figures before tax unless you set your own tax profile under "Taxes", where you choose your country and can adjust every rate and the commission your broker charges per trade — those figures are an estimate, not a tax calculation; and they convert every amount to euro at the daily exchange rate. The fund universe only contains products that exist today, so funds that closed in the past are missing, which tends to flatter results. Many funds have short histories, so some results cover only a few years of a largely rising market. Rules may be refined over time and each refinement is applied to the full history, so past results can change. Past or simulated performance does not guarantee future results, and every investment can lose value, including the capital invested.

About the data. Data is collected from publicly available open sources and third-party providers and is not independently verified. No warranty is given as to its accuracy, completeness or timeliness. Always check figures with the fund provider before acting. Fund reference data (expense ratio, fund size, domicile) is completed with the EU ETF Universe dataset by Danimoth, used under the CC BY 4.0 licence: danimoth.com/dataset.

Terms · Privacy · Cookies · Wiki · Pricing

Intellectual property. Fund names and trademarks belong to their respective owners. Content from this site may not be reproduced or redistributed without written permission.