Application of Moving Averages in ETF Performance Analysis and Forecasting

Research facts

Data period: January 2015–December 2025
Completed: 2026
Forecast horizon: 2026

What I studied

Compare SMA, EMA and WMA across three UCITS ETFs with different risk profiles and use the results to prepare a 12-month forecast for 2026.

Funds included

SXR8.DE

iShares Core S&P 500 UCITS ETF USD (Acc)

Higher-risk, higher-volatility broad equity-market benchmark.

MVOL.L

iShares Edge MSCI World Minimum Volatility UCITS ETF USD (Acc)

Moderate-risk equity fund with a reduced-volatility approach.

SXRP.DE

iShares Euro Government Bond 3–7yr UCITS ETF (Acc)

Lower-risk, lower-volatility bond-market benchmark.

Moving averages

SMA EMA WMA

The comparison included short-period and long-period crossover strategies, including 3-month and 12-month combinations.

Data and methods

SMA, EMA and WMA
Historical volatility
Crossover-signal analysis
Scenario forecasting

Monthly closing prices covered January 2015 to December 2025. Base, optimistic and cautious scenarios were prepared for a 12-month 2026 forecast. Calculations were performed in Microsoft Excel.

Main findings

No moving-average method worked best for all three funds.
Moving averages were more useful as an additional analysis tool than as a standalone forecasting method.

The best historical strategy differed by fund: WMA(3)/WMA(12) for the S&P 500 fund, EMA(3)/EMA(12) for the minimum-volatility equity fund and SMA(3)/SMA(12) for the euro government bond fund.

Limitations

The study covered only three ETFs.
Transaction costs and bid–ask spreads were not included.

What I would test next

Compare monthly, weekly and daily data.
Combine moving-average signals with RSI or MACD.
Test on a broader out-of-sample dataset.

Data and calculations

I used monthly closing prices from January 2015 to December 2025 for SXR8.DE, MVOL.L and SXRP.DE. All calculations were completed in Microsoft Excel.

For 2026, I prepared base, optimistic and cautious forecast scenarios.