Beta (β) is the slope coefficient from a linear regression of the ETF's monthly returns against the benchmark's monthly returns over 3 years (36 observations). Formula: β = Cov(RETF, Rbenchmark) / Var(Rbenchmark). For UK ETF tools, the benchmark default is MSCI ACWI (in GBP) for global ETFs, FTSE 100 (in GBP) for UK equity ETFs, and asset-class-appropriate for everything else. Beta ranges from negative (rare, inverse exposure) to highly positive (>2 for leveraged products). A beta near 1.0 means market-like sensitivity. Beta is useful for portfolio construction (knowing how much market exposure you're getting), not as a quality score.
The formula
Beta is the regression slope coefficient:
β = Cov(RETF, Rbenchmark) / Var(Rbenchmark) where: Cov(X, Y) = Σ[(Xi − X̄)(Yi − Ȳ)] / (n − 1) Var(X) = Σ[(Xi − X̄)²] / (n − 1) RETF = monthly return of the ETF, period i Rbenchmark= monthly return of the benchmark, period i X̄ = sample mean of X n = number of observations (36 for 3-year monthly)
Equivalently: Beta is the slope of the line in a scatter plot of ETF returns (y-axis) vs benchmark returns (x-axis) over the lookback period.
Inputs we use
| Input | Source | Frequency / window |
|---|---|---|
| ETF monthly returns | Issuer factsheet + LSE end-of-day close, cross-checked | 36 months (3 years) |
| Benchmark monthly returns | Index provider (MSCI / FTSE Russell / S&P Dow Jones) | 36 months (3 years) |
| Currency | Both series converted to GBP via Bank of England daily spot | Month-end FX |
| Total return basis | Distributions reinvested at ex-div date | Always TR, not price-return |
Benchmark choice — the critical decision
The same ETF can have wildly different beta values against different benchmarks. We use this benchmark-by-asset-class default:
| ETF type | Default benchmark | Why |
|---|---|---|
| Global equity (e.g. VWRL) | MSCI ACWI (GBP TR) | Closest match — global all-cap, all-country |
| UK equity (e.g. VUKE) | FTSE 100 (GBP TR) or FTSE All-Share for broader | Native UK benchmark |
| US equity (e.g. VUSA) | S&P 500 (GBP TR — for UK investor) | Native US benchmark |
| European equity | MSCI Europe ex-UK (GBP TR) | Excludes UK to avoid double-counting |
| Emerging market | MSCI Emerging Markets (GBP TR) | Native EM benchmark |
| Bond — global aggregate | Bloomberg Global Aggregate (GBP-hedged) | Standard ballast benchmark |
| Bond — UK gilts | FTSE Actuaries UK Gilts All Stocks | Standard UK gilt benchmark |
| Smart beta / factor | Underlying parent index (e.g. MSCI World for World-Quality) | Like-for-like comparison |
| Sector / thematic | Closest sector benchmark + market benchmark for cross-check | Disclosed on tool |
When you see a beta on UK Tax Drag, the benchmark used is always disclosed in the same row or panel. If you want beta against a different benchmark, the per-tool methodology explains how to recompute.
Worked example — VUSA against S&P 500 (GBP)
Computing 3-year monthly beta for Vanguard S&P 500 UCITS ETF (VUSA) against S&P 500 GBP Total Return Index. Lookback: 36 months ending April 2026.
| Month | VUSA monthly return (%) | S&P 500 GBP TR monthly return (%) |
|---|---|---|
| May 2023 | +1.42 | +1.40 |
| Jun 2023 | +5.85 | +5.92 |
| Jul 2023 | +2.95 | +2.99 |
| ... (34 more rows) ... | ... | ... |
| Apr 2026 | +1.85 | +1.89 |
Step 1 — Calculate sample means: VUSA mean = 0.95% / month; S&P 500 GBP TR mean = 0.96%.
Step 2 — Calculate covariance: Cov(VUSA, S&P) = 0.00154 (in decimal returns).
Step 3 — Calculate benchmark variance: Var(S&P) = 0.00155.
Step 4 — Beta = 0.00154 / 0.00155 = 0.993.
VUSA's beta vs S&P 500 GBP TR is approximately 1.00 — exactly what we'd expect for an S&P 500 tracker with low tracking error. The 0.007 difference from exact 1.00 is OCF drag + small sampling differences.
How to reproduce this yourself
- Download VUSA monthly prices from Vanguard UK or Yahoo Finance (ticker VUSA.L) for 36+ months.
- Download S&P 500 Total Return index data — easiest source is spindices.com. Convert to GBP using Bank of England daily spot rates.
- In Excel or Google Sheets, calculate monthly returns: =(Pt − Pt-1)/Pt-1.
- Use SLOPE(VUSA_returns, SP500_returns). Result is beta.
- Alternative: =COVARIANCE.S(VUSA_returns, SP500_returns) / VAR.S(SP500_returns).
Cross-check: VUSA's own factsheet often publishes beta or implies it via "tracking" stats. Numbers should match within ~5%.
What beta tells you
- β = 1.0: moves in line with the benchmark.
- β > 1.0: amplifies benchmark moves. A 1.3 beta on a 10% market rise = ~13% expected ETF rise. Same on the downside.
- β < 1.0: dampens benchmark moves. Defensive sectors, bond-heavy products, low-volatility ETFs.
- β ≈ 0: uncorrelated with benchmark. Commodities, alternatives, currency hedges.
- β < 0: moves opposite to benchmark. Inverse ETFs, some bond ETFs vs equity benchmarks during certain regimes.
What beta does NOT tell you
- Beta isn't a quality score. A 1.0 beta ETF tracking the S&P 500 is "perfectly market-like" — that's its job. A 1.4 beta thematic ETF isn't "worse" — it's just more aggressive.
- Beta doesn't measure idiosyncratic risk. Two ETFs with the same beta can have very different volatility from the benchmark — the rest of the variance is "specific risk" that beta misses.
- Beta is regime-dependent. An ETF with beta 0.7 during a bull market might have beta 1.2 during a crash. Use rolling beta to see this.
- Wrong benchmark = wrong beta. A tech ETF measured against the FTSE 100 will look weird (because UK has little tech). Always check the benchmark used.
- Beta isn't volatility. Use standard deviation for absolute volatility. Beta is volatility relative to a benchmark.
Beta interactions with R-squared
Beta should always be read alongside R² (R-squared) — the proportion of the ETF's return variance explained by the benchmark.
- R² > 0.9: beta is highly meaningful — the benchmark explains 90%+ of the ETF's movement.
- R² 0.7–0.9: beta is meaningful but other factors matter.
- R² < 0.7: beta is partially misleading — much of the ETF's behaviour isn't captured by the benchmark relationship. Use a different benchmark or treat the beta with caution.
UK Tax Drag tools publish R² alongside beta where available, so you can see how reliable the beta interpretation is.
Common Beta pitfalls in UK retail decisions
- Comparing beta across asset classes. A bond ETF's beta against MSCI ACWI is ~0.1 — that's structural, not a quality signal.
- Beta against the wrong benchmark. Comparing an EM ETF's beta against the S&P 500 produces a misleading number.
- Treating low beta as "safe." Low-beta ETFs can still have large drawdowns — beta only describes relative volatility.
- Forgetting beta is averaged. An ETF with beta 1.0 might have months at 1.5 and months at 0.5 — the average is 1.0 but the experience could be erratic.
Sources and methodology
Regression mathematics follow standard statistical practice (Wooldridge, Introductory Econometrics; Brooks, Introductory Econometrics for Finance). Benchmark data from index providers as documented in ETF Metrics Transparency. The ETF Data Methodology documents data sources in full. The site methodology documents the broader review process.
Related metric pages
How UK Tax Drag holds itself to account
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