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Sharpe ratio explained: what the metric tells you — and what it does not
Excess return per unit of variability — the number says no more, and no less.
Last updated: 14 August 2026 · By The Acutic Research Team
The Sharpe ratio measures risk-adjusted return: excess return over the risk-free rate divided by the standard deviation of excess returns — SR = (Rp − Rf) / σ (W. F. Sharpe, 1966; revised 1994). It answers exactly one question: how much excess return per unit of variability? What it does not answer: whether that trade-off fits any particular situation.
The formula, worked through
Risk-free rate in all examples: 2%. Every figure in the table is computed from the formula at render time:
| Example | Return p.a. | Volatility p.a. | Sharpe ratio |
|---|---|---|---|
| A | 8% | 10% | 0.60 |
| B | 12% | 20% | 0.50 |
| C | 4% | 2% | 1.00 |
Reading: in this example, C delivers the most excess return per unit of volatility (1.00) even though B has the highest absolute return. Which return-variability trade-off fits which purpose is not something the ratio states — it only describes the ratio.
What it can compare — and what it cannot
- Comparable: series over the same period, at the same data frequency, in the same currency, against the same risk-free basis.
- Not comparable: different windows or market regimes (the interest-rate level sits inside the formula), monthly against daily data, and strategies with strongly asymmetric return profiles — option strategies, say, whose rare large losses barely register in a standard deviation until they occur.
Known limits
- Upside variability counts as risk: standard deviation penalizes moves up exactly like moves down; the Sortino ratio uses downside deviation for that reason.
- Window sensitivity: three- and five-year values of the same strategy can sit far apart; a single figure without its period is not interpretable.
- Smoothed series flatter: autocorrelation — typical for illiquid assets — lowers measured volatility and lifts the ratio without any reduction in risk. The √12 annualization assumes independent returns.
- Negative values rank wrongly: at the same shortfall, the higher-volatility variant appears less negative — the table shows it (D and E, both 0% return):
| Example | Return p.a. | Volatility p.a. | Sharpe ratio |
|---|---|---|---|
| D | 0% | 5% | -0.40 |
| E | 0% | 20% | -0.10 |
Frequently asked questions
What counts as a “good” Sharpe ratio?
There is no context-free threshold. The value depends on the period, the asset class and the interest-rate level — the same strategy can score 0.3 in one decade and 0.9 in the next. The metric is meaningful only in relative terms: same period, same data frequency, same risk-free basis.
Why does monthly data distort the annualized Sharpe ratio?
The usual annualization multiplies the monthly figure by √12, assuming independent, identically distributed returns. Smoothed or autocorrelated series — illiquid assets, for instance — understate true variability, and the annualized figure comes out too high.
What does a negative Sharpe ratio mean?
Only that the return was below the risk-free rate. Ranking negative values is misleading: at the same shortfall, the higher-volatility variant appears less negative — the ordering then runs against intuition.
How does the Sortino ratio differ?
The Sortino ratio divides excess return by downside deviation only, so upside variability is not treated as risk — the Sharpe ratio penalizes both directions equally. Both metrics describe past data, not the future.
Further reading: TER explained: what the total expense ratio covers — and what it does not and ROIC vs ROE vs ROCE: what each measures. Create free account.
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