Definition. The Sharpe ratio — mean excess return over volatility — measures statistical efficiency, not investability. “High Sharpe” and “makes money for you” are connected by a chain with seven links, and any one can fail while the Sharpe stays technically true. This is the checklist for the gap, and it applies double to every paper in the archive claiming SR > 2.
The seven failure links
1. It’s a maximum, not a draw. Before anything else: a high Sharpe selected from many attempts is expected under the null — best-of-100 on a year of data prints ≈2.5 from noise. Everything below assumes the Sharpe survived that filter; most never get that far.
2. It doesn’t scale. Sharpe is size-invariant; returns aren’t. A 3-Sharpe strategy earning 4%/yr on $500k capacity is a nice salary, not a fund — and impact’s square-root law guarantees the Sharpe itself degrades as size grows. The investable statistic is dollar alpha at achievable size, which a ratio cannot express. High-frequency strategies are the extreme: SR 5+ with tiny capacity is common and uninvestable for outside capital.
3. Volatility isn’t risk — skew is. Sharpe’s denominator treats a short-vol premium harvest and a balanced strategy identically until the cliff. Strategies that manufacture smooth-then-catastrophic profiles — selling tails, providing liquidity into crashes, carrying pegs — post high Sharpes because the risk hides in moments the ratio ignores. Check skew, kurtosis, and worst-month before admiring the ratio; the PSR formula does this arithmetically.
4. You can’t always lever the efficiency into returns. The textbook says lever the high-Sharpe/low-return strategy up. Reality bills for it: margin limits, borrow availability, volatility-targeting’s hidden gap risk, and financing costs that scale with leverage while the edge doesn’t. An unleverageable 2-Sharpe earning 3%/yr loses to a leverageable 1-Sharpe earning 12%.
5. Correlation arrives exactly when it matters. A standalone Sharpe ignores when the strategy loses. Many premia — carry, liquidity provision, short-vol — earn steadily and then lose precisely in the states where your portfolio, your investors, and your margin desk are all bleeding. Conditional correlation in stress is the investability statistic; unconditional correlation flatters it.
6. Costs and slippage are convex in urgency. The backtest’s cost assumption was calibrated to calm markets and patient execution. The strategy’s worst days are fast markets — where spreads triple and the fill-quality gap yawns open. Sharpe measured on close-to-close fills quietly assumes liquidity that disappears on the days that define the drawdown.
7. Drawdown duration vs capital patience. Even a true, robust SR 1.5 spends years-long stretches underwater with meaningful probability — run the fan and look at the 5th percentile path. Investability includes the sociology: will you (or your investors, or your risk committee) still be funding the strategy at month 30 of a statistically-normal drawdown? A Sharpe that outlives its capital’s patience was never investable, just admirable.
The checklist
- ☐ DSR-adjusted for trials
- ☐ Capacity stated; dollar alpha at that capacity computed
- ☐ Skew/kurtosis/worst-month reported; short-vol content identified
- ☐ Leverage required for target returns actually available at quoted financing
- ☐ Stress-conditional correlation to your existing book
- ☐ Costs modeled in the states where losses occur
- ☐ Drawdown-duration distribution vs realistic capital patience
Questions to ask when a paper leads with its Sharpe
- At what AUM does this Sharpe halve?
- What does the return distribution’s left tail look like, and what economic state produces it?
- Is the ratio computed on gross, net, or close-to-close fantasy fills?
- Sharpe over what period — and what happened after?
What our scoring captures — and misses
Papers reporting capacity, skew, and net-of-cost results score higher on rigor — the axis was built to reward exactly these disclosures. What no paper score can know is link 7: investability relative to your capital structure and patience is a property of the reader, not the research. The ratio ranks papers; the seven links decide allocations.
Companions: Capacity constraints → · Volatility targeting’s fine print → · Run the numbers: capacity estimator → · Kelly under uncertainty → · See the drawdown fan: equity-curve simulator →