Return skewness measures asymmetry in a return distribution.
Negative skew means the left tail is longer or more influential than the right tail. Positive skew means the opposite. A symmetric distribution has skewness near zero under the usual moment-based definition.
Mean and volatility do not reveal asymmetry
Two strategies can have similar average return and volatility while one has frequent small gains and occasional large losses and the other has the reverse shape.
That is why skewness adds information beyond a Sharpe ratio.
It should be read with return kurtosis, which describes tail weight, and effective sample size, which addresses dependence rather than distribution shape.
The Probabilistic Sharpe Ratio and Deflated Sharpe Ratio calculations both use return-shape inputs when evaluating Sharpe evidence.
Sample skewness is itself uncertain
A short backtest can produce an unstable skewness estimate, especially when a few extreme returns dominate the sample.
Skewness therefore should not be treated as a fixed property inferred perfectly from limited historical data.
Sources: NIST, Measures of Skewness and Kurtosis and Bailey and López de Prado, Deflated Sharpe Ratio.
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Test Sharpe uncertainty
Use return asymmetry with sample length, kurtosis, and a benchmark Sharpe in probabilistic Sharpe inference.
Adjust selected Sharpe
Carry non-Normal return shape into the search-adjusted Deflated Sharpe Ratio calculation.
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