Direct Answer
Skewness measures the asymmetry of a distribution around its mean. Positive skew means a longer right tail, with occasional large gains, and negative skew means a longer left tail, with occasional large losses. Zero means a symmetric distribution, negative values signal a heavier left tail, and positive values a heavier right tail.
Skewness: Formula, Meaning, Signals, Examples and How to Use It
How Is Skewness Calculated?
Skewness measures the asymmetry of a distribution around its mean. Positive skew means a longer right tail, with occasional large gains, and negative skew means a longer left tail, with occasional large losses.
Formula: g1 = m3 / m2^1.5, where m2 and m3 are the second and third central moments; the sample-adjusted form is G1 = g1 * sqrt(n*(n-1)) / (n-2)
Inputs:
- Returns or other series values
- Window length n
Worked example. A third central moment of -12 with a variance of 25 gives a skewness of -0.096. The inputs are illustrative numbers, not market data.
Reading it. Zero means a symmetric distribution, negative values signal a heavier left tail, and positive values a heavier right tail. Return series commonly show negative skew in equity indexes, so a change over time matters more than the level.
Limits. Skewness depends on the third power of deviations, so one extreme value can dominate it. Estimates in short windows are very unstable.
What is Skewness?
Skewness measures the asymmetry of a distribution around its mean. Positive skew means a longer right tail, with occasional large gains, and negative skew means a longer left tail, with occasional large losses.
How to use Skewness
Zero means a symmetric distribution, negative values signal a heavier left tail, and positive values a heavier right tail. Return series commonly show negative skew in equity indexes, so a change over time matters more than the level. A reading alone is never a buy or sell signal; it needs context from price structure, volume, volatility and the broader market regime.
Strengths
- A signed number separates a distribution with a long right tail from one with a long left tail, which standard deviation cannot do.
- Cubing the standardized deviations keeps the sign of each move, so the measure reflects direction as well as size.
- Reproducible when the formula and data convention are fixed.
Weaknesses and limitations
- A skewness of zero is consistent with many different shapes, so symmetric but fat-tailed data shows nothing about its tail weight.
- Return skewness varies with the sampling interval, with daily and monthly returns often showing different values for the same asset.
- Correlated indicators can create false confidence by repeating the same underlying information.
Editorial and risk note
This page is educational content, not individualized investment advice. It should not imply guaranteed prediction, accuracy or outperformance.
Frequently Asked Questions
Is Skewness a buy or sell signal?
No. Zero means a symmetric distribution, negative values signal a heavier left tail, and positive values a heavier right tail. Skewness depends on the third power of deviations, so one extreme value can dominate it. A trade built on it still needs a trigger, an invalidation rule, a position size and realistic execution assumptions.
What is the best setting for Skewness?
Skewness takes these inputs: returns or other series values; window length n. There is no universal best setting for the tunable ones, so test them across instruments, regimes and out-of-sample periods and favor stable parameter regions over one historical winner.
Can Skewness be used by itself?
Skewness depends on the third power of deviations, so one extreme value can dominate it. Estimates in short windows are very unstable. For that reason it is read next to measures of direction, participation, valuation, liquidity and risk, not alone.
Does Skewness work on every timeframe?
The calculation (g1 = m3 / m2^1.5, where m2 and m3 are the second and third central moments; the sample-adjusted form is G1 = g1 * sqrt(n*(n-1)) / (n-2)) can be run on any bar size, but behavior is not portable: a reading on a five-minute chart describes a very different market window from the same reading on a daily chart. Validate the exact timeframe before relying on it.
Why does Skewness give false signals?
A skewness of zero is consistent with many different shapes, so symmetric but fat-tailed data shows nothing about its tail weight. Return skewness varies with the sampling interval, with daily and monthly returns often showing different values for the same asset. Lag, noise and regime changes add to this, because market participants react to new information after the reading is calculated.
How should Skewness be backtested?
Rebuild Skewness from returns or other series values using point-in-time data, write the rules before testing, include delisted securities where relevant, model realistic fills and costs, reserve a validation sample and compare with a simpler baseline.