Skewness is an important statistical concept used in trading to describe the asymmetry of the distribution of returns or data points relative to the average (mean). In simpler terms, skewness measures whether the returns are more frequently tilted towards one side of the average—either to the right (positive skew) or to the left (negative skew). Understanding skewness helps traders assess the nature of risk and potential reward in their investments, especially beyond what standard deviation or volatility alone can reveal.

Mathematically, skewness is calculated using the third standardized moment of a distribution. The formula for sample skewness is:

Formula: Skewness = (n / ((n-1)(n-2))) * Σ((xi – x̄) / s)^3

Where:
– n = number of observations
– xi = each individual return or data point
– x̄ = mean of the returns
– s = standard deviation of the returns

A positive skew means the distribution has a longer or fatter right tail, indicating more frequent or larger gains relative to losses. This implies that while most returns might cluster below the mean, there are occasional significant positive returns pushing the average higher. Conversely, a negative skew means the distribution has a longer left tail, with more frequent or larger losses, signaling a higher risk of experiencing big downside moves.

For example, consider a stock whose daily returns mostly fall between -1% and +1%, but occasionally it experiences sharp gains of +5% or more. This stock’s returns distribution will exhibit positive skewness. On the other hand, a currency pair in the forex market that usually moves within a small range but occasionally suffers steep drops due to geopolitical events or central bank interventions will show negative skewness.

A real-life trading example can be seen in the behavior of certain technology stocks during market rallies. For instance, during the post-2020 recovery, many tech stocks showed positively skewed returns as they frequently produced outsized gains during bullish phases, with relatively fewer large drawdowns. Conversely, during the 2008 financial crisis, many financial sector stocks demonstrated negative skewness, with occasional sharp drops far exceeding typical daily losses.

One common misconception is to equate skewness directly with risk. While negative skewness often indicates higher downside risk, it does not necessarily mean the asset is “riskier” in the traditional sense. Some traders may prefer assets with negative skew if they offer higher expected returns to compensate for that risk. Another mistake is ignoring skewness entirely and focusing solely on volatility or standard deviation, which only measures the spread but not the direction of tail risk.

Another related query traders often ask is: “How does skewness affect option pricing?” In options markets, skewness is crucial because it affects implied volatility surfaces and the pricing of out-of-the-money options. For example, negative skewness in the underlying asset often results in higher implied volatility for put options relative to calls, reflecting greater demand for downside protection.

In summary, skewness is a valuable metric that provides insight into the shape and risk profile of return distributions. Traders who understand skewness can better anticipate the likelihood of extreme gains or losses and adjust their strategies accordingly. Whether analyzing stocks, forex pairs, or indices, incorporating skewness into your risk assessment helps paint a fuller picture beyond average returns and volatility alone.