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What is 'Skewness'

Skewness is a term in statistics used to describes asymmetry from the normal distribution in a set of statistical data. Skewness can come in the form of negative skewness or positive skewness, depending on whether data points are skewed to the left and negative, or to the right and positive of the data average. A dataset that shows this characteristic differs from a normal bell curve.


When data is skewed to the right, the mean and the median of the set are both greater than the mode. Further, the mean is greater than the median in most cases. Conversely, when data is skewed to the left, the mean and the median are both less than the mode. In addition, as a rule, the mean is less than the median.

Skewness is measured by the use of Pearson’s first coefficient of skewness. This measure subtracts the median from the mode and then divides the difference by the standard deviation. Peterson's second law is also sometimes used, where the mode is subtracted from the median, multiplied by three and then divided by the standard deviation.

Skewness in Business and Finance

Skewness is extremely important to finance and investing. Most sets of data, including stock prices and asset returns, have either positive or negative skew, rather than following the balanced normal distribution, which has a skewness of zero. By understanding which way data is skewed, an investor can better estimate whether a given future data point will be more or less than the mean.

Most advanced economic analysis models study data for skewness and incorporate this into their calculations. Skewness risk is the risk that a model assumes a normal distribution of data, when in fact data is skewed to the left or right of the mean.

An Example of Skewness

Skewness is used by investors every day. Even casual equity investors look at the chart of a stock's price and try to make investments in companies that have a positive skew. The idea is to invest in a company with a long tail, which in the equity markets is a stock price that is greatly skewed positively, such as Netflix or Microsoft.

However, when skewness is combined with poor judgement, it can have adverse effects. For example, prior to the 2008 financial crisis, the market was booming and showing positive skewness. Many investors bought into the market at its high point in 2007, only to see it massively decline in 2008 and 2009. Then, noticing a negative skew, market participants sold at the bottom of the market, realizing huge losses.

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