The ratio of unexpected earnings (or earnings surprise) to the standard deviation of analysts’ earnings forecasts. This ratio is used as a valuation indicator, where the standard deviation of forecasts reflects the variability in earnings per share (EPS) estimates provided by analysts. The forecast error of a given proportion relative to the mean would be more meaningful when analysts’ forecasts are less dispersed. The scaled unexpected earnings measure is given by:

SUE_t = (HE_t – AE_t)/ σ_t

Where: HE_t, AE_t, and σ_t denote high earnings estimate, actual earnings (reported earnings), and standard deviation of forecasts during a specific period.

This ratio is typically used to ensure that earnings surprises are comparable across different companies. For example, if the mean consensus earnings forecast for XYZ company for a given year ending December 31 was $2.10, and there are 40 estimates ranging between as low as $1.05 and as high as $3.15 with a standard deviation of $0.25, then, given that actual reported earnings for the year turned out to be equal to the high forecast, the scaled unexpected earnings for XYZ, taking into account the dispersion of forecasts, will be:

SUE_t = (3.15 – 2.10)/ 0.25 = 4.2

This an abstract figure that can be used to compare this company with other companies. If another company, for example, has scaled unexpected earnings of 5, then XYZ is said to be faring better as its earnings forecasts show less deviations.

This measure is also known as scaled earnings surprise.