The statement below is in reference to SUE (Standardized Unexpected Earnings) " A given size forecast error is more meaningful the smaller the size of the historical forecast errors"
it means the more precise the analyst’s forecasts were in the past, the more accurate his/her future forecasts on earnings will be. The less variability there is an analyst’s estimates over time from the actual earnings number, the more trust we put in to him/her to continue giving us accurate forecasts. If the analyst’s estimates suck, and are far from the actual number reported, when he/she gives us their expected earnings for a company in the future, we take it with a grain of salt. Therefore, the more variability there is in his/her estimates, the less meaningful it is to us. Make sense?
That’s what I initially thought, but then second-guessed myself; if we are indeed correct, think about adjusting anaylst’s forecasted alphas a la Treynor Black to understand the concept.
basically, to sum up with mp said, if there’s a forecast error today but in the past the errors were small, the error today is more meaninful because of the large deviation from past errors.