  This paper proposes a general method to handle forecasts exposed to
behavioural bias by finding appropriate outside views, in our case corporate
sales forecasts of analysts. The idea is to find reference classes, i.e. peer
groups, for each analyzed company separately that share similarities to the
firm of interest with respect to a specific predictor. The classes are regarded
to be optimal if the forecasted sales distributions match the actual
distributions as closely as possible. The forecast quality is measured by
applying goodness-of-fit tests on the estimated probability integral
transformations and by comparing the predicted quantiles. The method is
out-of-sample backtested on a data set consisting of 21,808 US firms over the
time period 1950 - 2019, which is also descriptively analyzed. It appears that
in particular the past operating margins are good predictors for the
distribution of future sales. A case study compares the outside view of our
distributional forecasts with actual analysts' forecasts and emphasizes the
relevance of our approach in practice.
