Reference Class Selection in Similarity-Based Forecasting of Sales Growth

This paper proposes a method to find appropriate outside views for sales forecasts of analysts. The idea is to find reference classes, i.e. peer groups, for each analyzed company separately. Hence, additional companies are considered 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 applied 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 with a comparison of our forecasts with actual analysts' estimates emphasizes the relevance of our approach in practice.