Reference Class Selection in Similarity-Based Forecasting of Sales Growth

TL;DR

This paper introduces a method for improving sales forecasts by selecting relevant peer groups based on similarity measures, enhancing forecast accuracy through distributional matching and backtesting on extensive historical data.

Contribution

The paper presents a novel approach for reference class selection in sales forecasting, utilizing goodness-of-fit tests to optimize peer group identification for better distributional predictions.

Findings

Past operating margins are strong predictors of future sales distributions.

The method improves forecast accuracy by matching forecasted and actual distributions.

Backtesting on 21,808 firms demonstrates the approach's practical effectiveness.

Abstract

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.