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.
翻译:本文提出一种一般方法,通过找到适当的外部观点处理受到行为偏差影响的预测,在我们的个案中,公司对分析师的销售预测,目的是为每个被分析的公司分别找到参考类别,即同级集团,这些公司在特定预测器方面与利益公司有相似之处。如果预测的销售分布与实际分布尽可能接近,这些类别被认为是最佳的。预测质量是通过对估计概率整体变化进行适当测试和比较预测的量化值来衡量的。该方法在1950至2019年期间由21 808家美国公司组成的数据集上进行了抽样反向测试,该数据集也作了描述性分析。看来,特别是过去的运营利润幅度是未来销售分布的良好预测。一项案例研究将我们分配预测的外部观点与实际分析员的预测进行比较,并强调了我们做法的实际相关性。