Artikel

Reference class selection in similarity‐based forecasting of corporate sales growth

This paper proposes a general method to handle forecasts exposed to behavioral bias by finding appropriate outside views, in our case corporate sales forecasts of analysts. The idea is to find reference classes, that is, 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.

Sprache
Englisch

Erschienen in
Journal: Journal of Forecasting ; ISSN: 1099-131X ; Volume: 42 ; Year: 2022 ; Issue: 5 ; Pages: 1069-1085 ; Hoboken, NJ: Wiley

Thema
Distributional Forecast
Goodness of Fit
Outside View
Prediction
Bias Correction

Ereignis
Geistige Schöpfung
(wer)
Theising, Etienne
Wied, Dominik
Ziggel, Daniel
Ereignis
Veröffentlichung
(wer)
Wiley
(wo)
Hoboken, NJ
(wann)
2022

DOI
doi:10.1002/for.2927
Letzte Aktualisierung
10.03.2025, 11:41 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Theising, Etienne
  • Wied, Dominik
  • Ziggel, Daniel
  • Wiley

Entstanden

  • 2022

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