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.
- Language
-
Englisch
- Bibliographic citation
-
Journal: Journal of Forecasting ; ISSN: 1099-131X ; Volume: 42 ; Year: 2022 ; Issue: 5 ; Pages: 1069-1085 ; Hoboken, NJ: Wiley
- Subject
-
Distributional Forecast
Goodness of Fit
Outside View
Prediction
Bias Correction
- Event
-
Geistige Schöpfung
- (who)
-
Theising, Etienne
Wied, Dominik
Ziggel, Daniel
- Event
-
Veröffentlichung
- (who)
-
Wiley
- (where)
-
Hoboken, NJ
- (when)
-
2022
- DOI
-
doi:10.1002/for.2927
- Last update
-
10.03.2025, 11:41 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Theising, Etienne
- Wied, Dominik
- Ziggel, Daniel
- Wiley
Time of origin
- 2022
Other Objects (12)
Forecasting VARs, model selection, and shrinkage
Forecasting VARs, model selection, and shrinkage
On the selection of forecasting models
On the selection of forecasting models
Judgmental demand forecasting in direct sales
Resource description and selection for similarity search in metric spaces
Automatic Selection and Combination of Descriptors for Effective 3D Similarity Search
Similarity
Variable selection and inference for multi-period forecasting problems
Hierarchical Sales Forecasting In Multichannel Distribution Considering Marketing Campaigns
Lasso-type and heuristic strategies in model selection and forecasting
A similarity matrix and its application in genomic selection for hedging haplotype diversity
Forecasting VARs, model selection, and shrinkage
Forecasting VARs, model selection, and shrinkage
On the selection of forecasting models
On the selection of forecasting models
Judgmental demand forecasting in direct sales
Resource description and selection for similarity search in metric spaces
Automatic Selection and Combination of Descriptors for Effective 3D Similarity Search
Similarity
Variable selection and inference for multi-period forecasting problems
Hierarchical Sales Forecasting In Multichannel Distribution Considering Marketing Campaigns
Lasso-type and heuristic strategies in model selection and forecasting
A similarity matrix and its application in genomic selection for hedging haplotype diversity
Forecasting VARs, model selection, and shrinkage
Forecasting VARs, model selection, and shrinkage
On the selection of forecasting models
On the selection of forecasting models
Judgmental demand forecasting in direct sales
Resource description and selection for similarity search in metric spaces
Automatic Selection and Combination of Descriptors for Effective 3D Similarity Search
Similarity
Variable selection and inference for multi-period forecasting problems
Hierarchical Sales Forecasting In Multichannel Distribution Considering Marketing Campaigns
Lasso-type and heuristic strategies in model selection and forecasting