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COMPARING FORECAST ACCURACY FOR EXPONENTIAL SMOOTHING MODELS OF EARNINGS‐PER‐SHARE DATA FOR FINANCIAL DECISION MAKING
Author(s) -
Brandon Charles,
Jarrett Jeffrey E.,
Khumawala Saleha B.
Publication year - 1986
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1986.tb00220.x
Subject(s) - exponential smoothing , econometrics , earnings per share , earnings , portfolio , computer science , point (geometry) , sample (material) , finance , economics , mathematics , chemistry , geometry , chromatography
This paper relates recent research in predicting accounting earnings per share (EPS) to an experiment comparing the performance of extrapolative forecasting models. The paper points out the usefulness of the results to decision‐making processes such as those used in portfolio analysis or financial management. The statistical results of the experiment point to the usefulness of the Holt‐Winter (HW) model in predicting EPS for a random sample of firms over a 20‐year horizon. For short‐term forecasting, the HW model provides relatively accurate forecasts in comparison to other methods used. HW is likely to be a costeffective alternative to more time‐consuming and expensive techniques.