Title
COMPARING FORECAST ACCURACY FOR EXPONENTIAL SMOOTHING MODELS OF EARNINGS‐PER‐SHARE DATA FOR FINANCIAL DECISION MAKING
Document Type
Article
Date of Original Version
1-1-1986
Abstract
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. Copyright © 1986, Wiley Blackwell. All rights reserved
Publication Title, e.g., Journal
Decision Sciences
Volume
17
Issue
2
Citation/Publisher Attribution
Brandon, Charles, Jeffrey E. Jarrett, and Saleha B. Khumawala. "COMPARING FORECAST ACCURACY FOR EXPONENTIAL SMOOTHING MODELS OF EARNINGS‐PER‐SHARE DATA FOR FINANCIAL DECISION MAKING." Decision Sciences 17, 2 (1986): 186-194. doi: 10.1111/j.1540-5915.1986.tb00220.x.