The purpose of this study is to determine if an earnings forecasting model based on factors hypothesised to result in differential profits across firms (industries) reduces model error relative to the model developed by Ou (1990). Initial research attempting to forecast earnings found that the random walk model, where current year's earnings are the prediction for next year, provides the best forecast of annual earnings (Ball and Watts 1972; Foster 1973; Beaver, Kettler, and Scholes 1970; Albrecht, Lookabill, and McKeown 1977; Brealey 1969). Ou (1990) developed an earnings forecasting model using financial statement information beyond prior years' earnings as the explanatory variables that outperformed the random walk model in predicting annual earnings.
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1 November 1996
This article was originally published in
Management Research News
Review Article|
November 01 1996
Earnings Forecasting Models: Adding a Theoretical Foundation for the Selection of Explanatory Variables Available to Purchase
John E. Sneed
John E. Sneed
Assistant Professor, College of Business and Technology, Department of Accounting/Finance, University of Nebraska at Kearney, West Center E200,Kearney,NE 68849–4420, USA
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Publisher: Emerald Publishing
Online ISSN: 1758-6135
Print ISSN: 0140-9174
© MCB UP Limited
1996
Management Research News (1996) 19 (11): 42–57.
Citation
Sneed JE (1996), "Earnings Forecasting Models: Adding a Theoretical Foundation for the Selection of Explanatory Variables". Management Research News, Vol. 19 No. 11 pp. 42–57, doi: https://doi.org/10.1108/eb028504
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