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Refining financial analysts’ forecasts by predicting earnings forecast errors

Tatiana Fedyk (Department of Accounting, University of San Francisco, San Francisco, California, USA)

International Journal of Accounting & Information Management

ISSN: 1834-7649

Article publication date: 2 May 2017




The purpose of this paper is to examine the way serial correlation in quarterly earnings forecast errors varies with firm and analyst attributes such as the firm’s industry and the analyst’s experience and brokerage house affiliation. Prior research on financial analysts’ quarterly earnings forecasts has documented serial correlation in forecast errors.


Finding that serial correlation in forecast errors is significant and seemingly independent of firm and analyst attributes, the consensus forecast errors are modeled as an autoregressive process. The model of forecast errors that best fits the data is AR(1), and the obtained autoregressive coefficients are used to predict consensus forecast errors.


Modeling the consensus forecast errors as an autoregressive process, the present study predicts future consensus forecast errors and proposes a series of refinements to the consensus.


These refinements were not presented in prior literature and can be useful to financial analysts and investors.



Fedyk, T. (2017), "Refining financial analysts’ forecasts by predicting earnings forecast errors", International Journal of Accounting & Information Management, Vol. 25 No. 2, pp. 256-272.



Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited

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