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Refining financial analysts’ forecasts by predicting earnings forecast errors

Refining financial analysts’ forecasts by predicting earnings forecast errors PurposeThe 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.Design/methodology/approachFinding 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.FindingsModeling 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.Originality/valueThese refinements were not presented in prior literature and can be useful to financial analysts and investors. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Accounting and Information Management Emerald Publishing

Refining financial analysts’ forecasts by predicting earnings forecast errors

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1834-7649
DOI
10.1108/IJAIM-06-2016-0065
Publisher site
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Abstract

PurposeThe 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.Design/methodology/approachFinding 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.FindingsModeling 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.Originality/valueThese refinements were not presented in prior literature and can be useful to financial analysts and investors.

Journal

International Journal of Accounting and Information Management – Emerald Publishing

Published: May 2, 2017

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