Refining Financial Analysts’ Forecasts by Predicting Earnings Forecast Errors

International Journal of Accounting and Information Management; 25(2), 2017

30 Pages Posted: 14 Mar 2017 Last revised: 17 Jan 2018

Date Written: June 6, 2016

Abstract

Prior research on financial analyst’ quarterly earnings forecasts has documented serial correlation in forecast errors. This paper examines 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. Finding that serial correlation in forecast errors is significant and seemingly independent of firm and analyst attributes, I model consensus forecast errors as an autoregressive process. I demonstrate that the model of forecast errors that best fits the data is AR(1), and use the obtained autoregressive coefficients 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.

Keywords: Financial analysts' earnings forecasts; forecast errors; predicting forecast errors, consensus forecast

Suggested Citation

Fedyk, Tatiana, Refining Financial Analysts’ Forecasts by Predicting Earnings Forecast Errors (June 6, 2016). International Journal of Accounting and Information Management; 25(2), 2017. Available at SSRN: https://ssrn.com/abstract=2931636 or http://dx.doi.org/10.2139/ssrn.2931636

Tatiana Fedyk (Contact Author)

University of San Francisco ( email )

2130 Fulton Street
San Francisco, CA 94117
United States

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