The Use of Forecast Revision in Reducing Built-in Biases in Mean Analyst Forecasts

34 Pages Posted: 8 Dec 1998  

Oliver Kim

University of Maryland

Steve C. Lim

Texas Christian University - M.J. Neeley School of Business

Kenneth W. Shaw

University of Missouri at Columbia - School of Accountancy

Date Written: December 1997

Abstract

We evaluate the ability of the mean analyst forecast to effectively summarize analysts' information. We show analytically that even if analysts possess the ability and intention to forecast earnings truthfully, the mean forecast underweights analysts' private information. Thus, the mean does not adequately aggregate the full set of information individual analysts use in making their forecasts. Since the mean underweights private information, the problem worsens as the number of analysts forecasting earnings increases. We show that a positive multiple of forecast revision can be used to reduce the impact of improper information aggregation. We show empirically that forecast errors are positively related to forecast revision, and this relation is increasing in the number of forecasts made. Our results have implications for researchers who use the mean analyst forecast to proxy for the market's expectations of earnings.

JEL Classification: G10, G20, G29, M41

Suggested Citation

Kim, Oliver and Lim, Steve C. and Shaw, Kenneth W., The Use of Forecast Revision in Reducing Built-in Biases in Mean Analyst Forecasts (December 1997). Available at SSRN: https://ssrn.com/abstract=140997 or http://dx.doi.org/10.2139/ssrn.140997

Oliver Kim

University of Maryland ( email )

Rm.4449, Van Munching Hall
College Park, MD 20742-1815
United States
301-405-2243 (Phone)

Steve ChongKol Lim

Texas Christian University - M.J. Neeley School of Business ( email )

2900 Lubbock Street
Fort Worth, TX 76129
United States
817-257-7536 (Phone)

Kenneth W. Shaw (Contact Author)

University of Missouri at Columbia - School of Accountancy ( email )

420 Cornell Hall
Columbia, MO 65211
United States
573-882-5939 (Phone)
573-882-2437 (Fax)

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