Abstract

https://ssrn.com/abstract=140997
 
 

References (31)



 
 

Citations (2)



 


 



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


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

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.

Number of Pages in PDF File: 34

JEL Classification: G10, G20, G29, M41


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Date posted: December 8, 1998  

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

Contact Information

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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