Earnings Surprises that Motivate Analysts to Reduce Average Forecast Error

Posted: 24 Aug 2007 Last revised: 14 Jul 2008

See all articles by Orie E. Barron

Orie E. Barron

Pennsylvania State University

Donal Byard

City University of New York - Stan Ross Department of Accountancy

Yong Yu

University of Texas at Austin

Date Written: September 2007

Abstract

Large earnings surprises and negative earnings surprises represent more egregious errors in analysts' earnings forecasts. We find evidence consistent with our expectation that egregious forecast errors motivate analysts to work harder to develop or acquire relatively more private information in an effort to avoid future egregious forecasting failures. Specifically, we find that after large or negative earnings surprises there is a greater reduction in the error in individual analysts' forecasts of future earnings, and these individual forecasts are based more heavily on individual analysts' private information. This increased reliance on private information reduces the mean forecast error for upcoming earnings (even after controlling for the effect of reduced error in individual forecasts). As reliance on private information increases, more of each individual forecast error is idiosyncratic, and thus averaged out in the computation of the mean forecast.

Keywords: BKLS, Analyst forecasts, Forecast Error, Earnings

JEL Classification: G14, G24, G29, M41

Suggested Citation

Barron, Orie E. and Byard, Donal and Yu, Yong, Earnings Surprises that Motivate Analysts to Reduce Average Forecast Error (September 2007). Accounting Review, Vol. 82, No. 2, 2008, McCombs Research Paper Series No. ACC-09-07, Available at SSRN: https://ssrn.com/abstract=1008339

Orie E. Barron

Pennsylvania State University ( email )

University Park, PA 16802-3306
United States
814-863-3230 (Phone)
814-863-8393 (Fax)

Donal Byard (Contact Author)

City University of New York - Stan Ross Department of Accountancy ( email )

One Bernard Baruch Way, Box B12-225
New York, NY 10010
United States
646-312-3187 (Phone)
646-312-3161 (Fax)

Yong Yu

University of Texas at Austin ( email )

1 University Station B6400
Austin, TX 78712
United States
(512)471-6714 (Phone)
(512)471-3904 (Fax)

Here is the Coronavirus
related research on SSRN

Paper statistics

Abstract Views
1,785
PlumX Metrics