A New Measure of Earnings Forecast Uncertainty

Posted: 4 Nov 2011 Last revised: 23 Oct 2012

See all articles by Xuguang Simon Sheng

Xuguang Simon Sheng

American University - Department of Economics

Maya Thevenot

Florida Atlantic University

Date Written: 2012

Abstract

Relying on the well-established theoretical result that uncertainty has a common and an idiosyncratic component, we propose a new measure of earnings forecast uncertainty as the sum of dispersion among analysts and the variance of mean forecast errors estimated by a GARCH model. The new measure is based on both common and private information available to analysts at the time they make their forecasts. Hence, it alleviates some of the limitations of other commonly used proxies for forecast uncertainty in the literature. Using analysts’ earnings forecasts, we find direct evidence of the new measure’s superior performance.

Keywords: Uncertainty, analyst dispersion, common information, private information, BKLS, GARCH

JEL Classification: M41, C01

Suggested Citation

Sheng, Xuguang Simon and Thevenot, Maya, A New Measure of Earnings Forecast Uncertainty (2012). Journal of Accounting & Economics (JAE), 53(1-2), 21-33, Available at SSRN: https://ssrn.com/abstract=1954080

Xuguang Simon Sheng

American University - Department of Economics ( email )

4400 Massachusetts Avenue, N.W.
Washington, DC 20016-8029
United States

HOME PAGE: http://https://www.american.edu/cas/faculty/sheng.cfm

Maya Thevenot (Contact Author)

Florida Atlantic University ( email )

University Tower
220 SE 2 Avenue
Boca Raton, FL 33431
United States

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