IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this article

A new measure of earnings forecast uncertainty

Author Info

Listed author(s):
  • Sheng, Xuguang
  • Thevenot, Maya

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.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.sciencedirect.com/science/article/pii/S0165410111000899
Download Restriction: Full text for ScienceDirect subscribers only

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Article provided by Elsevier in its journal Journal of Accounting and Economics.

Volume (Year): 53 (2012)
Issue (Month): 1 ()
Pages: 21-33

as
in new window

Handle: RePEc:eee:jaecon:v:53:y:2012:i:1:p:21-33
DOI: 10.1016/j.jacceco.2011.11.001
Contact details of provider: Web page: http://www.elsevier.com/locate/jae

Keywords: Uncertainty; Analyst dispersion; Common information; Private information; BKLS; GARCH;

Find related papers by JEL classification:

  • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting
  • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as
in new window


  1. Karl B. Diether & Christopher J. Malloy & Anna Scherbina, 2002. "Differences of Opinion and the Cross Section of Stock Returns," Journal of Finance, American Finance Association, vol. 57(5), pages 2113-2141, October.
  2. X. Frank Zhang, 2006. "Information Uncertainty and Stock Returns," Journal of Finance, American Finance Association, vol. 61(1), pages 105-137, 02.
  3. Degeorge, Francois & Patel, Jayendu & Zeckhauser, Richard, 1999. "Earnings Management to Exceed Thresholds," The Journal of Business, University of Chicago Press, vol. 72(1), pages 1-33, January.
  4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
  5. Kajal Lahiri & Xuguang Sheng, 2010. "Measuring forecast uncertainty by disagreement: The missing link," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 514-538.
  6. Michael Clement & Richard Frankel & Jeffrey Miller, 2003. "Confirming Management Earnings Forecasts, Earnings Uncertainty, and Stock Returns," Journal of Accounting Research, Wiley Blackwell, vol. 41(4), pages 653-679, 09.
  7. Timothy C. Johnson, 2004. "Forecast Dispersion and the Cross Section of Expected Returns," Journal of Finance, American Finance Association, vol. 59(5), pages 1957-1978, October.
  8. Abarbanell, Jeffery & Lehavy, Reuven, 2003. "Biased forecasts or biased earnings? The role of reported earnings in explaining apparent bias and over/underreaction in analysts' earnings forecasts," Journal of Accounting and Economics, Elsevier, vol. 36(1-3), pages 105-146, December.
  9. Orie E. Barron, 2002. "High-Technology Intangibles and Analysts' Forecasts," Journal of Accounting Research, Wiley Blackwell, vol. 40(2), pages 289-312, 05.
  10. Abarbanell, Jeffery S. & Lanen, William N. & Verrecchia, Robert E., 1995. "Analysts' forecasts as proxies for investor beliefs in empirical research," Journal of Accounting and Economics, Elsevier, vol. 20(1), pages 31-60, July.
  11. Doukas, John A. & Kim, Chansog (Francis) & Pantzalis, Christos, 2006. "Divergence of Opinion and Equity Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 41(03), pages 573-606, September.
  12. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
Full references (including those not matched with items on IDEAS)

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

as
in new window


Cited by:

  1. Bert de Bruijn & Philip Hans Franses, 2015. "How Informative are the Unpredictable Components of Earnings Forecasts?," Tinbergen Institute Discussion Papers 15-032/III, Tinbergen Institute.
  2. Bert de Bruijn & Philip Hans Franses, 2013. "Forecasting Earnings Forecasts," Tinbergen Institute Discussion Papers 13-121/III, Tinbergen Institute.
  3. Xuguang Sheng & Maya Thevenot, 2013. "Differential Interpretation of Public Information: Estimation and Inference," Working Papers 2013-03, American University, Department of Economics.
  4. Xuguang Sheng & Orie Barron & Maya Thevenot, 2012. "Information Environment and the Cost of Capital: A New Approach," Working Papers 2012-12, American University, Department of Economics.
  5. Sheng, Xuguang (Simon) & Thevenot, Maya, 2015. "Quantifying differential interpretation of public information using financial analysts’ earnings forecasts," International Journal of Forecasting, Elsevier, vol. 31(2), pages 515-530.
  6. Sanjay W. Bissessur & David Veenman, 2016. "Analyst information precision and small earnings surprises," Review of Accounting Studies, Springer, vol. 21(4), pages 1327-1360, December.
  7. Orie Barron & Xuguang Sheng & Maya Thevenot, 2013. "Information Environment and The Cost of Capital," Working Papers 2013-003, The George Washington University, Department of Economics, Research Program on Forecasting.
  8. Bert de Bruijn & Philip Hans Franses, 2012. "What drives the Quotes of Earnings Forecasters?," Tinbergen Institute Discussion Papers 12-067/4, Tinbergen Institute.

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Access and download statistics

When requesting a correction, please mention this item's handle: RePEc:eee:jaecon:v:53:y:2012:i:1:p:21-33. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.