IDEAS home Printed from https://ideas.repec.org/a/eee/jaecon/v53y2012i1p21-33.html
   My bibliography  Save this article

A new measure of earnings forecast uncertainty

Author

Listed:
  • 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.

Suggested Citation

  • Sheng, Xuguang & Thevenot, Maya, 2012. "A new measure of earnings forecast uncertainty," Journal of Accounting and Economics, Elsevier, vol. 53(1), pages 21-33.
    • Handle: RePEc:eee:jaecon:v:53:y:2012:i:1:p:21-33
    DOI: 10.1016/j.jacceco.2011.11.001
    as

    Download full text from publisher

    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 search for a different version of it.

    References listed on IDEAS

    as
    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, February.
    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, September.
    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, May.
    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

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


    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. Sam Han & Justin Yiqiang Jin & Tony Kang & Gerald Lobo, 2014. "Managerial Ownership and Financial Analysts’ Information Environment," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 41(3-4), pages 328-362, April.
    4. Xuguang Sheng & Maya Thevenot, 2013. "Differential Interpretation of Public Information: Estimation and Inference," Working Papers 2013-03, American University, Department of Economics.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Bert de Bruijn & Philip Hans Franses, 2012. "What drives the Quotes of Earnings Forecasters?," Tinbergen Institute Discussion Papers 12-067/4, Tinbergen Institute.

    More about this item

    Keywords

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

    JEL classification:

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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. 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). General contact details of provider: http://www.elsevier.com/locate/jae .

    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 CitEc recognized a reference but did not link an item in RePEc 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 RePEc Author Service 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.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.