IDEAS home Printed from https://ideas.repec.org/a/kap/rqfnac/v45y2015i4p803-818.html
   My bibliography  Save this article

Are aggregate corporate earnings forecasts unbiased and efficient?

Author

Listed:
  • Bruno Deschamps

    ()

Abstract

In this article, we analyze the properties of professional aggregate corporate earnings forecasts with regards to accuracy, unbiasedness, and efficiency. Using a large panel of forecasts for the years 1992–2011, we find that forecast errors are in general large, and the magnitude of forecast errors varies substantially across forecasters. Forecasts are however directionally accurate, especially during periods of slowdown. We find evidence of an underprediction bias, as forecasters failed to predict the strong growth of corporate earnings that took place over the past two decades. Forecasts biases and forecast errors are particularly large during periods of economic instability such as recession years, suggesting that biases originate in forecasters’ slow adjustment to structural shocks. Finally, we reject forecast efficiency, and find evidence of overreaction to new information, as evidenced by the negative autocorrelation of forecast revisions. Forecasters overreact equally strongly to good and bad aggregate earnings news, resulting in excessive forecast volatility. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Bruno Deschamps, 2015. "Are aggregate corporate earnings forecasts unbiased and efficient?," Review of Quantitative Finance and Accounting, Springer, vol. 45(4), pages 803-818, November.
  • Handle: RePEc:kap:rqfnac:v:45:y:2015:i:4:p:803-818
    DOI: 10.1007/s11156-014-0456-2
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11156-014-0456-2
    Download Restriction: Access to full text is restricted to subscribers.

    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. Han, Bong H & Manry, David & Shaw, Wayne, 2001. "Improving the Precision of Analysts' Earnings Forecasts by Adjusting for Predictable Bias," Review of Quantitative Finance and Accounting, Springer, vol. 17(1), pages 81-98, July.
    2. Terence Lim, 2001. "Rationality and Analysts' Forecast Bias," Journal of Finance, American Finance Association, vol. 56(1), pages 369-385, February.
    3. Carlo Altavilla & Paul De Grauwe, 2010. "Forecasting and combining competing models of exchange rate determination," Applied Economics, Taylor & Francis Journals, vol. 42(27), pages 3455-3480.
    4. Masako N. Darrough, 2002. "A Positive Model of Earnings Forecasts: Top Down versus Bottom Up," The Journal of Business, University of Chicago Press, vol. 75(1), pages 127-152, January.
    5. Anilowski, Carol & Feng, Mei & Skinner, Douglas J., 2007. "Does earnings guidance affect market returns? The nature and information content of aggregate earnings guidance," Journal of Accounting and Economics, Elsevier, vol. 44(1-2), pages 36-63, September.
    6. Tilman Ehrbeck & Robert Waldmann, 1996. "Why Are Professional Forecasters Biased? Agency versus Behavioral Explanations," The Quarterly Journal of Economics, Oxford University Press, vol. 111(1), pages 21-40.
    7. Kothari, S.P. & Lewellen, Jonathan & Warner, Jerold B., 2006. "Stock returns, aggregate earnings surprises, and behavioral finance," Journal of Financial Economics, Elsevier, vol. 79(3), pages 537-568, March.
    8. Batchelor, Roy, 2007. "Bias in macroeconomic forecasts," International Journal of Forecasting, Elsevier, vol. 23(2), pages 189-203.
    9. Sadka, Gil & Sadka, Ronnie, 2009. "Predictability and the earnings-returns relation," Journal of Financial Economics, Elsevier, vol. 94(1), pages 87-106, October.
    10. Ager, P. & Kappler, M. & Osterloh, S., 2009. "The accuracy and efficiency of the Consensus Forecasts: A further application and extension of the pooled approach," International Journal of Forecasting, Elsevier, vol. 25(1), pages 167-181.
    11. Isiklar, Gultekin, 2005. "On aggregation bias in fixed-event forecast efficiency tests," Economics Letters, Elsevier, vol. 89(3), pages 312-316, December.
    12. Abarbanell, Jeffery S., 1991. "Do analysts' earnings forecasts incorporate information in prior stock price changes?," Journal of Accounting and Economics, Elsevier, vol. 14(2), pages 147-165, June.
    13. Cowen, Amanda & Groysberg, Boris & Healy, Paul, 2006. "Which types of analyst firms are more optimistic?," Journal of Accounting and Economics, Elsevier, vol. 41(1-2), pages 119-146, April.
    14. Davies, Anthony & Lahiri, Kajal, 1995. "A new framework for analyzing survey forecasts using three-dimensional panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 205-227, July.
    15. Deschamps, Bruno & Ioannidis, Christos, 2013. "Can rational stubbornness explain forecast biases?," Journal of Economic Behavior & Organization, Elsevier, vol. 92(C), pages 141-151.
    16. Ray Ball & Gil Sadka & Ronnie Sadka, 2009. "Aggregate Earnings and Asset Prices," Journal of Accounting Research, Wiley Blackwell, vol. 47(5), pages 1097-1133, December.
    17. Prof Roy Batchelor, 2007. "Forecaster Behaviour and Bias in Macroeconomic Forecasts," ifo Working Paper Series 39, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    18. repec:bla:joares:v:35:y:1997:i::p:167-199 is not listed on IDEAS
    19. April Knill & Kristina Minnick & Ali Nejadmalayeri, 2012. "Experience, information asymmetry, and rational forecast bias," Review of Quantitative Finance and Accounting, Springer, vol. 39(2), pages 241-272, August.
    20. Qi Chen & Wei Jiang, 2006. "Analysts' Weighting of Private and Public Information," Review of Financial Studies, Society for Financial Studies, vol. 19(1), pages 319-355.
    21. Lahiri, Kajal & Sheng, Xuguang, 2010. "Learning and heterogeneity in GDP and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 26(2), pages 265-292, April.
    22. Harrison Hong & Jeffrey D. Kubik, 2003. "Analyzing the Analysts: Career Concerns and Biased Earnings Forecasts," Journal of Finance, American Finance Association, vol. 58(1), pages 313-351, February.
    23. Kiridaran Kanagaretnam & Gerald Lobo & Robert Mathieu, 2012. "CEO stock options and analysts’ forecast accuracy and bias," Review of Quantitative Finance and Accounting, Springer, vol. 38(3), pages 299-322, April.
    24. William M. Cready & Umit G. Gurun, 2010. "Aggregate Market Reaction to Earnings Announcements," Journal of Accounting Research, Wiley Blackwell, vol. 48(2), pages 289-334, May.
    25. John C. Easterwood & Stacey R. Nutt, 1999. "Inefficiency in Analysts' Earnings Forecasts: Systematic Misreaction or Systematic Optimism?," Journal of Finance, American Finance Association, vol. 54(5), pages 1777-1797, October.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Forecast efficiency; Aggregate earnings; Overreaction; Forecast biases; E17; E37; G29;

    JEL classification:

    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • G29 - Financial Economics - - Financial Institutions and Services - - - Other

    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:kap:rqfnac:v:45:y:2015:i:4:p:803-818. 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: (Sonal Shukla) or (Mallaigh Nolan). General contact details of provider: http://springer.com .

    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.