All Title Author
Keywords Abstract

The Measurement of Analysts’ Earnings Forecast Uncertainty

DOI: 10.4236/me.2015.64041, PP. 430-435

Keywords: Analysts’ Earnings Forecast, Measurement, Uncertainty

Full-Text   Cite this paper   Add to My Lib


Analysts’ earnings forecast began in the early 20th century in America, researchers and investors are especially interested in estimating uncertainty about future earnings, because it reveals important characteristics of the firm’s information prior to the release of accounting results. Since uncertainty is inherently unobservable, evaluating its estimates poses challenging methodological problems. As a result, researchers have put forward alternative proxies for earnings forecast uncertainty. Here, we will review the measurement used in the study of foreign scholars of analysts’ earnings forecast uncertainty, and make a comparison among various methods. Considering the background of information, prediction model and analysts cannot be expected to know the cause of the situation, GARCH as an ex ante measure, will be one of the most accurately measures of uncertainty. Studying the methods of analysts’ earnings forecast uncertainty will be conducive to market participants to understand the characteristics of analysts’ earnings forecast, so as to make more rational decisions.


[1]  Baginski, S.P., Conrad, E.J. and Hassell, J.M. (1993) The Effects of Management Forecast Precision on Equity Pricing and on the Assessment of Earnings Uncertainty. Accounting Review, 68, 913-927.
[2]  Diether, K.B., Malloy, C.J. and Scherbina, A. (2002) Differences of Opinion and the Cross Section of Stock Returns. The Journal of Finance, 57, 2113-2141.
[3]  Clement, M., Frankel, R. and Miller, J. (2003) Confirming Management Earnings Forecasts, Earnings Uncertainty, and Stock Returns. Journal of Accounting Research, 41, 653-679.
[4]  Yeung, P.E. (2009) Uncertainty and Expectation Revisions after Earnings Announcements. Contemporary Accounting Research, 26, 273-301.
[5]  Imhoff Jr., E.A. and Lobo, G.J. (1992) The Effect of Ex Ante Earnings Uncertainty on Earnings Response Coefficients. Accounting Review, 67, 427-439.
[6]  Barron, O.E. and Stuerke, P.S. (1998) Dispersion in Analysts’ Earnings Forecasts as a Measure of Uncertainty. Journal of Accounting, Auditing & Finance, 13, 245-270.
[7]  Zhang, X. (2006) Information Uncertainty and Stock Returns. The Journal of Finance, 61, 105-137.
[8]  Abarbanell, J.S., Lanen, W.N. and Verrecchia, R.E. (1995) Analysts’ Forecasts as Proxies for Investor Beliefs in Empirical Research. Journal of Accounting and Economics, 20, 31-60.
[9]  Johnson, T.C. (2004) Forecast Dispersion and the Cross Section of Expected Returns. The Journal of Finance, 59, 1957-1978.
[10]  Barron, O.E., Stanford, M.H. and Yu, Y. (2009) Further Evidence on the Relation between Analysts’ Forecast Dispersion and Stock Returns. Contemporary Accounting Research, 26, 329-357.
[11]  Bomberger, W.A. (1996) Disagreement as a Measure of Uncertainty. Journal of Money, Credit and Banking, 28, 381-392.
[12]  Giordani, P. and Soderlind, P. (2003) Inflation Forecast Uncertainty. European Economic Review, 47, 1037-1059.
[13]  Brown, L.D. and Han, J.C.Y. (1992) The Impact of Annual Earnings Announcements on Convergence of Beliefs. Accounting Review, 67, 862-875.
[14]  Barron, O.E. and Stuerke, P.S. (1998) Dispersion in Analysts’ Earnings Forecasts as a Measure of Uncertainty. Journal of Accounting, Auditing & Finance, 13, 245-270.
[15]  Barron, O.E., Byard, D., Kile, C., et al. (2002) High-Technology Intangibles and Analysts’ Forecasts. Journal of Accounting Research, 40, 289-312.
[16]  Liang, L. (2003) Post-Earnings Announcement Drift and Market Participants’ Information Processing Biases. Review of Accounting Studies, 8, 321-345.
[17]  Botosan, C.A. and Stanford, M. (2005) Managers’ Motives to Withhold Segment Disclosures and the Effect of SFAS No. 131 on Analysts’ Information Environment. The Accounting Review, 80, 751-772.
[18]  Doukas, J.A., Kim, C.F. and Pantzalis, C. (2006) Divergence of Opinion and Equity Returns. Journal of Financial and Quantitative Analysis, 41, 573-606.
[19]  Sheng, X. and Thevenot, M. (2012) A New Measure of Earnings Forecast Uncertainty. Journal of Accounting and Economics, 53, 21-33.
[20]  Degeorge, F., Patel, J. and Zeckhauser, R. (1999) Earnings Management to Exceed Thresholds. The Journal of Business, 72, 1-33.
[21]  Abarbanell, J. and Lehavy, R. (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, 36, 105-146.
[22]  Bollerslev, T. (1986) Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31, 307-327.
[23]  Engle, R.F. (1982) Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica: Journal of the Econometric Society, 50, 987-1007.


comments powered by Disqus