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Earnings Estimates and the Accuracy of Expectational Data

Author

Listed:
  • Edwin J. Elton

    (New York University)

  • Martin J. Gruber

    (New York University)

Abstract

This paper examines the accuracy of forecasts produced by mechanical forecasting techniques and three groups of analysts. The nine mechanical forecasting techniques are variations of exponentially weighted moving averages, naive models, simple moving averages, and regressions. One-, two- and three-year forecasts are used to evaluate these techniques. The mechanical techniques exhibit statistically significant differences in their ability to forecast earnings per share, with the exponentially weighted moving averages producing the best forecasts. One-year forecasts produced by the best of the mechanical forecasting techniques were compared to the corresponding analysts' projections. No statistically significant difference could be discerned.

Suggested Citation

  • Edwin J. Elton & Martin J. Gruber, 1972. "Earnings Estimates and the Accuracy of Expectational Data," Management Science, INFORMS, vol. 18(8), pages 409-424, April.
    • Handle: RePEc:inm:ormnsc:v:18:y:1972:i:8:p:b409-b424
    DOI: 10.1287/mnsc.18.8.B409
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    Cited by:

    1. Marco Aiolfi & Marius Rodriguez & Allan Timmermann, 2010. "Understanding Analysts' Earnings Expectations: Biases, Nonlinearities, and Predictability," Journal of Financial Econometrics, Oxford University Press, vol. 8(3), pages 305-334, Summer.
    2. Baljit Sidhu & Hwee Cheng Tan, 2011. "The Performance of Equity Analysts During the Global Financial Crisis," Australian Accounting Review, CPA Australia, vol. 21(1), pages 32-43, March.
    3. Frank J. Finn & G. P. Whittred, 1982. "On the Use of Naive Expectations of Earnings per Share as Experimental Benchmarks," The Economic Record, The Economic Society of Australia, vol. 58(2), pages 169-173, June.
    4. Carter, Richard B. & Strader, Troy J., 2009. "The market versus the analyst: Biases and predictive ability," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(2), pages 398-416, May.
    5. JS Armstrong, 2004. "Relative Accuracy of Judgmental and Extrapolative Methods in Forecasting Annual Earnings," General Economics and Teaching 0412007, University Library of Munich, Germany.
    6. Neil Hartnett, 2006. "Management disclosure bias and audit services," Review of Quantitative Finance and Accounting, Springer, vol. 26(4), pages 369-390, June.
    7. Michele Bagella & Leonardo Becchetti & Rocco Ciciretti, 2007. "Earning Forecast Error in US and European Stock Markets," The European Journal of Finance, Taylor & Francis Journals, vol. 13(2), pages 105-122.
    8. Rä‚Zvan Popa, 2020. "Improving Earnings Predictions With Neural Network Models," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 26, pages 77-96, December.
    9. Neil Hartnett, 2006. "The representativeness of management financial forecasts vis-à-vis naïve forecasts," Asian Review of Accounting, Emerald Group Publishing, vol. 14(1), pages 5-23, July.
    10. Schreuder, H. & Klaassen, J., 1982. "Confidential revenue and profit forecasts by management and financial analysts : evidence from the Netherlands," Serie Research Memoranda 0026, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.

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