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Improving the Precision of Analysts' Earnings Forecasts by Adjusting for Predictable Bias

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  • Han, Bong H
  • Manry, David
  • Shaw, Wayne

Abstract

This research demonstrates that publicly-available information can be used to develop estimates of analysts' optimistic bias in earnings forecasts. These bias estimates can be used to produce more accurate forecasts, resulting in significant reductions of both cross-sectional mean forecast error and error variance. When bias estimates are based on past observations of forecast error alone, however, reductions in mean forecast error are smaller, and forecast precision is unimproved. Further tests provide evidence of a significant association between returns and the bias predictable from contemporaneously-available information, suggesting that predictable bias is only partially discounted by market participants. This study has significant implications for researchers and investors. The pricing of predictable bias in analysts' forecasts may add error to inferences which are based on the association between returns and analyst forecast errors, and knowledge of the market's partial discounting of predictable bias may help investors to make more efficient resource allocations. Copyright 2001 by Kluwer Academic Publishers

Suggested Citation

  • 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.
    • Handle: RePEc:kap:rqfnac:v:17:y:2001:i:1:p:81-98
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    Cited by:

    1. Kryzanowski, Lawrence & Mohsni, Sana, 2015. "Earnings forecasts and idiosyncratic volatilities," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 107-123.
    2. Vitor Azevedo & Patrick Bielstein & Manuel Gerhart, 2021. "Earnings forecasts: the case for combining analysts’ estimates with a cross-sectional model," Review of Quantitative Finance and Accounting, Springer, vol. 56(2), pages 545-579, February.
    3. David Newton, 2019. "Are All Forecasts Made Equal? Conditioning Models on Fit to Improve Accuracy," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-32, September.
    4. C. S. Agnes Cheng & K. C. Kenneth Chu & James Ohlson, 2020. "Analyst forecasts: sales and profit margins," Review of Accounting Studies, Springer, vol. 25(1), pages 54-83, March.
    5. 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.
    6. 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.
    7. Zhaoyang Gu & Jian Xue, 2007. "Do analysts overreact to extreme good news in earnings?," Review of Quantitative Finance and Accounting, Springer, vol. 29(4), pages 415-431, November.
    8. Karine Michalon & Sandrine Lardic & François Dossou, 2005. "Earnings forecast bias - a statistical analysis," Post-Print halshs-00142773, HAL.
    9. Shirley Liu, 2017. "Does the requirement of an engagement partner signature improve financial analysts’ information environment in the United Kingdom?," Review of Quantitative Finance and Accounting, Springer, vol. 49(1), pages 263-281, July.

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