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Analyst information production and the timing of annual earnings forecasts

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Abstract

We investigate whether the reputation-herding theory or the tradeoff theory explains variation in the timing of individual analysts’ forecasts. Using forecast accuracy improvements, forecast boldness, and the price impact of forecasts as measures of forecast quality, we find that in the information discovery phase that precedes an earnings announcement, earlier forecasts have higher quality than later forecasts. We also find a similar pattern in the information analysis phase that begins with the earnings announcement date. Our findings suggest that consistent with the herding theory, analysts who are more capable participate early in discovering and analyzing information, and therefore earlier forecasts in the information discovery and analysis phases are of higher quality than later forecasts in that phase.

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Notes

  1. Several studies explore this theory’s predictions about analyst herding behavior. Hong et al. (2000); Clement and Tse (2005); and Clarke and Subramanian (2006) all examine analyst characteristics associated with herding and the career consequences of herding. They find that experience, prior forecast accuracy, and brokerage size are negatively associated with an analyst’s tendency to herd. In contrast, we use the herding theory to predict timing-related differences in forecast quality within the information discovery and analysis phases.

  2. Researchers cannot directly observe this competition. We infer the effects of competition from analysts’ timing patterns and ex post forecast quality.

  3. We observe such leader and follower forecast patterns in our sample.

  4. Bias and accuracy both contribute to forecast quality, but we focus on accuracy in this study. Forecast bias may reflect analyst incentives (e.g., investment banking relationship and favored access to management). Chen and Jiang (2006) examine analyst incentives to issue forecasts that overweight favorable private information and underweight unfavorable information. We examine forecast timing patterns in the general population, so such incentives are beyond our scope.

  5. In a different setting, Gul and Lundholm (1995) demonstrate that analysts with extreme news (i.e., innovative estimates) are likely to forecast early. Their prediction is consistent with the prediction of the herding theory.

  6. Trueman (1994, p. 109) argues that ability cannot be the sole determinant of forecast timing. If it were, then investors could infer analyst ability from timing, removing analysts’ ability to hide low ability and hence the incentive for delaying forecasts. Thus analysts must have other (exogenous) reasons to release forecasts at certain dates for the reputation-related timing incentives to function.

  7. There are 252 trading days in a typical year and an average of 62 trading days between two quarterly earnings announcements. Almost all forecasts fall in one and only one 60-trading-day window.

  8. Chen et al. (2010) refer to “information analysis” as “information interpretation.”

  9. Chen et al. (2010) find no statistically significant association in weeks 3 and 4 (i.e., trading days 10 to 14) and a significantly negative association in weeks 5 and 6 (i.e., trading days 15 to 29), suggesting that analysts resume information discovery by day 29.

  10. We pool four earnings announcement events in a year because we find almost identical results when we analyze the prior-year announcement and the interim announcements separately.

  11. Our proxy for peers’ outstanding forecasts is consistent with Brown and Caylor (2005, see footnote 8), who argue that this measure is superior to the often-used analyst consensus because long-window consensus forecasts may include stale forecasts. Moreover, this proxy better captures the daily change in information than does the analyst consensus in our setting.

  12. This conjecture is confirmed by the upward trend in the absolute forecast accuracy chart in Fig. 1.

  13. Although it is not our focus, the steeper negative slope for the information analysis period than for the information discovery period suggests that analysts compete much more intensely in information analysis than in information discovery (perhaps because information analysis is confined to a very short window). Such intense competition facilitates price discovery after corporate disclosure.

  14. A concern arising from our measurement of Improve and Bold is that the arrival of corporate news may bias these measures upward at the earnings announcement date. Our results are similar if we exclude forecasts issued on days 0 and 1 (untabulated).

  15. Although it is not the focus of our study, we observe that FRC is higher on several days in the information discovery phase than in the entire information analysis phase, suggesting that investors sometimes value information discovery more highly than they do information analysis.

  16. This test addresses three issues: (1) confounding corporate news in the earnings announcement, (2) after-hour announcements (Berkman and Truong 2009), and (3) exclusion of earnings announcement date by prior studies.

  17. We use annual earnings forecasts to construct our measure for consistency with our other analyses. Shroff et al. (2013) use forecasts of quarterly earnings.

  18. The differences are statistically significant at better than the 1 % level (untabulated).

  19. Similar to Shroff et al. (2013), we require that the firm be followed by at least five analysts to calculate the leader–follower ratio. When we estimate our primary models on a sample of firms with too few analysts to calculate the leader–follower ratio, we still find results consistent with the herding theory, indicating that our timeliness measure provides a measure of forecast quality for a broader sample than the method of Shroff et al.

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Acknowledgments

We thank Anwer Ahmed, Shuping Chen, Xia Chen, Michael Clement, Gus De Franco, Matt Hart, Joost Impink, Marcus Kirk, Paul Madsen, Tom Omer, David Reppenhagen, Kathy Rupar, Jim Vincent, Greg Waymire, David Weber, two anonymous referees, and the participants of the 2011 AAA Annual Conference and the accounting workshops at the University of Connecticut, University of Florida, Peking University, University of Toronto, and Zhongshan University.

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Authors and Affiliations

  1. Department of Accounting, Sam Walton College of Business, University of Arkansas, Fayetteville, AR, USA

    Sami Keskek

  2. Department of Accounting, Mays Business School, Texas A&M University, College Station, TX, USA

    Senyo Tse

  3. Fisher School of Accounting, University of Florida, Gainesville, FL, USA

    Jennifer Wu Tucker

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  1. Sami Keskek
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  2. Senyo Tse
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Correspondence to Senyo Tse.

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Keskek, S., Tse, S. & Tucker, J.W. Analyst information production and the timing of annual earnings forecasts. Rev Account Stud 19, 1504–1531 (2014). https://doi.org/10.1007/s11142-014-9278-7

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