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Team earnings forecasting

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Abstract

While brokerage houses use both teams of sell-side analysts and individual analysts to conduct earnings research, there is no empirical research examining whether teams and individuals differ with regard to their forecasting performance or purpose. We first examine the most-often researched dimension of forecasting performance, earnings forecast accuracy, and show that teams are less accurate than individual analysts in general and their own individual team members in particular. We conjecture that teams focus their efforts on an alternative dimension of forecasting performance, timeliness, and show that team forecasts are timelier than those of individual analysts in general and their own individual team members in particular. Consistent with the notion that teams trade-off forecast accuracy for timeliness to comply with a market research demand, we show that team forecast revisions are associated with larger market responses than those of individuals. Finally, we illuminate the nature of team assignments by documenting that the firms that teams follow are in greater financial distress and larger in size.

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Notes

  1. The top business schools. The Wall Street Journal. September 20, 2006: R6.

  2. See Kothari (2001) and Ramnath et al. (2006) for summaries of the literature on brokerage research and Bonner (2007) for a summary of the judgment and decision-making literature on teams.

  3. In a supplemental analysis, we also show empirically two natural traits of our forecast timeliness measure: (1) a negative association with forecast accuracy and (2) a positive association with the stock market reaction.

  4. We provide descriptive evidence consistent with this assertion.

  5. Of the 10 analysts whom we were able to contact, all 10 verified the I/B/E/S coding.

  6. Results based on the Hong and Kubik (2003) relative measure of accuracy yield inferentially similar results.

  7. To preserve the number of analysts following a firm-year, we base our tabulated analysis on N = 1; however, our results are qualitatively similar for N = 2.

  8. The market adjustment is value-weighted; however, in sensitivity checks, we used an equal-weighted adjustment and obtained similar results. We also conducted a sensitivity check on the accumulation window by utilizing a 5-day window to accumulate returns for both value- and equal-weighted adjustments.

  9. I/B/E/S started updated analysts’ forecasts on a daily basis at the beginning of 1993 (Cooper et al. 2001).

  10. Ninety-four percent of our teams consist of two members. Sensitivity analyses reveal that restricting our analyses to only two-person teams results in inferentially similar findings. We were able to code 98.6% of the observations as being associated with a team or individual. The remaining 1.4% of observations had either an ambiguous or a missing name field and were excluded from our sample.

  11. In all regression models, reported t-statistics are based on standard errors adjusted for heteroskedasticity and intra-analyst error correlation (Rogers 1993); however, we also verify that our results are not sensitive to alternative strategies for addressing heteroskedasticity and dependence of residuals, including White (1980) heteroskedasticity consistent t-statistics and those based on Newey and West (1987) heteroskedasticity and autocorrelation consistent standard errors. All reported p-values are based on two-tailed tests.

  12. Since an accuracy model using annual observations and controlling for forecast horizon linearly may not adequately address the differential information sets available to analysts who forecast before versus after the t-n quarterly earnings forecast announcements, we also evaluate team earnings forecast accuracy in models 1 and 2 based on quarterly observations. Consistent with the results based on annual observations, the model 1 results based on quarterly observations indicate a negative and significant coefficient on the analyst team variable, TEAM, (αq = −0.029, t = −4.68, p < 0.01), and the model 2 results based on quarterly observations also indicate a negative and significant coefficient on the analyst team variable, TEAM, (αq = −0.021, t = −4.33, p < 0.01).

  13. To clarify, we show that individual analysts functioning as members of teams and who also issue their own individual forecasts issue their individual forecasts after analyst teams in general (not their own teams). Consistent with intuition, we do not find that individuals follow the same firms as their respective teams during the same forecasting periods.

  14. Ramnath et al. (2005) show that I/B/E/S consensus forecasts outperform Value Line individual forecasts due to the aggregation principle. We believe the aggregation principle is most applicable in the context of earnings forecasting where analysts independently generate their own earnings forecasts and then combine them. We view the primary reason that teams outperform individuals with respect to timeliness to be their ability to subdivide their work; specifically, the gathering, filtering, and analyzing of relevant financial information.

  15. An alternative construct related to both the accuracy and timeliness of earnings forecasting is “usefulness,” which measures the resolution of uncertainty with respect to a recent consensus (Mozes 2003; Williams 1996). In an untabulated analysis, we modify our performance model to include usefulness as the dependent variables and find that, relative to individual forecasts, team forecasts perform better on this earnings forecasting dimension.

  16. Due to space limitations, we exclude t-values in the table.

  17. An anecdotal account from a manager at a large brokerage relates that teams provide some marketing benefit to coverage, presumably from increased channels arising from added contacts.

  18. In this estimation, the score is increasing in the probability of bankruptcy.

  19. Our results are inferentially similar for an analysis based on a standard logistic regression.

  20. The lack of results with respect to the number of segments as a measure of complexity may be due to offsetting effects between a company’s different business segments (which could increase complexity in terms of necessary industry knowledge) and more consistent overall earnings (which could decrease complexity) due to a diversified business.

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Acknowledgements

This paper was previously circulated under the title, “Are Two Heads Better than One? The Case of Earnings Forecast Accuracy.” We thank Sarah Bonner, Marcus Caylor, Qiang Chang, Qi Chen, Jennifer Francis, Rebecca Hann, S. P. Kothari, Bob Lipe, Martien Lubberink, Russell Lundholm, Michael Mikhail, D. J. Nanda, Siva Nathan, Arianna Pinello, Beverly Walther, Ross Watts, Joseph Weber, Peter Wysocki, Yun Zhang, an anonymous referee, and participants at Duke University, M.I.T., University of British Columbia, University of Oklahoma, the AAA 2006 Annual Meeting, the 5th London Business School Accounting Symposium, and the 16th Annual Conference on Financial Economics and Accounting at University of North Carolina for helpful comments. We thank Frank Luo for his research assistance. We appreciate the financial support of the J. Mack Robinson College of Business.

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  1. J. Mack Robinson School of Business, Georgia State University, Atlanta, GA, 30303, USA

    Lawrence D. Brown & Artur Hugon

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  1. Lawrence D. Brown
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Correspondence to Artur Hugon.

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Brown, L.D., Hugon, A. Team earnings forecasting. Rev Account Stud 14, 587–607 (2009). https://doi.org/10.1007/s11142-008-9076-1

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