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Using earnings conference calls to identify analysts with superior private information

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Abstract

We examine the extent to which analysts who participate in earnings conference calls by asking questions possess superior private information relative to analysts who do not ask questions. Using a large sample of earnings conference call transcripts over the period 2002–2005, we find that annual earnings forecasts issued immediately after a conference call are both more accurate and timelier for participating analysts relative to nonparticipating analysts. These results hold after controlling for observable analyst characteristics, suggesting conference call participation can serve as a mechanism to identify analysts possessing superior private information. The economic magnitude of the superior private information contained in participating analyst forecasts is small but comparable with magnitudes reported in prior studies with respect to other analyst characteristics. Our mediation analysis does not support the notion that the superior private information stems exclusively from the information received during the call. Therefore, from a regulatory stand point, our results suggest that regulatory intervention to allow for equal participation during conference calls may be unwarranted.

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Notes

  1. Conference call transcripts are not available prior to 2002, which inhibits an investigation of conference call access before and after Regulation FD. Subsequent to 2005, we are no longer able to obtain I/B/E/S translation tables that identify the names of the analysts in I/B/E/S, which is necessary to match observations in I/B/E/S with analysts listed in the conference call transcripts.

  2. Barron et al. (1998) have developed the BKLS measure, which proxies for the extent of private information collectively held among all analysts following a firm but does not accommodate private information measurement at the individual analyst level, nor whether such private information is differentially superior. As such, we are unable to use this measure in our empirical tests.

  3. This assumes analysts ask questions conditional on their existing private information and that all analysts do not possess the same private information. In such a case, the public answer to these questions will uniquely complement the information set of the asking analyst but at the same time will not inform competing analysts who are listening in on the same call.

  4. In testimony to the U.S. Senate Committee on Banking, Housing, and Urban Affairs, Mike Mayo, a leading analyst on Wall Street, suggested that being denied conference call access by management could informationally handicap an analyst (Mayo 2002).

  5. Bagnoli et al. (2008) provide a list of analyst attributes institutional investors value. During the post RegFD period analyzed, providing “useful and timely calls” ranked fourth while earnings estimates and stock selection ranked between 9th and 12th. Consistent with this notion, Groysberg et al. (2011) note that the properties of earnings forecasts such as accuracy and timeliness are not explicitly part of analysts’ compensation contracts. In private discussion, the managing director and director of research at a prominent sell-side research firm also noted that institutional investor clients have their own personnel to map information into projections of earnings and stock recommendations and therefore value individual insights from sell-side analysts more than their aggregated overall opinions about future earnings and firm value.

  6. We do not impose any restrictions regarding staleness of either the quarterly earnings forecast or the stock recommendation at this point in the sample selection process.

  7. The theoretical potential set of participants include I/B/E/S analysts who cover the firm, I/B/E/S analysts who do not cover the firm, analysts not on I/B/E/S, bankers, institutional investors, and individual investors. Identifying this theoretical population and measuring the nature of the information sets contained by participants other than I/B/E/S analysts following the firm is cost prohibitive. Mayew (2008) documents that, at the median, managers take questions from nine noncorporate participants, firms are covered by six I/B/E/S analysts, and three I/B/E/S analysts ask questions.

  8. It is not empirically possible to identify the set of analysts who sought participation but were not chosen by management to participate, because the question queue is unobservable.

  9. Relaxing the restriction of the difference in propensity score from .01 to .05 does not impact our inferences.

  10. These values are obtained from Table 3 Column 1 of Ke and Yu (2006). The two analyst specific characteristics examined are relative firm experience and relative number of firms covered, which have statistically significant marginal effects of 0.013 and −0.006, respectively. These point estimates are multiplied by 100 to estimate differences in relative annual forecast accuracy, because relative firm experience and relative number of firms covered are ranked and bounded between 0 and 100.

  11. Since Delay is not a relative measure, firm characteristics not considered in Eq. (1) may impact participation and timeliness. To address this issue, we re-estimate Eq. (2b) but include the sign and magnitude of unexpected earnings, quarter of year fixed effects, and year fixed effects. The coefficient on Participate remains negative and significant (β1 = −0.657, p = 0.047). Further, since Delay is a count variable, OLS may not be the appropriate estimation technique, particularly if extreme values in Delay influence our inferences. If we re-estimate this augmented version of (2b) using logistic regression and replace Delay with an indicator that equals one if Delay ≥ 2 and zero otherwise, we continue to observe a negative and statistically significant coefficient on Participate (−0.243, p ≤ 0.01). Inferences are also unchanged if we use relative delay as the dependent variable.

  12. Decomposing the treatment and control groups by timeliness does not preserve propensity score balance across the cells we compare statistically. If we remove observations until we achieve insignificant differences at the 10 % significance level in propensity scores and redo the analysis, our inferences are unchanged.

  13. An alternative proxy to curry favor is using the optimistic/pessimistic (OP) pattern in annual forecasts (Richardson et al. 2004; Ke and Yu 2006; Libby et al. 2008). However, since we analyze quarterly earnings conference calls, whether a given annual earnings forecast updated after a conference call will ultimately fall into the OP category is unclear until annual earnings are reported. As a result, a manager may not know whether the annual forecasts we analyze are pleasing at the time they are provided to the market. Recommendation changes occurring between consecutive conference calls, however, are signals observable to managers before they make decisions about conference call access and can be relatively unambiguously identified as currying favor or not.

  14. We thank an anonymous referee for suggesting this mediation analysis.

  15. Testing this difference using the bootstrap method of Preacher and Hayes (2004), where statistical significance for mediation effects is estimated from 3,000 iterations, yields a similar p value of 0.184.

  16. A test of the hypothesis that this difference in coefficients equals zero using the Sobel test cannot be rejected (p value = 0.195.).

  17. To avoid the negative inference of analyst ability that might be associated with inaccurate forecasts, the analyst could wait and free ride on other analysts. However, free riding inflicts a different type of cost on the analyst because clients will infer that the analyst is not providing value over the other analysts who have previously forecasted (and on which the analyst is free riding).

  18. Including the interaction between Downgrade and ∆Participate in Eqs. (3c) and (3d) does not change our inferences and the coefficient on the interaction terms are not statistically significant. Also, the inclusion of fiscal quarter fixed effects does not change the inferences drawn from any of the changes analysis presented in Table 4.

  19. An alternative approach to capturing analyst information possession would be to systematically analyze the contents of the question-and-answer dialog between each analyst and management (Hollander et al. 2010). Such an analysis is beyond the scope of this study and would require subjectivity in coding both the topic of the dialog and whether the answer would suggest the analyst learned something new. A more refined set of outcome variables would also likely be necessary (such as perhaps revenue forecasts in the event an analyst discusses revenue issues).

  20. Sample attrition stems from I/B/E/S not providing a time stamp on every forecast in our sample, from analysts issuing forecasts outside of NYSE trading hours, or from firms not covered or having prices within the requisite window in the TAQ database.

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Acknowledgments

We appreciate helpful comments and suggestions from three anonymous referees, Larry Brown, Michael Clement, Yonca Ertimur, Jennifer Francis, Richard Frankel, Doron Nissim (the editor), Beverley Walther, Hal White, Richard Willis, Yong Yu, the managing director and director of research at a prominent sell-side research firm, members of the National Investor Relations Institute (NIRI) Triangle chapter and seminar participants at the Duke Accounting mini-brown bag, Fuqua summer brown bag, Texas A&M summer brown bag, Southeast Summer Accounting Research Conference at the University of Georgia, Washington University at St. Louis, and the AAA Financial Accounting and Reporting Section 2010 Midyear meeting.

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Authors and Affiliations

  1. Fuqua School of Business, Duke University, 100 Fuqua Drive, Durham, NC, 27708, USA

    William J. Mayew & Mohan Venkatachalam

  2. Mays Business School, Texas A&M University, 460 Wehner, Mailstop 4353, College Station, TX, 77843, USA

    Nathan Y. Sharp

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  1. William J. Mayew
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Correspondence to William J. Mayew.

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This paper was previously titled “Are there private information benefits to participating in a public earnings conference call?”

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Mayew, W.J., Sharp, N.Y. & Venkatachalam, M. Using earnings conference calls to identify analysts with superior private information. Rev Account Stud 18, 386–413 (2013). https://doi.org/10.1007/s11142-012-9210-y

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