Abstract
We examine the long-term stock performance of analyst recommendations and the properties of accompanied earnings forecasts for initiations and non-initiations to evaluate the reporting, selection, and processing explanations for analyst optimism. We find that Strong Buy and, to a lesser degree, Buy initiation recommendations underperform their non-initiation counterparts after controlling for analyst, brokerage, and firm characteristics associated with the initiation decision and expected long-term stock returns. Yet, earnings forecasts accompanying Strong Buy and Buy initiation recommendations are less optimistic and more accurate than those accompanying non-initiation recommendations. Our findings suggest that conflicts of interest (that is, the reporting explanation) are the dominant source for favorable recommendations.
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Notes
Throughout the paper we use “optimistic” and “optimism” to refer to the ex ante optimism in recommendations, i.e., the skewness towards favorable opinions. Approximately 25 and 32% of recommendations in I/B/E/S over the 1994 through 2006 period fall in the Strong Buy and Buy categories, respectively, compared with only 4% in the Sell and 2% in the Strong Sell categories. The remainder, about 37%, is Hold recommendations (see Fig. 1).
The literature uses various terms for the three explanations. For example, reporting and processing explanations are also referred to as “conflicts of interest” and “selection bias with winners’ curse” explanations, respectively. We borrow the terminology from Francis (1997).
Figure 1 displays the distribution of initiations and non-initiations. Strong Buy recommendations comprise 30.92% (23.19%) of initiations (non-initiations), and Buy recommendations comprise 36.52% (30.12%) of initiations (non-initiations). Pearson χ2 suggests that distributions differ (p-value <0.001).
Ljungqvist, Marston, and Wilhelm (2006) find that, while analyst behavior does not influence the likelihood of analysts’ employers being awarded underwriting business, analysts appear to inflate their recommendations when potential investment banking related income is at stake.
Krigman et al. (2001) document that nearly one-third of the firms that completed a seasoned equity offering within 3 years of their initial public offering in the 1993 through 1995 period, switched to a new underwriter in their following deal. They also find that research coverage is one of the top reasons firms switch underwriters.
Research guarantees were one of the activities that were recently under the scrutiny of the SEC in the context of the Global Analyst Research Settlement in which 12 major investment banks agreed to pay approximately $1.4 billion while not acknowledging any wrongdoing.
Baker and Wurgler (2007) argue that valuation mistakes are more likely in small, young, and growth companies.
Malmendier and Shanthikumar (2007) report that I/B/E/S data contain an unusually high number of recommendations during the first 3 months (starting from October 1993) and focus on the period starting from February 1994 to avoid potential issues with the consistency of the early data. The earliest recommendation in our final sample dates from May 2, 1994, because, as we explain below, we exclude all recommendations within the first 6 months of an analyst’s appearance on the I/B/E/S recommendation tape from our sample.
McNichols and O’Brien (1997) also treat multiple initiations that occur on a single date after the first 6 months of analysts’ tenure as “original” coverage, because such initiations may represent changes in analysts’ assignments. We treat these observations as “initiations” for two reasons. First, multiple initiations may also reflect the now-experienced analyst’s decision to add several (related) companies to her portfolio. Second, multiple initiations may be due to I/B/E/S lumping together analysts’ recommendations that are a few days apart from each other, especially in the earlier years of the database (Clement and Tse 2005; Frankel, Kothari, and Weber 2006). Multiple initiations constitute less than 1% of the initiations in our sample, and our results are qualitatively similar when we treat these initiations as “original” coverage.
In 2002, some brokerage houses switched from the five-tier system to a three-tier system, which combines Strong Buy and Buy recommendations (Kadan et al. 2009). I/B/E/S continues to translate these recommendations into a five-tier system. We analyze a random sample of brokers’ original recommendations and I/B/E/S’ translations and find that I/B/E/S somewhat arbitrarily translates Buy recommendations in a three-tier system to Buy or Strong Buy recommendations in a five-tier system. The only pattern we observe is that Buys of some brokerage houses are more often than not translated as Strong Buys, while those of other brokerage houses are predominantly translated as Buys. This switch adds noise or measurement error to our coding of Strong Buy and Buy recommendations. In untabulated analysis, we find that our results are similar if we only use the pre-2002 period.
An alternative to (1) is to adopt a two-stage estimation approach. First, estimate x = a 0 + a 1 Z + e 1 where x is Initiation and Z is a vector of variables related to the initiation decision. Second, estimate y = b 0 + b 1 r(x|Z) + b 2 K + e 2 where y is Returns, r(x|Z) is the residual from the first stage, and K is a vector of factors potentially associated with the returns. This two-stage procedure is equivalent to estimating an expanded regression of the form y = c 0 + c 1 x + c 2 Z + c 3 K + e 3, i.e., the approach we adopt in Eq. 1 above. In particular, c 1 = b 1 (see Kothari and Shanken 1992 for a proof).
There are some confounding factors that we cannot control for. For example, analysts may do more extensive research for initiations resulting in longer and more detailed reports for initiations than for non-initiations. Such factors would suggest that initiation recommendations result from “better” research (in line with the selection argument) and thus have more positive returns, a prediction that works against the reporting and processing explanations.
We repeat our analysis using alternative return windows to assess the sensitivity of our results to the inclusion/exclusion of announcement returns for recommendations and revisions. The results are qualitatively similar across these alternative return windows. In particular, the coefficient of Initiation for Strong Buy recommendations is (1) −0.032 when we exclude the revision announcement window, significant at 1% level, (2) −0.026 when we exclude the recommendation announcement window, significant at 1% level, and (3) −0.023 when we exclude both the recommendation and revision announcement windows, also significant at 1% level.
In our main tests, we estimate Eq. 1 for the pooled recommendation-level data with year fixed effects. As an alternative, we also try the Fama and Macbeth (1973) estimation approach and find similar results. That is, we estimate Eq. 1 annually without the year fixed effects and compute averages of the annual coefficient estimates. The results (not tabulated) show that Strong Buy initiations underperform their non-initiation counterparts. The coefficient of Initiation in the Strong Buy sample is −0.0194 with a Fama and MacBeth (1973) t-statistic of −2.87, significant at 1% level. The coefficient of Initiation remains insignificant for the Buy sample.
We also estimate Eq. 1 after excluding recommendations issued during the bubble years (1997 through 2001). The results are qualitatively similar. The coefficient of Initiation in the Strong Buy sample is −0.0275 and significant at 1% level.
The length of window over which we search for the earnings forecasts is arbitrary. In sensitivity analysis, we conduct the earnings forecast accuracy and optimism tests on a sample of earnings forecasts issued over the period starting 60 days before and ending 3 days after the recommendation date. The results (not reported) are qualitatively similar to the results in Table 3.
The significance of the individual or aggregate results in Table 4 does not affect the analysis of the returns to initiation and non-initiation recommendations, because we form matched pairs annually based on the corresponding propensity score model.
Because there are statistically significant differences in some analyst and firm characteristics across the initiation and non-initiation samples after the matching process, we also estimate Eq. 1 for initiations and the p-score matched non-initiations. We find similar results—the coefficients on initiations are −0.106 (t = −2.06) and −0.027 (t = −0.50) for Strong Buys and Buys, respectively.
The Carter-Manaster rankings are based on the hierarchy of the listing of underwriters in the prospectus of the security offering where prestigious underwriters are typically listed higher in the underwriting section. We use the updated Carter-Manaster ranks constructed by Loughran and Ritter (2004).
McNichols and O’Brien (1997) also study return on equity after coverage initiations. In univariate tests, they report higher median return on equity and median industry adjusted return on equity for firms with continuous coverage (corresponding to non-initiations in our setting) than for added stocks (corresponding to initiations in our setting). They focus on return on equity in the fiscal year during which analysts initiate coverage. In contrast, we focus on return on equity in the year after the fiscal year during which the recommendation is issued. This focus is appropriate for our setting because we are interested in whether the long-run returns to stock recommendations reflect firm fundamentals. When we estimate Eq. 7a with industry-adjusted contemporaneous return on equity as the dependent variable, the coefficient of Initiation is insignificant for the sample of Strong Buy recommendations (α 1 = 0.0002, p-value = 0.930).
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Acknowledgments
We appreciate the helpful comments of Russell Lundholm (editor), an anonymous referee, Bob Ashton, Patricia O’Brien, Brian Cadman, Xia Chen, Jennifer Francis, Russell Lundholm (editor), Stan Markov, Bill Mayew, Devin Shanthikumar, Brett Trueman, Mohan Venkatachalam, two anonymous referees, and seminar participants at 2008 American Accounting Association Annual Meeting, 2009 Financial Accounting and Reporting Section Mid-Year Meeting, Southern Methodist University, University of California at Los Angeles, University of Connecticut, University of Texas at Dallas, University of Utah, University of Waterloo, and Washington University.
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This paper was formerly titled “The Long-Run Performance of Analyst Coverage Initiations”.
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Ertimur, Y., Muslu, V. & Zhang, F. Why are recommendations optimistic? Evidence from analysts’ coverage initiations. Rev Account Stud 16, 679–718 (2011). https://doi.org/10.1007/s11142-011-9163-6
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DOI: https://doi.org/10.1007/s11142-011-9163-6