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Review of Accounting Studies

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01-03-2015

Who’s heard on the Street? Determinants and consequences of financial analyst coverage in the business press

Authors: Lynn Rees, Nathan Sharp, Brady Twedt

Published in: Review of Accounting Studies | Issue 1/2015

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Abstract

This study examines the determinants of financial analyst coverage in the business press and the effects of such coverage on analysts’ careers. Using a sample of business press articles that quote a financial analyst, we compare each cited analyst with the other analysts who were actively following the same firm and could have been cited by the reporter but were not. In general, we find that analysts quoted in the business press are more likely to possess characteristics that prior research has found to be positively associated with analyst quality and that higher quality analysts are allotted more space in the articles. However, we cannot rule out the possibility that at least some of our analyst quality measures capture the effects of familiarity. After controlling for analyst quality and article tone, we find that analysts with unfavorable stock recommendations are more likely to be cited than analysts with favorable recommendations. We provide evidence of significant career benefits for analysts who receive publicity in the business press, which suggests that the press is a source of valuable exposure for analysts.

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Cited in Groysberg (2010; p. 56).

See Ramnath et al. (2008) for an in-depth review of the literature on financial analysts and their role in capital markets.

Analysts create value for their brokerage houses in two ways. First, institutional investors often allocate brokerage commissions based on the quality of the brokerage’s analyst research. Second, firms generally direct their corporate finance business to banks with well-known analysts because the reports of these analysts can produce greater institutional investor awareness and liquidity for their stock (Groysberg et al. 2011).

Former all-star analyst Dan Reingold stated that, “Some clients voted for the name they knew best, meaning it was helpful to be quoted regularly in The Wall Street Journal…. Getting ranked in II was good for everyone. It made it easier for the retail brokers to attract clients, helped the traders and institutional sales people generate more commissions, and, lest we forget, it helped the bankers attract investment banking business from corporate executives” (Reingold and Reingold 2006, pp. 89–91).

Emery and Li (2009) provide evidence that achieving all-star status reflects analyst popularity more than analyst quality. However, Fang and Yasuda (2014) find that all-star status primarily reflects analyst quality. Similarly, brokerage size could also proxy for either analyst quality or recognition.

Bonner et al. (2007, p. 498) briefly describe untabulated results from estimating a determinants model of analyst media coverage at an annual level. Their determinants analysis represents a minor part of their study; nevertheless, we discuss in Sect. 3 how our model differs from and extends their analysis.

See, for example, Groysberg et al. (2011), Groysberg (2010), Cliff and Denis (2004), Hong and Kubik (2003), Krigman et al. (2001), and Stickel (1992).

Researchers have even observed significant price reactions to articles in the business press that appear to provide the market with little or no new information (Tetlock 2011; Huberman and Regev 2001). For example, Huberman and Regev (2001) report that a favorable article in The New York Times boosted biotechnology company EntreMed’s stock price by $39.75 overnight (a 330 % increase), although the information contained in the article had already been published months earlier in both an academic journal and the Times itself. They also find that this increase was relatively permanent, with the price remaining at more than twice its pre-article level six months after the article was published.

This reasoning is based on the investor recognition hypothesis posited by Merton (1987). For additional empirical evidence, refer to Bushee et al. (2010), Kothari et al. (2009), and Barber and Odean (2008).

The determination of which firms to cover and the sample selection criteria used to obtain articles are based on the internal methodology developed by Media Tenor and are thus beyond our control.

Fang and Peress (2009) report that media coverage of firms, in general, is surprisingly low, with over 25 % of large NYSE stocks not featured anywhere in the business press in a typical year. They also find that, for those firms that are covered in the press, the mean (median) number of total articles in a given year is only 12 (5). Based on this evidence, our sample appears to be relatively large, especially given our stringent sample requirements.

We follow Mayew (2008) in modeling favorableness on an absolute basis rather than relative to other analysts following the firm. Also, Kadan et al. (2009) find that roughly 75 % of all stock recommendations are issued using a three-tiered system since the regulatory reforms of 2002 (NASD Rule 2711, NYSE Rule 472, and the Global Analyst Research Settlement). Accordingly, we aggregate strong buy and buy recommendations, as well as sell and strong sell recommendations (see also Ertimur et al. 2007).

We also provide results using an alternative measure of article tone provided by Media Tenor, as discussed in more detail in Sect. 5.

Because Rel_Accuracy is calculated using absolute values of forecast errors, it is not necessary to use reverse rankings for this variable. The lowest forecast error represents the analyst with the highest accuracy and is assigned a rank of one; thus higher values of Rel_Accuracy indicate superior forecast accuracy.

Complete descriptions of all variables used in the study are provided in the Appendix.

We do not attempt to reconcile differences in results between this study and the determinants model in Bonner et al. (2007) because (1) their determinants analysis represents a minor part of their study and is less developed, (2) the research designs and sample periods between the studies are substantially different, and (3) Bonner et al. do not tabulate the results of their determinants analysis, which makes it difficult to precisely determine how our results differ. Where results differ, the results in our study are consistent with ex ante expectations.

We discuss the results of tests of reverse causality and other additional analyses related to endogeneity in Sect. 5.

For example, one analyst stated, “The II ranking had always been the yardstick by which all of us were measured, on the assumption that without a high II rating investors wouldn’t follow our advice and thus companies wouldn’t hire our firms for big deals” (Reingold and Reingold 2006, p. 180).

In Eq. (1), we measure analyst characteristics as relative rankings because each cited analyst is compared directly to all noncited analysts following the firm at the time of each article. In Eq. (2), we are no longer directly comparing cited analysts with matched noncited analysts at the article level. Instead, we are estimating how aggregate media coverage in one year affects career outcomes in the following year, while controlling for analyst characteristics across each analyst’s portfolio of firms covered during the year. Thus relative rankings are not suitable for this model.

It is possible that analysts drop from the all-star ranking from year t to year t + 1 because the analyst left the industry (Hong et al. 2000) or became a research executive (Wu and Zang 2009), and not because the analyst failed to receive a sufficient number of votes to retain the position. Accordingly, we eliminate analysts from our sample who are not found in the I/B/E/S database in year t + 1.

As an alternative test of the relation between analyst quality and press coverage, we categorize each of the quality measures for analyst j as high (= 1) or low (= 0) quality based on whether the value for analyst j was above or below the median value. We then sum the quality measures for each analyst to produce an overall summary measure of quality and include this value in the regression equation in place of the individual analyst characteristics. Consistent with our tabulated results, the coefficient on this aggregated variable is positive and significant (p < 0.01) across all regression specifications, indicating that higher quality analysts are more likely to be cited in the business press.

We find similar results when we use a two-day market reaction window (0,1) or market-adjusted returns instead of size-adjusted returns. We also examine the robustness of our results to changes in the window used to define a high visibility period and find that using a 30-day window or a 90-day window does not affect our inferences.

Additionally, we examine whether the news content of the revisions differs fundamentally between the high and low visibility periods. Comparing each revised recommendation to the prior recommendation the analyst had outstanding for the firm, we find that the distribution of reiterations, upgrades, and downgrades is virtually identical across the two sets of revisions (untabulated).

In an additional (untabulated) test, we replace Media_Coverage with an indicator variable for analysts who were cited in year t. Consistent with our tabulated results, we find no association between media coverage in year t and the profitability of analysts’ recommendations in year t + 1. We also examine whether future analyst forecast accuracy or boldness is associated with media coverage in period t, but we continue to find no relation.

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Title: Who’s heard on the Street? Determinants and consequences of financial analyst coverage in the business press
Authors: Lynn Rees
Nathan Sharp
Brady Twedt
Publication date: 01-03-2015
Publisher: Springer US
Published in: Review of Accounting Studies / Issue 1/2015
Print ISSN: 1380-6653
Electronic ISSN: 1573-7136
DOI: https://doi.org/10.1007/s11142-014-9298-3

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