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Review of Accounting Studies

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24.08.2022

Equity analyst social interactions and geographic information transmission

verfasst von: Qi Chen, William J. Mayew, Huihao Yan

Erschienen in: Review of Accounting Studies | Ausgabe 1/2024

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Abstract

We find that earnings forecasts by analysts with more local peers, defined as analysts working in the same brokerage office who cover different firms headquartered in the same area, are more accurate. These heightened accuracy effects are concentrated in settings where local peers are particularly valuable, such as when analysts have less access to corporate management, when earnings are harder to forecast, and when analysts have stronger incentives to work hard. In examining the nature of the information transmitted by local peers, we find that earnings forecasts by analysts with more local peers better reflect negative geographic shocks in firm earnings. In addition, geographic momentum in stock returns is attenuated for firms that are followed by more local peers, especially when area returns are negative. These findings suggest that social interactions among local peer analysts facilitate the transmission of complex, soft information about geographic factors to investors.

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These include social interactions for investors (Hong et al. 2005; Cohen et al. 2008; Pool et al. 2015; Ivković and Weisbenner 2007), corporate directors and managers (Shue 2013), and analysts (Cohen et al. 2010; Fang and Huang 2017; Bradley et al. 2020; Gu et al. 2019).

See Handbook of Regional and Urban Economics (Herderson and Thisse 2004) for a comprehensive review of the related literature.

The complexity of geography information is recognized in the existing literature. For example, while Dougal et al. (2015) outline a number of spillovers and externalities at the local level, they acknowledge that “for the most part, we cannot distinguish between the various people-based explanations” (page 169). Instead, they broadly refer to these geographic spillovers as “urban vibrancy.”

It is well established that analysts tend to specialize by industry (Piotroski and Roulstone 2004; Brown et al. 2015; Parsons et al. 2020).

Similarly, Jack’s forecasts for Whole Foods may also be of a different quality from his forecasts for Kroger, especially when compared with those by Jane.

Our paper is also related to, but distinct from, the strand of the analyst literature showing that geographic distance to an information source facilitates first-hand information acquisition, which in turn matters for analyst forecast quality (Malloy 2005; Bae et al. 2008; Jennings et al. 2017) and coverage decisions (O'Brien and Tan 2015; Engelberg et al. 2018).

See, for example, Dougal et al. (2015), Addoum et al. (2016), Core et al. (2016), and Matsumoto et al. (2022). For asset pricing specifically, see Pirinsky and Wang (2006), Feng and Seasholes (2004), Loughran and Schultz (2005), Loughran (2007), Hong (2008), and Garcia and Norli (2012).

The Nelson’s Directory of Investment Research continued until 2008, but only the 1993–2005 volumes are available at our institution. The lack of data beyond 2005 limits the generalizability of our findings to more recent time periods when advances in technology such as social media applications potentially minimize the benefits of in-person social interaction.

The IBES recommendation detail file only contains each analyst’s last name and first name initial. We rely on the match in brokerage names to make sure the link is correct. If there is still ambiguity, we exclude these observations from the sample.

https://www.census.gov/geo/maps-data/data/gazetteer2010.html. If the city name was not included in the Gazetteer file, we manually searched for the latitude and longitude on http://www.latlong.net/.

The results (not tabulated) are qualitatively unchanged if we instead use AFE without scaling by price.

Following prior literature (Clement 1999; Malloy 2005; Bradley et al. 2017), we de-mean both the dependent variable and the independent variables in alternative specifications and obtain qualitatively similar results (not tabulated). We opt to use the firm-year fixed effect specification, as Gormley and Matsa (2014) show that the de-mean approach can generate biased results.

Column (4) shows an insignificant coefficient for ln_dist because we include more controls than Malloy (2005). We can uncover the Malloy (2005) finding when we only include firm-year fixed effects and do not control for the number of local peers.

To be consistent with our earlier analysis, we estimate Eqn. (3) after scaling both FE_{i, j(a), k, t} and \( {\hat{X}}_{a,t} \) with the firm’s equity price at the beginning of year t. In a sensitivity test (not reported), we obtain qualitatively similar results when we use unscaled versions of FE_{i, j(a), k, t}and \( {\hat{X}}_{a,t} \).

This effect is hypothesized to be evolutionary in nature, as human survival is maximized by knowing when negative situations exist.

One concern for this analysis is that the dependent variable in Eqn. (3) is part of the sample used in estimating the area-specific shock; thus, the coefficient estimate on the interaction term of LN_OEA and \( {\hat{X}}_{a,t} \)may simply capture the mechanical correlation between the dependent variable and \( {\hat{X}}_{a,t} \). We note that the average number of firms headquartered in an area in our sample is 140, and the consensus for an average firm is based on 7.3 analyst forecasts. This means the dependent variable in Eqn. (3) is only one of the 1022 (=7.3*140) forecast errors used in constructing area-specific shock, suggesting that there are small mechanical correlation effects. Nonetheless, in an untabulated robustness check, we re-estimate Eqn. (3) by estimating an area-year specific shock without including the consensus forecast for the firm followed by analyst j. We find that our main results remain qualitatively similar.

Following Parsons et al. (2020), we only include firms in the 20 largest EAs in the sample.

Returns are never exactly equal to zero in our sample.

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Titel: Equity analyst social interactions and geographic information transmission
verfasst von: Qi Chen
William J. Mayew
Huihao Yan
Publikationsdatum: 24.08.2022
Verlag: Springer US
Erschienen in: Review of Accounting Studies / Ausgabe 1/2024
Print ISSN: 1380-6653
Elektronische ISSN: 1573-7136
DOI: https://doi.org/10.1007/s11142-022-09714-z

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