Abstract
We examine how research posted by “social media analysts” (SMAs)—individuals posting equity research online via social media investment platforms—is related to research subsequently produced by professional sell-side equity analysts. Using data from Seeking Alpha, we find that the market reaction to sell-side analyst research is substantially reduced when the analyst research is preceded by the report of an SMA, and that this is particularly true of sell-side analysts’ earnings forecasts. We further find that this effect is more pronounced when SMA reports contain more decision-useful language, are produced by SMAs with greater expertise, and relate to firms with greater retail investor ownership. We also provide evidence that the attenuated response to sell-side research is most likely explained by SMA research preempting information in sell-side research and that analysts respond to SMA preemption with bolder and more disaggregated forecasts. Collectively, our results suggest that equity research posted online by SMAs provides investors with information that is similar to but arrives earlier than sell-side equity research, and speak to the connected and evolving roles of information intermediaries in capital markets.
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Notes
Given our research objective, we focus on investment-related social media platforms (Seeking Alpha in particular). We do not use the term “crowdsourced” because, unlike venues such as Estimize or Glassdoor, the research, opinions, and analyses we examine are not aggregated or crowdsourced in any way.
As of May 2021, Seeking Alpha reports over 40 million monthly site visits per month spread across 15.2 million unique users (Seeking Alpha 2021).
Because of the flurry of news released by various financial market participants following firms’ disclosures of earnings news, we conduct our primary tests using a restricted sample of forecasts issued outside of periods when firms disclose earnings or earnings guidance, but in untabulated analyses we find generally consistent results using a full, unrestricted sample of forecasts.
“How Hedge Funds Rate Wall Street Analysts”, Alpha Magazine, November 21, 2005. This anecdotal evidence may initially appear inconsistent with the conclusion in Amiram et al. (2016) that sell-side analyst forecasts represent new information only to less sophisticated, retail investors. However, it is likely that the timing of the “high-touch” services provided to institutional clients does not correspond with the timing of analysts’ public forecasts. In this case, one could still observe the result in Amiram et al. (2016) even with a shift in focus towards institutional clients.
According to Chen et al. (2014), contributors on Seeking Alpha earn $10 per one thousand page views. Seeking Alpha also helps authors promote their work on major media outlets and hosts networking events, both of which help contributors build their reputations in the investment community and potentially monetize their skills through other means (Seeking Alpha 2019). In addition, Seeking Alpha hosts a “marketplace” where authors can sponsor their own “paid-for” research platform, which further incentivizes SMAs to produce high-quality analysis.
Seeking Alpha now puts most research behind a relatively inexpensive paywall. Users may still freely access current and recent analysis for stocks in the portfolios they maintain in their user accounts. During our sample period, the Seeking Alpha content we analyze was free to all users.
We do not collect content with “news” URLs, as those typically represent news flashes or dissemination of news published elsewhere.
One concern is that SMA reports that precede sell-side analyst reports systematically differ from those that do not. We evaluate whether this is the case in untabulated analyses. The mean (median) absolute returns for reports that precede analyst reports is 0.022 (0.013) percent, compared to 0.021 (0.013) percent for those that do not. We also find that SMA reports preceding sell-side analyst reports are slightly shorter than SMA reports not preceding them (average word count of 826 vs. 846, respectively). Thus, it is unlikely that significant differences in content contribute to our results.
We also consider a measure of forecast news derived from the consensus forecast and find similar results.
For SMA reports and sell-side forecasts issued after 4 p.m., we adjust the announcement date to the next trading day so that our return windows (described later) correctly identify the event day. We also delete a small number of sell-side forecasts dated after the firm’s earnings announcement, which likely reflect data errors.
Despite our best efforts to control for factors that likely contribute to the SMA’s decision to publish research and to the pricing of analyst research, the potential for an omitted variable remains. We evaluate parameters under which an omitted variable would alter our inferences using the method developed in Oster (2019). We implement this procedure as in Call et al. (2018). Untabulated analyses suggest that, in order to alter our inferences in Table 3, omitted correlated variables would need to be 1.1 to 5.2 times more important than the combined effect of the vector of controls we include. While omitted variables remain a limitation of our study, we believe that these diagnostics suggest that the likelihood of such a variable playing a significant role in our analyses is low.
To the extent that SMA reports increase the likelihood that news about the upcoming analyst forecast is disseminated either on social media (SMA[0,1]) or by the business press (BizPress[0,1]), these two controls may not be appropriate, as they are not predetermined with respect to our variable of interest (Whited et al. 2021). However, if we exclude them, then our results could plausibly be driven by dissemination (since business press coverage and social media coverage surrounding the forecast correlate with coverage prior to the forecast). If we exclude these two variables, our results are qualitatively similar (untabulated). In addition, we do not include a control for other analyst forecasts contemporaneous to the forecast of interest (i.e., ProfAnalyst[0,1]), because we collapse our dataset to the firm-day level. Finally, we note that results are similar if we use the logged count of business press articles and prior analyst forecasts rather than indicator variables (untabulated).
Results are robust to clustering standard errors one-dimensionally by industry-month-year instead (untabulated).
Leone et al. (2019) note that winsorization is not an effective method for addressing significant outliers. As such, we tabulate results after applying Cook’s distance. We obtain similar results if we exclude observations with studentized residuals exceeding 2 or estimate the models using robust regressions (with industry-year-month fixed effects). Consistent with winsorization not effectively addressing outliers, we find inconsistent results for the first four columns of Table 3 using winsorization alone.
As an additional means of comparing the effects of these various intermediaries, in untabulated analysis we include all possible interaction terms of SMA[−7,−1], ProfAnalyst[−7,−1], and BizPress[−7,−1]. We find that the attenuating effect of SMA reports is observed in the presence of an analyst report, a business press article, or both. In contrast, the main effects of ProfAnalyst and BizPress are not consistently significant.
Note that we estimate these models at the analyst level, so there can be more than one observation per trading day. In addition, we only estimate these models using the analyst forecast sample (i.e., excluding recommendations and price targets), because IBES scrambled analyst identifiers prior to our accessing data related to price targets and recommendations. This scrambling makes analyst fixed effects impossible in tests that combine research outputs, such as those in our Table 3 analyses.
To illustrate the column 1 calculation, 0.164 divided by 0.387 equals 0.424.
We use Python’s “textblob” package to measure sentence-level sentiment. This measure of sentiment has two parts: polarity, which is analogous to tone and varies between −1 (negative) and +1 (positive), and subjectivity, which varies between 0 and 1. We consider a sentence to generate higher uncertainty if it conveys relatively strong sentiment, defined as an absolute value of polarity that is greater than 0.75, and high subjectivity, defined as greater than 0.50. Note that we also considered dictionary-based measures of uncertainty from both Loughran and McDonald (2014) and General Inquirer dictionaries and observed no significant differences.
The correlation between institutional holdings and retail trading intensity is − 0.44, suggesting that these two proxies capture similar but not identical aspects of ownership structure.
We use sample partitions (fully interacted models) for these tests since our partitioning variable is defined for all observations. For the analyses discussed in Sect. 5.1.1, the partitioning variable (e.g., expertise) is only defined when SMA = 1. Therefore, we use SMAhigh and SMAlow in those analyses.
This method identifies retail trades using a regulatory restriction that retail orders can receive price improvements (measured in small fractions of a cent per share) but institutional orders cannot. Using TAQ data, we divide the transaction price by 1 cent (0.01). If the remainder is in the interval (0.0, 0.4], then we identify the trade as a retail sell transaction; if the remainder is in the interval [0.6, 1.0), then we identify the trade as a retail buy transaction. Trades that occur at a round penny (remainder = 0) or those with remainders that fall around the half-penny (remainders in the interval (0.4, 0.6)) are not categorized as retail for conservatism. We then aggregate retail trading volume over each trading day.
To evaluate whether SMAs generally agree with sell-side analysts, we compare the tone of SMA reports, computed using the Loughran and McDonald (2014) sentiment dictionary, to AF. As expected, we observe a significantly positive association (untabulated), suggesting that the tenor of the news contained in the reports of SMAs and those of sell-side analysts generally track one another.
In untabulated analysis, we find similar (non)results using intraperiod timeliness over the same three windows (though starting at day 0) to measure the efficiency of the price formation process (Twedt 2016).
We find similar inferences using the natural log of the number of SMA reports over the 90-day period instead of SMA[− 90,− 1].
Note that Bloomberg data begins in 2010, resulting in a smaller sample for this test.
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Acknowledgements
We thank two anonymous reviewers, John Campbell, Ted Christensen, Eric Condie, Braiden Coleman, Matthew DeAngelis, Peter Easton (editor), Mac Gaulin (discussant), Ilan Guttman, Chad Ham, Mirko Heinle, Wen Hi (discussant), Mike Iselin, Ajay Kohli, Josh Madsen, Saby Mitra, Joseph Pacelli, Marlene Plumlee, Christina Zhu (discussant), and workshop participants at Georgia Tech, University of Calgary, University of Minnesota, University of Oregon, the 2020 Financial Accounting and Reporting Section Midyear Meeting, the 2020 UTS Summer Accounting Conference, and the 2020 Utah Winter Accounting Conference for helpful comments and suggestions.
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Drake, M.S., Moon, J.R., Twedt, B.J. et al. Social media analysts and sell-side analyst research. Rev Account Stud 28, 385–420 (2023). https://doi.org/10.1007/s11142-021-09645-1
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DOI: https://doi.org/10.1007/s11142-021-09645-1