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Review of Accounting Studies

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07-07-2019

Skin in the game: personal stock holdings and investors’ response to stock analysis on social media

Authors: John L. Campbell, Matthew D. DeAngelis, James R. Moon Jr

Published in: Review of Accounting Studies | Issue 3/2019

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Abstract

Motivated by concerns that financial positions impair analyst objectivity, we examine investor perceptions of the financial positions of nonprofessional analysts (hereafter NPAs) writing on the social media outlet Seeking Alpha. We find that NPA positions contribute directly to short-window returns surrounding the article’s publication, holding constant the information in the article as well as contemporaneously issued news from professional analysts, managers, and the business press. Contrary to concerns that stock positions are associated with biased analysis, we find no evidence that NPA positions reduce investor responses to the tone of the article. In fact, our evidence suggests that holding a position magnifies investor responses to both positive and negative tone, although this effect is limited to tone that is contrary to the NPA’s stock position. Overall, our findings suggest that, contrary to regulators’ concerns, NPA stock positions do not decrease the credibility and informativeness of their analyses.

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Unlike professional analysts or firm insiders, NPAs publishing on sites like Seeking Alpha do not face legally enforced “blackout periods” that limit trading activity around the publication of their reports. However, they are still subject to US laws regarding market manipulation (15 U.S. Code § 78i).

There are a number of social media venues considered by prior research, such as Motley Fool, Estimize, StockTwits, Yahoo! Finance, and online stock message boards. We focus on Seeking Alpha because (1) it requires contributors to provide written, edited analysis (suggesting a certain level of rigor and sentiment, or tone), and (2) it requires contributors to disclose whether they have a financial position in the firms about which they write. Chen, De, Hu, and Hwang (2014) find that Seeking Alpha content represents value-relevant information. Additionally, Drake, Thornock, and Twedt (2017) identify Seeking Alpha as a credible internet-based information intermediary.

Based on data provided by George Moriarty, SeekingAlpha’s editor, SA is used by both institutional and retail investors. His data suggests that the professional investors that use SA control $15 trillion in managed assets (from both institutions and high net-worth clients) and retail investors report $1.3 trillion in savings and investments. Thus, if SA position disclosures are informative, users of the information have the purchasing power to significantly move stock prices.

We expect first-time disclosures to be most informative because they indicate new information about an author’s position that may be value relevant. However, repeat disclosures indicate a continued commitment to the position and likely still provide information.

As previously mentioned, US law may provide an enforcement mechanism for truthful reporting of positions: while SA cannot observe the portfolios of its NPAs, the SEC certainly can. However, because NPAs are often writing about their own analysis and opinion, readers of SA articles must still pay attention to their credibility.

The current analyst disclosure rules were initially developed by the New York Stock Exchange (NYSE) and the Financial Industry Regulatory Authority (FINRA) and then adopted by the SEC in 2002 (SEC 2016).

Bradley (2018) argues that the most “interesting, controversial, and convincing evidence” in Chan et al. (2018) is the evidence in their Table 7 showing that, in 424 instances, analysts appear to sell their ownership positions while maintaining a buy recommendation, an act that is generally prohibited by law. However, this activity is exceedingly rare, as it represents 0.06% of all buy recommendations in the sample (i.e., 424 out of 749,606 buy recommendations). Importantly, this evidence does not conflict with our finding that, on average, position disclosures appear to enhance analysts’ credibility.

According to SeekingAlpha’s website, one benefit of being a contributor is “access to company management” (https://seekingalpha.com/page/become-a-seeking-alpha-contributor). There, the publication mentions that “[c]ompanies pay close attention to what is written about them on SA. Some companies also contribute via articles and comments. Many contributors have been given exclusive access to company executives to get their side of the story.”

A related stream of literature examines how firm insiders, such as managers or employees, communicate with market participants via social media (e.g., Blankespoor et al. 2014; Hales et al. 2018).

The passage of MiFID II in Europe has also led some to predict the demise of sell-side analysts (e.g., Morris 2017, Armstrong 2018), further suggesting platforms like SA will be important sources for investment news.

See http://www.investopedia.com/articles/investing/112514/top-sites-latest-stock-market-news.asp and http://www.thetoptens.com/financial-news-websites/. Both sites accessed in Summer 2017.

As a rule, SA articles themselves do not uniformly include an author’s recommendation, though the author’s analysis may come with in implicit recommendation to buy or sell a stock. In addition, SA uses keyword algorithms to generate categories of stock analysis, and two of those categories are “long ideas” or “short ideas.” However, not all articles by long (short) authors are tagged as “long (short) ideas,” and not all authors writing a long and short idea have a position in the stock they recommend. Note that our sample begins with all SA articles (which have many different categories, including long and short ideas, among others).

During our sample period, SA offered a “pro” subscription that gave subscribers early access to content selected by editors. Per our discussion with the SA editor, this access lasted 24 h after which the article is made public for 30 days. After that period, the article is archived behind a paywall and available only to pro subscribers. Due to our sampling procedures, we did download a limited number of pro articles that were published in the 30 days preceding our query of SA, but these articles were not accessible when we extracted comments at a later date and are therefore excluded from our final sample. Thus, our sample is fully comprised of articles which were available to the public on the timestamp appearing in the article. Note that, as of mid 2018, this process appears to have changed. Immediate access to articles is limited to tickers included in a user’s portfolio, and access to archived content requires a pro subscription.

Disclosures of positions were relatively rare until 2012 (no more than 20% each year), when SA started requiring these disclosures. In more recent years, most (over 90%) of articles with a primary ticker designation include disclosures.

For brevity, we refer to articles authored by investors with long (short) positions as “long articles” (“short articles”) and to those holding no position as “no position articles.”

While the sample attrition in Table 1 may at first glance appear dramatic, the majority of sample attrition is due to (1) the removal of SA articles that do not relate to one specific ticker symbol (i.e., that are industry or macroeconomy articles), (2) the requirement to disclose whether the NPA holds a position, and (3) the ability to match the identified ticker symbol with a firm listed in CRSP and Compustat. Thus, when put in context, our sample attrition is largely driven by factors that are necessary to answer our questions of interest. Given our research design, we do not believe these data restrictions induce any systematic biases in our sample.

SA prohibits authors from publishing the full content of their analysis in other locations. Therefore it is unlikely articles written by authors with their own blogs could be published in advance of clearing the publication’s editorial process. Nonetheless, in a sensitivity analysis, we exclude articles written by authors appearing to have personal website. All of our results are quantitatively and qualitatively unchanged.

We provide detailed variable definitions in Appendix 1.

Following Loughran and McDonald (2011), we do not code words as positive if they are preceded by a negating word (no, not, none, neither, never, or nobody).

The long-form nature of SA articles makes it unlikely that an event on day t leads to an article written on day t. Furthermore, discussions with an editor at SA suggest that the editorial process can be lengthy—as long as 12 h in some cases. This delay makes it unlikely that reactions to content reflect contemporaneously issued news. Nonetheless, we control for several aspects of contemporaneous news in our models, and we conduct additional analyses in Section 5 to rule out the alternative explanation that contemporaneous events explain our findings.

We include SAFollowers to control for the author’s reputation, ability, or both. Our results are unchanged if, in addition to SAFollowers, we also include as control variables (1) a measure of the number of prior posts, (2) the number of prior posts about the target firm, (3) the author “track record,” following Chen et al., and (4) whether the author is deemed a “financial professional” (coded as 1 if the author mentions “analyst” or “CFA” in his or her bio or if the account name appears to be a business).

PosGuidance and NegGuidance take the value of 1 if management issues a forecast that is greater or less than the analyst consensus before the guidance is issued, respectively, while Guidance is equal to 1 if any forecast is issued. The sum of the mean values for PosGuidance and NegGuidance slightly exceeds the value of Guidance because managers may issue multiple forecasts of varying horizons in a given window with different news (i.e., PosGuidance and NegGuidance could both equal 1).

In all tables, we multiply the dependent variable by 100 to facilitate presentation of coefficient estimates. All specifications also include industry and year-month fixed effects. Significance is assessed from standard errors clustered by year-month to correct for cross-sectional correlation in returns.

To the extent that the absolute (rather than signed) reaction to an author’s work varies with his or her reputation, using signed returns in Panel B may not adequately control for unobserved author characteristics. Therefore, in untabulated tests, we replace the dependent variable with the natural log of 1 plus the absolute value of AbRet_i,[0,1] and repeat tests in Panel B of Table 5. While we continue to find significant coefficients on Short in all specifications (t-statistics between 2.54 and 4.75), coefficients on Long are significant only in full-sample models.

We present results for H2 using sample partitions. To assess significance, we estimate equation [1] using a series of fully interacted models. Reported significance levels reflect the significance of the interaction distinguishing the two compared columns. We report one-tailed p-values when comparing “position” articles versus no position (i.e., columns 2, 3, or 4 versus column 1) and two-tailed otherwise.

We center these three variables at 0 for this analysis to maintain interpretability of coefficients on Long and Short. In other words, main effects on Long and Short represent the response to position for average levels of the interacted variables. This is especially useful, as another explanation for our results related to H1 is that, for articles written by NPAs with a long (short) position, article length captures positive (negative) tone not measured in PosPct (NegPct). If this were the case, then we would observe a significant interaction between each position variable and lWordCount but insignificant main effects on Long and Short.

One may argue that the first-time disclosure of a position should be the only time this knowledge matters. However, multiple articles disclosing the same position affirm the author’s beliefs over time, similar to management affirming a previously issued forecast, thus providing additional relevant signals.

We also considered whether a change in disclosures represents a credible signal. However, this only occurs in about 8% of articles.

In untabulated tests, we replicate their result using size and book-to-market matched portfolio returns as well as simple market-adjusted returns. Thus the difference in our result appears to be driven by return definition (ours corrects for momentum, theirs does not) rather than differences in sample period or control variables. Additionally, Chen et al. (2014) still provide important evidence that SA articles predict future earnings news.

If we partition at the median following for the author population, the low (high) partition has approximately 8000 (96,000) observations. Nonetheless, we find similar evidence as that reported, except we also observe stronger responses to short position in the high-following sample (p = 0.06).

Specifically, we regress CountToDate_i,k,t, which is the number of times author k has written about firm i as of date t on calendar month (“year-month”) fixed effects and compute the residual. Positive (negative) residuals are considered high (low) levels of coverage. We orthogonalize this measure to time period since article counts are increasing over time, and to better emulate an author’s record relative to other authors, at the time each article is published.

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Title: Skin in the game: personal stock holdings and investors’ response to stock analysis on social media
Authors: John L. Campbell
Matthew D. DeAngelis
James R. Moon Jr
Publication date: 07-07-2019
Publisher: Springer US
Published in: Review of Accounting Studies / Issue 3/2019
Print ISSN: 1380-6653
Electronic ISSN: 1573-7136
DOI: https://doi.org/10.1007/s11142-019-09498-9

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