Place Your Bets? The Value of Investment Research on Reddit’s Wallstreetbets

Abstract

We examine the value of due diligence recommendations on Reddit’s Wallstreetbets (WSB) platform. Before the Gamestop (GME) short squeeze, recommendations are significant predictors of returns and cash-flow news. This predictability is eliminated post-GME. Post-GME, the fraction of reports emphasizing price-pressure or attention-grabbing stocks dramatically increases, and the decline in informativeness is concentrated in these reports. Similarly, retail trade informativeness is particularly strong following DD reports in the pre-GME period, but not post-GME. Our findings are consistent with the view that the Gamestop event altered the culture of WSB, leading to a deterioration in investment quality that adversely affected smaller investors.

On February 18, 2021, the CEOs of Reddit and Robinhood along with a Reddit user testified before Congress for their role in the well-publicized Gamestop (GME) short squeeze that sent shares to almost $500 before plummeting to around $50 a few days later. One of the main concerns among regulators can be summed up with the following quote during testimony: “The Reddit discussions are in many ways quite worrisome. They create volatility in the markets, and volatility is generally bad. It creates all kinds of dislocations.”¹

While regulators have previously raised alarm about the impact of social media on stock market efficiency and investor welfare, several unique attributes of Reddit’s Wallstreetbets (WSB) platform have exacerbated these concerns.² First, unlike other social media platforms, users’ postings are anonymous. During testimony, several Congressional representatives questioned this policy. Reddit’s CEO countered suggesting that WSB would not exist if users had to reveal their identity.³ In addition, in contrast to other prominent investment-related social media platforms, such as Seeking Alpha, WSB posts have virtually no editorial review and tend to focus on speculative strategies that emphasize small probabilities of large gains, possibly at the expense of lower expected returns (Bali, Cakici, and Whitelaw 2011). Further, anecdotal evidence from the Gamestop episode suggests that WSB could induce trading frenzies that can potentially harm smaller investors, destabilize prices, and even affect the real economy (Goldstein, Ozdenoren, and Yuan 2013).

We take a first look at the determinants and capital market consequences of the investment research provided on WSB. To disentangle the general effects of social media with the more unique features of WSB, we contrast WSB research with research provided by Seeking Alpha. Presumably, many people were attracted to WSB based on the extraordinary success of the Gamestop short squeeze. Given investors tendency to extrapolate from salient recent events, one concern is that new users will flood the site with research reports on attention-grabbing stocks that emphasize strategies unrelated to firm fundamentals (e.g., short squeezes). To the extent that users overestimate the effectiveness of such strategies, WSB research after the GME event may be particularly uninformative.⁴

We primarily focus on single firm “Due Diligence” (DD) reports, which are reports identified by the poster (and verified by moderators) as containing some type of analysis and a clear buy or sell signal. DD reports contain clear investment recommendations and potentially new value-relevant information, which makes them most comparable to other forms of crowdsourced investment research, such as Seeking Alpha reports. We also separately examine non-research-related WSB posts (e.g., meme posts, bragging about recent gains and losses). Although nonresearch posts are unlikely to contain useful information, they could still influence prices through attention-based buying (Barber and Odean 2008). Our sample includes 5,050 DD reports and 13,255 non-research-related WSB posts issued between July 2018 and June 2021.

Consistent with the view that WSB emphasizes high-risk investments, we find that both DD reports and nonresearch posts on WSB tilt toward young, volatile stocks with high skewness and high short interest. WSB preference toward speculative investments also substantially increases in the post-GME period. For example, WSB tendency to cover more volatile stocks increases by 150%, while WSB coverage of stocks with heavy short interest increases by nearly 500%. The time-series patterns are consistent with the Gamestop event attracting even more risk-seeking users. While SA coverage also tilts toward volatile firms, SA coverage of speculative stocks does not increase in the post-GME period. These findings suggest that the GME event affected WSB in a way that did not generalize across all social media platforms.

In the pre-GME period, we find DD reports are significant predictors of future returns. For example, an incremental DD buy recommendation is associated with a 5.17% increase in 1-month-ahead returns for the full sample and a 2.33% increase after excluding GME and AMC.⁵ However, the 1-month return predictability is fully eliminated in the post-GME period. Consistent with prior work (e.g., Chen et al. 2014), we find that SA research report recommendations also predict future returns. However, we find no evidence that SA research reports exhibit a decline in informativeness in the post-GME period, which alleviates the concern that broad economic forces resulted in a deterioration in investment research across all social media platforms.

One concern is that the informativeness of DD reports in the pre-GME period is largely a consequence of the unusual pandemic market, which amplified market volatility and generated a surge in retail trading (Ozik, Sadka, and Shen 2021). To explore this conjecture, we partition the pre-GME period into prepandemic and post-pandemic. We find that the informativeness of DD reports are slightly larger (albeit insignificantly so) for reports issued prior to the pandemic, which is inconsistent with the view that the results are driven by factors related to the pandemic (e.g., excess volatility, working from home, stimulus checks). More generally, we find that the predictive ability of DD reports was stable in 2020, and then immediately and permanently declines in the post-GME period. The lack of pre-trends in the pre-GME period and the sharp decline in informativeness in the post-GME period is consistent with the GME event itself contributing to the decline in report informativeness.

We also consider that the predictive ability of WSB research in the pre-GME period is not driven by reports containing value-relevant information (information), but rather because DD reports induce uninformed demand shocks that push prices beyond fundamentals (price pressure). However, we find no evidence that the returns patterns reverse over longer-horizons (up to 12 weeks following the report), which is inconsistent with the joint hypothesis of price pressure and short-term downward-sloping demand curves. We also document a positive relation in the pre-GME period between DD reports and future media sentiment, earnings surprises, and analyst earnings forecast revisions, which points to the possibility that at least a portion of the return predictability associated with DD reports in the pre-GME period is attributable to forecasting future cash-flow news. However, in the post-GME period, the positive relation between DD reports and cash-flow news is fully eliminated, and in some cases, it even becomes significantly negative.

Why does report informativeness decline in the post-GME period? We conjecture that the remarkable success of the GME short squeeze may have attracted new users who (1) place too much emphasis on coordinated trading strategies, possibly at the expense of analyzing firm fundamentals and (2) have the tendency to trade attention-grabbing stocks, which has been shown to negatively affect retail traders (Barber et al. 2022). We find support for both conjectures. In particular, the fraction of reports focusing on price pressure strategies increases by 165% in the post-GME period, while the fraction of reports issued on attention-grabbing stocks increases by 75%. Further, the decline in return predictability of DD reports in the post-GME period is significantly stronger among these reports. In fact, while post-GME DD reports are, on average, uninformative, we find evidence of significant negative return predictability among this subset of price pressure or attention-grabbing reports.

Our last set of tests examine how investors of differing sophistication levels trade following DD reports. We consider three groups of investors: institutional investors, large retail investors (as proxied by volume-based measures of retail order imbalance), and small retail investors (as proxied by trade-based measures of retail order imbalance). We compute a measure of standardized abnormal imbalances for each investor type, which captures the intensity of the directional order imbalances for a firm relative to a benchmark period. We find that abnormal institutional imbalances are uncorrelated with DD report recommendations, abnormal large retail imbalances are modestly correlated, and abnormal small retail imbalances exhibit a substantial correlation. We also find that both nonresearch reports and SA recommendations correlate with small retail imbalances. However, the increase in abnormal small retail imbalances following a DD buy recommendation is roughly four times larger than the corresponding increase following a nonresearch report or an SA recommendation. The association between DD report recommendations and small retail trading imbalances is particularly strong for the right tail of the distribution. For example, in the post-GME period, an additional DD buy recommendation is associated with a 42% decline in the likelihood that the stock falls within the bottom 1st percentile of small retail imbalances compared to a 294% increase in the likelihood of it landing in the top 1st percentile.

Since small retail investor imbalances are highly correlated with DD report recommendations, the decline in DD report informativeness in the post-GME period may lead to a decline in small retail trade informativeness. Consistent with this view, we find that small retail trade informativeness is significantly larger following DD reports issued in the pre-GME period, but this relation is fully eliminated in the post-GME period. The finding is consistent with the decline in the informativeness of WSB adversely affecting less-sophisticated retail investors.

Our study contributes to the literature about the informativeness of investment research provided on social media. While some papers find a significant positive relation between investment opinions on social finance sites and future stock returns (e.g., Chen et al. 2014; Jame et al. 2016; Crawford et al. 2018; Bartov, Faurel, and Mohanram 2018), others do not (e.g., Tumarkin and Whitelaw 2001; Kim and Kim 2014; Giannini, Irvine, and Shu 2018; Ammann and Schaub 2021). During the GME trading frenzy, WSB became the most influential social finance site by many metrics.⁶ Further, WSB offers many features that are distinct from other social media sites. These features give rise to its apparent ability to induce trading frenzies among smaller investors and coincidently attract greater scrutiny from regulators. Consistent with these differences being important, we find that the influence of WSB on financial markets is very different from Seeking Alpha. In this sense, our finding echoes recent work by Cookson et al. (2022) who find that sentiment exhibits very little correlation across three different social media platforms (Twitter, StockTwits, and Seeking Alpha), and who caution readers from drawing general conclusions about social media based on evidence from one specific platform.

Our study also extends the nascent literature that explores the growing importance of WSB and its impact on financial markets. Several contemporaneous papers focus on the dynamics between WSB activity and 1-day-ahead returns, trading volume, short interest, volatility, and market quality (e.g., Aharon et al. 2023; Semenova and Winkler 2021; Hu et al. 2021; Long, Lucey, and Yarovaya 2021; Eaton et al. 2022; and Allen et al. 2021). Relative to this literature, we make at least three contributions. First, we distinguish between DD reports and nonresearch posts. We show that retail trading is far more correlated with DD reports than nonresearch posts, and we encourage future research on WSB to separately analyze DD reports (ie, investment research) from other WSB posts. Second, we benchmark our findings from WSB to Seeking Alpha. This contrast highlights several important differences between the two sites. For example, we find that small retail imbalances are much more strongly related to WSB reports, which is consistent with regulators’ concerns that WSB posts are more likely to induce retail trading frenzies. Lastly, we contrast the value of DD reports in the pre-GME and post-GME period. Our findings highlight a dramatic decline in the informativeness of WSB in the post-GME period that is at least partially attributable to an increased emphasis on coordinated trading strategies and attention-grabbing stocks.

Finally, we add to the literature on retail trading. Several recent papers highlight a surge in retail trading spurred by innovations by fintech brokerage firms (Barber et al. 2022) and pandemic-related disruptions (Ozik, Sadka, and Shen 2021). Our results suggest that the growth in WSB is another factor that contributed to the recent growth in retail trading. A related literature examines the informativeness of retail trading. Early work finds that retail traders are uninformed “dumb money” (e.g., Hvidkjaer 2008; Frazzini and Lamont 2008; Barber, Odean, and Zhu 2009). However, more recent evidence suggests that retail trade imbalances correctly predict future stock returns (e.g., Kaniel et al. 2012; Kelley and Tetlock 2013; Boehmer et al. 2021; Welch 2022). Farrell et al. (2022) suggest that the improvements in retail trading over time may be partially attributable to the increasing democratization of investment research. Consistent with Farrell et al. (2022), we find evidence consistent with WSB reports improving the informativeness of retail trading, but this relation only holds during the pre-GME period.

1 Data and Descriptive Statistics

1.1 Background of Reddit and Wallstreetbets

Reddit is a social media platform founded in June 2005. Like many other social media websites, contributors post content, and users can add comments in response to the original post. The Reddit community is a collection of forums, where each forum is dedicated to a particular topic called a subreddit. Each subreddit is then organized into several pages based on users’ ranking criteria. For instance, the default page is the “Hot Page,” which lists the currently most viewed posts or posts with the most active commentators. “New Posts” lists posts based on the listing timestamp, and “Top posts” lists posts with the most likes (upvotes) and comments for a specified period. When a new post is written, it is only visible in the new post category. The post can then move up to the hot page if it reaches sufficient traffic.

Wallstreetbets (WSB) is one of many subreddits within the Reddit community. It was created on January 31, 2012, with a particular emphasis on highly speculative trading strategies. While this is not the only subreddit dedicated to investing strategies (ie, r/Investing, r/Personalfinance, r/Stocks, etc), we focus on this particular subreddit for three primary reasons. First, with over 13 million subscribers, it is by far larger than other finance-related subreddits. Second, it is the subreddit that has recently attracted significant media and regulatory attention for its role in the GameStop short squeeze and ensuing trading frenzies targeting meme stocks. The conventional view is that this forums’ userbase is predominantly unsophisticated retail investors who are more interested in gambling than investing.⁷ There has also been significant concern that the “research” on WSB is at best uninformative, and at worse, a force that destabilizes stock prices and contributes to significant retail trading losses. Lastly, as we will discuss in greater detail in the next section, WSB differs significantly from other social finance sites (e.g., Seeking Alpha), which suggests that prior work on social finance may not apply to WSB.

1.2 A comparison of WSB to Seeking Alpha

WSB shares important similarities with other social finance platforms such as Seeking Alpha (SA). Both sites allow nonprofessional investors to share their investment research, and both sites allow for readers to provide comments on the report and engage in discussions with other users. Prior work on SA has suggested that a large fraction of SA reports and contributors are skilled (see, e.g., Chen et al. 2014; Farrell, Jame, and Qiu 2020). However, several prominent differences between WSB and SA suggest that WSB research may be less informative than SA research.

First, while SA employs an editorial team to review all research reports to ensure quality, the quality control on WSB is very limited. Relatedly, WSB allows users greater anonymity than SA.⁸ Greater anonymity reduces the incentives to develop a strong reputation and potentially allows users with more nefarious motives (e.g., pump and dump schemes) to switch identities without accountability. WSB reports also tend to be considerably less in-depth than the average SA report, and the userbase of WSB is likely to have less financial sophistication.⁹ Anecdotal evidence suggests that WSB also places a larger emphasis on highly speculative trading strategies. As a result, investment research on the site may gravitate toward strategies that tend to earn lower expected returns such as buying stocks with high volatility (Ang et al. 2006) or stocks with lottery-like features (Bali, Cakici, and Whitelaw 2011).

Finally, in contrast to Seeking Alpha which has steadily grown over the past 15 years, much of WSB growth is attributable to the GME short squeeze event. For example, Figure 1 shows the forum grew from 500,000 users in July of 2018 to 10.7 million users as of June 2021, with a clear spike during the GameStop short squeeze in January of 2021. One concern is the dramatic increase in new users, most of whom were attracted to the site by the extraordinary price increases in Gamestop, can have a profound shift on the culture of the site. For example, given investors tendency to forecast expected returns based on recent performance (e.g., Greenwood and Shleifer 2014), new users may overestimate the effectiveness of strategies that are unrelated to fundamentals, such as price pressure induced short squeezes. Indeed, ample anecdotal evidence suggests that new members of WSB tend to emphasize coordinated buy-and-hold strategies for a handful of meme stocks with little regard to the company’s fundamentals.¹⁰

Given the significant shift in both the userbase and culture of WSB following the GME event, our analysis will separately examine WSB reports issued in the pre-GME period and post-GME period. We define the GME event as occurring on January 13, 2021, the first day when GME experienced a dramatic increase in returns and trading volume (see Figure IA.1 for daily GME trading from December 2020 through January 2021). Accordingly, we define the pre-GME period as July 2018, to January 12, 2021, the post-GME period as January 14, 2021, to June 2021, and we exclude DD reports issued on the event day itself. We also confirm that our main results are robust to excluding the week prior to and after the GME event day (see Table IA.1).

1.3 The WSB sample

We scrape all posts on WSB from July of 2018 through June of 2021 using the Pushshift API, which collects new posts in almost real-time.¹¹ Posts can be deleted by the original author, moderator of the subreddit, or an “automod” (which is a spam filter robot operated and constructed by moderators). Deletions by the automod typically occur in less than a minute. Deletions of posts by moderators take longer (usually up to a day) if the post breaks the rules of the subreddit and was not already captured by the automod. Lastly, a post can be deleted by the author at any time. Importantly, the API retains posts deleted by authors, and these posts are included in our sample.

WSB contains more than 100,000 different posts spanning several different categories including: News (links to news stories WSB users found interesting), Discussion (open-ended discussions, frequently on macroeconomic forces, such as proposed regulations, supply chain disruptions), YOLO (posts reporting large upcoming bets), Gains/Losses (posts highlighting major investment successes and failures), Sh$tposts (ironic investment theses meant to entertain rather than inform), and Due Diligence reports (posts that contain investment analysis and a clear investment recommendation). The contributor of the post selects the category, and the classification is immediately known to user participants. However, posts that are incorrectly classified by users, such as a DD report that does not provide any new investment research, are deleted by moderators/bots very quickly (generally within 2–3 minutes of posting). We limit the sample to posts that have more than one upvote (which is automatic from the user). This filter should minimize the impact of posts that were immediately removed by moderators/bots because they failed to meet the criteria for a specific category.

Our analysis focuses primarily on Due Diligence (DD) reports. These reports are vetted by the moderators as containing information where (1) at least some analysis has been performed and (2) the author provides a clear investment recommendation (long or short). DD reports are most similar to other forms of social-finance investment research (e.g., Seeking Alpha research reports) that have been studied in the prior literature, and they are the most likely to contain value-relevant information.

We limit the sample to DD reports focused on a single ticker (e.g., we eliminate DD reports that focus on market-wide or industry analysis) and to common stocks (CRSP share codes 10 and 11) with available data in the CRSP-Compustat merged database. For each DD report, we manually review the report to identify the investment recommendation and ticker. Although the author’s investment recommendation is clear to anyone reading the report, there is no standardized format for listing the recommendations which necessitates a manual review of each report. The manual review of tickers is also needed for two reasons. First, users may place special characters before or after a ticker symbol that a program would misclassify. Second, users sometimes intentionally report a wrong ticker to misdirect hedge funds and other institutional investors that monitor message boards using algorithms.¹²

 Appendix A provides an example of a DD report in our sample. A manual reading of the report indicates that the author is recommending a “Buy” for “BYND.” The header of the report also includes the username and the timestamp of the report. For DD reports that occur outside of trading hours, we set the date of the report equal to the date on which an investor could have first traded on the report.¹³

Panel A of Table 1 provides summary statistics. The sample includes 5,015 DD reports covering 3,782 firm days and 906 different firms. The overwhelming majority of DD reports (88%) are buy recommendations. We also find the average contributor, as measured by the username on the DD report, issues only 1.32 reports during the sample period. These estimates likely significantly understate the number of reports per person since users often get temporary bans for violating moderator rules and circumvent the ban by joining the forum with a different username. Nevertheless, the lack of repeated posts by the same username suggests that WSB users are not especially concerned with developing and maintaining a reputation.

Summary statistics for the pre- and post-GME period are presented in the second and third rows of panel A. Although the post-GME period is substantially shorter in calendar time, it accounts for a slight majority (∼51%) of all DD reports. DD reports in the post-GME are more likely to recommend a long position (95% vs. 81%), and they are substantially more likely to discuss GME or AMC (19.6% vs. 4.2%). The substantial differences in report characteristics in the pre- and post-GME period are consistent with the GME event resulting in a significant shift in the culture of WSB. We also provide summary statistics of the distribution of DD report coverage at the firm-month level. The average firm has only 0.04 DD reports per month, although there is considerable dispersion in the intensity of coverage, as evidenced by the relatively large standard deviation (0.80).

We contrast WSB DD reports with nonresearch posts. We define a WSB post as nonresearch related if it belongs to one of the following categories: News, Losses, Gains, Charts, and Shi$posts. We focus on these posts categories because they are not designed to contain any new value-relevant information and they do not provide a clear directional recommendation (ie buy vs. sell).¹⁴ For example, News include links to articles and does not provide any analysis or interpretation of the news, Gains and Losses report previous successful and unsuccessful investments, Charts are typically graphs of past returns that could be found on any website, and Shi$posts are typically satirical posts that are not intended to be taken seriously. Panel B reports summary statistics for the nonresearch post sample. The sample includes 13,255 nonresearch posts, 57% of which are in the post-GME period. Nonresearch posts are much more likely to be on GME or AMC. In fact, in the post-GME period more than 70% of all nonresearch posts are on GME and AMC.

1.4 Other variable construction

We collect Seeking Alpha research reports over the same sample period (July 2018-June 2021). For each report, we collect the following information: a report ID assigned by Seeking Alpha, the date and time of the publication, the ticker (or tickers) assigned to each report, the author of the report, and the authors stated recommendation (e.g., “bullish,” “neutral,” or “bearish”). To parallel the WSB sample, we convert SA recommendations to a “buy” indicator, which equals one if the recommendation is “very bullish” or “bullish” and zero otherwise. We exclude reports that do not have a stated recommendation, and we limit the sample to reports that are focused on single-ticker articles for U.S. common stocks.

Panel C of Table 1 reports summary statistics for the Seeking Alpha sample. The sample of SA reports is much larger than DD reports (23,659 vs. 5,015), and the SA sample spans a larger cross-section of firms. SA research also tends to be overwhelming bullish, with 85% of all reports being classified as buy recommendations. The average SA contributor writes 10.98 reports compared to only 1.32 reports for WSB. This finding is consistent with anecdotal evidence that contributors on SA are more interested in using the site to build a reputation.¹⁵ Even in the post-GME period, less than 1% of all SA reports cover GME or AMC. This is consistent with the GME event having a less pronounced impact on SA research relative to WSB research.

We combine the data on social media research from WSB and SA with several additional data sources. We obtain financial statement data, including book value of equity, book value of debt, book value of assets, short interest, and total common shareholders from Compustat. We obtain financial market data, including daily data on share price, shares outstanding, volume, and stock returns from CRSP. Earnings announcement dates and sell-side analyst earnings forecast data are from the I/B/E/S unadjusted U.S. detail history file. We collect the number of shares held by institutions from the Thomson Reuters Institutional Holdings database, and media coverage is collected from Bloomberg.

We identify retail trading from TAQ data using the approach of Boehmer, Jones, Zhang, and Zhang (2021) (hereafter BJZZ). Specifically, we classify trades with TAQ exchange code “D” and prices just below a round penny (fraction of a cent between 0.6 and one) as retail purchases, while trades with exchange code “D” and prices just above a round penny (fraction of a cent between zero and 0.4) are classified as retail sales. This classification is conservative in the sense that it has a low type 1 error (ie, trades classified as retail are very likely to be retail). However, this classification omits retail trades that occur on exchanges as well as limit orders that are not immediately executable.

1.5 Determinants of WSB coverage

In this section, we examine the determinants of WSB coverage. We expect that many of the firm characteristics that influence research coverage on other social finance sites (e.g., Seeking Alpha) are likely to be relevant on WSB as well. However, relative to SA, we expect that WSB users will tend to issue reports on more speculative stocks, and we expect that such effects may be amplified in the post-GME period.

The dependent variable, Coverage, is either equal to WSB DD coverage, defined as the natural logarithm of one plus the total number of DD reports issued for firm i during month t, WSB Nonresearch coverage defined as the natural logarithm of one plus the total number of nonresearch reports issued for firm i during month t, or SA Coverage, defined as the natural logarithm of one plus the total number of SA reports issued for firm i during month t. Chars contains the vector of firm characteristics used in Farrell et al. (2022) to explain SA coverage, namely, the percentage of the firm’s shares held by institutional investors at the end of the prior year (Inst. Ownership), the number of common shareholders (Breadth of ownership), market capitalization (Size), book-to-market ratio (BM), return volatility (Volatility), share turnover (Turnover), past 1-month returns (Return$m−1$⁠), past returns over the prior 2 to 12 months (Ret$m−2,m−12$⁠), the number of unique media articles mentioning the firm in the prior year (Media coverage), and the number of sell-side analysts issuing a forecast for the firm in the prior year (IBES coverage). In addition, given the ample anecdotal evidence that WSB users target stocks with lottery like features, stocks with heavy short interest, and stocks that recently went public, we add indicator variables equal to one if the firm is in the top quintile of the maximum daily return in the previous month (High max), the top quintile of short interest in the previous month (Heavy short), or if the firm went public in the past 6 months (Recent IPO). Finally, given the large percentage of reports issued on GME and AMC, we include a separate GME/AMC indicator. We allow the coefficient for all the characteristics to vary in the pre- and post-GME period by interacting the characteristics with Post, an indicator equal to one in the post-GME period (February to June of 2021) and zero for the pre-GME period (July 2018 to December 2020), and we exclude January 2021 since it spans both the pre- and post-GME period. We include month fixed effects and cluster standard errors by firm and month. We log all continuous variables other than past returns, and we standardize all continuous variables, including the dependent variables, to have zero mean and unit variance.

Specification 1 of Table 2 reports the results. Consistent with prior work on the determinants of SA coverage, we find that WSB coverage in the pre-GME period is increasing in firm size, volatility, and media coverage, and decreasing with institutional ownership. We also find that WSB coverage is significantly greater for lottery-like stocks (ie, High max), stocks with high short interest, stocks that recently went public, and GME/AMC. The coefficients for the post-GME interaction terms indicate that WSB coverage of speculative stocks, including stocks with higher volatility, higher max returns, higher shorter interest, recent IPO stocks, and GME/AMC are significantly greater in the post-GME sample. The magnitudes are economically large. For example, WSB coverage of stocks with high max returns increases by nearly 500% in the post-GME period (from 0.03 to 0.17), and we observe a similar large increase in the coverage of stocks with high short interest and the coverage of GME/AMC. These dramatic increases are consistent with the extreme GME returns attracting even more speculative investors.

Specification 2 reports the determinants of WSB nonresearch coverage. The findings in the pre-GME period are generally similar to the results for DD Report Coverage. However, the shift in Nonresearch coverage in the post-GME period is primarily driven by an increased emphasis on GME/AMC rather than a more general shift toward other more speculative stocks.

In Specification 3, we examine the determinants of SA coverage. Perhaps surprisingly, in the pre-GME period, we find that SA and WSB have similarly strong preferences for lottery-like stocks, stocks with heavy short-interest, and stocks that recently went public. However, in sharp contrast to WSB DD Coverage, SA coverage of speculative stocks does not dramatically increase in the post-GME period. In fact, SA’s coverage of volatile stock and stocks with heavy short interest declines in the post-GME period. The findings suggest that the effects of the GME event were not uniform across all social media platforms.

2 The Informativeness of WSB Research

2.1 WSB Research and the cross-section of stock returns

In this section, we examine whether DD report recommendations forecast future stock returns. Our null hypothesis is WSB reports are unable to forecast returns. This hypothesis is consistent with either WSB reports containing no useful information, or the market immediately incorporating all value-relevant information on the day of the report.¹⁶ In contrast, it is possible that WSB reports could positively predict returns. While this prediction is inconsistent with efficient markets, it is in line with a large literature that documents longer-horizon predictability following investment research including drifts following sell-side analyst recommendations (Womack 1996), Seeking Alpha research reports (Chen et al. 2014), and the tweets of professional investors on StockTwits (Cookson and Niessner 2020). Finally, a more pessimistic view is that WSB either reflects or causes investor sentiment, in which case WSB reports could negatively predict future returns.

The dependent variable is the stock return measured over the subsequent week (ie, x = 5 trading days) or the subsequent month (x = 21 trading days). Net DD is the number of buy DD recommendations for stock I on day t less the number of sell DD recommendations for stock i on day t, and Net DD × Post, interacts Net DD with an indicator equal to one for the post-GME period. Thus, Net DD captures the average predictive ability of DD reports in the pre-GME period, and Net DD × Post captures the incremental predictive ability of DD reports in the post-GME period.

Nonresearch is the is the number of non-research-related postings on WSB for stock i on day t, Net SA is the number of SA reports issuing a buy recommendation for stock i on day t less the number of SA reports issuing a sell recommendation for stock i on day t, Nonresearch × Post interacts Nonresearch with the post-GME indicator, and Net SA × Post is defined analogously. We winsorize Net DD, Nonresearch, and Net SA at the 1st and 99th percentiles of the distribution of firm-days where the variable is not equal to zero. Following Kelley and Tetlock (2013), the controls include Size, Book-to-market, returns measured from days [0], [–5, –1], and [–26, –6] and media sentiment measured similarly. See  Appendix C for detailed definitions. Day denotes calendar-day fixed effects. To account for the correlation in the residuals induced by the overlapping holding periods, we cluster standard errors by both firm and month. The regression is estimated across all firm-days, including firm days with no reports from social media outlets.

Specifications 1 and 2 of Table 3 report the results for 1-week and 1-month holding periods for the full sample, and Specifications 3 and 4 report analogous results after excluding GME and AMC. Across all four specifications, the coefficient for Net DD is positive and statistically significant. The economic magnitudes are also sizeable. For example, an incremental buy DD report issued in the pre-GME period is associated with a 1.11% increase in 1-week-ahead returns and a 5.17% increase in 1-month-ahead returns. The extraordinary returns of GME and AMC contribute to the sizeable magnitudes, however the predictability of WSB reports is not driven purely by these two firms. In particular, after excluding GME and AMC, an incremental buy DD report issued in the pre-GME period is associated with a 0.91% increase in 1-week-ahead returns and a 2.33% increase in 1-month-ahead returns. While this finding is consistent with prior work that suggests that crowdsourced investment research can forecast future returns, it is perhaps surprising that this relation continues to hold in a setting with complete user anonymity, minimal oversight, and limited reputational incentives.

In contrast, the coefficient for Net DD × Post is significantly negative at the 1-month holding period. In particular, in the full sample, the predictive ability of DD reports declines by 5.30% (to –0.13%), and in the sample that excludes GME and AMC the estimate declines by –3.45% (to –1.12%). Both the –0.13% and the –1.12% estimate are not significantly different from zero. Thus, the ability of WSB reports to forecast returns is completely eliminated in the post-GME period. We more deeply explore the factors driving the time-series decline in the predictability of WSB reports in Section 5.

The coefficient for WSB nonresearch is statistically insignificant. We also confirm that after excluding GME and AMC, the coefficient for Net DD is significantly larger than the coefficient for Nonresearch. This finding alleviates the concern that any WSB post, independent of its content, can predict returns simply because it correlated with naïve investor demand. We also find some evidence that the informativeness of nonresearch reports declines in the post-GME period, and in Table IA.2 of the Internet Appendix, we confirm that the informativeness on nonresearch posts in the post-GME period (ie, WSB nonresearch + WSB nonresearch × Post) is significantly negative after excluding GME and AMC. This finding is consistent with nonresearch posts in the post-GME period reflecting naïve investor sentiment that is associated with overpricing and subsequent reversals.

The coefficient for Net SA is positive and significant. This finding is consistent with prior work suggesting that SA research is informative (e.g., Chen et al. 2014; Dim 2021). However, the estimates on Net SA are smaller than the estimates on Net DD, although the differences in the coefficients are significantly different from each other in only one of the four specifications. Nevertheless, at a minimum, the findings suggest that WSB research was at least as informative as SA research in the pre-GME period. In the Internet Appendix, we explore whether differences in risk can account for the somewhat stronger predictability of WSB research relative to SA research in the pre-GME period. We find that WSB recommendations are stronger predictors of future volatility than SA report recommendations (Specifications 1 and 2 of Table IA.4), and WSB superior performance relative to SA is eliminated when considering volatility-adjusted performance (Specifications 3 and 4 of Table IA.4) or when focusing on the left-tail of the distribution using quantile regressions (Table IA.3). However, the superior performance of WSB recommendations is robust to controlling for common measures of systematic risk such as size, book-to-market, and momentum (Specifications 5 and 6 of Table IA.4).

Importantly, we find no evidence that the informativeness of SA research declined in the post-GME period. This finding is inconsistent with the view that broad macroeconomic forces contributed to a widespread decline in the informativeness of investment research across all social media platforms. Lastly, we note that the coefficients for the control variables are consistent with prior work. In particular, the negative coefficient for prior day and prior week returns is in line with prior work on short-term return reversals (e.g., Nagel 2012; Jame 2018), and the positive coefficient for prior day media sentiment echoes the findings in Tetlock, Saar-Tsechansky, and Macskassy (2008). The coefficient for the other control variables, including size and book-to-market, exhibit no significant relation with returns, which is consistent with an insignificant size and value premium in more recent times (Smith and Timmerman 2022).

2.2 WSB research and the cross-section of stock returns: Robustness and alternative horizons

In Table IA.1 of the Internet Appendix, we examine whether the findings reported in Table 3 are robust to different research design choices. We briefly summarize the results below and offer a more detailed discussion in the Internet Appendix. In particular, we confirm that the results are similar if we (1) exclude the “Robinhood 50” stocks, defined as the 50 stocks that Robinhood imposed trading restriction on beginning on January 28, 2021, and ending February 5, 2021, from the sample; (2) interact all the control variables with a Post indicator; (3) exclude the 5 days prior to and after the GME-event; or (4) replace Net DD with three indicator variables (Heavy buy, Light buy, and Sell). We also examine the relation between Net DD and stock returns over longer horizons. Specifically, we construct a monthly measure of Net DD by aggregating all reports over the calendar month, and then examine the 1-month-ahead return predictability. We find that the magnitudes of the estimates decline (in absolute value) which is consistent with the predictability of DD reports being stronger over shorter horizons. However, aggregating over longer windows also results in more precise estimates, and as a result, the statistical significance of the estimates remains similar.

Next, we examine the predictive ability of WSB reports for various alternative holding periods. Specifically, we repeat Equation (2) for the current day (ie, day 0), each of the first 5 days, weeks 2, 3, and 4, and weeks 5 through 12. Panel A of Table 4 reports the estimates on Net DD, Net DD × Post-GME, and Net DD + Net DD × Post-GME for the full sample, and panel B reports the results after excluding GME and AMC.

In panel A, we observe a significant positive relation between Net DD and day 0 returns. In particular, one additional buy recommendation is associated with a 0.88% increase in same-day returns. We continue to find positive estimates ranging from 0.10% to 0.33% over the 5 days after the DD report, and most of these estimates are at least marginally significant (p < .10). We also observe that the returns continue to drift upward over the subsequent 60 trading days. The absence of a return reversal is inconsistent with short-term price pressure being the primary driver of the informativeness of WSB research in the pre-GME period (additional price pressure tests in Section 4.3 reinforce this finding). Consistent with the return predictability declining in the post-GME period, the coefficient for Net DD × Post is negative in 9 of the 10 holding periods considered. Although the individual estimates are typically insignificant, as we show in Table 3, the cumulative estimates for the 1-month holding period are significantly negative, and this effect does not reverse over the subsequent 2 months. The estimates of the combined coefficient (ie, Net DD + Net DD × Post) are typically insignificantly different from zero. The results that exclude GME and AMC (panel B) are generally smaller in magnitude but yield qualitatively similar conclusions.

To better visualize the cumulative returns, we also estimate Equation (2) for horizons ranging from 1-week (ie, x = 5) through 12 weeks (ie, x = 60), and then plot the estimates. Figure 2, panels A and B, plots the estimates and 95% confidence intervals for Net DD and Net DD × Post for the full sample and the sample that excludes GME and AMC. Consistent with Table 4, we see that the predictive ability of WSB reports in the pre-GME period does not reverse over longer horizons. In addition, the decline in the predictive ability of DD reports in the post-GME period remains sizeable over longer horizons. For example, at the end of 12 weeks, the coefficient for Net DD × Post is –6.32% for the full sample and –3.40% for the sample that excludes GME and AMC.

3 Why Did WSB Reports Predict Returns in the Pre-GME period?

The results from the previous section suggest that WSB research reports were able to forecast returns in the pre-GME period. In this section, we seek to better understand the forces contributing to this return predictability.

3.1 The role of the pandemic

A significant portion of the pre-GME period coincides with the COVID-19 pandemic. Prior work has found that retail trading increased substantially during the pandemic (e.g., Ozik, Sadka, and Shen 2021). Stay-at-home orders and the cancellation of sports and other forms of entertainment resulted in many retail investors having extra time that could potentially be devoted to understanding financial markets. Reduced spending and stimulus checks may have also provided investors a catalyst to trade financial securities. In addition, lockdowns limited institutional investors’ ability to meet privately with management, which may have reduced the informational advantage of institutional investors relative to retail investors (Bai and Massa 2022; Bradley, Jame, and Williams 2022). Thus, a natural question is whether the informativeness of DD reports in the pre-GME period is primarily attributable to the unusual pandemic period.

To test this idea, we partition the pre-GME period into Prepandemic (July 2018 to February 25, 2020) and Post-pandemic (February 26, 2020, to January 12, 2021).¹⁷ The February 26$th$ date corresponds to the first case of suspected U.S. transmission of COVID-19, and Cookson, Engelberg, and Mullins (2020) find that investor beliefs on social media changed significantly following this date. The prepandemic and post-pandemic sample include 837 and 1,513 reports, respectively. We repeat Equation (2) after partitioning Net DD into Net DD × Prepandemic and Net DD × Post-pandemic. We also perform analogous partitions for Nonresearch and Net SA.

Table 5 reports the results. We find that the estimates on Net DD × Prepandemic are statistically significant. In fact, after excluding GME and AMC, the point estimates on Net DD × Prepandemic are larger (albeit insignificantly so) than the estimates on Net DD × Post-pandemic. This evidence suggests that the informativeness of DD reports in the pre-GME period is not purely driven by the pandemic period.

To more generally understand how the predictive ability of WSB reports varies over the time series, in Figure 3, we estimate Specifications 2 and 4 of Table 3 over the following intervals: the-pre 2020 sample, quarters 1 and 2 of 2020, and each of the remaining quarters in 2020 and 2021. We combine the pre-2020 sample because there are a relatively small number of DD reports (606) prior to 2020; we combine quarters 1 and 2 of 2020 to accommodate the small sample of reports in Q2 of 2020 because of missing data from the API; and we include the small number of pre-GME DD reports issued in January of 2021 in the Q4 2020 estimates. After excluding GME and AMC, the predictive ability of DD reports was stable in 2020, with point estimates ranging from 2.14% to 3.92%. We also observe a sharp decline in informativeness in Q1 of 2021 (–1.57%), which remained negative in Q2 of 2021 (–0.36%). Including GME and AMC yields similar results except that the return predictability in Q4 of 2020 is substantially larger (7.49%) due to the very large returns of GME in January of 2021.

Collectively, the results from this section suggest that the informativeness of WSB was stable in the pre-GME period. In particular, it was of similar magnitude in both the prepandemic and post-pandemic periods, and it was economically sizeable for every quarter in 2020. One important caveat, however, is that the pre-GME time series is relatively short, and even the prepandemic period may not be representative of a “normal” environment. Given the short time-series of WSB reports, we are unable to assess whether the informativeness of WSB reports in the pre-GME period would generalize to other market environments.¹⁸

3.2 Information processing versus information production

Next, we explore the economic channel underlying the investment value of WSB reports in the pre-GME period. If the return predictability following DD reports is primarily a consequence of DD reports piggybacking off other news events (e.g., Alt ink il iç and Hansen 2009) or skillfully interpreting public news (e.g., Engelberg, Reed, and Ringgenberg 2012), then we would expect the results to be significantly stronger for reports that coincide with major information events (ie, Information processing). On the other hand, if WSB users independently produce novel information, then the return predictability results may be stronger for reports not issued during major news events (Information production). While both channels are potentially valuable to users who rely on WSB for investment research, distinguishing these explanations provides insight into the source of WSB investment value in the pre-GME period.

We classify a report as information processing if the firm had an earnings announcement, analyst revision, or abnormally high media coverage (as defined in  Appendix C) on the day prior to the DD report or the day of the DD report. Roughly 40% of all DD reports are categorized as information processing, with the remaining 60% of reports classified as information production. We then repeat Equation (2) after partitioning Net DD into Net DD processing and Net DD production. Table 6 reports the results. In the pre-GME period, we find that the coefficients for both Net DD processing and Net DD Production are always positive, and the estimates are both significantly different from zero for the 1-month horizon. The evidence suggests that both information processing and information production contribute to the predictive ability of WSB in the pre-GME period.

3.3 Information versus price pressure

The existing evidence is consistent with DD reports issued in the pre-GME period containing value-relevant information that is impounded into prices over the subsequent month (information). However, an alternative view is that DD reports cause (or are correlated with) uninformed demand shocks that induce significant price pressure over the subsequent month (Price pressure). The lack of reversal over the 12-week holding period is inconsistent with a temporary price pressure explanation, but it is still possible that WSB induces price pressure that persists for even longer holding periods.

To more directly test whether DD reports contain value-relevant information, we examine whether DD reports forecast cash-flow news over the subsequent week or month. Specifically, we repeat Equation (2) after replacing returns with one of three proxies for cash-flow news. The first measure is Media Sentiment obtained from Bloomberg. Specifically, for each firm day, Bloomberg assigns a sentiment score ranging from –1 (very negative news) to 1 (very positive news), with a median value of zero (neutral articles). We assign firms with no media coverage a value of zero, and we sum the daily media sentiment over the 5- or 21-day holding period. Our second measure is Positive forecast error, which equals one if realized earnings exceed the median quarterly forecast across all I/B/E/S analysts as of day t, and zero otherwise. The 5-day (21-day) sample is limited to firms that will announce earnings within 5 (21) trading days of day t, and we also require that the firm have at least one I/B/E/S earnings forecast. While Positive forecast error is a common proxy for cash-flow news (e.g., Kelley and Tetlock 2013), one limitation is that it restricts the sample to firms that will shortly announce earnings. As a broader measure of earnings-related news, we also compute Positive forecast revision, which equals the total number of upward revisions scaled by the total number of revisions. In computing this measure, we consider both quarterly and annual earnings forecast revisions. We exclude firms with zero I/B/E/S coverage, and we set Positive forecast revision to 50%, the median value across the sample, for firms with I/B/E/S coverage but no forecast revisions over the holding period.¹⁹

Table 7 reports the results for the full sample.²⁰ In all six specifications, the estimates on Net DD are positive, and three of the six estimates are statistically significant. The economic magnitudes are also sizeable. For example, the estimate in Specification 3 indicates that an incremental buy DD recommendation issued within 5 days of the earnings announcement is associated with a 4.22-percentage-point increase in the likelihood of beating the sell-side earnings consensus forecast, which corresponds to roughly a 7% increase relative to the sample mean of 60%. These findings are consistent with DD reports containing value-relevant information related to a firm’s future cash flows. An alternative view is that DD reports themselves cause changes in the cash-flow measures. The latter view is perhaps most plausible for media sentiment (ie, news coverage is either influenced by DD reports or by the price increase caused by DD reports); however, it seems unlikely that DD reports will directly affect earnings over the subsequent week or month.²¹ Thus, we believe the totality of evidence suggests that WSB reports in the pre-GME period contain value-relevant information. In contrast, we find no evidence that WSB reports in the post-GME period predict cash-flow news. Further, in the case of Positive forecast error, the post-GME estimate (ie, Net DD + Net DD × 2021) is significantly less than zero indicating that WSB reports in the post-GME period are negative predictors of forecast revisions.

4 Why Did Return Predictability of WSB Reports Decline in the Post-GME Period?

In this section, we explore factors that may have contributed to the decline in informativeness of WSB research in the post-GME period. Our primary conjecture is that the success of the GME event altered the composition and culture of WSB research. As Figure 1 shows, WSB experienced a nearly tenfold increase in the number of users during January of 2021. The shift in the userbase naturally altered the composition of DD contributors. In fact, we find that only 176 of the 2,717 post-GME DD reports (∼6.5%) were written by contributors who issued a DD report in the pre-GME period.²² In Sections 5.1 and 5.2 we document that the shift in the userbase also results in significant shifts in contributor strategy, and in Section 5.3 we link the shifts in strategy to the decline in WSB return predictability.

4.1 Shift in contributor strategy: Price pressure versus fundamentals

While the impact of the GME event on the culture of WSB is likely far-reaching and multifaceted, anecdotal evidence suggests that a particularly important change was that the site became more focused on identifying potential profit opportunities due to coordinated price pressure strategies, possibly because the massive (and salient) success of the GME short-squeeze resulted in upwardly biased expectations of the profitability of this strategy.²³ We begin by exploring whether there is an increase in WSB reports emphasizing coordination and/or price pressure following the GME event. We conduct textual analysis to identify whether the report focuses on strategies related to price pressure. We randomly read approximately 100 DD reports and wrote a list of words that appeared to be related to price pressure strategies (e.g., short selling) or company fundamentals (e.g., earnings). See  Appendix B for the word list. In Table IA.7 of the Internet Appendix, we also examine the relative importance of each word on the price pressure list and the impact each word has on the main results. We find that the three most frequent price pressure words are “squeeze,” “short interest,” and “float.” However, the main findings are qualitatively unchanged if we exclude any particular word.

We define a report as focusing on price pressure if the number of price pressure words exceeds the number of fundamental words (PP report). As a robustness check, we also classify a report as focusing on price pressure if there is at least one price pressure word in the report (PP report2). To test whether the frequency of price pressure reports increase in the post-GME period, we regress the PP report indicator on a post-GME indicator. The results of this analysis, reported in panel A of Table 8, indicate that the fraction of price pressure reports increases by 19.4 percentage points in the post-GME period. This reflects a roughly 165% increase relative to the pre-GME sample mean of 11.6%. Figure 4, panel A, plots the fraction of PP reports by quarter (where the construction of quarters follows Figure 3) for the full sample of firms. We find that the fraction of price pressure reports is relatively stable in the pre-GME period and then jumps to 33% and 30% in Q1 and Q2 of 2021, respectively. Specification 2 confirms that the results are robust to excluding GME and AMC, and Specifications 3 and 4 show the results are also very similar if we consider the alternative measure of price pressure reports (PP report2).

4.2 Increases in behavioral biases: Attention-based trading

Our second conjecture is that the composition of contributors on the site shifted toward a less sophisticated userbase, which could result in post-GME reports being more influenced by behavioral biases. One prominent behavioral bias that has been shown to adversely affect retail trading is their tendency to purchase stocks that catch their attention (e.g., Barber et al. 2022). Accordingly, we test whether the composition of DD reports in the post-GME period shifts toward more attention-grabbing stocks.

We consider two proxies for attention. The first is the absolute return on the day prior to the DD report. To facilitate comparison with the PP report indicator variable, we convert the absolute return to an indicator based on whether the absolute return was in the top decile of the distribution (High absolute return). Our second measure is the number of nonresearch posts on WSB over the previous week. We again convert this to an indicator equal to one if there was more than one nonresearch report issued on WSB over the previous 5 trading days (High WSB posts). Finally, we consider a composite attention measure, High attention, which is equal to one if either High absolute return or High WSB posts is equal to one.

Panel B of Table 8 reports the results for High attention. The fraction of DD reports that are classified as high attention increases from 32.5% in the pre-GME period to 57.1% in the post-GME period. Figure 4, panel B, indicates a sharp increase in attention-based DD reports beginning in Q1 of 2021, and Specifications 2–4 confirm that the results are robust to excluding GME and AMC or considering either of the attention measures (High absolute return or High WSB posts) in isolation.

4.3 The impact of price pressure reports and high-attention reports on dd informativeness

The increase in PP reports and High-attention reports in the post-GME period could contribute to the decline in report informativeness for at least two reasons. The first view is that PP and High-attention reports are always uninformative (even in the pre-GME period) and thus a higher percentage of these types of reports results in a decline in the average report informativeness. Alternatively, it is possible that PP and High-attention reports become significantly less informative in the post-GME period. Time-varying informativeness of PP reports would be consistent with users overestimating the value of price pressure strategies in the post-GME period, perhaps because contributors extrapolate the investment value of price pressure signals (e.g., the level of short interest) based on the recent success of the GME event. Similarly, the informativeness of High-attention reports could decline if the dramatic shift in the userbase of WSB following the GME event shifted the motivation for issuing reports on high-attention stocks from primarily rational reasons (e.g., skillful interpretation of major news announcements) to primarily behavioral reasons (e.g., attention-induced trading).

To examine whether PP reports (High-attention reports) are less informative than other reports, we repeat Equation (2) after including Net DD PP (Net DD attention) defined as the Net DD measure computed for the subset of reports classified as a price pressure (high-attention) report. We also consider a composite measure (PP or Attention) which is equal to one if the report is classified as either a PP report or a High-attention report. We continue to allow the informativeness to vary in the post-GME period by interacting the different measures of Net DD with the post-GME indicator.

Specifications 1 and 2 of Table 9 report the results for PP reports before and after excluding GME and AMC, respectively. We find that the coefficient for Net DD PP is positive, albeit statistically insignificant after excluding GME and AMC. The positive coefficient is inconsistent with the view that PP reports are always uninformative. In contrast, the coefficient for Net DD PP × Post is significantly negative, although the estimate is only marginally significant (p < .10) for the sample that excludes GME and AMC. Specifications 3 and 4 reveal that the informativeness of High-attention reports declines in the post-GME period. In particular, the coefficient Net DD attention × Post is negative and statistically significant at a 1% level in both specifications. Furthermore, the results using the composite measure (PP or Attention) reported in Specifications 5 and 6, yield even stronger statistical support. Collectively, the evidence supports the view that the shift in contributor focus to high-attention and price pressure reports contributed to the decline in informativeness in the post-GME period.²⁴

Next, we repeat the analysis in Specifications 2, 4, and 6 of Table 9 after replacing the 1-month return, with returns for horizons ranging from 1-week (ie, x = 5) through 12 weeks (ie, x = 60). In Figure 5, panel A, we report the estimates and 95% confidence intervals for Net DD + Net DD PP (ie, the informativeness of PP reports in the pre-GME period) and Net DD × Post-GME + Net DD PP × Post-GME (ie, the change in the informativeness of PP reports in the post-GME period). Figure 5, panels B and C, reports analogous results after replacing price pressure reports with attention-based reports and the composite measure (PP or Attention), respectively. Some evidence indicates that the decline in informativeness for price pressure reports is attenuated over longer horizons. For example, the estimate falls from –6.00% at a 5-week horizon to –1.96% over a 12-week horizon. However, the decline is not reliably different from zero (untabulated). Further, there is no corresponding attenuation for attention-based reports (Figure 5, panel B) or the composite measure (Figure 5, panel C).

An important question is whether price pressure reports and high-attention reports in the post-GME period were simply uninformative or whether such reports are associated with mispricing. In Table 10, we formally test for the significance of PP or Attention reports return predictability in the pre-GME period, the post-GME period, and the difference between the two estimates for the sample that excludes GME and AMC (ie, Specification 6 of Table 9). We find that PP or Attention reports are associated with a 3.87% increase in 1-month-ahead returns in the pre-GME period, and this effect is reliably different from zero. This effect declines by a significant 5.53% in the post-GME period. The estimate for the post-GME period only (ie, PP or Attention reports + PP or Attention reports × Post) is –1.67%, which is marginally significant (p < .10). The 1-month return might mask interesting intramonth return dynamics. Accordingly, we also examine the returns for the other holding periods considered in Table 4. In the 11–15 days after the report, PP or Attention reports are associated with a –0.54% return in the post-GME period. This estimate is statistically significant, although some caution is needed in interpreting the statistical significance since the tests in Table 10 consider multiple holding periods. We also find that PP or Attention reports are associated with a further decline of –1.02% in the fourth week, and an additional –1.08% decline over weeks 5–12, but neither estimate is reliably different from zero. Collectively, the results from Table 10 provides evidence consistent with PP or Attention reports negatively forecasting returns in the post-GME period over certain horizons, although the imprecision of the estimates prevents us from drawing very strong conclusions.

5 WSB Reports and Retail Trading

A concern among regulators is that WSB induces uninformed trading frenzies that can destabilize prices and potentially harm investors, particularly less sophisticated investors. In this section, we explore this concern by investigating how WSB reports correlate with the trading direction and trade informativeness of investors with differing sophistication levels. In conducting this analysis, we acknowledge that we cannot cleanly distinguish the extent to which WSB directly affects retail trading versus simply proxies for retail sentiment. Nevertheless, the absence of a strong relation between WSB research and retail trading would cast doubt on regulators concerns that WSB induces trading frenzies, while the presence of such a relation would be consistent with this concern.

5.1 Investor order imbalances following DD Reports

We begin by examining the relation between WSB reports and investor order imbalances.²⁵ We consider three groups of investors: small retail investors, large retail investors, and institutional investors. We proxy for small retail traders by equally weighting retail trades, which tend to be dominated by relatively smaller traders, and we proxy for large retail traders by examining retail share volume, which is heavily influenced by large trades. Finally, any trade not classified as retail is classified as an institutional trade. We sign retail trades using the algorithm of BJZZ, and we sign institutional trades using the Lee and Ready (1991) algorithm.

For each investor type, we consider two measures of order imbalances. First, following BJZZ, we define Percent imbalance as the difference between buys and sales (Imbalance) scaled by the sum of buys and sells. For example, Inst percent imbalance is defined as the difference between institutional buy share volume and institutional sell share volume scaled by total institutional share volume, and Large retail percent imbalance and Small retail percent imbalance are defined analogously. While Percent imbalance is commonly used in the literature, extreme values (e.g., Percent imbalance $=1$⁠) could be associated with very small trading volume, and presumably relatively small price impact. Accordingly, we construct an alternative measure, SD abnormal imbalance defined as the imbalance on day t less the average Imbalance over days $t−120$ through $t−240$⁠, scaled by the standard deviation of imbalance estimated over the same 6-month estimation window.²⁶ In contrast to Percent imbalance, Std. abnormal imbalance captures the intensity of the imbalance, and is thus arguably a better proxy for the buying frenzies commonly associated with WSB.²⁷ To facilitate comparison across the two measures, we convert both Percent imbalance and Std. abnormal imbalance into percentile rankings.

The dependent variable is one of the six order imbalance variables (ie, three investor types × two order imbalance measures), and the key independent variables, Net DD, Nonresearch, and SA, and Post are defined as in Equation (2). Controls include a similar set of controls as in Equation (2) with a few modifications. First, we add the lag of all the order imbalance variables measured over the previous trading day, which helps control for persistence in order imbalances (BJZZ 2021). Second, we exclude the contemporaneous return and contemporaneous media sentiment since they are measured at the same time as investor order imbalances.²⁸ Third, we include the absolute return and the absolute news sentiment measured over days [–1], [–5, –2], and [–26, –6] to control for attention-grabbing events that could potentially influence imbalances (Barber and Odean 2008). Standard errors are double-clustered by firm and month.

Table 11 reports the results. Specifications 1–3 report the results for Percent imbalance for institutional investors, large retail investors, and small retail investors, respectively, and Specifications 4-6 report analogous results for SD abnormal imbalance. The results using the two order imbalance measures are typically similar, so the remaining discussion will focus on the SD abnormal imbalance measure.

In the pre-GME period, we find that Inst SD abnormal imbalance is uncorrelated with Net DD. Both Large retail SD abnormal imbalance and Small retail SD abnormal imbalance are significantly correlated with Net DD, but the estimate for Small retail SD abnormal imbalance is more than twice as large (6.09 vs. 2.43). This finding is consistent with WSB recommendations influencing retail investors, particularly smaller retail investors, during the pre-GME period. Small retail imbalances are also correlated with nonresearch posts and SA recommendations. However, the magnitude of the effect is noticeably weaker. Specifically, for small retail traders the estimated coefficient for Net DD (6.09) is roughly four times as large as the estimated effects on NonResearch (1.74) and Net SA (1.46).

The correlation between Small retail SD abnormal imbalance and Net DD remains economically large in the post-GME period (Net DD + Net DD × Post $=4.82$⁠). In contrast, the relation between Large retail SD abnormal imbalance and Net DD declines in the post-GME period, and there is no significant relation between Large retail SD abnormal imbalance and Net DD in the post-GME period. These findings point to the possibility that larger, and presumably more sophisticated retail investors are better able to recognize the decline in report quality in the post-GME period.²⁹

Next, we examine the association between Net DD and extreme quantiles of small retail trading imbalances. In particular, we replace Small retail SD abnormal imbalance with indicators equal to one if the imbalance is greater than the 90th, 95th, or 99th percentile or less than the 10th, 5th, or 1st percentile. We report the coefficient for Net DD and Net DD + Net DD × Post-GME. To better compare the economic magnitudes, we convert the percentage point estimates to percentage effects by scaling by the mean of the dependent variable.³⁰ The results, reported in Figure 6, reveal an asymmetric pattern. In particular, the estimates are much larger for the right tail of the distribution relative to the left-tail of the distribution. This is consistent with DD reports being more likely to induce buying frenzies than selling frenzies. We also see that the results are stronger as you move into more extreme right quantiles, particularly in the post-GME period. For example, in the post-GME period, an additional buy DD recommendation is associated with a 71% increase in the likelihood that the stock falls within the top 10% of small retail imbalances compared to a 294% increase in the likelihood of it landing in the top 1%. Collectively, the findings are consistent with regulators’ concern that WSB has a particularly pronounced effect on retail buying frenzies.

5.2 The informativeness of retail trading following DD reports

Next, we examine whether the informativeness of small retail investor trading changes following DD reports. We focus on small retail investors because, as shown in Table 11, their trading is most strongly related to DD reports.³¹ Given that DD reports are informative in the pre-GME period, but not the post-GME period (Table 3), and that small retail traders closely follow DD reports (Table 11), we conjecture that small retail imbalances are particularly informative following DD reports in the pre-GME period, but not the post-GME period. However, this need not be the case. For example, if smaller retail investors are less likely to trade following lower quality reports than the decline in DD report informativeness would not necessarily translate to a decline in the informativeness of small retail investor trading.

The dependent variable is the 1-month-ahead return. Imb is Small retail SD abnormal imbalance, and we interact Imbalance with Post, which tests whether the informativeness of small retail trading changes in the post-GME period. We also interact Imbalance with an indicator for whether there was a DD report and an indicator for a DD report in the post-GME period. Thus, Imbalance × DD tests whether trade informativeness following DD reports is different from trade informativeness on non-DD report days during the pre-GME period, and Imbalance × DD × Post examines whether this relation changes in the post-GME period. We also contrast the effects of DD reports on trade informativeness with the effects of nonresearch postings and SA reports by interacting Imbalance with NR indicator, NR indicator × Post, SA, and SA × Post, where NR indicator and SA are indicators equal to one if there was at least one nonresearch post or at least one SA report, respectively. Finally, Controls and Day are defined in Equation (2). We also consider specifications that control for the directional recommendation of the reports on the day (e.g., Net DD and Net DD × Post). Comparing the estimates across specifications that do and do not control for the recommendation of the DD report allows us to examine the importance of Net DD in explaining changes in retail trade informativeness following DD reports.

Table 12 reports the results. Specifications 1 and 2 report the results for the full sample and the sample that excludes GME and AMC, prior to controlling for the recommendation of the DD reports. We find some evidence that small retail imbalances in the pre-GME period are a stronger predictor of 1-month-ahead returns on days with DD Reports. A one-decile increase in small retail order imbalance is associated with a 0.14% increase in 1-month-ahead returns for the full sample and a 0.05% increase for the sample that excludes GME and AMC, and both estimates are marginally significant (⁠$p<.10$⁠). We also find that these effects are fully eliminated in the post-GME period. In particular, the coefficient for Imbalance × DD × Post is negative, and larger (in absolute value) than the coefficient for Imbalance × DD. Below the regression estimates, we also formally test for whether retail trade informativeness following DD reports in the post-GME period (ie, Imbalance × DD + Imbalance × DD × Post) is significantly negative. We find that the estimates are not reliably different from zero for the full sample, but they are significantly negative for the sample that excludes GME and AMC. We also find that small retail trade informativeness following nonresearch reports is significantly negative for the sample that excludes GME and AMC. Overall, these results provide some support for regulators’ concern that WSB can result in significant trading losses for retail investors.³²

In Specifications 3 and 4 we explore the extent to which the decline in report informativeness contributes to the decline in small retail trade informativeness by including Net DD and Net DD × Post as controls. The inclusion of these variables attenuates the estimates. For example, for the full sample, the coefficient for Imbalance × DD falls by more than half (from 1.39 to 0.62), while the estimate on Imbalance × DD × Post falls by roughly one-third. We observe qualitatively similar patterns after excluding GME and AMC. These findings suggest that the decline in DD report informativeness contributes to some, but not all, of the decline in small retail trading informativeness following DD reports. One potential explanation for the remaining decline is that the GME event shifted the composition of traders following DD reports toward less sophisticated investors.

6 Conclusion

Wallstreetbets (WSB) has become an increasingly prominent source of investment research, particularly for risk-seeking retail investors. This paper offers a first look at the investment value of WSB due-diligence (DD) reports. We find that prior to the GME short squeeze event, WSB was a source of valuable investment research. In particular, in the pre-GME period, WSB DD reports positively forecasted 1-month-ahead returns. WSB research also positively forecasted media sentiment, earnings surprises, and earnings forecast revisions suggesting that WSB research contained useful information about future cash-flows news. However, all of the above findings are eliminated following the GME event. In the post-GME period, we find a dramatic increase in reports emphasizing price pressure strategies and on reports focused on attention-grabbing stocks, and we show that these shifts in strategy contributed to the significant decline in report informativeness. We also find that the informativeness of smaller retail trading increases following DD reports in the pre-GME period, but this relation fails to persist post-GME. Collectively, the evidence is consistent with the surge in new users stemming from the GME short squeeze event significantly altering the content of reports, deteriorating the informativeness of WSB research, and consequently, its potential benefits to less sophisticated investors.

Our findings should be of relevance to both regulators and investors. From a regulatory perspective, our evidence suggests that the impact of WSB research on financial markets is likely to be more nuanced than many of the black-and-white views expressed during the congressional hearings. For example, the pre-GME results suggest that the WSB can provide informative research despite the fact that WSB caters to a risk-seeking userbase, promises complete anonymity, offers minimal oversight, and provides limited reputational incentives. On the other hand, the post-GME results suggest that the culture and informativeness of WSB can change quickly, and such changes can have adverse consequences. For example, in the post-GME period, we find that WSB research was associated with trading frenzies that had potentially negative implications for smaller retail traders.

From an investor’s perspective, the decline in the informativeness of WSB research in the post-GME period should provide caution to the 10+ million WSB subscribers who turn to WSB for investment research. Indeed, our evidence casts doubt on the view that simply following all DD report recommendations will generate significant abnormal returns going forward. However, WSB may still be a useful source of information for investors who are adept enough to discern between higher and lower quality WSB research. Our findings suggest that users should be particularly cautious of reports that focus on price pressure strategies or reports issued on attention-grabbing stocks. Identifying additional attributes of WSB reports that are associated with better performance, particularly in the post-GME period, is a potentially interesting area for future research.

The replication code and data are available in the Harvard Dataverse at: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/X9ZFV9.

Acknowledgement

We thank Gregory Eaton (discussant), John Campbell (discussant), Tyson Van Alfen (discussant), Itay Goldstein (Editor), Paul Irvine (discussant), Andriy Shkilko (discussant), Michael Farrell, two anonymous referees, and seminar participants at the FARS Midyear Meeting, the Stockholm Business School (Future of Financial Information Webinar), the Texas A&M Young Scholars Finance Consortium, the 2022 Financial Management Association Meeting, the 2022 Southern Finance Association Meeting, and the University of Kentucky. Supplementary data can be found on The Review of Financial Studies web site.

Appendix

A Sample Report

Posted by u/MikeThePutz

Post time: Wednesday, Jul 10, 2019, 02:55:11 PM EST.

BYND Is at Costco DD

DD

Costco is now carrying Beyond Burgers. I don’t see this in any press releases by either Costco or BYND. There was a vegan blog that mentioned this information (it was brought to my attention by relevant_pet_bug) and I called the stores to confirm it. The item # is 1338620. It is in approximately 15 stores nationwide. A store in San Diego has it for sure, 2345 Fenton Parkway, and the Costco on 1890 S University Drive in Davie, Florida has it. I don’t know the other 13 stores. When I called a purchasing manager in the regional area to find out why the stores only carried a limited supply of the Beyond Burgers, he said that Costco would buy as many Beyond Burgers as they could get their hands on, but that BYND only sold a limited amount because they couldn’t keep up with demand. He said that Beyond Burgers are selling really well and they are selling out in “just a few days.” Apparently, BYND will be installing new manufacturing lines by the end of 2019 to increase supply and supposedly they will be able to provide Costco nationwide with Beyond Burgers by next year.

I asked why Costco would sell Beyond Burgers when they already sell Morning Star and Don Lee Farm’s Veggie Burgers and the purchasing manager said that Beyond Burgers just taste different and customers want them. AGAIN: This man is responsible for buying items for Costco in a large region of the US and he said that they would buy as many BYND Burgers as they could, but that supply was limited and that they will stock Beyond Burgers nationwide once BYND can meet demand in early 2020. This is hugely positive news and I don’t see any news reports about it or analyst reports mentioning it (please let me know if I am wrong!)

Photos taken from other groups about it: https://imgur.com/a/FcalTI9

If any reporters want my sources for this story feel free to PM me.

I want to thank u/relevant_pet_bug for pointing me in the right direction and bringing the vegan blog post, where this was first mentioned, to my attention.

Edit: fixed an error with the years. I legitimately forgot we are living in 2019.

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95% Upvoted

B List of Keywords in Price Pressure Analysis

This table lists the keywords assigned as “price pressure” words or “fundamental” words.

C Variable Definitions

C.1 Outcome Variables

• WSB DD coverage (Table 2) – the natural logarithm of one plus the total number of Wallstreetbets (WSB) due diligence (DD) reports written for a firm during the calendar month (Source: WSB)

• WSB nonresearch coverage (Table 2) – the natural logarithm of one plus the total number on WSB nonresearch posts reports for a firm during a calendar month, where a nonresearch post includes post belonging to one of the following WSB categories: News, losses, gains, charts, and Sh$tpost (Source: WSB)

• SA coverage (Table 2) – the natural logarithm of one plus the total number of Seeking Alpha (SA) reports written for a firm during the calendar month (Source: Seeking Alpha)

• Ret$t+1,t+x$ (Tables 3, 4, 5, 6, 9, 10, and 12) – the buy and hold return starting on day t + 1 and ending on day t + x, where x typically equals 5 or 21 trading days

• News sentiment$t+1,t+x$ (Table 7) – the sum of a daily sentiment score starting on day t + 1 and ending on day t + x. The sentiment scores are obtained from Bloomberg and range from –1 (very negative news) to 1 (very positive news), with a median value of zero (neutral articles). We assign firms with no media coverage a value of zero (Source: Bloomberg)

• Positive forecast error$t+1,t+x$ (Table 7) – An indicator equal to one if the realized quarterly earnings reported between day t + 1 and day t + x exceed the median forecast across all I/B/E/S analysts. The value is set missing for firms that do not have I/B/E/S coverage or for firms that will not announce earnings over the forecast horizon being analyzed (ie, 5 or 21 trading days) (Source: I/B/E/S)

• Positive forecast revision$t+1,t+x$ (Table 7) – the total number of upward revisions issued between day t + 1 and days t + x, scaled by the total number of revisions issued over the same period. In computing this measure, we consider both quarterly and annual earnings forecast revision. This value is set to missing for firms that do not have I/B/E/S coverage, and the value is set to 50%, the median value across the sample, for firms with I/B/E/S coverage but no forecast revisions over the holding period (Source: I/B/E/S)

• PP (Table 8) – an indicator equal to one if the number of price pressure words in the report exceeds the number of fundamental words, where the list of price pressure and fundamental words is available in  Appendix C (Source: WSB)

• PP2 (Table 8) – an indicator equal to one if the number of price pressure words is greater than zero, where the list of price pressure words is available in  Appendix C (Source: WSB)

• High absolute return (Table 8) – an indicator equal to one if the absolute return on the day prior to the DD report was in the top decile (Source: CRSP)

• High WSB posts (Table 8) – an indicator equal to one the firm had more than one nonresearch post issued on WSB over the previous 5 trading days (Source: WSB)

• High attention (Table 8) – an indicator equal to one if either High absolute return or High WSB posts is equal to one (Sources: CRSP and WSB)

• Institutional percent imbalance (Table 11) – institutional buy share volume less institutional sell share volume scaled by total institutional share volume. Institutional trades are assigned as buys or sells based on the Lee and Ready (1991) algorithm (Source: TAQ Intraday Indicators)

• Large retail percent imbalance (Table 11) – retail buy share volume less retail sell share volume scaled by total retail share volume. Retail trades are assigned as buys or sells based on the BJZZ algorithm (Source: TAQ Intraday Indicators)

• Small retail percent imbalance (Table 11) – the number of retail buy trades less the number of retail sell trades scaled by total retail trades. Retail trades are assigned as buys or sells based on the BJZZ algorithm (Source: TAQ Intraday Indicators)

• Institutional SD abnormal imbalance (Table 11) – institutional buy share volume less institutional sell share volume (institutional imbalance) less the average institutional imbalance over days $t−120$ through $t−240$⁠, scaled by the standard deviation of institutional imbalance estimated over days $t−120$ through $t−240$⁠. Institutional trades are assigned as buys or sells based on the Lee and Ready (1991) algorithm (Source: TAQ Intraday Indicators)

• Large retail SD abnormal imbalance (Table 11) – retail buy share volume less retail sell share volume (large retail imbalance) less the average large retail imbalance over days $t−120$ through $t−240$⁠, scaled by the standard deviation of large retail imbalance estimated over days $t−120$ through $t−240$⁠. Retail trades are assigned as buys or sells based on the BJZZ algorithm (Source: TAQ Intraday Indicators)

• Small retail SD abnormal imbalance (Table 11) – the number of retail buy trades less the number of retail sell trades (small retail imbalance) less the average small retail imbalance over days $t−120$ through $t−240$⁠, scaled by the standard deviation of small retail imbalance estimated over days $t−120$ through $t−240$⁠. Retail trades are assigned as buys or sells based on the BJZZ algorithm (Source: TAQ Intraday Indicators)

C.2 Main Independent Variables

• Net DD—the total number of WSB due diligence (DD) reports that recommend buying the firm over a time period (e.g., one day) less the total number of DD reports that recommend selling the firm during the time period. We winsorize this value at the 1st and 99th percentiles of the distribution of firm days where Net DD is not equal to zero (Source: WSB)

• NonResearch Posts—the total number of nonresearch posts where a ticker is mentioned in the title over a time period (e.g., one day). We classify posts in the following WSB categories as nonresearch related: News, Gains, losses, charts, and Shi$posts. We winsorize this value at the 1st and 99th percentiles of the distribution of firm days where NonResearch Posts is not equal to zero (Source: WSB)

• Net SA—the total number of Seeking Alpha research reports that recommend buying the firm over a time period (e.g., one day) less the total number of Seeking Alpha reports that recommend selling the firm during the time period. We winsorize this value at the 1st and 99th percentiles of the distribution of firm days where Net SA is not equal to zero (Source: SA)

• Post-GME (Post) – an indicator equal to one for the post-GME period (January 14, 2021, to June 30, 2021) and zero for the pre-GME period (July 1, 2018, to January 12, 2021)

• Prepandemic—an indicator equal to one for the pre-GME period that coincides with the period prior to the pandemic (July 2018 to March 15, 2020) and zero otherwise

• Post-pandemic—an indicator equal to one for the pre-GME period that coincides with the period that coincides with the pandemic (March 16, 2020, to January 12, 2021) and zero otherwise

• Processing—an indicator equal to one if the report is issued around a major information event, defined as an earnings announcement issued on the previous or current day (–1, 0), an analyst revision on the previous or current day (–1, 0) or abnormal media coverage on the previous or current day (–1, 0)

  • Earning announcement—a quarterly or annual earnings announcement (Source: I/B/E/S)

  • Analyst revision—a quarterly or annual earnings forecast revision (Source: I/B/E/S)

  • Abnormal media coverage—an indicator equal to one if the number of articles on the firm is in the top 20% relative to the firm’s average media coverage over the previous 60 days [–60, –1] (Source: Bloomberg)

• Net DD processing—Net DD computed using only the subset of reports where Processing $=1$ (Source: WSB, I/B/E/S, and Bloomberg)

• Net DD production –Net DD computed using only the subset of reports where Processing $=0$ (Source: WSB, I/B/E/S, and Bloomberg)

• Net DD PP—Net DD computed using only the subset of reports where PP report $=1$ (Source: WSB)

• Net DD attention—Net DD computed using only the subset of reports where High attention $=1$ (Sources: WSB and CRSP)

• Net DD PP/Attenttion—Net DD computed using only the subset of reports where either PP report $=1$ or High attention $=1$ (Sources: WSB and CRSP)

• DD—an indicator equal to one if a DD report was issued for firm i on day t and zero otherwise (Source: WSB)

• NR indicator—an indicator equal to one if a Nonresearch post was issued for firm i on day t and zero otherwise (Source: WSB)

• SA—an indicator equal to one if an SA report was issued for firm i on day t and zero otherwise (Source: Seeking Alpha)

C.3 Other Control Variables

• Size—the market capitalization computed as share prices times total shares outstanding at the end of the year (Source: CRSP)

• Book-to-market (BM) – the book-to-market ratio computed as the book value of equity during the calendar year scaled by the market capitalization at the end of the calendar year. Positive values are winsorized at the 1st and 99th percentiles. Negative value and missing values are set equal to zero and we include a corresponding “Missing BM” indicator (Source: CRSP/Compustat)

• Volatility—the standard deviation of daily returns during the month (Source: CRSP)

• Turnover—the average daily turnover (ie, share volume scaled by shares outstanding) during the month.

• Ret [0]—the buy-and-hold return on the current day (Source: CRSP)

  • Ret [–5, –1]—the buy-and-hold return over the previous 5 trading days

  • Ret [–26, –6]—the buy-and-hold return over the previous 6 to 26 trading days

  • Return (m—1) – the buy-and-hold return in the previous calendar month

  • Return (m—2, m—12) – the buy-and-hold return over the previous 2 to 12 calendar months. (Source: CRSP)

  • Abs. ret—the absolute value of Ret

• Sentiment [0]—The average sentiment scores across all news articles on the current day, where the score ranges from -1 (very negative news) to 1 (very positive news), with a median value of zero (neutral articles) Firms with no media coverage are assigned a sentiment score of zero (Source: Bloomberg)

  • Sentiment [–5, –1]—the sum of the sentiment score over the previous 1 to 5 trading days

  • Sentiment [–26, –6]—the sum of the sentiment score over the previous 6 to 26 trading days

  • Abs. sentiment—the absolute value of Sentiment

• Institutional ownership—the percentage of the firm’s shares held by institutions at year-end (Source: Thomson Reuters Institutional Holdings S34)

• Breadth of ownership—the total number of common shareholders (Source: Compustat)

• IBES coverage—the number of unique brokerage houses issuing earnings forecast for a firm during the calendar year (Source: I/B/E/S)

• Media coverage—the total number of media articles about a firm during the calendar year (Source: Bloomberg)

• High max—an indicator equal to one if the maximum daily return of the firm in the prior month was in the top quintile of the distribution (Source: CRSP)

• Heavy short—an indicator equal to one if the firm is in the top quintile of short interest, defined as the number of shares that have been sold short scaled by shares outstanding (Source: Compustat)

• Recent IPO—an indicator equal to one if the firm went public in the past 6 months (Source: CRSP)

• GME/AMC—an indicator equal to one for GME or AMC and zero otherwise

Footnotes

Author notes

Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

References