Social media has become an increasingly popular forum for market participants to post and exchange their opinions about financial securities, especially since the inception of Twitter in 2006. That increased popularity has been accompanied by heightened interest from academic researchers who have sought to determine if there is valuable information in the social media postings. For example, the June 2020 study “Do Individual Investors Trade on Investment-related Internet Postings?” investigated whether social media postings help individual investors identify investment strategies that deliver superior performance in the future and found that

“it is mainly unsophisticated individuals who rely on investment-related Internet postings when making investment decisions, but this does not help them identify traders with superior skills.”

These findings are consistent with those of the authors of the November 2020 study “Attention Induced Trading and Returns: Evidence from Robinhood Users,” who found:

“Large increases in Robinhood users are often accompanied by large price spikes and are followed by reliably negative returns.”

The findings are also consistent with those of the authors of the March 2021 paper “The Rise of Reddit: How Social Media Affects Retail Investors and Short-sellers’ Roles in Price Discovery,” who found that “Reddit social media activity encourages retail buying behavior, and deters shorting.” They added: “Social media activity and retail flows cultivate price bubbles, while the shortsellers correct the bubbles created by social media activity and retail order flows.”

And the author of the August 2020 study “Investor Emotions and Earnings Announcements” had a particularly interesting finding. While he found that investors are typically excited about firms that do end up exceeding expectations, their enthusiasm was excessive and resulted in negative post-announcement returns.

Unfortunately, the body of evidence demonstrates that naive retail investors can be easily convinced they have an edge—they know something the market hasn’t yet incorporated into prices. Sadly, the evidence also shows that while these less sophisticated investors can be convinced they “know” something by finding an “expert” on a social media platform, the results of trading activities based on following “experts” show negative outcomes. As usual, the ones benefiting are the platforms (like Robinhood), not the investors who use them.

Sell-Side Analysts and Social Media

Ann Marie Hibbert, Qiang Kang, Alok Kumar and Suchi Mishra took a different approach to the issue of how social media impacts markets. In their March 2024 study, “Negativity Bias, Social Media, and Analyst Behavior,” they examined whether sell-side equity analysts are able to effectively extract information from social media to improve their earnings forecasting performance. They used Bloomberg’s daily Twitter sentiment data on S&P 500 firms over the period 2015-2019 to determine if that was the case.

The authors began by noting:

“One strand of psychology literature shows that, across a broad range of contexts, negative information is processed more thoroughly than positive information. … Consequently, negative information would be more influential than comparable positive information.”

They added:

“The same psychology literature also demonstrates that negative information elicits more thorough and careful information processing than positive information. Therefore, negative information may capture more attention and receive more conscious processing.”

And finally, they noted:

“Due to either conflict of interest or economic incentives, analysts issue overly optimistic earnings forecasts. Innovation in information technology such as the advent and prevalence of social media have intensified competition in information production, which may induce analysts to make less biased forecasts.”

And in fact, the authors of the 2016 study “The Value of Crowdsourced Earnings Forecasts” found such behavior among firms on Estimize, an open platform that crowdsources short-term earnings forecasts. 

Because of the asymmetry of how negative and positive information is received, Hibbert, Kang, Kumar and Mishra hypothesized that

“when analysts look to social media as an increasingly important and competing information source, their optimism bias may be moderated by negative information, but not necessarily exacerbated by positive information. Consequently, negative and positive Twitter information would affect analyst earnings forecasts in an asymmetric way.”

To establish a causal relation between Twitter information and analyst behavior, they used two events as proxies for exogenous changes in the information content of individual tweets: (1) the Twitter character limit doubling to 280 in November 2017 and (2) the StockTwits character limit increasing to 1,000 in May 2019. The authors explained:

“Both events likely increased the information content of individual tweets. As a result, the impact of negative Twitter information on analyst earnings performance enhancements is likely to be stronger following these events.”

Following is a summary of their findings:

  • On average, analysts’ forecasts are too optimistic.
  • Twitter information tends to be relatively more pessimistic than traditional news.
  • Positive Twitter information had little or no impact on analyst forecasts. However, more negative Twitter information was associated with more pessimistic (less optimistic) and more accurate earnings forecasts—Twitter information reduces forecast optimism and improves forecast accuracy of equity analysts. The effect is distinct from the impact of traditional news sources and was greater after the two exogenous events. It was also greater for smaller firms with greater information asymmetry.
  • The predictive relation between negative Twitter information and forecast accuracy was significantly weaker in the fiscal year-end month than in the other three fiscal quarter-end months, confirming the less important role played by (negative) Twitter information in a richer information environment. The authors explained: “Analysts are likely to have access to a richer and more accurate information set during the months of the firm’s fourth fiscal quarter-end, i.e., fiscal year-end, compared to the other months of the year including the other three fiscal quarter-end months.”
  • The short-term market responses to earnings surprises were significantly weaker for highly Twitter-sensitive firms.

At the aggregate level, Twitter-sensitive firms have smaller earnings surprises and consequently, weaker stock market reaction—the post-earnings-announcement drift (PEAD) anomaly (the tendency for a stock’s cumulative abnormal returns to drift in the direction of an earnings surprise for several weeks, or even several months, following an earnings announcement) was reduced, especially for negative earnings surprises.

Their findings led Hibbert, Kang, Kumar and Mishra to conclude:

“Collectively, these results suggest that financial analysts extract useful information from Twitter, improving their overall forecasting performance and market efficiency. Investors recognize this relation and respond to this phenomenon accordingly.”

Investor Takeaway

In our book The Incredible Shrinking Alpha, Andrew Berkin and I provided the evidence demonstrating that it is persistently more difficult for active managers to add alpha (outperform appropriate risk-adjusted benchmarks). We also demonstrated that there are four main themes that explain the shrinking alpha:

  • Academic research has been converting what was once alpha into beta.
  • The pool of victims that can be exploited has been shrinking.
  • The competition has been getting tougher.
  • The supply of dollars chasing the shrinking pool of alpha has increased.

Hibbert, Kang, Kumar and Mishra provided us with yet another explanation: social media is providing analysts with information that reduces their forecasting errors. The result has been an increase in market efficiency, leading to a reduction in the PEAD anomaly. The bottom line is that the ability to generate alpha continues to be under assault—trying to outperform the market by stock selection is becoming even more of a loser’s game.  

For informational and educational purposes only and should not be construed as specific investment, accounting, legal, or tax advice.  Certain information is based upon third party data which may become outdated or otherwise superseded without notice.  Third party information is deemed to be reliable, however its accuracy and completeness cannot be guaranteed. Information from sources deemed reliable, but its accuracy cannot be guaranteed.  By clicking on any of the links above, you acknowledge that they are solely for your convenience, and do not necessarily imply any affiliations, sponsorships, endorsements or representations whatsoever by us regarding third-party websites. We are not responsible for the content, availability or privacy policies of these sites, and shall not be responsible or liable for any information, opinions, advice, products or services available on or through them. The opinions expressed by featured authors are their own and may not accurately reflect those of the Buckingham Strategic Wealth® or Buckingham Strategic Partners®, collectively Buckingham Wealth Partners. Neither the Securities and Exchange Commission (SEC) nor any other federal or state agency have approved, determined the accuracy, or confirmed the accuracy of this article. LSR-22-247

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About the Author: Larry Swedroe

Larry Swedroe
Larry Swedroe is the author or co-author of 18 books on investing, including his latest Enrich Your Future.

Important Disclosures

For informational and educational purposes only and should not be construed as specific investment, accounting, legal, or tax advice. Certain information is deemed to be reliable, but its accuracy and completeness cannot be guaranteed. Third party information may become outdated or otherwise superseded without notice.  Neither the Securities and Exchange Commission (SEC) nor any other federal or state agency has approved, determined the accuracy, or confirmed the adequacy of this article.

The views and opinions expressed herein are those of the author and do not necessarily reflect the views of Alpha Architect, its affiliates or its employees. Our full disclosures are available here. Definitions of common statistics used in our analysis are available here (towards the bottom).

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