{"id":205949,"date":"2024-05-02T11:10:35","date_gmt":"2024-05-02T15:10:35","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=205949"},"modified":"2024-05-06T13:23:35","modified_gmt":"2024-05-06T17:23:35","slug":"social-media-analyst-behavior-and-market-efficiency","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/social-media-analyst-behavior-and-market-efficiency\/","title":{"rendered":"Social Media, Analyst Behavior and Market Efficiency"},"content":{"rendered":"\n

The article “Social Media, Analyst Behavior and Market Efficiency” first appeared on the\u00a0 Alpha Architect Blog<\/a><\/em>.<\/p>\n\n\n\n

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 \u201cDo Individual Investors Trade on Investment-related Internet Postings?<\/a>\u201d investigated whether social media postings help individual investors identify investment strategies that deliver superior performance in the future and found that<\/p>\n\n\n\n

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\u201cit 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.\u201d<\/em><\/p>\n<\/blockquote>\n\n\n\n

These findings are consistent with those of the authors of the November 2020 study \u201cAttention Induced Trading and Returns: Evidence from Robinhood Users<\/a>,\u201d who found:<\/p>\n\n\n\n

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\u201cLarge increases in Robinhood users are often accompanied by large price spikes and are followed by reliably negative returns.\u201d<\/em><\/p>\n<\/blockquote>\n\n\n\n

The findings are also consistent with those of the authors of the March 2021 paper \u201cThe Rise of Reddit: How Social Media Affects Retail Investors and Short-sellers\u2019 Roles in Price Discovery<\/a>,\u201d who found that \u201cReddit social media activity encourages retail buying behavior, and deters shorting.\u201d They added: \u201cSocial media activity and retail flows cultivate price bubbles, while the shortsellers correct the bubbles created by social media activity and retail order flows.\u201d<\/p>\n\n\n\n

And the author of the August 2020 study \u201cInvestor Emotions and Earnings Announcements<\/a>\u201d 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.<\/p>\n\n\n\n

Unfortunately, the body of evidence demonstrates that naive retail investors can be easily convinced they have an edge\u2014they know something the market hasn\u2019t yet incorporated into prices. Sadly, the evidence also shows that while these less sophisticated investors can be convinced they \u201cknow\u201d something by finding an \u201cexpert\u201d on a social media platform, the results of trading activities based on following \u201cexperts\u201d show negative outcomes. As usual, the ones benefiting are the platforms (like Robinhood), not the investors who use them.<\/p>\n\n\n\n

Sell-Side Analysts and Social Media<\/strong><\/h2>\n\n\n\n

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, \u201cNegativity Bias, Social Media, and Analyst Behavior,\u201d 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\u2019s daily Twitter sentiment data on S&P 500 firms over the period 2015-2019 to determine if that was the case.<\/p>\n\n\n\n

The authors began by noting:<\/p>\n\n\n\n

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\u201cOne strand of psychology literature shows that, across a broad range of contexts, negative information is processed more thoroughly than positive information. \u2026 Consequently, negative information would be more influential than comparable positive information.\u201d<\/em><\/p>\n<\/blockquote>\n\n\n\n

They added:<\/p>\n\n\n\n

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\u201cThe 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.\u201d<\/em><\/p>\n<\/blockquote>\n\n\n\n

And finally, they noted:<\/p>\n\n\n\n

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\u201cDue 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.\u201d<\/em><\/p>\n<\/blockquote>\n\n\n\n

And in fact, the authors of the 2016 study \u201cThe Value of Crowdsourced Earnings Forecasts<\/a>\u201d found such behavior among firms on Estimize<\/a>, an open platform that crowdsources short-term earnings forecasts. <\/p>\n\n\n\n

Because of the asymmetry of how negative and positive information is received, Hibbert, Kang, Kumar and Mishra hypothesized that<\/p>\n\n\n\n

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\u201cwhen 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.\u201d<\/em><\/p>\n<\/blockquote>\n\n\n\n

To establish a causal relation between Twitter information<\/a> 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:<\/p>\n\n\n\n

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\u201cBoth 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.\u201d<\/em><\/p>\n<\/blockquote>\n\n\n\n

Following is a summary of their findings:<\/p>\n\n\n\n