What studies find on social media posts and market moves

Summary

Published studies of social media posts and the stock market, most of them on posts from one U.S. president's account, find effects that depend on topic, tone, asset and data frequency, and many of the effects fade within hours or days.

• Six studies of a U.S. president's posts naming listed companies disagree. Two found price moves that faded or partly reversed within days [8, 9], one found declines after negative posts about non-media firms, largely reversed the next day [12], one found no lasting price effect [13] and two found no significant average effect [10, 11].
• Tone changes the answer between samples. Brans and Scholtens found strongly negative posts followed by lower company value and supportive posts with no significant effect [11]. Kinyua and coauthors found positive market reactions to both strongly positive and strongly negative posts [15].
• With minute-by-minute futures data on 8,686 posts from a U.S. president's account to September 2019, Gjerstad and coauthors found that most topics did not move markets, and that trade-war posts were followed by lower U.S. stock prices, more trading and more uncertainty [14].
• Posts from a U.S. president's account criticizing the Federal Reserve, timed to the second against fed funds futures from 2015 to 2021, lowered expected policy rates, while randomly chosen other posts from the account showed no significant effect [23].
• Currency studies of a U.S. president's account found effects tied to content: higher Mexican peso volatility after Mexico policy posts, a weaker ruble after negative Russia-related posts, and a stronger dollar after posts on macroeconomic policy or trade [19, 20, 21].
• In 2018 the SEC alleged that an electric-car maker's chief executive posted false and misleading statements about taking the company private, and he and the company each agreed to a $20 million penalty without admitting or denying the allegations [4, 5].
• Posts by many individual users, studied together as crowd posts, form a separate literature. Aggregate opinion in individual posts predicted firms' quarterly earnings and announcement returns from 2009 to 2012 [29], and a 2017 replication found no out-of-sample support for a 2011 finding that a Twitter mood series predicted the Dow Jones Industrial Average [32].
• For 2025, a May 2026 bachelor's thesis using daily data found no significant association between a U.S. president's tariff posts and daily VIX changes, and its authors do not rule out intraday reactions [35].

Background

Executives, companies and heads of state post on social media, and investors read the posts. Two questions follow: when a post counts as disclosure, and whether it moves prices. This report sets out the evidence on the second, with the record on the first as background.

Posts as disclosure. On 2 April 2013 the SEC said that companies can use social media outlets such as Facebook and Twitter to announce key information in compliance with Regulation Fair Disclosure (Regulation FD), provided investors have been alerted to the channels a company uses [2]. Regulation FD requires companies to distribute material information "in a manner reasonably designed to get that information out to the general public broadly and non-exclusively" [2].

The statement came with a report of investigation into a post by a streaming company's chief executive on his personal Facebook page on 3 July 2012, announcing 1 billion hours of streaming in June, with no press release or Form 8-K [3]. The news "reached the securities market incrementally", and the stock "continued a rise that began when the market opened", from $70.45 at the time of the post to $81.72 at the close of the following trading day [3]. The Commission decided not to pursue an enforcement action [3].

Posts as market events. Journals published event studies of one U.S. president's posts from 2017 to 2025 [8, 18], set out in Findings 1 to 5, and a 2026 bachelor's thesis on 2025 posts is Finding 9 [35]. A chief executive's posts led to an SEC case in 2018 and to studies of crypto assets (Findings 6 and 7) [4, 25]. Crowd posts form a separate literature (Finding 8) [29, 31].

Method

What was examined. Published studies of how prices, trading volume and volatility moved around social media posts, with each study's sample, data frequency, window, tone coding and result, plus the regulatory and court record on executives' posts.

The measure. An event study measures the effect of an event on the value of firms by examining security prices around it [1]. Brans and Scholtens, for example, measure each company's move against a market model on the S&P 500, fitted over an estimation window of 250 days that ends the day before each post, and report the average abnormal return (AAR) across posts [11]. Gjerstad and coauthors compare returns after each post with all non-overlapping returns of the same length during the presidency [14]. Bianchi, Kind and Kung compare their results with randomly drawn placebo posts [23].

Sources. Studies of a U.S. president's posts (Findings 1 to 5 and 9), of one chief executive's posts on crypto assets (Finding 7) and of company and crowd posts (Findings 6 and 8), with SEC and court records [2, 3, 4, 5, 6, 7, 34]. All the studies are journal articles except an NBER working paper, published in revised form in the Journal of Monetary Economics in 2023 [23], a Federal Reserve discussion paper whose page says its conclusions "are often preliminary" [33], and a bachelor's thesis that is not peer-reviewed [35].

How. Full texts were read for [3, 6, 11, 14, 19, 23, 27, 34, 35], and abstracts, record pages or press releases for the others. Each result is stated in the study's own terms, under its own sample and window, and results are not averaged. Null results are reported beside effects. Figures marked "by arithmetic" are computed from a source's numbers. Posting accounts are described by their role. Every source was viewed on 8 November 2026.

Findings

1. Presidential posts naming companies

Six studies tested whether a U.S. president's posts that named listed companies moved those companies' stocks [8, 9, 10, 11, 12, 13].

Born, Myers and Clark studied posts between the election of 8 November 2016 and the inauguration of 20 January 2017 that identified ten publicly traded firms [8]. Positive posts were followed by positive abnormal returns and negative posts by negative ones on the event date, and virtually all of the effect came between the opening price and the close [8]. Within five trading days the cumulative abnormal returns were no longer statistically significant [8]. Trading volume and Google search activity rose, which the authors read as small or noise traders acting on the posts with a transitory impact [8].

Ge, Kurov and Wolfe found that posts naming companies moved stock prices and raised trading volume, volatility and institutional investor attention, with a stronger impact before the inauguration [9]. They found some evidence that the initial price impact reversed in the next few trading days [9].

Juma'h and Alnsour related the posts' content to daily changes in major indexes and in the named companies' excess returns. On average they found no significant effect on the indexes or on most of the named companies' share prices [10].

Brans and Scholtens collected over 5,600 posts from 8 November 2016 to 8 November 2018 and kept 100 that named a publicly listed company [11]. They left out retweets, posts about non-listed companies, non-specific posts, posts that shared a window with a post about another company, and companies with mergers, splits, profit warnings or top-management changes [11]. Posts during trading hours were matched to closing prices, posts outside them to the next day's open, and weekend and holiday posts to the next trading day [11]. Neither daily AAR was significant, so the null hypothesis of no abnormal returns could not be rejected [11].

Ajjoub, Walker and Zhao took every post from 26 May 2016 to 30 August 2018, and selected the 513 of 6,983 that mention companies publicly traded in the United States [12]. For media firms, positive posts had a pronounced positive price impact and negative and neutral posts little or none [12]. For non-media firms they found the opposite: negative posts were associated with significant price declines, which to a large extent reversed the following day [12]. Posts repeating information the market already knew drew an additional price reaction [12].

With minute-by-minute data, Machus, Mestel and Theissen found that the posts raised trading activity and had no lasting effect on prices [13]. Abnormal returns, trading volume and investor attention also rose before the posts, which the authors read as the posts commenting on events that had already drawn investor attention [13].

2. Tone: negative and positive posts

Brans and Scholtens scored each post automatically with SentiStrength, a word-list tool, from -5 to +5, where Ge and coauthors coded tone by hand [11]. Strongly negative posts were followed by reduced market value of the company named, and supportive posts had no significant effect [11]. The difference between negative and positive posts' AARs was 0.7% and 1.0%, significant at the 10% level [11]. One event, a drop of almost 20%, was removed from their sample, and the authors note that including it would have strengthened their results [11]. Ge and coauthors, with 48 posts, found the difference between negative and positive posts not statistically significant [11].

Kinyua, Mutigwe, Cushing and Poggi used a 30-minute window for each post and intraday data on the Dow Jones Industrial Average and the S&P 500 [15]. They found a significant negative reaction when posts came during open market hours, and positive market reactions to posts with strong positive or strong negative sentiment [15].

3. Topics, volatility and the whole market

Gjerstad, Meyn, Molnár and Næss started from 40,842 posts by 22 September 2019, of which 10,911 came after the 2016 election, and kept 8,686 after removing retweets [14]. They used minute-by-minute futures data on the S&P 500, the VIX, WTI crude oil, the Hang Seng and gold to 18 September 2019, with post times rounded down to the minute [14].

Pooled over all posts, the S&P 500 was negatively affected, but the effect was not very significant and seemed to vanish within an hour [14]. The return effect lasted at most 45 minutes, while trading volume stayed higher for at least an hour [14]. After posts containing "products", the average cumulative return fell about 30 basis points over the next 60 minutes, and after posts containing "tariff" about ten basis points, and these effects vanished within two hours [14]. With Latent Dirichlet Allocation, a method that groups posts into topics, they found that most topics did not affect markets [14]. Posts on a trade-war topic were followed by a U.S. price decline, higher trading volume and higher uncertainty, a negative response in the Chinese stock market and a positive one in gold [14]. That topic's S&P 500 effect stayed significant for the full two hours tested [14].

Burggraf, Fendel and Huynh analyzed more than 3,200 posts and found that posts on the U.S.-China trade war negatively predict S&P 500 returns and positively predict the VIX, with one-directional Granger causality and results that vary across industries by their trade intensity with China [16].

Nishimura, Dong and Sun found that posting positively and significantly affected volatility and jumps in the later part of their sample, and that realized volatility responded more than continuous volatility because of the jump component [17]. Nishimura and Sun, in 2025, found that posts from the earlier term raised volatility in the German, French and UK stock markets, more as attention to the posts rose, with a weaker effect in the UK [18].

4. Currencies and oil

Benton and Philips counted over 14,500 posts between 1 January 2015 and 2 February 2018 and used 17 keywords to find 438 Mexico-related posts sent on 239 days [19]. With the daily percentage change in the USD/MXN exchange rate, they found that Mexico-related policy posts raised peso volatility while the policy views were unknown and also afterwards, as signals of resolve on that agenda [19].

Afanasyev, Fedorova and Ledyaeva found that escalating negative sentiment in Russia-related posts led to a ruble depreciation of 4% to 10% over about three days [20]. Those episodes tended to coincide with U.S. sanctions, and sanctions that were not accompanied by negative posts did not cause depreciation [20].

Filippou, Gozluklu, Nguyen and Viswanath-Natraj identified the subset of posts with information on macroeconomic policy or trade [21]. Those posts were followed by a U.S. dollar appreciation and a decline in intraday volatility, robust to controlling for macroeconomic announcements [21].

Dragomirescu-Gaina, Philippas and Goutte, with a nonlinear regime model, found that some posts increased the likelihood of a wider spread between WTI and Brent crude, with a negative impact on proxies for liquidity and speculative activity [22].

5. Posts about monetary policy at high frequency

Bianchi, Kind and Kung timed posts criticizing the Federal Reserve to the second and measured them against tick-by-tick fed funds futures in a benchmark window from 0.1 minutes before to 5 minutes after each post, from 16 June 2015 to 20 January 2021 [23]. The average effect on the expected fed funds rate was negative and statistically significant, and grew with the horizon, and the posts were followed by higher stock prices and lower long-term Treasury yields [23]. In a placebo test of 100 randomly selected other posts, repeated 100 times, the coefficients were all close to zero and not significant [23].

Tillmann found that, on average, the president's statements on the Fed led to lower long-term interest rates, consistent with lower expected future short rates, and that the impact declined over time [24].

6. An executive's posts and the SEC's response

On 27 September 2018 the SEC charged an electric-car maker's chief executive and chairman with securities fraud for "a series of false and misleading tweets" about a potential transaction to take the company private [4]. According to the SEC, on 7 August 2018 he posted to his 22 million followers that he could take the company private at $420 per share, a substantial premium to its trading price at the time, and that funding had been secured [4]. The SEC's complaint alleged that the posts caused the stock price to jump by over 6% that day and led to significant market disruption [4].

Two days later both settled without admitting or denying the SEC's allegations [5]. The company was charged with failing to have required disclosure controls and procedures relating to his posts, although it had told the market in 2013 that it intended to use his account to announce material information [5]. The settlements, subject to court approval, required him to step down as chairman for three years, the company to add independent directors and controls over his communications, and each to pay a $20 million penalty, the $40 million to be distributed to harmed investors [5].

On 15 May 2023 the Second Circuit affirmed the district court's judgment on appeal from an order of 27 April 2022 [6]. The consent decree required him to follow company procedures for communications in any format, including posts on social media, and the company's policy sent covered communications through pre-approval by the general counsel or an in-house securities lawyer [6]. Coverage first turned on materiality and later on a list of specified subjects [6]. The court noted that the record showed just three SEC inquiries into his posts since 2018 [6]. The order is a summary order without precedential effect, and the Supreme Court denied a petition for a writ of certiorari on 29 April 2024 [6, 7].

In a sample of technology firms, Blankespoor, Miller and White found that using Twitter to send links to press releases was associated with lower abnormal bid-ask spreads and greater abnormal depths, consistent with less information asymmetry, mainly for firms that are not highly visible [28].

7. Crypto assets and one executive's posts

Ante studied 47 crypto-related posting events by the same chief executive and found significant positive abnormal returns and trading volume after them [25]. On average, price effects were significant for Dogecoin-related posts and not for Bitcoin, because the positive and negative effects on Bitcoin cancel each other out [25]. Non-negative posts were followed by significantly positive abnormal Bitcoin returns, and individual posts raised Bitcoin's price by 16.9% or reduced it by almost 11.8% [25].

Shahzad, Anas and Bouri, with four-hourly data, found that his general crypto posts contributed to price explosivity in Bitcoin and his Dogecoin posts to price explosivity in Dogecoin [26]. Huynh measured the tone of 10,850 posts containing 157,378 words, from December 2017 to May 2021, with financial dictionaries [27]. With daily data, tone had "a Granger causal relation with returns", reversal effects appeared, and no link was found between the sentiment and Bitcoin volatility [27].

8. Crowd posts: aggregate opinion, mood and a short squeeze

Crowd studies measure the combined opinion of many users. Bartov, Faurel and Mohanram found, in a broad sample from 2009 to 2012, that the aggregate opinion in individual posts predicted a firm's forthcoming quarterly earnings and announcement returns [29]. The results were stronger for firms in weaker information environments and held after controlling for opinion in traditional media [29]. Yang, Fernandez-Perez and Indriawan found intraday that the price impact of buyer-initiated trades rose with the number of posts and their sentiment, and that the resulting mispricing took several days to correct [30].

Bollen, Mao and Zeng built daily mood series from posts and reported an accuracy of 87.6% in predicting the daily up and down changes of the Dow Jones Industrial Average [31]. Lachanski and Pav could not reproduce its p-value pattern [32]. They found a significant mood effect in the original subsample, from 28 February to 3 November 2008, and none in a sample extended backwards, which they call consistent with data snooping, and no evidence that mood measures helped predict the market out of sample [32].

Adams, Ajello, Silva and Vazquez-Grande built a Twitter Financial Sentiment Index that correlates highly with corporate bond spreads [33]. Overnight sentiment helped predict next-day stock returns, a deterioration in sentiment the day before a Federal Open Market Committee statement predicted the size of restrictive monetary policy shocks, and sentiment worsened after an unexpected tightening [33].

The SEC staff found that GameStop and other stocks rose sharply in January 2021 "as bullish sentiments of individual investors filled social media" [34]. Price and volume moves coincided with substantial interest in certain online investing forums, and some posts heavily touted GameStop's prospects [34]. The staff concluded that positive sentiment sustained the weeks-long rise and that buying to cover short positions did not [34]. The report gives staff views and is not a statement of the Commission [34].

9. Posts in 2025

Gustavsson and Hansson studied a U.S. president's posts from 3 March to 30 June 2025 in a bachelor's thesis at Lund University, with events taken from The American Presidency Project's archive of the account [35]. Of 85 trading days, 56 were tariff-related event days and 29 were not, and the main regression used 83 days because two lacked financial data [35]. They found no statistically significant association between tariff-related event days and daily close-to-close changes in the VIX [35]. They do not rule out intraday reactions, effects of specific types of tariff posts, or reactions absorbed by broader equity-market moves [35]. With tariff posts on about two thirds of trading days (56 of 85, by arithmetic), the design had 29 comparison days [35].

10. Why results differ

Selection. Samples range from 15 to 513 posts about companies [11, 12] and up to 8,686 posts in all [14]. Brans and Scholtens selected posts manually and list that, with their use of daily data, as limitations [11]. Filippou and coauthors kept only posts with macroeconomic policy or trade content [21].

Data frequency and windows.

In Finding 3, the pooled effect was gone within an hour and the "products" and "tariff" effects within two hours, while the trade-war topic's S&P 500 effect was still significant at the end of the two-hour window [14]. Two daily studies name the limit themselves: Brans and Scholtens call the lack of high-frequency data a limitation [11], and Gustavsson and Hansson do not rule out intraday reactions [35].

Moves before the post. Machus and coauthors found abnormal returns, volume and attention rising before posts [13]. Gjerstad and coauthors saw the trade-war effect start slightly before posts, judged leakage unlikely and attributed it to consecutive posts on the same topic [14].

Outliers and missing posts. Brans and Scholtens removed one event with a drop of almost 20% [11]. Their archive collected posts about once every 24 hours until 27 January 2017 and in real time after, so posts deleted within 24 hours before that date are missing, and the authors removed all posts deleted within 24 hours to keep sampling consistent [11].

Tests and replication. Lachanski and Pav read the 2011 mood result as consistent with data snooping, a pattern in one subsample that did not hold in a longer one [32]. Placebo posts [23] and all ordinary returns of the same length [14] set the baseline in two high-frequency studies.

11. What the evidence supports and what it leaves open

Trading activity rose after posts more consistently than prices moved in one direction. Volume rose in [8, 9, 13, 14, 25], attention in [8, 9], and volatility in [9, 16, 17, 18, 19], while informative posts on policy or trade lowered intraday currency volatility [21]. Price effects on named companies were short or reversed in [8, 9, 12, 13] and not significant on average in [10, 11]. Market-wide effects were tied to policy content in several studies: trade [14, 16], the Federal Reserve [23, 24] and relations with Mexico and Russia [19, 20].

The evidence leaves three questions open. First, cause: two minute-level studies found moves starting before posts [13, 14]. Second, other accounts, since the studies of single posters cover one president and one chief executive [8, 25]. Third, recent posts: the only opened study of a U.S. president's posts after January 2021 is a bachelor's thesis with a null result [35].

Implications

For researchers.

• Use timestamps at least to the minute and intraday prices. In one minute-level study, most effects faded within one to two hours, and the trade-war effect lasted the full two-hour window [14]. Tick-level work timed posts to the second [23].
• Keep every post as issued, deletions included. An archive gap removed early deleted posts from one sample [11].
• Compare each post window with ordinary windows or placebo posts [14, 23].
• State how posts were selected and how many were kept [11, 12, 14, 21], and report null results with the same detail as effects [10, 11, 35].
• Record how tone was scored, since word lists, hand coding and firm-type splits give different groups [11, 12, 27].
• Test a finding outside the sample that produced it [32].

For risk teams. The more consistent response across these samples is higher volume, volatility and attention [8, 9, 13, 14, 17, 18]. Currencies, oil spreads and rate expectations responded in several studies [19, 20, 21, 22, 23]. On disclosure, the SEC's 2013 position lets companies use social media once investors are alerted to the channels [2], and its 2018 case charged a company that had named an executive's account as a channel with lacking disclosure controls over it [5].

Limits of this analysis

• Most studies were read from abstracts or record pages. The data frequency and windows of Ge, Kurov and Wolfe [9], Ajjoub, Walker and Zhao [12], Burggraf, Fendel and Huynh [16] and Ante [25] are not stated in what was opened, and this report states none.
• The post counts for Born, Myers and Clark (15) and Ge, Kurov and Wolfe (48), and the result for Ge and coauthors' tone difference, come from Brans and Scholtens' summary [11].
• Brans and Scholtens' text also gives a two-day cumulative figure that does not equal the sum of its two daily figures, so this report leaves it out [11].
• 18 of the 27 studies here concern one account, and three concern one chief executive (by count), so the evidence on single posters rests on two accounts.
• No peer-reviewed study of posts from 2025 or 2026 could be opened for this report. The only opened study of 2025 posts is a bachelor's thesis, which is not peer-reviewed [35].
• The 2018 SEC matter is the SEC's allegation, settled without admitting or denying [4, 5]. The Second Circuit order has no precedential effect [6].

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