Can Social Media Inform Corporate Decisions: Cookson, Niessner & Schiller (2026)

What this is. The paper’s core results, datasets, and formal specifications: enough to know what it found and how without reading all 52 pages. To replicate or extend it, access the original via the DOI: 10.1111/jofi.13508 (paywalled).

TL;DR

Using 260 million StockTwits posts (2010–2021) matched to 6,438 U.S. M&A deals from SDC Platinum, the paper measures firm-specific abnormal social media sentiment in the four days after a merger announcement. A one-standard-deviation decrease in abnormal sentiment predicts a 0.64 percentage point higher probability of deal withdrawal (16.6% of the unconditional rate). This effect survives controls for acquirer CARs, traditional news sentiment (RavenPack), analyst recommendation changes (IBES), and deal characteristics. The effect is absent for deals withdrawn by regulators or target boards (only one-third the magnitude), is concentrated after firms register corporate Twitter accounts (especially high-follower or verified accounts), and is driven by fundamental investor tweets and M&A-relevant tweet topics, not meme or technical tweets.

Core results

Magnitudes and significance are as reported; \*/\*\*/\*\*\* = 10%/5%/1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Negative abnormal social media sentiment predicts merger withdrawal, controlling for market reactions, news, and analyst signals	Table II Panel A, pp. 107–108	beta = -0.643*** (col. 1, no controls) to -0.711*** (col. 6, full controls); 1-SD decrease in AbnSent associated with 0.64 pp higher withdrawal probability (16.6% of baseline 3.89%)
R2	StockTwits and Twitter sentiment each predict withdrawal independently	Table II Panel B, pp. 107–108	StockTwits col. (1): -0.706**; Twitter (SMA) col. (2): -1.075***; both significant when included jointly in col. (3) (StockTwits: -0.593**, Twitter: -0.998***)
R3	Effect is not driven by governance channel: coefficients are similar for positive-CAR and negative-CAR deals	Figure 5, p. 111	Estimated AbnSent coefficient indistinguishable across CAR < 0 and CAR >= 0 subsamples; governance-metric splits (ISS, board independence, antitakeover provisions) also yield no significant differences (Table IA.VI)
R4	Social media sentiment predicts market-favorable deal outcomes: initial negative sentiment followed by withdrawal yields higher post-announcement BHARs	Table IV, p. 119	Interaction AbnSent x 1(Deal Withdrawn): -0.101** to -0.153*** across columns; withdrawal predicted by negative AbnSent associated with 10.09%–15.28% higher BHAR from day 11 to deal conclusion
R5	Effect strengthens after acquirer registers corporate Twitter account, especially for high-follower or verified accounts	Table V, pp. 122–123	AbnSent x Post(Twitter, HighFollow) col. (4): -0.956**; AbnSent x Post(Twitter, Verified) col. (5): -1.301**; coefficients on AbnSent alone (pre-Twitter) near zero and insignificant
R6	Effect is driven by fundamental investor tweets, not technical investor tweets	Table VI Panel A, pp. 126–127	Fundamental (non-technical) AbnSent: -1.105*** (col. 1); technical AbnSent: -0.350 (insignificant); result holds after adding media controls (col. 2): fundamental -1.080***, technical -0.274 (insig.)
R7	Effect is driven by longer tweets and by M&A-relevant topics (Company/Business, Deal Terms, Disclosure), not by meme or trading tweets	Table VI Panels A–B, pp. 126–127	Long tweets: -1.214*** to -1.222***; short tweets: -0.053 to +0.014 (insig.); Company/Business: -1.202***; Deal-Terms: -1.139***; Disclosure: -0.868**; Meme: -0.280 (insig.); Technical: -0.487 (insig.)
R8	Effect is stronger when AbnSent disagrees with market and news signals, and when social media volume is higher	Table VII, pp. 129, 130	High N Tweets vs. low: -2.030*** vs. -0.516* (coef. diff. t = 2.28, p = 0.023); split by social media vs. news disagreement drives the result; news-article-count split shows no statistically significant difference

Overall (paper’s conclusion). Building on the parallel test of traditional news sentiment for M&A decisions in Liu and McConnell (2013), the paper adds social media as a distinct channel. Social media sentiment contains information about M&A deal outcomes that is not subsumed by market prices, traditional media, or analyst signals. The evidence is consistent with a revelatory channel: managers learn from social media, particularly after engaging with the platform through a corporate Twitter account.

Theory / model

The paper has no original structural model. It tests a conceptual distinction introduced by Bond, Edmans, and Goldstein (2012) and Bai, Philippon, and Savov (2016) between two channels of informativeness:

• Revelatory: social media reveals genuinely new information to the firm manager, analogous to revelatory price efficiency (RPE), where prices aggregate dispersed information that managers subsequently learn from.
• Forecasting: social media merely correlates with information the manager already has (market prices, analyst signals, news), providing no incremental input.

The key empirical test distinguishes these channels: under the revelatory channel the predictive coefficient on $\text{AbnSent}_i$ is large and robust to controlling for all other signals managers are known to rely on; it grows stronger after the firm engages actively with social media (registering a corporate Twitter account); and it weakens for deals where withdrawal cannot reflect manager learning (regulator- or target-rejected deals). Under a pure forecasting channel these patterns would not arise. Under a governance channel, predictability would be concentrated in negative-CAR mergers and in firms with weaker governance, which is not observed. Whereas the Chinese message board study of Ang et al. (2021) emphasizes the governance channel, this US-sample paper finds no governance-channel pattern and instead supports revelatory learning.

Identification strategy. The baseline design of using market reactions to predict M&A withdrawal follows Luo (2005), which the paper extends by adding social media signals. The paper exploits deal-level cross-sectional variation in $\text{AbnSent}_i$ (eq. 1, p. 103), which is constructed as the difference between announcement-window and pre-announcement-window sentiment, removing firm-level baseline sentiment differences. The main specification (eq. 2, p. 106) further controls for the acquirer CAR, traditional news sentiment, analyst recommendations, deal characteristics, year-by-quarter time fixed effects, and acquirer-industry fixed effects. Coefficient stability as controls are added ($R^2$ rising from 0.001 to 0.216 with a stable beta, following Oster (2019)) is the primary guard against omitted-variable concerns. The Twitter-account timing test (eq. 4, p. 121) uses within-acquirer before/after variation as an additional quasi-experimental design.

Method

Step 1: Constructing abnormal sentiment (equation 1, p. 103).

$$\text{AbnSent}_i = \left( \frac{1}{|J_{i,[0,3]}|} \sum_{j \in J_{i,[0,3]}} \text{Sentiment}_{i,j(t)} \right) - \left( \frac{1}{|J_{i,[-13,-7]}|} \sum_{j \in J_{i,[-13,-7]}} \text{Sentiment}_{i,j(t)} \right) \tag{1}$$

• $\text{Sentiment}_{i,j(t)}$ is the sentiment of tweet $j$ about acquiring firm $i$ occurring $t$ days after the merger announcement date (day 0).
• $J_{i,[t_1,t_2]}$ is the set of tweets about firm $i$ between day $t_1$ and $t_2$.
• The first term averages sentiment over the four-day announcement window $[0, 3]$; the second term averages over a reference period $[-13, -7]$ (7 to 13 days prior to announcement), omitting the 6 days immediately before the announcement to address information leakage.
• The primary sentiment scores are from StockTwits’ proprietary MarketLex classifier, bounded in $[-1, 1]$; robustness uses maximum entropy and naive Bayes classifiers trained on user-labelled bullish/bearish tags, and SMA Twitter sentiment bounded in $[0, 1]$.

Step 2: Estimating abnormal sentiment in a text classifier. To construct alternative sentiment measures, a maximum entropy (MaxEnt) classifier and a naive Bayes (Bayes) classifier are trained on StockTwits posts with user-provided sentiment tags (bullish/bearish), following Antweiler and Frank (2004) and Cookson and Niessner (2020). The classifiers assign a continuous sentiment score to each tweet and are validated against held-out samples (Section I.A of the Internet Appendix; cross-sample correlations 0.85-0.90, Table IA.I, p. 99 of the Internet Appendix).

Step 3: Topic classification via Biterm Topic Model (BTM, p. 128). To decompose the social media signal into fundamental vs. meme/technical content, the paper trains a BTM with eight topics on all tweets in the $[0, 3]$ window around merger announcements, following Yan et al. (2013). Six topics are retained: Company/Business, Disclosure, Deal Terms, Trading, Technical, and Memes. Separate $\text{AbnSent}$ measures are constructed for each topic subset.

The method builds on panel-regression for the estimating equation, event-study for the CAR controls, and text-classification for the sentiment and topic scores.

Empirical specifications

Baseline withdrawal regression (equation 2, p. 106, produces R1–R3).

$$\text{Deal\_Withdrawn}_i = \beta_1 \cdot \text{AbnSent}_i + \beta_2 \cdot \text{CAR}_i + \Gamma \cdot X_i + \alpha_t + \gamma_j + \epsilon_i \tag{2}$$

• $\text{Deal\_Withdrawn}_i$ is an indicator equal to 1 if M&A deal $i$ was subsequently withdrawn, multiplied by 100.
• $\text{AbnSent}_i$ is eq. (1) above, standardized to mean 0, SD 1.
• $\text{CAR}_i$ is the acquirer CAR[-1, 10] from the Fama-French three-factor model (100-day pre-event window, 10-day gap), also standardized.
• $X_i$ includes CAR[-5, -1], news sentiment (RavenPack ESS), analyst recommendation changes (IBES), deal value, pct. shares held, white-knight/competing-bidder/rumored/hostile deal indicators, termination fee, N tweets, N news articles, and acquirer firm size, leverage, cash.
• $\alpha_t$ = year-by-quarter fixed effects; $\gamma_j$ = acquirer industry (GIC 2-digit) fixed effects.
• Standard errors clustered at the year-by-quarter level. Sample: 5,932-6,306 deal-level observations (2010-2021).

BHAR regression (equation 3, p. 118, produces R4).

$$\text{BHAR}_{i,[11, T_{\text{conclusion}}]} = \beta_1 \cdot \mathbf{1}(\text{Deal Withdrawn})_i + \beta_2 \cdot \text{AbnSent}_i + \beta_3 \cdot \mathbf{1}(\text{Deal Withdrawn})_i \times \text{AbnSent}_i + \Gamma \cdot X_i + \epsilon_i \tag{3}$$

• $\text{BHAR}_{i,[11, T_{\text{conclusion}}]}$ is the buy-and-hold abnormal return from day 11 after the merger announcement until deal conclusion (withdrawal or completion), using the Fama-French three-factor model.
• $\mathbf{1}(\text{Deal Withdrawn})_i$ equals 1 for withdrawn deals.
• The coefficient of interest is $\beta_3$ on the interaction: a negative $\beta_3$ means that initial negative social media reaction predicts the market eventually responds positively to a merger withdrawal.
• Industry (GIC2) and year-by-quarter fixed effects included. Standard errors clustered at year-by-quarter level.
• Sample restricted to deals with interim period > 25 days (and > 75 days for robustness) to avoid overlap with the announcement window; target-rejected deals excluded. N = 1,784-3,343 (Table IV, p. 119).

Twitter-account timing test (equation 4, p. 121, produces R5).

$$\text{Deal\_Withdrawn}_i = \beta_1 \cdot \text{AbnSent}_i + \beta_2 \cdot \text{AbnSent}_i \times \text{Post}_{i,t} + \beta_3 \cdot \text{Post}_{i,t} + \Gamma \cdot X_i + \alpha_t + \gamma_j + \epsilon_i \tag{4}$$

• $\text{Post}_{i,t}$ equals 1 for acquisitions by firm $i$ announced after firm $i$ registered its corporate Twitter account.
• Columns vary whether any Twitter account counts (cols 1-2), whether the account must have an above-median follower count (cols 3-4), or whether it must be a verified account (cols 5-6).
• $X_i$ includes the same controls as eq. (2) including $\text{CAR}_i$.
• The identification relies on within-acquirer before/after variation in Twitter engagement.
• Sample: 5,932 observations with year-by-quarter and industry FE; acquirer FE added in cols 2, 4, 6 (Table V, pp. 122-123).

Content heterogeneity regressions (Table VI, pp. 126-127, produces R6-R7). Variants of eq. (2) replacing the single $\text{AbnSent}_i$ with two simultaneous signals: $\text{AbnSent}_i(\text{Technical}=\text{N})$ and $\text{AbnSent}_i(\text{Technical}=\text{Y})$ in Panel A columns 1-2 (technical vs. fundamental traders); $\text{AbnSent}_i(\text{Long}=\text{Y})$ and $\text{AbnSent}_i(\text{Long}=\text{N})$ in columns 3-4 (above-/below-median word count); and one topic-specific $\text{AbnSent}$ per topic in Panel B (Company/Business, Deal Terms, Disclosure, Meme, Technical, Trading). Same FE structure and clustering.

Information-source heterogeneity regressions (Table VII, p. 129, produces R8). Variants of eq. (2) with sample split at median N Tweets (cols 1-2), N News Articles (cols 3-4), and $|\text{CAR}|$ (cols 5-6); coefficient difference tested with a Wald t-statistic.

Datasets used

Dataset	Role in paper	Wiki page
StockTwits (260M posts, Jan 2010–Dec 2021, proprietary via Social Market Analytics / direct)	Primary social media sentiment measure (AbnSent); 6,438 M&A deals matched	StockTwits (licensed)
SDC Platinum (Thomson Reuters)	M&A deal universe (announcement dates, deal values, completion/withdrawal status, deal characteristics)	SDC Platinum (licensed)
CRSP	Stock returns for acquirer and target CARs; Fama-French three-factor model inputs	WRDS / CRSP / Compustat (licensed)
Compustat North America	Acquirer firm controls (market cap, leverage, cash holdings, M/B ratio)	WRDS / CRSP / Compustat (licensed)
RavenPack News Analytics (v. RPA 1.0)	Traditional news media sentiment (Event Sentiment Score) for M&A-related articles	RavenPack (licensed)
IBES (via Refinitiv)	Analyst recommendation changes as external signal control	WRDS / CRSP / Compustat (licensed via WRDS)
Ken French Data Library	Fama-French three-factor returns for CAR estimation	Ken French Library
Social Market Analytics (SMA) Twitter data (2012–2021)	Robustness alternative sentiment measure from Twitter	No page yet
Refinitiv Streetevents (M&A conference call transcripts)	Conference call textual analysis (% constrained/negative words in presentation vs. Q&A)	No page yet
BoardEx / LinkedIn	CEO professional network proxies (education, employment, digital connections)	No page yet

Sample: 6,438 M&A announcements (6,187 completed, 251 withdrawn); acquirers are U.S. public firms; deal values >= $25M; 2010–2021.

When to read the full paper

Use the original DOI link if you are: examining the Internet Appendix robustness tables (Tables IA.I–IA.XIV); studying the conference call presentation vs. Q&A split in detail (Table VIII); using the BTM tweet-topic methodology; or replicating the BHAR analysis (Table IV). The locators above point to the main tables. For “what did this paper find,” the table above covers the core results.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 81(1), February 2026. © 2025 the American Finance Association. Published by Wiley; paywalled. Licence confirmed via Crossref (Wiley VOR terms, no Creative Commons entry).

This distillation was extracted by an LLM on 2026-05-31 and augmented on 2026-06-01; not human-verified or independently reproduced. Extract-only: no PDF mirror is hosted here.

Cookson, J. Anthony, Marina Niessner, and Christoph Schiller. “Can Social Media Inform Corporate Decisions? Evidence from Merger Withdrawals.” The Journal of Finance 81, no. 1 (February 2026): 91–142. DOI: 10.1111/jofi.13508. © 2025 the American Finance Association. All rights reserved. This page contains extracted findings only; no reproduction of the original text.