This paper studies whether social media sentiment predicts merger withdrawals. We find that a one‐standard‐deviation increase in social media sentiment after a merger announcement is associated with a 0.64 percentage point lower probability of withdrawal (16.6% of the average). This effect is unexplained by abnormal price reactions, traditional news, and analyst recommendations. Consistent with manager learning, the informativeness of social media strengthens after firms start corporate Twitter accounts. The informativeness is driven by longer acquisition‐related tweets by fundamental investors, rather than memes and price trend tweets. These findings suggest that social media signals can be important for corporate decisions.

We study the effect of personal wealth on entrepreneurial decisions using data on mineral payments from Texas shale drilling to individuals throughout the United States. Large cash windfalls increase business formation by 0.8 to 2.1 percentage points, but do not affect transitions to self-employment. By contrast, cash windfalls significantly extend self-employment spells, but do not affect the duration of business ownership. Our findings help reconcile contrasting findings in prior work: liquidity constraints have different effects on entrepreneurial activity that may depend on the entrepreneur’s motivations.

We find evidence of selective exposure to confirmatory information among 400,000 users on the investor social network StockTwits. Self-described bulls are five times more likely to follow a user with a bullish view of the same stock than are self-described bears. Consequently, bulls see 62 more bullish messages and 24 fewer bearish messages than bears do over the same 50-day period. These “echo chambers” exist even among professional investors and are strongest for investors who trade on their beliefs. Finally, beliefs formed in echo chambers are associated with lower ex post returns, more siloing of information, and more trading volume.

We examine social media attention and sentiment from three major platforms: Twitter, StockTwits, and Seeking Alpha. We find that, even after controlling for firm disclosures and news, attention is highly correlated across platforms, but sentiment is not: its first principal component explains little more variation than purely idiosyncratic sentiment. Using market events, we attribute differences across platforms to differences in users (e.g., professionals versus novices) and differences in platform design (e.g., character limits in posts). We also find that sentiment and attention contain different return-relevant information. Sentiment predicts positive next-day returns, but attention predicts negative next-day returns. These results highlight the importance of considering both social media sentiment and attention, and of distinguishing between different investor social media platforms.