The Effects of Bots on Market Reactions to Earnings News

ABSTRACT Social media platforms such as Twitter influence capital markets by rapidly disseminating information; yet, this environment is increasingly shaped by nonhuman bots. Building on theories of investor attention and information salience, we examine whether bots amplify market reactions to earnings news by directing attention toward larger surprises. Using machine learning to classify 12.02 million tweets discussing S&P 1,500 firms in 2018, we measure firm-specific abnormal bot activity and analyze its association with market responses to earnings announcements. We find that bot activity amplifies the relationship between earnings surprises and abnormal returns. Additional analyses reveal that this effect is stronger when bot sentiment is positive but diminishes with excessive positivity, varies by bot type, and is most pronounced for firms with fewer analysts, further supporting our investor attention argument. Our findings highlight bots’ roles as “attention amplifiers” and underscore the need for greater scrutiny of algorithmic actors in financial markets. Data Availability: Data are available from the public sources identified in the text. JEL Classifications: D83; E71; G14; G41; M41.