Two old market sayings hold that news is already priced in by the time it is published, and that the rumor is bought while the news is sold. Both place the price move associated with a piece of news before and at publication rather than after it. Whether the claims hold, for which kinds of news, and by how much are basic questions about how fast markets absorb public information. We test them on 4.57 million financial news articles covering roughly 3,000 US stocks (2023-2026). A large language model teacher, distilled into a compact classifier through active learning, assigns each article one of 17 event tags and five attributes; articles are clustered into stories to separate first reports from follow-up coverage; and beta-adjusted abnormal returns are measured around the resulting 1.68 million stock-day events, with 364,405 neutral-sentiment events as a placebo group. Three results follow. First, the price move associated with news concentrates before and at publication: pooled across all signed events, the cumulative move in the news direction by the close of publication day is 2.8 times its value 20 days later, and for rumor-flagged events the rumor day captures the entire move while the subsequent confirmation contributes nothing. Second, measured against the placebo of comparable stocks, markets underreact to numbers and overreact to stories: quantified fundamental news (earnings, dividends, guidance, analyst actions) keeps drifting in the direction of the news for weeks, while soft story-driven news (launches, macro commentary, leadership) gives back its move. Third, news carries width as well as direction: publicity raises volatility before the publication day, and volatility declines once the news is out, because publication resolves uncertainty. The study also produces a table of measured drift for each event tag, usable as a prior in news-conditioned forecasting models.
Prediction-market probabilities increasingly appear in news coverage, yet little is known about which market movements become news or how much trading money sits behind the numbers journalists quote. Unlike a poll, a market price can be moved by anyone willing to trade, so the cost of manufacturing a number that circul...
We show that increased media coverage strongly predicts lower subsequently announced firm fundamentals, earnings surprises, and higher likelihoods of bankruptcy, dividend cuts, and delistings. Additionally, we find that media articles often convey negative sentiment, with investor attention increasing around the public...
Betty Liu, Marina Niessner, Eric C. So· Management Sciences· 0 citations
The study explores how sentiment analysis of financial news can be used to predict the stock price movement in the emerging markets. The study aims to fill a gap in the literature by considering Brazil (Bovespa), India (Nifty 50), and China (Shanghai Composite) between 2020 and 2025 and using available tools to test th...
This paper asks whether firms'exposures to news about different types of AI risk are priced in U.S. stock returns. Using AI and risk keywords, I identify 7,787 Wall Street Journal articles from January 2016 to December 2025. I combine latent Dirichlet allocation (LDA) with the Domain Taxonomy in the MIT AI Risk Reposit...
The growth of digital financial media has led to the faster and larger dissemination of market information while varying levels of emotionality in the news reporting from different sources can create conflicting public signals and add to information asymmetry. While there is some existing literature on the connection b...
Yue-Tong Yang· Applied and Computational En...· 0 citations
This study examines whether measurable features of public financial news can help explain short-term stock return volatility. Public announcements, financial news, institutional opinions, social media heat and market data are converted into five variables: sentiment, topic type, popularity, publication timing and marke...
Xianrui Lin· Finance & Economics· 0 citations
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