Using textual data extracted by Causality Link platform from a large variety of news sources (news stories, call transcripts, broker re-search, etc.), we build aggregate news signals that take into account the tone, the tense and the prominence of various news statements about a given firm. We test the informational content of these signals and examine how news is incorporated into stock prices. Our sample covers 1,701,789 news-based signals that were built on 4,460 US stocks over the period January 2014 to December 2021. We document large and significant market reactions around the publication of news, with some evidence of return predictability at short horizons. News about the future drives much larger reactions than news about the present or the past. Stock returns also react more to high-coverage news, fresh news and purely financial news. Finally, firms’ size matters: stocks that are not components of the Russell 1000 index experience larger reactions to news compared to those that are Russell 1000 components. Implications of our results for financial analysts and investors are of-fered and related to the links between news, firms’ market value and investment strategies.
Natural Language Processing; Textual Analysis; Efficient Market Hypothesis; ESG;
Marie Brière, Karen Huynh, Olav Laudy, and Sébastien Pouget, “What do we Learn from a Machine Understanding: News Content? Stock Market Reaction to News”, TSE Working Paper, n. 23-1401, January 2023.
TSE Working Paper, n. 23-1401, January 2023