We develop a classification methodology for the context and content of news articles to predict risk and return in stock markets in 51 developed and emerging economies. A parsimonious summary of news, including topic-specific sentiment, frequency, and unusualness (entropy) of word flow, predicts...

We find that an increase in the ``unusualness'' of news with negative sentiment predicts an increase in stock market volatility. Similarly, unusual positive news forecasts lower volatility. Our analysis is based on more than 360,000 articles on 50 large financial companies, published in...

We study information and portfolio choices when securities' dividends depend on an aggregate (macro) risk factor and idiosyncratic (micro) shocks, and when investors can acquire costly dividend information. We establish a general result under which investors endogeneously specialize in either...