We examine information spillover as a source of stock return synchronicity, where information about highly-followed "prominent" stocks is used to price other "neglected" stocks sharing a common fundamental component. We find that stocks followed by few analysts co-move significantly with...

We introduce a new text-mining methodology that extracts sentiment information from news articles to predict asset returns. Unlike more common sentiment scores used for stock return prediction (e.g., those sold by commercial vendors or built with dictionary-based methods), our supervised...

We propose a new measure of time-varying tail risk that is directly estimable from the cross section of returns. We exploit firm-level price crashes every month to identify common fluctuations in tail risk across stocks. Our tail measure is significantly correlated with tail risk measures...