This paper uses a dataset of more than 900,000 news stories to test whether news can predict stock returns. We measure sentiment with a proprietary Thomson-Reuters neural network. We find that daily news predicts stock returns for only 1 to 2 days, confirming previous research. Weekly news,...

This paper uses a dataset of more than 900,000 news stories to test whether news predicts stock returns. We measure sentiment with the Harvard psychosocial dictionary used by Tetlock, Saar-Tsechansky, and Macskassy (2008), the financial dictionary of Loughran and McDonald (2011), and a...

This paper uses a dataset of more than 900,000 news stories to test whether news can predict stock returns. We measure sentiment with a proprietary Thomson-Reuters neural network. We find that daily news predicts stock returns for only 1 to 2 days, confirming previous research. Weekly news,...

This paper explores the variance risk premium in option returns across twenty different futures, including equities, bonds, currencies, and commodities (energy, metals, and grains). We implement a novel model-free methodology that constructs tradable option portfolios, which replicate realized...