Abstract: Over the past 30 years, nearly all the gains in the U.S. stock market have been earned overnight, while average intraday returns have been negative or flat. We find that a large part of this effect can be explained by differences between intraday and overnight news and the response to news. Our analysis uses a collection of over a million news articles. We use supervised topic analysis technique that selects news topics based on their ability to explain contemporaneous market returns. We find that differences in the mix of news topics and differences in the responses to news topics both contribute to the difference in intraday and overnight returns. In out-of-sample tests, our approach forecasts which stocks will do particularly well overnight and particularly poorly intraday. We contrast the effect of news with other mechanisms proposed in the literature to explain overnight returns. This is joint work with Kriste Krstovski, Paul Laliberte, and Harry Mamaysky.

Bio: Paul Glasserman is the Jack R. Anderson Professor Business at Columbia Business School, and he is chair of the Financial and Business Analytics center in Columbia University's Data Science Institute. His research interests lie at the intersection of data science and quantitative finance; recent projects include work on properties of implied volatility, stress testing, systemic risk, swing pricing, and applications of natural language processing in finance. Paul received the 2006 Lanchester Prize for his book, Monte Carlo Methods in Financial Engineering, and he is also a past recipient of Risk magazine's Quant of the Year Award (2007) and the IAQF Financial Engineer of the Year Award (2020).