Mathematical Finance & Financial Data Science Seminar

Expected Returns and Foundation Models of Language

Speaker: Dacheng Xiu, Booth School of Business, University of Chicago

Location: Online Zoom access provided to registrants

Date: Tuesday, November 8, 2022, 5:30 p.m.


We extract contextualized representations of news text to predict returns using the state-of-the-art foundation models in natural language processing. The contextualized representation of news reflects its content more accurately than the bag-of-words representation prevalent in the literature. In particular, the latter approach is more prone to errors when negation words appear in news articles. Moreover, we provide polyglot evidence on news-induced return predictability in 16 international equity markets with news written in 13 different languages. Information in newswires is assimilated into prices with an inefficient delay that is broadly consistent with limits-to-arbitrage, yet can be exploited in a real-time trading strategy. Furthermore, a trading strategy that exploits fresh news in the form of news alerts leads to even higher Sharpe ratios.

Speaker Bio:

Dacheng Xiu’s research interests include developing statistical methodologies and applying them to financial data, while exploring their economic implications. His earlier research involved risk measurement and portfolio management with high-frequency data and econometric modeling of derivatives. His current work focuses on developing machine learning solutions to big-data problems in empirical asset pricing.

Xiu’s work has appeared in Econometrica, Journal of Political Economy, Journal of Finance, Review of Financial Studies, Journal of the American Statistical Association, and Annals of Statistics. He is a Co-Editor for the Journal of Financial Econometrics, an Associate Editor for the Review of Financial Studies, Management Science, Journal of Econometrics, Journal of Business & Economic Statistics, Journal of Applied Econometrics, the Econometrics Journal, and Journal of Empirical Finance. He has received several recognitions for his research, including Fellow of the Society for Financial Econometrics, Fellow of the Journal of Econometrics, Swiss Finance Institute Outstanding Paper Award, AQR Insight Award, and Best Conference Paper Prize from the European Finance Association. In 2017, Xiu launched a website that provides up-to-date realized volatilities of individual stocks, as well as equity, currency, and commodity futures. These daily volatilities are calculated from intraday transactions and the methodologies are based on his research of high-frequency data.

Xiu earned his PhD and MA in applied mathematics from Princeton University, where he was also a student at the Bendheim Center for Finance. Prior to his graduate studies, he obtained a BS in mathematics from the University of Science and Technology of China.


This event is free, but requires registration.  Please click here to register.  You will then receive the Zoom link by email about a day or so before the event.