主题：Expected Returns and Large Language Models

主讲人：Dacheng Xiu，芝加哥大学布斯商学院计量经济学与统计学教授

时间：3月29日（周三）上午10:00-11:30

地点：4-101教室

语言：英文

摘要：

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.

主讲人介绍：

Dacheng Xiu is Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His current research focuses on developing machine learning solutions to big-data problems in empirical finance. Xiu’s work has appeared in the Journal of Finance, Review of Financial Studies, Econometrica, Journal of Political Economy, the Journal of the American Statistical Association, and the Annals of Statistics. He is a Co-Editor for the Journal of Financial Econometrics, an Associate Editor for the Review of Financial Studies, Journal of the American Statistical Association, Journal of Econometrics, Management Science, etc. He has received several recognitions for his research, including the Fellow of the Society for Financial Econometrics, Fellow of the Journal of Econometrics, AQR Insight Award, EFA Best Paper Prize, and Swiss Finance Institute Outstanding Paper Award. Xiu earned his PhD and MA in applied mathematics from Princeton University.