内容简介:

We leverage state-of-the-art large language models (LLMs) such as ChatGPT and LLAMA to extract contextualized representations of news text for predicting stock returns.  Our results show that prices respond slowly to news reports indicative of market inefficiencies and limits-to-arbitrage.  Predictions from LLM embeddings significantly improve over leading technical signals (such as past returns) or simpler NLP methods by understanding news text in light of the broader article context.  For example, the benefits of LLM-based predictions are especially pronounced in articles where negation or complex narratives are more prominent.   We present comprehensive evidence of the predictive power of news on market movements in 16 global equity markets and news articles in 13 languages.

演讲人简介:

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 has served as Co-Editor for the Journal of Financial Econometrics and has been on the editorial board as an Associate Editor for many prestigious journals, including the Review of Financial Studies, Journal of the American Statistical Association, Journal of Econometrics, and Management Science. 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. He has been recognized as one of Poets & Quants’ Best 40-under-40 Business School Professors of 2023. Xiu earned his PhD and MA in applied mathematics from Princeton University.

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