Expected Returns and Large Language Models

69 Pages Posted: 21 Apr 2023 Last revised: 13 Sep 2023

See all articles by Yifei Chen

Yifei Chen

University of Chicago - Booth School of Business

Bryan T. Kelly

Yale SOM; AQR Capital Management, LLC; National Bureau of Economic Research (NBER)

Dacheng Xiu

University of Chicago - Booth School of Business

Date Written: November 22, 2022

Abstract

We extract contextualized representations of news text to predict returns using the state-of-the-art large language models in natural language processing. Unlike the traditional word-based methods, e.g., bag-of-words or word vectors, the contextualized representation captures both the syntax and semantics of text, thus providing a more comprehensive understanding of its meaning. Notably, word-based approaches are more susceptible to errors when negation words are present in news articles. Our study includes data from 16 international equity markets and news articles in 13 different languages, providing polyglot evidence of news-induced return predictability. We observe that information in newswires is incorporated into prices with an inefficient delay that aligns with the limits-to-arbitrage, yet can still be exploited in real-time trading strategies. Additionally, we find that a trading strategy that capitalizes on fresh news alerts results in even higher Sharpe ratios.

Keywords: natural language processing (NLP), foundation models, BERT, GPT, OPT, ChatGPT, Bag-of-Words, Word2vec, machine learning, return prediction

JEL Classification: G10, G11, G14, C14, C11, C21, C22, C23, C58

Suggested Citation

Chen, Yifei and Kelly, Bryan T. and Xiu, Dacheng, Expected Returns and Large Language Models (November 22, 2022). Available at SSRN: https://ssrn.com/abstract=4416687

Yifei Chen

University of Chicago - Booth School of Business ( email )

5807 S Woodlawn Ave
Chicago, IL 60637
United States

Bryan T. Kelly

Yale SOM ( email )

135 Prospect Street
P.O. Box 208200
New Haven, CT 06520-8200
United States

AQR Capital Management, LLC ( email )

Greenwich, CT
United States

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Dacheng Xiu (Contact Author)

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

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