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Topic
Webinar Registration | Large Language Models and Return Prediction in China
Date & Time

Selected Sessions:

Nov 21, 2024 10:00 AM

Description
We examine whether large language models (LLMs) can extract contextualized representation of Chinese public news articles to predict stock returns. Based on representativeness and influences, we consider seven LLMs: BERT, RoBERTa, FinBERT, Baichuan, ChatGLM, InternLM, and their ensemble model. We show that news tones and return forecasts extracted by LLMs from Chinese news significantly predict future returns. The value-weighted long-minus-short portfolios yield annualized returns between 35% and 67%, depending on the model. Building on the return predictive power, we further investigate its implications for information efficiency. We show the assimilation speed of the LLM signals is two days, and they contain fundamental information. The signals can be especially helpful under higher frictions, when firms have less efficient information environments, more complex news, and higher retail holdings. Interestingly, heterogeneous investors load their future trades oppositely on LLM signals upon news releases. These findings suggest LLMs can be helpful in processing public news, and thus contribute to overall market efficiency. Time Nov 21, 10 AM (Beijing/Singapore Time)/ Nov. 20, 9 PM (Eastern Time) Session Chair Bernard Y. Yeung Emeritus Professor, National University of Singapore and Emeritus President, ABFER Presenter Lin Tan PhD Candidate PBC School of Finance Tsinghua University Co-Author Huihang Wu Research associate PBC School of Finance Tsinghua University Xiaoyan Zhang Xinyuan Chair Professor of Finance, Associate Dean PBC School of Finance Tsinghua University Discussant Yinan Su Assistant Professor of Finance Carey Business School Johns Hopkins University