We examine whether large language models (LLMs) can extract contextualized representation of Chinese news articles and predict stock returns. The LLMs we examine include BERT, RoBERTa, FinBERT, Baichuan, ChatGLM and their ensemble model. We find that …
Using a proprietary granular database of a major Chinese stock exchange, we examine heterogenous investors’ trading dynamics around one of the most important macro announcements of the Chinese central bank, the monthly release of monetary aggregates …
We study two important questions regarding trading dynamics in China. How do retail and institutional investors trade, and what are the underlying factors for these behaviors? Different from the United States, China's stock market has two prominent …
Using the Boehmer, Jones, Zhang, and Zhang (2021) algorithm, we identify a broad swath of marketable retail investor orders in the U.S. market during the pandemic. The marketable retail trading volumes rapidly rise from $325 billion in 2019 to $852 …
We find that anomaly returns are generally unchanged during FOMC days. The average return on the long- and short-leg, of a comprehensive set of 207 anomalies, increases by 26.3 bps and 28.8 bps, respectively, prior to the FOMC and reverses back …
We find that anomaly returns are generally unchanged during FOMC days. The average return on the long- and short-leg, of a comprehensive set of 207 anomalies, increases by 26.3 bps and 28.8 bps, respectively, prior to the FOMC and reverses back …
Using a proprietary granular database of a major Chinese stock exchange, we examine heterogenous investors’ trading dynamics around one of the most important macro announcements of the Chinese central bank, the monthly release of monetary aggregates …
Using a proprietary granular database of a major Chinese stock exchange, we examine heterogenous investors’ trading dynamics around one of the most important macro announcements of the Chinese central bank, the monthly release of monetary aggregates …