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Cross-Market News Sentiment and Trading Volume
时 间：2025年12月2日（Tue.）
14:00 - 15:30 p.m.
地 点：同德楼 111
主 讲：何靖宇（香港城市大学）
论文摘要
We examine how public news affects trading activity across a firm’s equity and corporate bond markets. Using Dow Jones Newswires articles from 2002 to 2023 and DeepSeek to extract asset-specific sentiment, we show that stronger sentiment about an asset predicts higher trading volume in that asset, even after controlling for established determinants. News sentiment also transmits across markets: equity-specific sentiment is associated with higher bond trading activity, and bond-specific sentiment is associated with higher equity trading activity. Sentiment extracted from article bodies is more informative than headline sentiment for volume. A multi-asset Kyle-type model with a public signal provides a theoretical framework that rationalizes the empirical patterns.
主讲简介
Prof. Jingyu He is an Associate Professor of Finance and Business Statistics at City University of Hong Kong. He received his Ph.D. and M.B.A. from the University of Chicago Booth School of Business, as well as an M.S. in Statistics from the University of Chicago and a B.S. from the University of Science and Technology of China. His research focuses on applying machine learning and Bayesian statistical methods to finance – particularly in empirical asset pricing, FinTech, and quantitative finance. Prof. He’s work has been published in leading academic journals in finance and statistics, including the Journal of Financial Economics and the Journal of the American Statistical Association. He has earned several honors from both academia and practitioners, such as the Best Paper Award at the 2024 China FinTech Research Conference and an INQUIRE Europe Research Grant Award in 2022. At CityU, he teaches courses on FinTech, AI in finance, and predictive modeling, and co-organises the Hong Kong Conference on FinTech, AI & Big Data in Business.
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