主讲人Speaker: 张国昌   教授   香港大学商学院

时间Date & Time: 2025年10月17日（周五），10:00--11:30

地点Venue:粤海校区汇星楼565会议室

内容简介/ Abstract:

The literature has documented the usefulness of analysts’ questions during conference calls; however, the dynamics of their information demand throughout the earnings season remain largely unexplored. In this study, we examine analysts’ inquiries into macroeconomic and industry conditions during conference calls, with a focus on how their attention shifts over the course of the earnings season. Using a word embedding model to classify question types, we find that analysts tend to ask more macro- and industry-related questions to firms reporting earlier in the season. This tendency is especially strong when macroeconomic uncertainty is high. By leveraging ChatGPT to identify the content of macro and industry-related questions, we observe that early-season questions are more general and broader, while questions posted later in the season become more specific and detailed. We further find that macro and industry questions are positively associated with absolute macro- and industry-level returns and earnings response coefficients, and negatively associated with post-earnings-announcement drifts—particularly for calls held early in the season. Finally, macro and industry questions directed at firms reporting earlier are linked to smaller analyst forecast dispersions and more frequent updates of peer firm forecasts.

主讲人介绍/Biography of the speaker：

张国昌，现任香港大学商学院会计学教授，鍾瀚德基金教授(會計學)。之前曾在香港科技大学及加拿大滑铁卢大学任职。获得上海交通大学工学学士，英属哥伦比亚大学会计学硕士及金融学博士。研究方向包括基于会计信息的公司估值、信息披露、盈余管理，与准则设计等。论文发表于Accounting Review, Journal of Accounting and Economics, Journal of Accounting Research, Contemporary Accounting Research, Review of Accounting Studies, Management Science 等刊物。并发表专著“Accounting Information and Equity Valuation: Theory, Evidence and Applications”。曾担任欧洲会计研究（European Accounting Review)刊物副编和香港会计师公会财务报告准则委员的成员。