Learning about Economy: Analysts’ Dynamic Demand for Macro and Industry Information from Conference Calls
Prof. Yingzhen JIANG
Assistant Professor in Accounting
FBA, UM
Date: 21 October 2024 (Monday)
Time: 13:00 to 14:00
Venue: FBA Lobby
Abstract
The literature has documented the usefulness of analysts’ questions during conference calls, yet the dynamics of their information demand remain largely unexplored. We examine how analysts dynamically adjust their demand for macroeconomic and industry information during conference calls in the earnings season. Employing a word embedding model to capture macro- and industry- information, we find that analysts demand more macro and industry questions for firms reporting earlier, and the results are more prominent for the earnings seasons with increasing uncertainty in the macro environment. Using ChatGPT to summarize the topics, we find that the questions for earlier reporting firms are more general and broad, and become specific and detailed for later firms. Further, we find that these macro- and industry-focused questions are associated with higher absolute macro returns, higher absolute industry returns, larger earnings response coefficients, and smaller post-earnings-announcement drift, with stronger associations for earlier conference calls. We also find that analysts’ forecasts are less dispersed and the frequencies of peer firm forecasts are higher for earlier deciles.
Speaker
Prof. Yingzhen JIANG currently serves as an assistant professor in Accounting at Faculty of Business Administration, University of Macau. His research interests include voluntary disclosure, machine learning, big data, auditing and macro-accounting.
All are welcome!
