时间：9月7日（周一）10:00
地点：浙大管院A423会议室
主题：Inside the Black Box of All-American Analyst Rankings
主讲人：Lily Chen, 澳大利亚国立大学副教授
主持人：吴依，浙江大学管理学院研究员
主讲人简介：
Lily Chen is an Associate Professor in Accounting. She joined ANU in July 2023. Prior to that, she worked at the University of Auckland where she obtained her PhD. Lily’s research interests include financial reporting and disclosure, financial analysts, corporate governance, sustainability, and machine learning. Her research has been published in international journals such as The Accounting Review, Accounting, Organizations and Society, Journal of Banking & Finance, Journal of Business Finance and Accounting, Journal of Management Accounting Research, British Accounting Review, Journal of Business Ethics, Accounting and Finance, Journal of International Accounting Research, Meditari Accountancy Research, IEEE Transactions on Knowledge and Data Engineering, and Natural Language Engineering, etc. Her work has been widely cited by regulatory bodies, major accounting firms, and professional bodies in their publications, and has received several manuscript awards and conference best paper awards. Lily is an editor for Pacific Accounting Review, deputy editor for Accounting and Finance, and serves on the editorial board for Meditari Accountancy Research. She also serves on the board of the Accounting and Finance Association of Australia and New Zealand.
摘要：
Each year, Institutional Investor (II) magazine surveys investors and selects All-American (AA) star analysts. Prior studies identify a range of analyst characteristics associated with All-Star status but generally observe only the final public designations. Using proprietary II voting data, we examine the underlying voting process in greater detail. We find that voting reflects multiple dimensions of analyst characteristics, but the relative importance is highly uneven. Broker/platform resources and industry expertise together account for approximately 75%-81% of the explanatory power attributable to analyst characteristics, while conventional research-performance measures contribute relatively little. Voting is also a staged process, with a broader set of characteristics explaining entry into the Top 10 than advancement to the highest-ranking positions. We further develop industry-level measures of Contestability and Stability, capturing how open the voting competition is and how persistently investor support is maintained. These voting environments vary systematically across industries and shape the criteria investors apply. Finally, voting-based scores predict future recognition and research performance. More detailed vote counts, however, do not consistently outperform publicly available ranking categories in out-of-sample prediction, indicating that public ranking information itself remains useful for identifying promising analysts.
