Does Confidence (in Fund Skill Estimates) Matter for Investors?
学术午餐会2024年第5期，总第196期
主讲人：罗荣华
罗荣华现任西南财经大学教授、博士生导师，金融学院院长、中国金融研究院院长，西南财经大学“光华杰出学者计划”青年杰出教授。他先后于南开大学数学科学学院和北京大学光华管理学院取得理学学士和经济学博士学位，主要关注金融计量、资本市场和商业银行相关领域的研究。在《经济研究》《管理世界》《经济学季刊》和Annals of Statistics、Journal of Banking and Finance、Journal of Business and Economic Statistics等国内外期刊发表论文60余篇，著有《FOF管理：策略与技术》《家庭资产配置与风险管理》等专著和教材。
讲座时间：11月15日10:30-12:00
讲座地点：经济学院305会议室
主持人：高明
报告摘要
Existing literature often assumes that mutual fund investors learn fund skills solely from individual fund performance, overlooking the critical role of model uncertainty in the fund return-generating process. This paper introduces the framework of “learning with model uncertainty,” where sophisticated investors balance a bias-variance tradeoff between individual fund performance and the group-average performance of comparable funds. We develop a novel metric, Confidence, derived from pairwise t-tests, to capture investors’ relative confidence in individual fund performance. Using data from US actively-managed domestic equity mutual funds, we demonstrate that Confidence significantly increases flow-performance sensitivity while reducing sensitivity to group-average performance. This framework offers explanatory power for fund flows comparable to, or exceeding, that of Morningstar ratings. Additionally, fund flows predicted by this framework positively forecast future fund performance. Finally, high-Confidence funds, particularly those with extreme performance, display a strategic shift from systematic to idiosyncratic risk. Our study thus broadens the understanding of investor confidence and provides a more comprehensive perspective on investor learning.
美编&校对 | 叶佳佳
