讲座题目

Heterogeneous Predictability on Mutual Fund Alphas: A Sparse Clustering GMM Approach

主讲嘉宾

崔丽媛

嘉宾介绍

崔丽媛，现为香港城市大学经济与金融系副教授，金融工程硕士项目主任。崔丽媛获武汉大学数学、金融学双学士，美国康奈尔大学经济学博士学位。主要研究方向包括金融计量经济学，高维数据分析，高频交易，非参数统计，资本资产定价等。以一作或通讯在Cities, International Economic Review, Management Science, Journal of Econometrics, Journal of Environmental Economic and Management, Journal of International Money and Finance, Land Use Policy, Real Estate Economics等英文期刊以及《经济研究》等中文期刊发表论文，主持国家自然科学基金两项（青年、面上），以及5项香港UGC研究基金项目。

讲座摘要

Mutual fund managers' skills, measured by risk-adjusted alphas, are predictable using fund characteristics identified via machine learning (e.g., Kaniel et al., 2023, JFE; DeMiguel et al., 2023, JFE). However, alpha's predictive power varies across funds and periods, with most funds exhibiting negligible alphas. To model the heterogeneous predictability, this paper introduces Sparse Clustering GMM (SCGMM), a nonparametric approach to uncover latent fund group structures. SCGMM clusters funds using estimated alphas and identifies group-specific parameters tied to market predictors. The method accounts for heterogeneity in cross-sectional grouping and time variation mechanisms across predictors, without requiring prior cluster information or time variation specifications. Our proposed estimator is theoretically grounded, ensuring consistency in grouping and estimation of time-varying parameters. Empirical analysis of U.S. data shows that only a small group of mutual funds exhibit alphas predictably driven by market predictors.

时间

2025年12月23日，周二，10:00-11:30

地点

学院南路校区学术会堂603

主办方

创新发展学院中国金融发展研究院

撰稿：陈翀

审稿：赵阳

编辑：沈嘉怡

审核：林艺茹