报告题目：Time-Varying Factor Selection: A Sparse Fused GMM Approach
报告人： 冯冠豪 香港城市大学
报告时间：2023年4月4日(周二)，16:30-18:00
报告地点：中科院数学与系统科学研究院南楼N204
腾讯会议 ID：479 3348 6244
内容摘要
This paper proposes a sparse fused GMM approach (SFGMM) to estimate a sparse time-varying coefficient model for selecting factors with heterogeneous structural breaks. SFGMM offers an alternative estimation to the dynamic stochastic discount factor model, where factor risk prices are sparse and time-varying, employing a high dimension set of conditioning variables and test assets. Evaluating U.S. equity factors, we find that ours outperforms several benchmark models, improving asset pricing and investment performance and providing insights into time-varying factor selection. Our results indicate that risk factors have the strongest explanatory power when the aggregate dividend yield or default yield is high, but their effectiveness is reduced when market liquidity is low. Moreover, our study reveals that the selection of factors changes over time, with some previously successful factors such as momentum and idiosyncratic volatility disappearing in the recent decade, while new factors like betting-against-beta and expected growth have emerged.
主讲人简介
冯冠豪，香港城市大学商业统计系助理教授，商业数据分析硕士项目负责人、数据科学学院教员以及AI驱动金融科技实验室项目负责人。他于2017年获得了芝加哥大学博士和MBA学位。研究兴趣包括贝叶斯统计、实证资产定价、金融科技以及机器学习在金融领域的应用。研究成果已经发表在Journal of Finance，Journal of Econometrics等国际权威学术期刊上。
