数字技术与经济金融学术午餐会（第20期）

摘要：

We propose a new approach to construct factors from firm characteristics. In contrast to existing studies, each of our factors comes from the same group of statistically related firm characteristics, making its economic interpretation possible. The number of groups is not chosen ad hocly, but rather determined by data. Applying our method to a set of 94 representative firm characteristics, we find that the factors chosen by our approach is not only easy to interpret economically, but the associated factor model outperforms existing models. We also apply our approach to the recent developed and highly effective IPCA model of Kelly, Pruitt and Su (2019), and find that our factors not only are well linked to the associated economic risks, but also can price assets no worse than the standard IPCA latent factors that are difficult to interpret. 

主讲人介绍：

焦宇晓，清华大学经济管理学院金融系博士生，研究方向为实证资产定价和机器学习。其论文被美国金融学年会AFA, 美国中西部金融学协会年会MFA, 中国金融学年会等十余个学术会议接收，求职论文被2024中国金融学博士生毕业意向交流会评为“优秀论文”。