文献类型：期刊文献

英文题名：Sparse principal component factors in asset pricing: evidence from the Chinese stock market

作者：Xu, Hai-Chuan[1];Wu, Meng[1];Zhou, Wei-Xing[1]

机构：[1]East China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China

年份：2026

卷号：357

期号：1

起止页码：505

外文期刊名：ANNALS OF OPERATIONS RESEARCH

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001493344200001)】；

基金：We acknowledge financial support from the National Natural Science Foundation of China (71971081, U1811462).

语种：英文

外文关键词：Factor models; Stochastic discount factor; Machine learning; Sparse hypothesis

摘要：Traditional sparse factor models (e.g., Fama-French) struggle to explain cross-sectional returns in high-dimensional settings due to the 'factor zoo'-a proliferation of anomalies with overlapping or noisy signals. We show that a principal component (PC)-based stochastic discount factor (SDF) using regularization techniques can aggregate characteristics into dominant risk sources, balancing parsimony and robustness. First, the SDF is estimated using a small sample of 25 portfolios double-sorted by size/book-to-market ratio, and it is found that only 2 principal component factors are needed to predict the cross-sectional returns well, which is consistent with the classical size premium and value premium. Then, the sample is further extended to 72 anomalous characteristics. The results show that the sparse PC-based SDF predicts the cross-sectional returns better than the sparse original characteristic-based SDF. We verify that sparse PC-based models outperform traditional sparse factor models even in emerging markets like China, where retail-driven trading and regulatory shifts amplify idiosyncratic risks.