Thousands of Alpha Tests
提出一个利用机器学习技术的多重假设检验框架，用于在线性资产定价模型中控制数据窥探导致的假阳性，并证明其在大规模检验下的渐近有效性，最后应用于对冲基金业绩评估。
Abstract Data snooping is a major concern in empirical asset pricing. We develop a new framework to rigorously perform multiple hypothesis testing in linear asset pricing models, while limiting the occurrence of false positive results typically associated with data snooping. By exploiting a variety of machine learning techniques, our multiple-testing procedure is robust to omitted factors and missing data. We also prove its asymptotic validity when the number of tests is large relative to the sample size, as in many finance applications. To improve the finite sample performance, we also provide a wild-bootstrap procedure for inference and prove its validity in this setting. Finally, we illustrate the empirical relevance in the context of hedge fund performance evaluation.