The traditional fund-by-fund alpha inference suffers from various econometric problems (e.g., cross-sectional independence assumption, lack of power, time-invariant coefficient assumption, multiple-hypothesis-testing). Recognizing the panel nature of fund industries, we tailor four high-dimensional cross-sectional tests to shed light into both the zero-alpha hypothesis and ratio of non-zero alphas. Particularly, we augment Gagliardini et al. (2016) with a time-varying alpha estimator. Our results reject the zero-alpha joint hypothesis as the statistical significance of alphas is too high to be explained by luck. After controlling for luck, our empirical studies show that the power enhancement helps to identify a large portion of significant fund alphas, which cannot be achieved using the usual Wald test. Meanwhile, the time-varying approach shows that fund alphas diverge during the late 2000s Global Financial Crisis, which cannot be observed using the time-invariant model. Overall, relative to the literature, we draw a more accurate and complete picture, and provide several powerful tools for future research.

传统的逐个基金Alpha推断会遭受各种计量经济学问题（例如，横截面独立性假设，缺乏能力，时不变系数假设，多重假设检验）。认识到基金行业的面板性质，我们定制了四个高维横截面检验以阐明零阿尔法假设和非零阿尔法比率。特别是，我们增加了Gagliardini等人。（2016）随时间变化的alpha估算器。我们的结果拒绝了零-α联合假设，因为α的统计显着性太高而无法用运气来解释。在控制好运气之后，我们的经验研究表明，增强功效有助于识别很大一部分重要的基金alpha，而这是使用常规Wald检验无法实现的。与此同时，时变方法表明，在2000年代末全球金融危机期间，基金的alpha值存在差异，而使用时不变模型则无法观察到。总体而言，相对于文献，我们得出了更加准确和完整的图景，并为将来的研究提供了一些强大的工具。