Low Power of Alpha Tests When Factors Are Constructed From Sorted Portfolios
研究发现，当因子由排序投资组合构建时，因子模型Alpha的统计检验可能因Alpha分解的两部分相互抵消而功效低下，导致错误接受错误模型。论文基于Alpha分解提出了新检验统计量，在传统检验失效时仍保持高功效。
Abstract When factors for asset pricing are constructed from a set of sorted portfolios, statistical tests of factor model alphas can suffer from low power. The low power arises because alpha can be decomposed into a sum of two parts, where each part is equal to zero under the null of a correct factor model. If the values of the two parts under the alternative hypothesis are nonzero, differ in sign, and have approximately equal magnitudes, they offset which results in a high probability of not rejecting a false model. Thus, insignificant alpha tests can be misleading when factors are constructed from sorted portfolios. This paper uses the parts of alpha to derive new test statistics that have high power when the traditional alpha test lacks power.