New Evidence on Conditional Factor Models
用多种条件信息估计多因子模型，发现条件模型能更好解释25个CAPM异象，但不同模型在解释动量、盈利和价值增长异象时表现有显著差异。
We estimate conditional multifactor models over a large cross section of stock returns matching 25 CAPM anomalies. Using conditioning information associated with different instruments improves the performance of the Hou, Xue, and Zhang (HXZ) (2015) and Fama and French (FF) (2015), (2016) models. The largest increase in performance holds for momentum, investment, and intangibles-based anomalies. Yet, there are significant differences in the performance of scaled models: HXZ clearly dominates FF in explaining momentum and profitability anomalies, while the converse holds for value–growth anomalies. Thus, the asset pricing implications of alternative investment and profitability factors (in a conditional setting) differ in a nontrivial way.