ABSTRACT

Recent studies advocate two new benchmark models (the Fama-French five-factor model and the Hou, Xue and Zhang four-factor model) with monthly data. Our daily data approach provides considerable supplement to the monthly data approach presented in recent studies. We adopt the advanced bootstrap methodology by replicating the original data sample, and this approach should effectively alleviate the problem of too much noise in the data of daily return. A two-pass cross-sectional regression and GMM with several useful testing statistics are used to more thoroughly diagnose the specifications of the model. The following consistency is observed when using different frequencies of sample data: the evidence indicates that the two newer benchmark models (the Fama-French five-factor model and the Hou, Xue and Zhang four-factor model) outperform the Fama-French three-factor model in estimating a few well-known portfolios (formed on different anomalies). However, several specification tests do not robustly accept the correct specifications of the Fama-French five-factor model and the Hou, Xue and Zhang four-factor model.

摘要

最近的研究提倡使用月度数据的两个新基准模型（Fama-French 五因素模型和 Hou、Xue 和 Zhang 四因素模型）。我们的每日数据方法为最近研究中提出的每月数据方法提供了相当大的补充。我们通过复制原始数据样本采用先进的引导方法，这种方法应该有效地缓解每日收益数据中噪声过多的问题。两遍横截面回归和具有多个有用测试统计数据的 GMM 用于更彻底地诊断模型的规格。当使用不同频率的样本数据时观察到以下一致性：证据表明两个较新的基准模型（Fama-French 五因素模型和 Hou，Xue 和 Zhang 四因子模型）在估计一些著名的投资组合（根据不同异常形成）方面优于 Fama-French 三因子模型。然而，一些规范测试并没有稳健地接受 Fama-French 五因素模型和 Hou、Xue 和 Zhang 四因素模型的正确规范。