Estimating Time-Varying Factor Exposures (Corrected October 2017)
提出一种利用横截面风险特征估计动态因子载荷的方法，应用于美国共同基金数据，将主动收益分解为恒定因子暴露、时变因子暴露和证券选择三部分，发现大盘成长基金集中于动量与质量因子，而大盘混合基金因子多样性最高，并指出常用经理技能指标可能具有误导性。
We develop a methodology to estimate dynamic factor loadings using cross-sectional risk characteristics. Applying it to a dataset of US-domiciled mutual funds, we distinguish the components of active returns attributable to (1) constant factor exposures (e.g., a tilt to value stocks), (2) time-varying factor exposures, and (3) security selection. We find that large-cap growth funds tend to be concentrated in two factors (momentum and quality) whereas large-cap blend funds have the most factor diversity. We also find that common measures to gauge manager skill may be misleading.