Using Stocks or Portfolios in Tests of Factor Models
研究在检验资产定价模型时，使用个股还是投资组合作为基础资产更有效。文献认为组合能降低异质波动、提高因子载荷估计精度，但本文证明组合缩小了贝塔值的分散度，反而导致风险溢价标准误更大。
We examine the efficiency of using individual stocks or portfolios as base assets to test asset pricing models using cross-sectional data. The literature has argued that creating portfolios reduces idiosyncratic volatility and allows more precise estimates of factor loadings, and consequently risk premia. We show analytically and empirically that smaller standard errors of portfolio beta estimates do not lead to smaller standard errors of cross-sectional coefficient estimates. Factor risk premia standard errors are determined by the cross-sectional distributions of factor loadings and residual risk. Portfolios destroy information by shrinking the dispersion of betas, leading to larger standard errors.