Identifying and Exploiting Alpha in Linear Asset Pricing Models with Strong, Semi-Strong, and Latent Factors
提出一种两步偏差校正估计量，用于识别线性资产定价模型中的非零阿尔法，并构建能超越均值-方差组合的phi投资组合，实证应用于美国证券数据。
Abstract The risk premia of traded factors are the sum of factor means and a parameter vector, we denote by ϕ, which is identified from the cross-sectional regression of αi on the vector of factor loadings, βi. If ϕ is non-zero, then αi are non-zero and one can construct “phi-portfolios” which exploit the systematic components of non-zero alpha. We show that for known values of βi and when ϕ is non-zero, there exist phi-portfolios that dominate mean–variance (MV) portfolios. This article then proposes a two-step bias corrected estimator of ϕ and derives its asymptotic distribution allowing for idiosyncratic pricing errors, weak missing factors, and weak error cross-sectional dependence. Small sample results from extensive Monte Carlo experiments show that the proposed estimator has the correct size with good power properties. This article also provides an empirical application to a large number of U.S. securities with risk factors selected from a large number of potential risk factors according to their strength and constructs phi-portfolios and compares their Sharpe ratios to MV and S&P portfolios.