Aggregation of Information About the Cross Section of Stock Returns: A Latent Variable Approach
提出一种新方法，利用偏最小二乘估计从大量公司特征中提取预期收益的潜变量，生成的预期收益估计能产生更大的横截面收益差异，并优于其他技术。
We propose a new approach for estimating expected returns on individual stocks from a large number of firm characteristics. We treat expected returns as latent variables and apply the partial least squares (PLS) estimator that filters them out from the characteristics under an assumption that the characteristics are linked to expected returns through one or few common latent factors. The estimates of expected returns constructed by our approach from 26 firm characteristics generate a wide cross-sectional dispersion of realized returns and outperform estimates obtained by alternative techniques. Our results also provide evidence of commonality in asset pricing anomalies.