REVIEW OF FINANCIAL STUDIES·doi:10.1093/rfs/hhw102·Published: 06 February 2017

關于股票收益的橫截面的信息的總結：潛在變量方法

作者：Nathaniel Light (American University in Dubai), Denys Maslov (Moody's Analytics), Oleg Rytchkov (Temple University - Department of Finance)

摘要：我們提出了一種新方法，用于從大量企業特征中估計單個股票的預期收益。我們将預期收益視為潛在變量，應用偏最小二乘（PLS）估計量把它們從一系列公司特征中過濾出來，同時假設這些特征通過一個或幾個共同潛在因素與預期收益率相聯系。運用我們的方法從26個公司特征中所構建的預期收益估計值産生了較廣泛的真實收益率橫截面離差，并優于通過相關替代技術獲得的估計值。另外，我們的結果也提供了關于資産定價異象的共性的證據。

Aggregation of Information About the Cross Section of Stock Returns: A Latent Variable Approach

Nathaniel Light (American University in Dubai), Denys Maslov (Moody's Analytics), Oleg Rytchkov (Temple University - Department of Finance)

ABSTRACT

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.

原文鍊接：

https://academic.oup.com/rfs/article-abstract/doi/10.1093/rfs/hhw102/2756101/Aggregation-of-Information-About-the-Cross-Section?redirectedFrom=fulltext

翻譯：何杉