REVIEW OF FINANCIAL STUDIES·doi:10.1093/rfs/hhw102·Published: 06 February 2017
关于股票收益的横截面的信息的总结：潜在变量方法
作者👨🏿🔬：Nathaniel Light (American University in Dubai), Denys Maslov (Moody's Analytics), Oleg Rytchkov (Temple University - Department of Finance)
摘要：我们提出了一种新方法🧑🏽🏫🙆🏼♂️，用于从大量企业特征中估计单个股票的预期收益。我们将预期收益视为潜在变量，应用偏最小二乘（PLS）估计量把它们从一系列公司特征中过滤出来🥺，同时假设这些特征通过一个或几个共同潜在因素与预期收益率相联系。运用我们的方法从26个公司特征中所构建的预期收益估计值产生了较广泛的真实收益率横截面离差，并优于通过相关替代技术获得的估计值🤸♂️。另外👊🏼，我们的结果也提供了关于资产定价异象的共性的证据。
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.
原文链接🤴🏼：
https://academic.oup.com/rfs/article-abstract/doi/10.1093/rfs/hhw102/2756101/Aggregation-of-Information-About-the-Cross-Section?redirectedFrom=fulltext
翻译👨🏼🔬🐶：何杉
