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

Nathaniel Light, D. Maslov, O. Rytchkov
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引用次数: 89

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.
股票收益横截面信息的聚合:一种潜在变量方法
我们提出了一种从大量公司特征中估计个股预期收益的新方法。我们将预期收益视为潜在变量,并应用偏最小二乘(PLS)估计器,该估计器在假设特征通过一个或几个共同潜在因素与预期收益相关联的情况下,将它们从特征中过滤出来。我们的方法根据26个公司特征构建的预期收益估计产生了实现收益的广泛横截面分散,并且优于替代技术获得的估计。我们的研究结果还提供了资产定价异常的共性证据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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