Journal of Portfolio Management · VOL43., NO. 3 · spring2017

纯因子组合和多元回归分析

作者：RG Clarke（Ensign Peak Advisors in Salt Lake City）

H De Silva（Analytic Investors in Los Angeles）

S Thorley（Brigham Young University in Provo）

摘要：将因子组合结构与标准化因素披露的证券收益之间的横截面回归联系起来，会导致对因子绩效的一个透明的和可投资的观点。在资本加权的情况下，多元回归系数转化为投资组合收益，这是与次要因子披露相比更清晰的基准。本文采用50年的数据集，对1000个美国大股票和5个因子进行了分析，它们分别为价值、动量、小规模、低beta值和盈利能力。通过对因子组合分析的两个案例研究，作者关注用收益率衡量的低成本和用10年期美国国债收益率的敏感性来衡量的利率风险。

Pure Factor Portfolios and Multivariate Regression Analysis

RG Clarke（Ensign Peak Advisors in Salt Lake City）；H De Silva（Analytic Investors in Los Angeles）；S Thorley（Brigham Young University in Provo）

ABSTRACT

Linking factor portfolio construction to cross-sectional regressions of security returns on standardized factor exposures leads to a transparent and investable perspective on factor performance. Under capitalization weighting, multivariate regression coefficients translate to portfolio returns that are benchmark relative and cleared of secondary factor exposures. The methodological contributions in this article are illustrated using a 50-year data set of 1,000 large U.S. stocks and five factor exposures: value, momentum, small size, low beta, and profitability. Using two case studies in factor portfolio analysis, the authors focus on cheapness, as measured by earnings yield, and interest rate risk, as measured by sensitivity to the 10-year Treasury bond return.

原文链接：

https://www.researchgate.net/publication/316479716_Pure_Factor_Portfolios_and_MultivariateRegression_Analysis

翻译：黄涛