Shrinking the cross-section

• 成果类型: Article
• 署名作者: Kozak, Serhiy; Nagel, Stefan; Santosh, Shrihari
• 署名单位: University System of Maryland; University of Maryland College Park; University of Chicago; National Bureau of Economic Research; Center for Economic & Policy Research (CEPR); University of Colorado System; University of Colorado Boulder
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/j.jfineco.2019.06.008
• 发表日期: 2020
• 页码: 271-292
• 关键词: Factor Models; SDF; Cross section; shrinkage; Machine Learning

摘要

We construct a robust stochastic discount factor (SDF) summarizing the joint explanatory power of a large number of cross-sectional stock return predictors. Our method achieves robust out-of-sample performance in this high-dimensional setting by imposing an economically motivated prior on SDF coefficients that shrinks contributions of low-variance principal components of the candidate characteristics-based factors. We find that characteristics-sparse SDFs formed from a few such factors-e.g., the four- or five-factor models in the recent literature cannot adequately summarize the cross-section of expected stock returns. However, an SDF formed from a small number of principal components performs well. (C) 2019 Elsevier B.V. All rights reserved.