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Shrinking the cross-section
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AI
DOI：10.1016/j.jfineco.2019.06.008.png)
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摘要
En 中文
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.
Keyword:
Factor models
SDF
Cross section
Shrinkage
Machine learning
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期刊
IF:
12
论文数:
3.8K
被引数:
5.5W
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