发帖
雷达卡
Joachim Freyberger
University of Wisconsin-Madison
Andreas Neuhierl
Olin Business School
MichaelWeber
University of Chicago
We propose a nonparametric method to study which characteristics provide incremental
information for the cross-section of expected returns. We use the adaptive group LASSO
to select characteristics and to estimate how selected characteristics affect expected returns
nonparametrically. Our method can handle a large number of characteristics and allows
for a flexible functional form. Our implementation is insensitive to outliers. Many of the
previously identified return predictors don’t provide incremental information for expected
returns, and nonlinearities are important. We study our method’s properties in simulations
and find large improvements in both model selection and prediction compared to alternative
selection methods. (JEL C14, C52, C58, G12)
Freyberger2020.pdf
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