Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/185385
Authors: 
Freyberger, Joachim
Neuhierl, Andreas
Weber, Michael
Year of Publication: 
2018
Series/Report no.: 
CESifo Working Paper 7187
Abstract: 
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 they affect expected returns nonparametrically. Our method can handle a large number of characteristics, allows for a flexible functional form, and our implementation is insensitive to outliers. Many of the previously identified return predictors do not provide incremental information for expected returns, and nonlinearities are important. We study the properties of our method in an extensive simulation study and out-of-sample prediction exercise and find large improvements both in model selection and prediction compared to alternative selection methods. Our proposed method has higher out-of-sample Sharpe ratios and explanatory power compared to linear panel regressions.
Subjects: 
cross section of returns
anomalies
expected returns
model selection
JEL: 
C14
C52
C58
G12
Document Type: 
Working Paper

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