Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/161830
Authors: 
Freyberger, Joachim
Neuhierl, Andreas
Weber, Michael
Year of Publication: 
2017
Series/Report no.: 
CESifo Working Paper 6391
Abstract: 
We propose a nonparametric method to test which characteristics provide independent 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 exible functional form, and is insensitive to outliers. Many of the previously identified return predictors do not provide incremental information for expected returns, and nonlinearities are important. Our proposed method has higher out-of-sample explanatory power compared to linear panel regressions, and increases Sharpe ratios by 50%.
Subjects: 
cross section of returns
anomalies
expected returns
model selection
JEL: 
C14
C52
C58
G12
Document Type: 
Working Paper

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