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Erscheinungsjahr: 
2017
Schriftenreihe/Nr.: 
CESifo Working Paper No. 6391
Verlag: 
Center for Economic Studies and ifo Institute (CESifo), Munich
Zusammenfassung: 
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%.
Schlagwörter: 
cross section of returns
anomalies
expected returns
model selection
JEL: 
C14
C52
C58
G12
Dokumentart: 
Working Paper
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