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Dissecting Characteristics Nonparametrically

Joachim Freyberger, Andreas Neuhierl and Michael Weber ()

No 7187, CESifo Working Paper Series from CESifo Group Munich

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.

Keywords: cross section of returns; anomalies; expected returns; model selection (search for similar items in EconPapers)
JEL-codes: C14 C52 C58 G12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ore
Date: 2018
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Related works:
Working Paper: Dissecting Characteristics Nonparametrically (2017) Downloads
Working Paper: Dissecting Characteristics Nonparametrically (2017) Downloads
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