Dissecting Characteristics Nonparametrically

Joachim Freyberger, Andreas Neuhierl, Michael Weber and Michael Weber
Authors registered in the RePEc Author Service: Michael Weber

No 7187, CESifo Working Paper Series from CESifo

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)
Date: 2018
New Economics Papers: this item is included in nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Dissecting Characteristics Nonparametrically (2020)
Working Paper: Dissecting Characteristics Nonparametrically (2017)
Working Paper: Dissecting Characteristics Nonparametrically (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_7187

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