Dissecting Characteristics Nonparametrically

Joachim Freyberger, Andreas Neuhierl and Michael Weber

No 23227, NBER Working Papers from National Bureau of Economic Research, Inc

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 flexible 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%.

JEL-codes: C14 C52 C58 G12 (search for similar items in EconPapers)
Date: 2017-03
New Economics Papers: this item is included in nep-ecm
Note: AP
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Citations: View citations in EconPapers (38)

Published as Joachim Freyberger & Andreas Neuhierl & Michael Weber & Andrew Karolyi, 2020. "Dissecting Characteristics Nonparametrically," The Review of Financial Studies, vol 33(5), pages 2326-2377.

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Related works:
Journal Article: Dissecting Characteristics Nonparametrically (2020)
Working Paper: Dissecting Characteristics Nonparametrically (2018)
Working Paper: Dissecting Characteristics Nonparametrically (2017)
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