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Dissecting Characteristics Nonparametrically

Listed author(s):
  • Joachim Freyberger
  • Andreas Neuhierl
  • Michael Weber

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%.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 23227.

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Date of creation: Mar 2017
Handle: RePEc:nbr:nberwo:23227
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