Dissecting Characteristics Nonparametrically

doi:10.1093/rfs/hhz123

Dissecting Characteristics Nonparametrically

2020-04-17

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DOI：10.1093/rfs/hhz123

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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 selected characteristics affect expected returns nonparametrically. Our method can handle a large number of characteristics and allows for a flexible functional form. Our implementation is insensitive to outliers. Many of the previously identified return predictors don't provide incremental information for expected returns, and nonlinearities are important. We study our method's properties in simulations and find large improvements in both model selection and prediction compared to alternative selection methods.

Keyword:

CAPITAL-ASSET PRICES

CROSS-SECTION

ADDITIVE REGRESSION

VARIABLE SELECTION

COMMON-STOCKS

RISK

INVESTMENT

EARNINGS

RETURNS

EQUILIBRIUM

Dissecting Characteristics Nonparametrically

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