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CESifo Working Paper Details

Dissecting Characteristics Nonparametrically

Joachim Freyberger, Andreas Neuhierl, Michael Weber (Website)

CESifo Working Paper No. 6391 (March 2017)

Primary CESifo Category: [12] Empirical and Theoretical Methods

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 exible 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%.

Keywords: cross section of returns, anomalies, expected returns, model selection

JEL Classification:
[C140] Semiparametric and Nonparametric Method: General
[C520] Model Evaluation, Validation, and Selection
[C580] Financial Econometrics
[G120] Asset Pricing; Trading volume; Bond Interest Rates

Additional CESifo Category:
[7] Monetary Policy and International Finance

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