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Dissecting Characteristics Nonparametrically

Author

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  • Joachim Freyberger
  • Andreas Neuhierl
  • Michael Weber
  • Michael Weber

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 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%.

Suggested Citation

  • Joachim Freyberger & Andreas Neuhierl & Michael Weber & Michael Weber, 2017. "Dissecting Characteristics Nonparametrically," CESifo Working Paper Series 6391, CESifo.
  • Handle: RePEc:ces:ceswps:_6391
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    More about this item

    Keywords

    cross section of returns; anomalies; expected returns; model selection;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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