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Detrending Persistent Predictors

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Abstract

Researchers in finance very often rely on highly persistent Ñ nearly integrated Ñ explanatory variables to predict returns. This paper proposes to stand up to the usual problem of persistent regressor bias, by detrending the highly auto-correlated predictors. We find that the statistical evidence of out-of-sample predictability of stock returns is stronger, once predictors are adjusted for high persistence

Suggested Citation

  • Christophe Boucher & Bertrand Maillet, 2011. "Detrending Persistent Predictors," Documents de travail du Centre d'Economie de la Sorbonne 11019, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    • Handle: RePEc:mse:cesdoc:11019
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    File URL: ftp://mse.univ-paris1.fr/pub/mse/CES2011/11019.pdf
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    1. Nelson, Charles R & Kim, Myung J, 1993. "Predictable Stock Returns: The Role of Small Sample Bias," Journal of Finance, American Finance Association, vol. 48(2), pages 641-661, June.
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    More about this item

    Keywords

    Forecasting; persistence; detrending; expected returns;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G1 - Financial Economics - - General Financial Markets

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