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Detrending Persistent Predictors

Author

Listed:
  • Christophe Boucher

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, A.A.Advisors-QCG - ABN AMRO)

  • Bertrand Maillet

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, A.A.Advisors-QCG - ABN AMRO, EIF - Europlace Institute of Finance)

Abstract

Researchers in finance very often rely on highly persistent - nearly integrated - 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," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00587775, HAL.
    • Handle: RePEc:hal:cesptp:halshs-00587775
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00587775
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    References listed on IDEAS

    as
    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.; Prévision; persistance; extraction de tendance; rendements espérés.;
    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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