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Detrending Persistent Predictors

Author

Listed:
  • Christophe Boucher

    (A.A.Advisors-QCG (ABN AMRO), Variances et Centre d'Economie de la Sorbonne)

  • Bertrand Maillet

    (A.A.Advisors-QCG (ABN AMRO), Variances et Centre d'Economie de la Sorbonne et EIF)

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," 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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    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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    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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