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Detrending Persistent Predictors

Author

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  • 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," Post-Print halshs-00587775, HAL.
    • Handle: RePEc:hal:journl: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

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    Full references (including those not matched with items on IDEAS)

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