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Detrending Persistent Predictors

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  • Christophe Boucher

    ()
    (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris I - Panthéon-Sorbonne, A.A.Advisors-QCG - ABN AMRO)

  • Bertrand Maillet

    ()
    (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris I - Panthéon-Sorbonne, 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.

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Paper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number halshs-00587775.

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Date of creation: Mar 2011
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Handle: RePEc:hal:cesptp:halshs-00587775

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Keywords: Forecasting; persistence; detrending; expected returns.;

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