Detrending Persistent Predictors

Christophe Boucher (christophe.boucher@univ-paris1.fr) and Bertrand Maillet (bertrand.maillet3@gmail.com)
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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

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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.

Keywords: Forecasting; persistence; detrending; expected returns.; Prévision; persistance; extraction de tendance; rendements espérés. (search for similar items in EconPapers)
Date: 2011-03
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00587775
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Published in 2011

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