Detrending Persistent Predictors

Christophe Boucher () and Bertrand Maillet

Documents de travail du Centre d'Economie de la Sorbonne from Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne

Abstract: Researchers in finance very often rely on highly persistent Ñ nearly integrated Ñ 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 (search for similar items in EconPapers)
JEL-codes: C1 C14 G1 (search for similar items in EconPapers)
Pages: 10 pages
Date: 2011-03
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ftp://mse.univ-paris1.fr/pub/mse/CES2011/11019.pdf (application/pdf)

Related works:
Working Paper: Detrending Persistent Predictors (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:mse:cesdoc:11019

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