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Detrending Persistent Predictors

Christophe Boucher () and Bertrand Maillet

Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL

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)
New Economics Papers: this item is included in nep-ets and nep-for
Date: 2011-03
Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-00587775
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Published in Documents de travail du Centre d'Economie de la Sorbonne 2011.19 - ISSN : 1955-611X. 2011

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Persistent link: https://EconPapers.repec.org/RePEc:hal:cesptp:halshs-00587775

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