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| HAL: halshs-00587775, version 1 |
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| Detrending Persistent Predictors |
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| Christophe Boucher 1, 2Bertrand Maillet 1, 2, 3 |
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| (2011-03) |
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| 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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| 1: | Centre d'économie de la Sorbonne (CES) |
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CNRS : UMR8174 – Université Paris I - Panthéon-Sorbonne |
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| 2: | A.A.Advisors-QCG |
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ABN AMRO |
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| 3: | EIF |
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Europlace Institute of Finance |
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| Subject | : | Humanities and Social Sciences/Economies and finances Humanities and Social Sciences/Business administration Humanities and Social Sciences/Methods and statistics Mathematics/Statistics Statistics/Statistics Theory |
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| Forecasting – persistence – detrending – expected returns. |
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| Attached file list to this document: | |||||
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| halshs-00587775, version 1 | |
| http://halshs.archives-ouvertes.fr/halshs-00587775 | |
| oai:halshs.archives-ouvertes.fr:halshs-00587775 | |
| From: Lucie Label | |
| Submitted on: Thursday, 21 April 2011 14:11:12 | |
| Updated on: Friday, 22 April 2011 09:46:03 | |
