Detrending Persistent Predictors for Forecasting Stock Returns

34 Pages Posted: 12 May 2011

Date Written: May 1, 2011

Abstract

Researchers in finance very often rely on highly persistent – nearly integrated – explanatory variables to predict returns. However, statistical inference in predictive regressions depends critically upon the stochastic properties of the posited explanatory variable, and in particular, of its persistence. This paper proposes to stand up to the usual problem of persistent regressor bias, by detrending the highly autocorrelated predictors. We find that some evidence of predictability at short horizons based on financial ratios.

Keywords: forecasting, persistence, wavelet, expected returns

JEL Classification: C14, C58, G17

Suggested Citation

Boucher, Christophe and Maillet, Bertrand B., Detrending Persistent Predictors for Forecasting Stock Returns (May 1, 2011). International Conference of the French Finance Association (AFFI), May 11-13, 2011. Available at SSRN: https://ssrn.com/abstract=1836907 or http://dx.doi.org/10.2139/ssrn.1836907

Christophe Boucher (Contact Author)

ESG ( email )

25 rue saint ambroise
Paris, 75011
France

Bertrand B. Maillet

EMLyon Business School (Paris Campus) ( email )

23 Avenue Guy de Collongue
Ecully, 69132
France

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