Return Predictability Under the Alternative
Texas A&M; University of Notre Dame - Department of Finance
Timothy T. Simin
Pennsylvania State University
Daniel R. Smith
Queensland University of Technology - School of Economics and Finance; Simon Fraser University; Financial Research Network (FIRN)
November 22, 2013
Long-horizon predictability is not a myth. We propose a new analytical standard error for predictive regressions that does not impose the null hypothesis that returns are unpredictable and exhibits substantial power gains relative to popular tests. Deriving the covariance matrix under the alternative hypothesis produces two new terms capturing the volatility of shocks to the regressor and their correlation with shocks to the prediction equation. Empirically, we show that failure to detect long-horizon predictability comes from lower power in tests derived under the null hypothesis. For many predictors, giving the alternative a chance allows short-run predictability to survive at long-horizons.
Number of Pages in PDF File: 54
Keywords: Predictability, overlapping observations, analytical standard errors, size, power
JEL Classification: G12, C52
Date posted: August 26, 2012 ; Last revised: December 3, 2013
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