To question the statistical significance of return predictability, we cannot specify a null that simply turns off that predictability, leaving dividend growth predictability at its essentially zero sample value. If neither returns nor dividend growth are predictable, then the dividend-price ratio is a constant. If the null turns off return predictability, it must turn on the predictability of dividend growth, and then confront the evidence against such predictability in the data. I find that the absence of dividend growth predictability gives much stronger statistical evidence against the null, with roughly 1-2% probability values, than does the presence of return predictability, which only gives about 20% probability values. I argue that tests based on long-run return and dividend growth regressions provide the cleanest and most interpretable evidence on return predictability, again delivering about 1-2% probability values against the hypothesis that returns are unpredictable. I show that Goyal and Welch's (2005) finding of poor out-of-sample R2 does not reject return forecastability. Out-of-sample R2 is poor even if all dividend yield variation comes from time-varying expected returns.
*Published:
Cochrane, John. "The Dog That Did Not Bark: A Defense of Return Predictability.” Review of Financial Studies 21, 4 (2008): 1533-1575.
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This paper was revised on June 29, 2006
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