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We study the size-power tradeoff of commonly employed tests of return predictability. For short horizon tests, we show analytically that the indirect dividend test is asymptotically more powerful than the direct return test when dividend growth is less volatile than returns, as appears to be true in the data. The asymptotic power advantages of the dividend test carry over to small samples. Asymptotically, the relative power of the short vs long-horizon return test may depend on size. For empirically relevant parameter values the short-horizon return test is asymptotically more powerful than the long-horizon test at the 1% level but the reverse is true at the 5% and 10% levels. Monte Carlo analysis indicates that, in small samples, the long-horizon return test is more powerful than the short-horizon return test for all sizes. The differences in the relative power of the tests in the small sample case is traced back to the correlation structure of the underlying shocks.

Size and power in tests of return predictability