Most studies of the predictability of returns are based on time series
data, and whenever panel data are used, the testing is almost always
conducted in an unrestricted unit-by-unit fashion, which makes for a
very heavy parametrization of the model. On the other hand, the few
panel tests that exist are too restrictive in the sense that they are based on homogeneity assumptions that might not be true. As a response to this, the current study proposes new predictability tests in the context of a random coefficient panel data model, in which the null of no predictability corresponds to the joint restriction that the predictive slope has zero mean and variance. The tests are applied to a large panel of stocks listed at the New York Stock Exchange. The results suggest that while the predictive slopes tend to average to zero, in case of book-to-market and cash flow-to-price the variance of the slopes is positive, which we take as evidence of predictability.