A Random Coefficient Approach to the Predictability of Stock Returns in Panels

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 unitby-
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 paper 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-toprice
the variance of the slopes is positive, which we take as evidence of predictability