Recently, a number of researchers-e.g., Barberis, Shleifer and Vishny (1997), Daniel, Hirshleifer and Subrahmanyam (1997), and Hong and Stein (1997)—have begun to develop behavioral models that aim to unify a range of previously documented ’’anomalies” in asset returns. In a critique of this work, Fama (1997) argues that one should not be too impressed if these models simply rationalize those existing patterns that they were specifically designed to capture. Rather, the acid test should be the “out-of-sample” one: the ability to generate new hypotheses that are ultimately borne out in future empirical work: ”The over-riding question should always be: does the new model produce coherent rejectable predictions…”
We agree wholeheartedly with this sentiment, and this paper represents an attempt to take one step in the indicated direction. The gradual-information-diffusion model of Hong and Stein (1997) was built for the express purpose of delivering both medium-term momentum and long-term reversals in stock returns; in the spirit of Fama (1997), then, it should be evaluated more on the basis of other, previously untested auxilliary predictions. Here we have focused on one relatively simple and clear-cut such hypothesis, namely: if momentum comes from gradual information flow, then there should be more momentum in those stocks for which information gets out more slowly.
Rather than restating all our findings, at this point it suffices to say that they are strongly consistent with the above hypothesis. This is not to claim that alternative interpretations of some or all of the evidence cannot be put forth. If concrete alternatives are in fact offered, it will be necessary to do more refined testing to sort things out. But in any case, we hope that this effort has demonstrated at least one point: non-classical models of asset pricing can do more than just provide ex-post rationalizations of existing anomalies; they can–and should—be subject to the same standards of out-of-sample empirical testing as more traditional theories.