In Tables 6-9, we redo the analysis of Table 4, using a variety of alternative specifications. First, in Table 6, we depart from Jegadeesh and Titman’s (1993) focus on raw returns. Given that our economic story is all about firm-specific information, it seems sensible to focus on returns adjusted for any market-wide factors. In Table 6 all the returns—both in the pre-formation and post-formation periods—are market-model adjusted, using individual stock betas. As can be seen, the use of this beta adjustment does not significantly alter our central results. The P3-P1 momentum measure for the entire sample actually rises somewhat, to 1.20% per month (it was 0.94% in Table 4), and the difference between the low-coverage SUB1 and the high-coverage SUB3 also goes up a bit, to 0.49%, with a t-stat of 4.04 (it was 0,42% in Table 4). Finally, the LAST strategy, which is long P1/SUB3 and short P1/SUB1, continues to do well—though not quite as well as before-generating an average beta-adjusted return of 0.50% per month (t-stat = 3.64).
In Table 7, we go back to using raw returns, but we now generate the coverage residuals from Model 2 of Table 2, which includes the 15 industry dummies. As can be seen, the results are not much changed. The difference in P3-P1 momentum between SUB1 and SUB3 falls slightly, to 0.33% per month, but is still strongly significant, with a t-stat of 3.06. As for our LAST strategy which operates only in PI, it now generates a monthly return of 0.60% (t-stat = 5.03). Note that given the combined results in Tables 6 and 7, it appears that one can design a profitable LAST strategy that is not only size-neutral and momentum-neutral, but beta-neutral as well as neutral to any industry factors. This makes it all the more improbable that one can explain the substantial returns to this strategy based on any kind of risk story.
However, a final caveat on this point is that we have not checked whether the profits to the LAST strategy continue to be large after controlling for book-to-market effects. One might think that this correction would be relevant in light of the evidence in Table 2 that analyst coverage is positively correlated with book-to-market. As it turns out, though, the differences in book-to-market across SUB I and SUB3 are too small to matter much. Using our Model 1 residuals, the median value of book-to-market is .57 in SUB1 and .69 in SUB3 (the means are .67 and .78 respectively). Based on the evidence in Fama and French (1992), this book-to-market spread corresponds to a return differential of roughly 0.10% per month, only a small fraction of the profits to our LAST strategy.