In Table 8. we again use raw returns, and this time generate the coverage residuals from Model 8 of Table 2. which includes the turnover variables. But before turning to the numbers, we should point out that it is far from clear that it makes economic sense to control for turnover in this way. As noted above, it may well be that the positive correlation of coverage and turnover reflects causality running from the former to the latter: high-coverage stocks have lower adverse-selection costs of trading, and hence attract more trading volume (Brennan and Subrahmanyam 1995). To the extent that this story is true, we should not use Model 8 to generate our residuals—we will just be reducing the exogenous variation in coverage by regressing it on a noisy proxy for itself, thereby weakening the power of our tests.
On the other hand, there are other stories, according to which it is more sensible to use Model 8. To take a simple example, one might argue that our basic measure of firm size is misleading, because for some stocks, the ’’float” (i.e.. those shares that trade on a regular basis in the public market) is much smaller than the market cap. And it is possible that both analyst coverage, as well as costs of arbitrage, are driven primarily by float, rather than by market cap. In this setting, a turnover control—presumably a good proxy for float—would be warranted.
Overall, this discussion suggests that by using a turnover control as in Table 8, we are erring on the side of being too conservative—the control may or may not make economic sense, and it potentially wastes some statistical power. We also end up sacrificing further power because of two data limitations: 1) we can only run the turnover-adjusted tests for the shorter sample period 1984-1996, due to a lack of earlier turnover data on NASDAQ; and 2) we also lose roughly 12% of the firms—typically among the smaller ones—from our Table 4 sample because of the requirement that turnover numbers be available for six months prior to the measurement of analyst coverage. With all these flags in mind, the results in Table 8 are surprisingly strong. The difference in P3-P1 momentum between SUB1 and SUB3 falls slightly relative to Table 4, to 0.31% per month, but even with the shorter sample it is still significant, with a t-stat of 2.23. The return to the LAST strategy is now 0.56% per month, with a t-stat of 3.58. The bottom line is that our results appear to be robust, even to this (possibly ill-conceived) control for the correlation between turnover and analyst coverage.