In Table 2, we examine the cross-sectional determinants of analyst coverage. When we actually implement our trading strategies in the next section, we run a separate regression every month to create our measure of residual coverage. Because the regressions look so similar month to month, we only present one set in Table 2 for illustrative purposes, corresponding to December of 1988, which is around the midpoint of our sample period. Again, note that in each case, the regression is only run on those stocks which are larger than the 20th percentile NYSE/AMEX breakpoint in the given month.

Model# | LogSize | NASD | Book/Mkt | Beta | 1/P | Var | Ro | Ri | r_{2} |
Яз | R4 | Turn-Over | NASD*Turnover | INDS | R^{2} |

1 | .54(52.67) | .03(0.99) | NO | .61 | |||||||||||

2 | .56(52.90) | .04(1.21) | YES | .63 | |||||||||||

3 | .55(53.03) | .05(1.50) | .12(3.15) | NO | .61 | ||||||||||

4 | .57(52.22) | .07(2.00) | .17(4.30) | YES | .63 | ||||||||||

5 | .50(48.41) | .07(2.28) | .38(11.54) | NO | .64 | ||||||||||

6 | .51 (46.11) | .09(2.62) | .40(10.94) | YES | .65 | ||||||||||

7 | .57(49.87) | .09(2.59) | -.52(‘3.12) | -1.27(-3.23) | -.50(-9.46) | -.28(-6.06) | -.28(-6.00) | -.04(-0.85) | -.16(-3.46) | YES | .65 | ||||

8 | .52(51.46) | -.02(-.54) | 3.82(8.18) | -.53(-.93) | NO | .64 |

The first point to note is that unlike some previous researchers who have run similar regressions (e.g., Bhushan i989 and Brennan and Hughes 1991) we use as our left-hand side variable log( 1 -t- ANALYSTS), rather than the raw number of analysts. We do this because it is crucial for our tests in Section 3 that the residuals from our regressions bear no relationship to firm size. Were we to use the raw number of analysts as the dependent variable instead, there would be a strong tendency for smaller firms to have lower absolute values of the residual.13 Even with the log(l+ ANALYSTS) specification, of course, we will have to check carefully that our regressions produce residuals with the desired properties, as the underlying relationship may not be a perfectly linear one.

In Model 1, we use OLS, and the only two right-hand side variables are log(SIZE), where SIZE is current market capitalization, and a NASDAQ dummy variable. The size variable is clearly enormously important, generating an R2 of .61. In Model 2, we add 15 industry dummies to the regression. This has a small effect, raising the R2 to .63.

In Models 3 and 4, we try adding the firm’s book-to-market ratio. We do this because book-to-market is well known to forecast returns (Fama and French 1992, Lakonishok, Shleifer and Vishny 1994) and we want to make sure that any return-predicting power we get out of analyst coverage is not simply capturing a book-to-market effect. As it turns out. the coefficient on book-to-market is positive and significant, but it adds nothing at all to the R2. Thus it is unlikely that any of the results we report below are driven by anything to do with book-to-market.-.28