from a firm with high growth is offset by the potential gains from learning about such
firms.
The results for the marginal effects of the changes of analysts' coverage are in
Table E-2. The table shows that the probability that the number of analysts stays the
same with an increase in the absolute value of the average error decreases; the same
happens to the probability that the number increases by one, although the fall is larger.
On the other hand, the probability that coverage decreases by one goes up. These
results indicate that previous errors represent a real cost in gathering information and
cause a decrease on the number of analysts. For example, all else equal, an increase
of one standard deviation in the lagged scaled error causes an increase of 0.003 in the
probability of a reduction in coverage by one analyst; this is an increase of 1.8%.
Errors in estimations are relevant determinants of both large and small changes in
coverage, and the changes caused by the realization of the inaccuracy may be
significant. For example, an increase of one standard deviation in the lagged scaled
error causes an increase of .04 in the probability of a reduction of around 10 analysts in
coverage. This is 10% of the estimated probability of observing such a decrease. A
decrease in past errors causes an almost equal increase in the probability of observing
around 10 more analysts covering a stock.
Similar estimations were performed for a more aggregated coverage variable.
Changes in coverage were separated into three groups: positive, negative or no
change. In this case all coefficients became insignificant. This happens because most
changes in the data are small, and hence they occur within each of the three groups, in
addition to the natural increase in standard errors and hence the tendency to find more