positive changes. In addition, most stocks are covered by one analyst (Figure C-2) and
most estimators are relatively small (less than 10 analysts work ng in it -Figure C-3-).
This means that possible profits are not highly concentrated, i.e. there are enough
niches for multiple small firms to succeed.
Since it is likely that higher coverage of a stock results in more accurate
estimations a test was performed to be certain of the interpretation of the error variable.
The full sample was separated in those stocks with average coverage over the last
three years above the median, and those below it. The median is 6.2 analysts. Although
the mean error for the subsample with large coverage is smaller than the mean error of
stocks with small coverage, the difference is statistically insignificant.
Research Model
The model is estimated as an ordered probit, using only observations from 2008.
In order to guarantee all the possible outcomes had enough observations to estimate
the probabilities, I regrouped the tai Is of the distribution so that every group had at least
18 observations in it. For changes in analysts greater than 9 or smaller than -8 the
observations were grouped and called 10' and -10', respectively. For changes in
estimators, the firms with absolute changes greater than 7 were grouped together, and
the groups were labeled 10' and -10'. Hence, the label 10' in the tables in the appendix
refer to large changes of around 10 analysts (or estimators). The final distributions are
in appendix D (Tables D-2 and D-3).
Different regressions are run to evaluate the effect of past errors and past
revisions on stock coverage. Both variables are not in the same regression to avoid the
high correlation between them to bias the coefficients. The ordered probit was
estimated with changes in coverage as the dependent variable. This would control for