The BKLS measures allow us to use observable attributes of analysts' forecasts to derive empirical estimates of two key (unobservable) dimensions of an information structure among multiple analysts: (1) the precision of their information (i.
Selection of Individual Forecasts Used to Calculate BKLS Measures
This procedure ensures that when comparing the BKLS measures of analysts' information before and after earnings announcements, we base our analysis on the same set of individual analysts.
We substitute ex post realized dispersion (D) and squared error in these analysts' mean forecast (SE) for the expected dispersion (D) and squared error in the mean forecast (SE) used in the BKLS model.
Substituting ex post realizations for expected values introduces measurement error into our measures of expected dispersion and squared error in the mean forecast and, in turn, into our estimates of BKLS measures.
We compute estimates of [rho], h, and s--[rho], h, and s--by substituting the number of our selected analysts forecasts (N), together with the ex post realized dispersion (D) and squared error in the mean forecast (SE) for these forecasts, both scaled by the absolute actual earnings per share, into the BKLS equations shown here in Equations (3), (4), and (5).
Table 1 shows the median and mean levels of BKLS correlation ([rho]) and the BKLS precision measures (h and s) for the [Q2.
Primary Results: Changes in BKLS Measures around Earnings Announcements
We first test for changes in BKLS correlation around earnings announcements.
Recall that the BKLS correlation measure [rho] directly captures the across-analyst correlation in forecast errors, which is the theoretical construct in the Fischer and Verrecchia (1998) model.
In addition, the BKLS framework would consider any common information disclosed privately to all analysts to be effectively public information.
8) In the BKLS framework, error in the mean forecast is defined as the squared difference between the mean analyst forecast and actual earnings.