Abstract
I show that dispersion among equity analysts’ forecasts increases following earnings announcements. I provide evidence that this is due to information selection: analysts rely on different subsets of public information revealed in earnings announcements when revising their forecasts in a high-dimensional setting. Using text data from analysts’ reports, I show that analysts cite different information when they revise their forecasts
following earnings announcements, providing evidence of information selection. Results from elastic net regressions corroborate these findings but further imply the existence of a common benchmark to which analysts can compare their forecasts. The existence of a common benchmark explains the decrease in forecast dispersion in the periods between earnings announcements. To formalize these findings, I develop a simple model
of forecast-making processes under a common benchmark to explain the dynamics of forecast dispersion. My results highlight the importance of information selection as a driver of disagreement, especially in high-dimensional environments.

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