Novel Methods for Aggregating Analyst Estimates

In equity investment management, sell side analysts provide estimates of companies' financial metrics. The capture of analyst estimates provide a systematic and quantitative proxy for market sentiment. Thus far the academic literature analysing this dataset has resolved to use relatively simple methods for aggregating the individual estimates to arrive at a consensus estimate. In this paper, we present a systematic review of academic literature relating to the study of analyst estimates. Iterative filtering algorithms are proposed as a novel alternative to the aggregation of analyst estimates. We present preliminary results of applying the algorithm to real-world datasets. The results suggest iterative filtering methods improve upon the forecast accuracy of the consensus forecast compared to a mean consensus.