In equity investment management, sell side analysts serve an important role in forecasting metrics of companies’ financial performance. These estimates are often produced in an opaque manner, namely, the process upon which the estimate is initiated or revised is not directly observable. With multiple analysts covering the same company, and an analyst covering multiple companies, we have an n-m relationship. The systematic 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 propose a novel method for aggregating analyst estimates utilising iterative filtering algorithms. This work is inspired by applications of such classes of algorithms to the robust aggregation of sensor network data and online reviews. We conduct experiments using real-world datasets to demonstrate the efficacy of this approach. The results suggest iterative filtering methods improve upon the forecast accuracy of the consensus forecast compared to the simple mean consensus.

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.