A0597
Title: Resolving conflicts in crowds: An earnings forecasts application
Authors: Houping Xiao - Robinson College of Business/GSU (United States) [presenting]
Shiyu Wang - University of Georgia (United States)
Abstract: Recently, investors can obtain earnings forecast information through traditional venues, such as Wall Street, Institutional Brokers' Estimate System (IBES), as well as modern social media platforms like Estimize, which generates consensus estimates based on the forecasts from individuals with different backgrounds. As a result, this will inevitably lead to conflicts in the earnings forecast. The aim is to present a novel and effective optimization-based approach to resolve such conflicts in earnings forecast data and further generate an accurate and robust earnings forecast consensus. Consistent with the wisdom-of-crowds effect, the new earnings forecast consensus is more accurate than the Wall Street consensus (67.5\% of estimations with error less than the Wall Street) and IBES consensus (67.4\% of estimations with error less than the IBES) of the time. Moreover, the new earnings forecast consensus can provide incrementally useful information in forecasting earnings, and the incremental information is further priced in the market after the earnings announcement.
