An Evaluation of Financial Analysts and Naïve Methods in Forecasting Long-Term Earnings

Abstract

We evaluate the performance of financial analysts versus naive models in making long-term earnings forecasts. Long-term earnings forecasts are generally defined as third-, fourth-, and fifth-year earnings forecasts. We find that for the fourth and fifth years, analysts' forecasts are no more accurate than naive random walk (RW) forecasts or naive RW with economic growth forecasts. Furthermore, naive model forecasts contain a large amount of incremental information over analysts' long-term forecasts in explaining future actual earnings. Tests based on subsamples show that the performance of analysts' long-term forecasts declines relative to naive model forecasts for firms with high past earnings growth and low analyst coverage. Furthermore, a model that combines a naive benchmark (last year's earnings) with the analyst long-term earnings growth forecast does not perform better than analysts' forecasts or naive model forecasts. Our findings suggest that analysts' long-term earnings forecasts should be used with caution by researchers and practitioners. Also, when analysts' earnings forecasts are unavailable, naive model earnings forecasts may be sufficient for measuring long-term earnings expectations.