Bias in European Analysts' Earnings Forecasts

Forecasting company earnings is a difficult and hazardous task. In an efficient market where analysts learn from past mistakes, there should be no persistent and systematic biases in consensus earnings accuracy. Previous research has already established how some (single) individual-company characteristics systematically influence forecast accuracy. So far, however, the effect on consensus earnings biases of a company's sector and country affiliation combined with a range of other fundamental characteristics has remained largely unexplored. Using data for 1993–2002, this article disentangles and quantifies for a broad universe of European stocks how the number of analysts following a stock, the dispersion of their forecasts, the volatility of earnings, the sector and country classification of the covered company, and its market capitalization influence the accuracy of the consensus earnings forecast. Earnings forecasts play a central role in most equity valuation models. In an efficient market where analysts learn from past mistakes, we would expect the average or consensus forecast to be an unbiased estimate of future realized earnings. Academic research has documented persistent and systematic biases, however, in earnings forecasts.To the extent that asset prices largely reflect the consensus earnings number, these biases present opportunities for active portfolio managers. In fact, buy-side analysts already try, at least in part, to distinguish themselves by improving upon the consensus forecast. Asset managers must be selective, however, in the number and types of companies they have in their investable universe. So, they would obviously like to focus their efforts on the companies that are most likely to be mispriced. This article is an attempt to help identify these companies by establishing the characteristics that are most likely to be associated with optimistic (positively biased) consensus earnings forecasts.Our study focused on a broad cross-section of European companies for which we collected the earnings forecasts for the 10 years from May 1993 to April 2002. We establish that the well-documented anomalies of herding (too narrow a dispersion of analyst forecasts for a given company) and optimism (systematic positive bias) were prevalent in our sample.We review the literature on factors that have been associated with the forecast bias. Virtually all of these studies concentrated on the effect of single variables. None systematically explored sector and country effects while simultaneously controlling for other fundamental company characteristics (such as market capitalization or number of analysts following a stock). Our empirical study contributes to filling this gap.We used a multiple-regression framework to verify which factors significantly affect the sign and magnitude of the forecast bias. We establish that analyst forecast dispersion and stock price volatility (which we used as a surrogate for earnings volatility) were consistently and significantly associated with larger forecast bias in the study period. The number of analysts following a stock had no impact on the forecast bias (but reduced the forecast error); the company's market capitalization did not affect the accuracy of the consensus number.We also confirmed the existence of significant sector effects. The forecasts for the consumer nondurables, health care, public utilities, and transportation sectors were, on average, more correct than those for the other industries.Significant geographical differences in forecast accuracy used to exist at the country level, with the companies in the core “Euroland” countries (France, Germany, and Italy) showing particularly poor earnings forecast accuracy. These geographical differences have lost significance in the recent past, however, which probably helps explain the disappearance of country effects in stock returns.By identifying which types of companies are most likely to have the largest earnings forecast bias, we hope to provide guidance to sell-side analysts as to how they can distinguish themselves from the consensus. For the buy-side analyst, these persistent and systematic anomalies should provide interesting investment opportunities.