Analysts' earnings forecasts are influenced by their desire to win investment banking clients. We hypothesize that the equity bull market of the 1990s, along with the boom in investment banking business, exacerbated analysts' conflict of interest and their incentives to adjust strategically forecasts to avoid earnings disappointments. We document shifts in the distribution of earnings surprises, the market's response to surprises and forecast revisions, and in the predictability of non-negative surprises. Further confirmation is based on subsamples where conflicts of interest are more pronounced, including growth stocks and stocks with consecutive non-negative surprises; however shifts are less notable in international markets

Analysts' earnings forecasts are influenced by their desire to win investment banking clients. We hypothesize that the equity bull market of the 1990s, along with the boom in investment banking business, exacerbated analysts' conflict of interest and their incentives to adjust strategically forecasts to avoid earnings disappointments. We document shifts in the distribution of earnings surprises, the market's response to surprises and forecast revisions, and in the predictability of non-negative surprises. Further confirmation is based on subsamples where conflicts of interest are more pronounced, including growth stocks and stocks with consecutive non-negative surprises; however shifts are less notable in international markets.

This study examines the consequences of the series of reforms targeting investment-banking-related conflicts of interest. We compare and contrast optimism biases in analysts' stock recommendations and earnings forecasts across different types of analyst firms in the post-reform period 2004-2007 versus the pre-reform period 1998-2001. We document a significant reduction in the relative optimism of sanctioned investment bank analysts' stock recommendations, but not their earnings forecasts. Moreover, we find little change in the profitability of their stock recommendations, but detect a drop in the accuracy of earnings forecasts made by investment bank analysts. In sum, the reforms achieve the objective of mitigating the apparent optimism in investment bank stock recommendations, but they do not provide benefit to investors in terms of more profitable recommendations or more accurate earnings forecasts.

Managerial behavior differs considerably when managers report quarterly profits versus losses. When they report profits, managers seek to just meet or slightly beat analyst estimates. When they report losses, managers do not attempt to meet or slightly beat analyst estimates. Instead, managers often do not forewarn analysts of impending losses, and the analyst's signed error is likely to be negative and extreme (i.e., a measured optimistic bias). Brown (1997 Financial Analysts Journal) shows that the optimistic bias in analyst earnings forecasts has been mitigated over time, and that it is less pronounced for larger firms and firms followed by many analysts. In the present study, I offer three explanations for these temporal and cross-sectional phenomena. First, the frequency of profits versus losses may differ temporally and/or cross-sectionally. Since an optimistic bias in analyst forecasts is less likely to occur when firms report profits, an optimistic bias is less likely to be observed in samples possessing a relatively greater frequency of profits. Second, the tendency to report profits that just meet or slightly beat analyst estimates may differ temporally and/or cross-sectionally. A greater tendency to 'manage profits' (and analyst estimates) in this manner reduces the measured optimistic bias in analyst forecasts. Third, the tendency to forewarn analysts of impending losses may differ temporally and/or cross-sectionally. A greater tendency to 'manage losses' in this manner also reduces the measured optimistic bias in analyst forecasts. I provide the following temporal evidence. The optimistic bias in analyst forecasts pertains to both the entire sample and the losses sub-sample. In contrast, a pessimistic bias exists for the 85.3% of the sample that consists of reported profits. The temporal decrease in the optimistic bias documented by Brown (1997) pertains to both losses and profits. Analysts have gotten better at predicting the sign of a loss (i.e., they are much more likely to predict that a loss will occur than they used to), and they have reduced the number of extreme negative errors they make by two-thirds. Managers are much more likely to report profits that exactly meet or slightly beat analyst estimates than they used to. In contrast, they are less likely to report profits that fall a little short of analyst estimates than they used to. I conclude that the temporal reduction in optimistic bias is attributable to an increased tendency to manage both profits and losses. I find no evidence that there exists a temporal change in the profits-losses mix (using the I/B/E/S definition of reported quarterly profits and losses). I document the following cross-sectional evidence. The principle reason that larger firms have relatively less optimistic bias is that they are far less likely to report losses. A secondary reason that larger firms have relatively less optimistic bias is that their managers are relatively more likely to report profits that slightly beat analyst estimates. The principle reason that firms followed by more analysts have relatively less optimistic bias is that they are far less likely to report losses. A secondary reason that firms followed by more analysts have relatively less optimistic bias is that their managers are relatively more likely to report profits that exactly meet analyst estimates or beat them by one penny. I find no evidence that managers of larger firms or firms followed by more analysts are relatively more likely to forewarn analysts of impending losses. I conclude that cross-sectional differences in bias arise primarily from differential 'loss frequencies,' and secondarily from differential 'profits management.' The paper discusses implications of the results for studies of analysts forecast bias, earnings management, and capital markets. It concludes with caveats and directions for future research.

Recent efforts of regulators have helped neutralize analysts' conflict of interest. Analysts tended to make overly optimistic earnings forecasts prior to Regulation FD and the Global Analyst Research Settlement. Regulation FD made analysts less dependent on insider information and, thereby, diminished analysts' motives to inflate their forecasts. The impact of Regulation FD is more apparent for firms with less informational transparency. The Global Settlement had an even bigger impact on analyst behavior. After the Global Settlement, the mean forecast bias declined significantly, whereas the median forecast bias essentially disappeared. These results are not limited to 12 banks covered by the Global Settlement, but are similar for all analysts.

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.