This paper examines cross-sectional differences in the optimistic behavior of financial analysts. Specifically, we investigate whether the predictive accuracy of past information (e.g., time-series of earnings, past returns, etc.) is associated with the magnitude of the bias in analysts' earnings forecasts. We posit that there is higher demand for non-public information for firms whose earnings are difficult to accurately predict than for firms whose earnings can be accurately forecasted using public information. Assuming that optimism facilitates access to management's non-public information, we hypothesize that analysts will issue more optimistic forecasts for low predictability firms than for high predictability firms. Our results support this hypothesis.

Financial Analysts' Forecasts and Stock Recommendations reviews research related to the role of financial analysts in the allocation of resources in capital markets. The authors provide an organized look at the literature, with particular attention to important questions that remain open for further research. They focus research related to analysts' decision processes and the usefulness of their forecasts and stock recommendations. Some of the major surveys were published in the early 1990's and since then no less than 250 papers related to financial analysts have appeared in the nine major research journals that we used to launch our review of the literature. The research has evolved from descriptions of the statistical properties of analysts' forecasts to investigations of the incentives and decision processes that give rise to those properties. However, in spite of this broader focus, much of analysts' decision processes and the market's mechanism of drawing a useful consensus from the combination of individual analysts' decisions remain hidden in a black box. What do we know about the relevant valuation metrics and the mechanism by which analysts and investors translate forecasts into present equity values? What do we know about the heuristics relied upon by analysts and the market and the appropriateness of their use? Financial Analysts' Forecasts and Stock Recommendations examines these and other questions and concludes by highlighting area for future research.

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.

If either economic incentives or psychological phenomena cause the bias in analysts' forecasts to persist long enough, it would be potentially discoverable and exploitable by investors. quot;Exploitationquot; in this context implies that investors, through examination of historical forecasting performance, can more or less reliably estimate the direction and extent of bias, and impute unbiased estimates for themselves, given analysts' forecasts. The absence of persistence in forecast errors would suggest that analysts' own behavior ultimately quot;self-correctsquot; within a time frame that eliminates the possibility that the patterns could be exploited by investors. We use two look-back methods that capture salient features of analysts' past forecasting behavior to form quintile portfolios that describe the range of analysts' forecasting behavior. Parametric and nonparametric tests are performed to determine whether the two portfolio formation methods provide predictive power with respect to subsequent forecast errors. The findings support a conclusion that analysts' behaviors in both optimistic and pessimistic extremes do not entirely self-correct, leaving open the possibility that investors may find historical forecast errors useful in making inferences about current forecasts.

This study investigates four properties of earnings forecasts made by financial analysts to determine if systematic differences in these properties exists failing and healthy firms. The four properties are: The level of forecasts, forecast error, forecast bias, and forecast dispersion. Measures reflecting the four properties are used in models to distinguish failing and healthy firms and predict future bankruptcy. Results indicate that measures developed from analysts forecasts of future earnings can be exploited to distinguish failing from healthy firms.