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Abstract:
This study examines the market response to confirming forecasts. Confirming management forecasts are voluntary disclosures by management that corroborate existing market expectations about future earnings. The study of confirming forecasts is important because it can provide evidence on the relation between voluntary disclosure and cost of capital. We find that the market's reaction to confirming forecasts is significantly positive, indicating that benefits may accrue to firms that disclose such forecasts. In addition, while we find no significant change in the mean consensus forecasts (a proxy for earnings expectations) around the confirming forecast date, evidence indicates a significant reduction in the mean and median consensus analyst dispersion (a proxy for earnings uncertainty). Finally, we document a positive association between reduction of dispersion of analysts' forecasts and the magnitude of the stock market response.

Abstract:
The primary objective of this study is to investigate the effect of prior and multiple restructuring charges on analysts? earnings forecasts. We investigate the effect of restructuring charges on analysts'forecasts by examining both forecast accuracy and dispersion. Chaney et al. (1999) provide evidence that analyst forecast accuracy is impaired by restructurings. However, they. find no empirical link between prior restructuring events and forecast accuracy. In contrast to the findings of Chaney et al., we predict that analysts will learn from prior restructuring charges. By "learn" we mean that current restructuring charges impair forecast accuracy to a lesser extent when prior restructuring charges are present. To document learning, we adapt the Chaney model and partition the sample to identify restructuring plans that have been fully implemented. Additionally, we control for the complexity of the prior charges.

Consistent with our prediction, we find that analysts are able to learn from prior restructuring events. Further tests suggest that this learning is only related to restructurings that have been fully implemented, that is, previous events that were announced more than two years prior to the current forecast. Additionally, we find that the relative magnitude of restructuring charges are associated with a decrease (increase) in forecast accuracy (dispersion) for up to two years after the announcement of the event. This result is consistent with the findings of Hanna (1999). Overall, our results are generally consistent with the conclusion that restructurings create uncertainty for analysts that lasts for at least two years subsequent to the announcement of the event and that analysts do in fact learn from the existence of prior events.

Abstract:
We reexamine the descriptive ability of the conventional wisdom that earnings announcements made after trading and on Friday are dominated by bad news in light of the 24/7 media coverage and other technological changes of the 1990s. We find that the change in media coverage has facilitated a significant change in earnings announcement times: only 27% of earnings announcements are now made during trading as opposed to 67% in prior research. However, our finding of continued dominance of bad news in Friday announcements in particular strongly suggests that the conventional wisdom is not solely the result of managers' desire to take advantage of limited media coverage. Instead, managers appear to be taking advantage of other aspects of investors' behavior, such as their anticipating negative Friday announcements earlier in the week, and the relatively quiet (in terms of trading) weekend period to manage stock price responses to their companies' financial news.

Earnings announcements, earnings surprises, analysts' forecasts, strategic timing, bad news, earnings disclosures, market efficiency, earnings response coefficients

Abstract:
In this study, we examine how analysts are affected by the public actions of investors and other analysts by closely examining how analysts revise their earnings forecasts after an earnings announcement. In particular, we hypothesize that analysts observe the actions of investors and other analysts in order to more accurately forecast earnings and have the expertise to determine when these actions are most informative about future earnings. Consistent with our hypotheses, we find that analysts revise their earnings forecasts more strongly in response to returns and other analysts' revisions when these signals are more informative about future earnings changes. We also find that, consistent with analysts being conservative while facing uncertain information, underreactions are strongest (not weakest) when analysts are responding most strongly to these signals (i.e., when the signals are most informative). Lastly, we find that analysts who are most sensitive to the informativeness of others' actions are relatively more accurate in forecasting earnings, suggesting that the ability to extract information from the actions of others serves as a source of expertise for at least some analysts.

Financial analysts, earnings forecasts, market efficiency, learning

Abstract:
This study investigates whether analysts who pay attention to investor sentiment issue more or less profitable stock recommendations than their peers. We find that analysts whose stock recommendations are positively correlated with recent or future investor sentiment tend to issue relatively less profitable recommendations. Our results suggest that analysts attempting to maximize the profitability of their stock recommendations may wish to focus on fundamentals such as earnings, cash flows, and discount rates rather than attempting to predict investor sentiment or other signals that may affect a firm’s stock price but that are not theoretically related to the firm’s underlying intrinsic value.

investor sentiment, financial analysts, stock recommendations

Abstract:
There is considerable debate in the literature about what analyst experience measures and whether analysts learn from their prior experiences. Extant research has argued that once an analyst's innate ability is considered, the general and firm-specific experiences of an analyst are not relevant to understanding his/her forecasting performance. This prior research posits that only the highest-ability analysts survive at the job and, as a result, performance is not a function of analysts learning from their experiences. Drawing on psychology-based research, we argue that previous measures of experience need to be expanded to not only include general and firm-specific experience, but also task-specific experience. We empirically test our ideas within the context of firms experiencing restructuring charges. Our results reveal that analysts' performance (i.e., forecast accuracy) around current restructurings is associated with both their innate ability and their task-specific restructuring experience. In addition, we find that forecast accuracy and task-specific experience are most highly correlated for those analysts who survive the longest and, thus, presumably have the greatest innate abilities.

analysts, forecast accuracy, analyst characteristics, analyst learning

Abstract:
This paper presents evidence for international markets about the characteristics of financial analysts who are able to provide more accurate earnings forecasts than their peers. The evidence is provided for ten individual countries and for country groups formed on the basis of a similar culture and corporate governance. While prior studies document that forecast accuracy in the U.S. is associated with several analyst characteristics, this topic has not been investigated in an international setting. We predict that differences in culture and corporate governance will cause the influence of some of the characteristics to differ by country. We find that relative forecast accuracy is influenced by years of experience, size of the analyst's employer, and frequency of forecast issuance in many of the countries and show that the significance of experience and employer is conditional on the type of culture and corporate governance of the country.

Abstract:
Brand value estimates are significantly positively related to prices and returns, incremental to accounting variables. Questionable brand value estimate reliability underlies lack of financial statement recognition for brands. Findings suggest estimates are relevant and sufficiently reliable to be reflected in share prices. Simultaneous equations estimation reveals inferences are unaffected by potential bias resulting from simultaneity between brand value estimates and equity market value. Brand value estimates are positively associated with advertising expense, operating margin, and market share. Yet, brand value estimates provide significant explanatory power for prices incremental to these variables, and to recognized brand assets and analysts earnings forecasts.

Abstract:
We find that brand value estimates reported by Financial World are significantly positively related to prices and returns, incremental to other accounting variables. Concerns about brand value estimate reliability underlie lack of financial statement recognition of brands. Our findings suggest the estimates are relevant to investors and sufficiently reliable to be reflected in share prices. Simultaneous equations estimation reveals our inferences are unaffected by potential bias resulting from simultaneity between brand value estimates and equity market value. Brand value estimates also are positively associated with advertising expense, operating margin, and market share. Yet, brand value estimates provide significant explanatory power for share prices incremental to these variables, and to recognized brand assets and analysts earnings forecasts.

Abstract:
Prior studies have identified systematic and time persistent differences in analysts' earnings forecast accuracy, but have not explained why the differences exist. Using the IBES Detail History database, this study finds that forecast accuracy is positively associated with analysts' experience (a surrogate for analyst ability and skill) and employer size (a surrogate for resources available), and negatively associated with the number of firms and industries followed by the analyst (measures of task complexity). The results suggest that analysts' characteristics may be useful in predicting differences in forecasting performance, and that market expectations studies may be improved by modeling these characteristics.