We examine the effects of analysts’ celebrity on investor reaction to earnings forecast revisions. We measure celebrity as the quantity of media coverage analysts receive in sources included in the Dow Jones Interactive database, and find that media coverage is positively related to investor reaction to forecast revisions. The effect of celebrity on the reaction to forecast revisions remains significant after controlling for forecast performance variables examined in prior studies (ex post forecast accuracy, ex ante accuracy, award status, and other variables shown to be related to forecast accuracy). While these results are consistent with the familiarity of the analyst’s name affecting the market reaction, we cannot rule out that our measure of celebrity is correlated with error in the performance measures we examine and/or correlated with other unexamined dimensions of forecast performance. A content analysis of a random subsample of the media coverage of our sample analysts suggests that our findings likely are not due to the increased availability of forecast revisions. Finally, an investigation of the excess returns around the quarterly earnings announcement date suggests that market participants react too strongly to forecast revisions issued by analysts with high levels of media coverage. Taken together, these findings suggest that an analyst’s level of media coverage can affect the initial market reaction to his forecast revisions.

Regulators’ interest in analyst reports stems from the belief that small investors are unaware of the conflicts sell-side analysts face and may, as a consequence, be misled into making suboptimal investment decisions. We examine who trades on security analyst stock recommendations by extending prior research to focus on investor-specific responses to revisions. We find that both large and small traders react to analyst reports; however, large investors appear to trade more than small traders in response to the information conveyed by the analyst’s recommendation and earnings forecast revision (proxied by the magnitudes of the recommendation change and the earnings forecast revision, respectively). We also find that small investors do not fully account for the effects of analysts’ incentives on the credibility of analyst reports, as captured by the type of recommendation (i.e., upgrade versus downgrade or buy versus sell). In particular, small investors not only trade more than large investors following upgrade and buy recommendations, but also trade more following upgrade and buy recommendations than they do following downgrade and hold/ sell recommendations. Furthermore, we observe that, on average, small traders are net purchasers following recommendation revisions regardless of the type of the recommendation; large traders tend to be net sellers following downgrades and sells. Consequently, large traders generate statistically positive returns from their trading, while small traders generate statistically negative returns from their trading. These findings are consistent with large investors being more sophisticated processors of information, and provide some support for regulators’ concerns that analysts may more easily mislead small investors.

We investigate if sell-side security analysts exhibit relative persistence in their stock picking ability. We find that analysts whose recommendation revisions earned the most (least) positive excess returns in the past continue to outperform (underperform) other analysts in the future. Further, we find that the market recognizes these performance differences in the five-day period surrounding the recommendation revision. This market reaction, however, is incomplete. Excess returns measured over the one and three trading months following the revision are significantly different from zero and positively associated with the analysts’ prior performance. A trading strategy taking long (short) positions in recommendation upgrades (downgrades) conditional on an analyst’s prior performance generates excess returns, but these returns are insufficient to cover transaction costs.

Controlling for other determinants of the cost of capital, we find that firms with repeated large earnings surprises experience a higher cost of equity capital. This finding holds regardless of the sign of the earnings surprises, but firms that consistently report negative surprises have relatively higher cost of equity capital. Although firms that frequently surprise the market experience a decrease in analyst following relative to no surprise firms, this reduction in monitoring cannot account for the higher cost of equity capital. Overall, these findings document that repeated earnings surprises are costly, and provide evidence that managers have incentives to avoid missing earnings targets.

We investigate if market participants’ reactions to dividend changes are related to "earnings quality," the extent to which a firm’s past earnings are associated with its future cash flows. Consistent with predictions from analytical work (Holthausen and Verrecchia [1988]; Kim and Verrecchia [1991]), we find that, controlling for the firm’s dividend change, information environment, investment opportunity set, operating risk, and dividend clienteles, the market reacts less to dividend increase announcements from firms with greater earnings quality. Controlling for the firm’s dividend change, information environment, and the release of other information around the dividend declaration date, we also document that analyst forecast revisions are significantly lower for firms with higher earnings quality following dividend increases. In both the market reaction and analyst forecast revision tests, our results for dividend decreases are generally not statistically significant.

Prior research suggests that various financial anomalies are related to investors’ inability to process historical earnings and price information. In particular, analysts’ failure to incorporate appropriately the serial correlation in earnings surprises provides at least a partial explanation for post-earnings-announcement drift. Because prior work documents that analysts more fully incorporate the information in prior earnings surprises as they gain experience, we examine if firms followed by more experienced analysts exhibit less drift. Measuring analyst firm-specific forecasting experience as the number of prior quarters for which the analyst has issued an earnings forecast for the firm, we find that post-earnings-announcement drift associated with firms with a more experienced analyst following is 18 percent less than that for firms with a less experienced analyst following. This result suggests that the efficiency of a firm’s market price is affected by the aggregate experience level of its analyst following.

The accuracy of sell-side analysts’ forecast revisions is related to a number of factors, including characteristics of the analyst and the age of the forecast. In this study we examine whether there are differences in how sophisticated and unsophisticated investors use these factors to predict the relative accuracy of forecast revisions. We adapt the lens model methodological approach from the judgment and decision-making literature to investigate these differences in an archival setting. Our results suggest that sophisticated investors have greater knowledge overall about the relation of the factors to forecast accuracy. Further, our evidence is consistent with sophisticated investors relying more on the specific factors that provide the most benefits (relative to their costs) for predicting relative forecast accuracy.

We examine whether analysts more fully incorporate prior earnings and returns information in their current quarter forecasts as their experience following a firm increases. We measure analyst firm-specific forecasting experience as the number of prior quarters the analyst has issued an earnings forecast for the firm. We find that analysts underreact to prior earnings information less as their experience increases, suggesting one reason why analysts forecast earnings more accurately with experience.

Theories of underinvestment propose a link between cash flow volatility and investment and subsequent cash flow and earnings levels. Consistent with these theories, our results indicate that forecasting models that include volatility as an explanatory variable have greater accuracy and lower bias than forecasting models that exclude volatility. The improvement in forecast accuracy and bias is greatest when the firm is most likely to experience underinvestment. The profitable implementation of a trading strategy based on these findings, however, suggests that equity market participants do not incorporate fully the information in historical volatility when forecasting future firm performance.

We examine the disclosure strategies managers follow when they "preannounce" quarterly earnings shortly before formal earnings announcements. We document that managers with bad news release essentially all of their news at the preannouncement date, while managers with good news only release about half of their news. Controlling for the combined news released at the preannouncement and earnings announcement dates, firms with negative earnings announcement surprises have significantly lower excess returns for the period from just before the preannouncement to just after the earnings announcement. This finding is consistent with the observed disclosure strategies whereby managers attempt to avoid negative earnings announcement surprises, and suggests that how information is presented can affect the market’s reaction to that information.

An earnings preannouncement is a public statement about an upcoming earnings announcement made shortly before the official announcement. Managers use preannouncements to guide analysts' and investors' expectations for the soon-to-be-announced earnings amount. Managers most commonly use preannouncements when the earnings number they will ultimately report is very far from analysts' earnings forecasts, when there is a large variation in analysts' forecasts, or when managers have bad news. In recent years, managers have issued earnings preannouncements with increasing frequency. This article summarizes the findings of our recent study of preannouncements (Leonard Soffer, Ramu Thiagarajan and Beverly Walther, "Earnings Preannouncement Strategies," Review of Accounting Studies, Vol. 5, No. 1, 2000) on how managers determine the tentative earnings numbers provided in their preannouncements, and how the market reacts to the preannouncements and the subsequent earnings announcements.

This paper provides evidence that managers strategically select the prior-period earnings amount that is used as a benchmark to evaluate current-period earnings in quarterly earnings announcements. Managers are more likely to separately announce a prior-period gain from the sale of property, plant, and equipment (PPE) than a loss. This strategy provides the lowest possible benchmark for evaluating current earnings, thereby allowing the manager to highlight the most favorable change in earnings. This strategic disclosure behavior is more likely to occur when it prevents a negative earnings surprise. The observed strategic disclosure decisions are consistent with a conjecture by managers that the nonrecurring nature of the prior-period gain/ loss will be forgotten unless it is separately announced. Consistent with this conjecture, there is some evidence that equity investors, one potential target of strategic reporting, use the benchmark that managers provide in earnings announcements to evaluate current earnings, even when the components of this benchmark have different persistence. However, cross-sectional analyses provide no evidence that managers ex post exploit the equity mispricing that occurs between the earnings announcement date and the release of the financial statements.

We investigate if earnings forecast accuracy matters to security analysts by examining its association with analyst turnover. Controlling for firm- and time-period effects, forecast horizon, and industry forecasting experience, we find that an analyst is more likely to turn over if his forecast accuracy is lower than his peers. We find no association between an analyst’s probability of turnover and his absolute forecast accuracy. We also investigate another observable measure of the analyst’s performance, the profitability of his stock recommendations. There is no statistical relation between the absolute or relative profitability of an analyst’s stock recommendations and his probability of turnover. We interpret our findings as indicating that forecast accuracy is important to analysts.

We investigate if sell-side security analysts generate more accurate quarterly earnings forecasts and more profitable stock recommendations as their experience following a specific firm increases. We measure firm-specific experience as the number of prior quarters for which the ana- lyst has issued an earnings forecast for the firm. If analysts improve their forecasting performance with repetition, as suggested by the "Learning By Doing" (LBD) model, an analyst's earnings forecasts will become more accurate with experience.

This paper investigates whether sophisticated investors rely more on analyst forecasts than on time-series model forecasts in forming expected earnings. Specifically, I investigate if earnings-announcement-related returns are more closely associated with analyst (SRW) forecasts for firms for which the marginal investor is more (less) likely to be sophisticated. My proxies for investor sophistication are institutional ownership, analyst following, and firm size (Atiase [1985], Hand [1990]). For a sample of 89,246 firm-quarter observations over 1980-1995, I find that the weight placed on the analyst (SRW) forecast is increasing (decreasing) in my proxies for investor sophistication. These results are consistent with an association between investor sophistication and the relative weight placed on analyst and SRW forecasts in forming expected earnings. Further analysis indicates that neither forecast availability (as captured by publication in The Wall Street Journal) nor forecast accuracy can account for these findings.

Published market concentration statistics have aroused concerns that industry leaders may have monopolized the U.S. accounting market. Using the law and advertising service industries as benchmarks, this paper analyzes local (single Metropolitan Statistical Area) concentration measures. Consistent with national measures, the average local concentration measures indicate that accounting Is statistically more concentrated than law or advertising. However, the relative difference between accounting and the benchmark advertising and law concentration measures declines considerably as one moves from the national to the local level. Moreover, accounting is statistically more concentrated than law or advertising only In the largest local markets; concentration measures of the three service industries are not statistically different in smaller local markets.These results are consistent with large discretionary expenditures (e.g., training, research and development, advertising) in accounting relative to advertising or law. Our findings suggest that, in smaller local markets, accounting is not more prone to collusion than other professional services. The results also suggest an important difference in the market structure of accounting and other service industries.