We posit and find an effect of disclosure and analyst reporting regulations implemented from 2000 through 2003 (including Regulation Fair Disclosure, the Sarbanes-Oxley Act, and the Global Settlement Act) on the importance of analyst and forecast characteristics for analyst forecast accuracy. Following the enactment of these regulations, more experienced analysts and All-Star analysts do not maintain their superior forecast accuracy, and analysts employed by large brokerage houses perform worse than other analysts. In addition, we find a decrease in the importance of analyst effort, the number of industries and firms followed, days elapsed since the last forecast, and forecast horizon. While the importance of bold upward forecast revisions does not change, bold downward revisions lose their relevance for forecast accuracy after 2003. Finally, we find an increase in the important of prior forecast accuracy. We find that the importance of these characteristics varies with the precision of publicly available information. Specifically, the decrease in the importance of most analyst and forecast characteristics and the increase in the importance of prior forecast accuracy are greater when the precision of publicly available information is low. Overall, our results suggest that the positive effects of experience, effort, brokerage house size, and All-Star status on forecast accuracy in the pre-regulation period were because of the information advantages that these analysts enjoyed (rather than their ability to generate private information). In contrast, our results suggest that prior forecast accuracy is related to analysts' ability to generate private information.

This study provides new evidence about how tax footnote disclosure characteristics can alleviate errors in analyst forecasts of a firm's effective tax rate (ETR). Despite the importance of income taxes to a firm's bottom line, prior studies indicate that analysts and investors do not fully grasp the complexities of income taxes. Additionally, the FASB is interested in improving the effectiveness of financial statement footnotes and is evaluating the income tax footnote as part of the Disclosure Framework Project. I find that disclosures containing more quantitative information, less jargon, fewer complex words, and/or less legal terminology alleviate analysts' ETR forecast errors related to complexity in income tax accounting. Collectively, my findings provide evidence regarding the areas of accounting for income taxes that make forecasting ETRs difficult and the disclosure characteristics that aid analysts' understanding of tax information and thereby improve their ETR forecasts.

Prior research has shown improvements in analysts' forecast accuracy around various events (e.g. new disclosure regulations or cross-listings), but these studies do not consider a change in the composition and ability of analysts providing forecasts over time. By studying foreign firms cross-listing on U.S. stock exchanges, we find that analyst composition changes by over 50 percent during the three-year period around cross-listing. We show that cross-listing is associated with a shift away from analysts who are less accurate forecasters and toward analysts who are more accurate forecasters. This shift in analyst composition accounts for a significant improvement of 9.5 percent in analyst forecast accuracy. In addition, we document that changes in both analyst ability and public information disclosure affect analyst forecast accuracy around cross-listing. Our results indicate that researchers should control for changes in analyst composition and ability when measuring the impact of specific events on analyst forecast accuracy.

This paper reports the results of an experiment that examines how analyst forecast accuracy (i.e., how close an analyst's forecast is to realized earnings) and forecast boldness (i.e. how far the analyst's forecast is from the consensus forecast) affect the analyst's perceived credibility and investors' willingness to rely on and purchase the analyst's future reports. We hypothesize and find that forecast boldness magnifies the effect of forecast accuracy on these variables. That is, analysts who provide accurate, bold forecasts experience more positive consequences than those who provide accurate, non-bold forecasts, and analysts who provide inaccurate, bold forecasts experience more negative consequences than those who provide inaccurate, non-bold forecasts. We also find that these effects are not symmetric - the negative consequences of being bold and inaccurate exceed positive consequences of being bold and accurate. Our results are not sensitive to the level of the analyst's prior reputation.

While numerous intuitively appealing characteristics of individual analyst earnings forecast accuracy have been identified, the extant literature has provided no evidence regarding the out-of-sample predictive validity of these characteristics. We examine whether these characteristics possess incremental predictive ability vis-a-vis forecast age, the control variable of these studies. We compare the predictive ability of a consensus forecast based solely on forecast age (Age) with that of a six-factor model consisting of forecast age plus five analyst characteristics suggested by the literature (Model). We use the dual evaluative criteria suggested by Foster (1977) and employed numerous times thereafter to assess predictive ability:(1) the ability to predict the next number in the time series and (2) the ability to approximate the capital market's earnings expectation.We show that Age predicts as well as Model using both predictive ability criteria. Because Model consists of Age plus five other factors, it is cost-beneficial to use Age in lieu of Model for predictive purposes. FASB Statement of Financial Accounting Concepts No. 2 (1980) identifies predictive value as a criterion of information relevance. We show that analyst characteristics have no relevance for the purpose of obtaining better predictions of the next quarterly earnings number vis-a-vis a simpler model based on forecast age. There are many reasons for identifying individual analyst-based characteristics, but predictive ability of the next quarterly number is not one of them.