[This paper presents a model that relates properties of the analysts' information environment to the properties of their forecasts. First, we express forecast dispersion and error in the mean forecast in terms of analyst uncertainty and consensus (that is, the degree to which analysts share a common belief). Second, we reverse the relations to show how uncertainty and consensus can be measured by combining forecast dispersion, error in the mean forecast, and the number of forecasts. Third, we show that the quality of common and private information available to analysts can be measured using these same observable variables. The relations we present are intuitive and easily applied in empirical studies.]

This study examines how financial disclosures with earnings announcements affect sell‐side analysts' information about future earnings, focusing on disclosures of financial statements and management earnings forecasts. We find that disclosures of balance sheets and segment data are associated with an increase in the degree to which analysts' forecasts of upcoming quarterly earnings are based on private information. Further analyses show that balance sheet disclosures are associated with an increase in the precision of both analysts' common and private information, segment disclosures are associated with an increase in analysts' private information, and management earnings forecast disclosures are associated with an increase in analysts' common information. These results are consistent with analysts processing balance sheet and segment disclosures into new private information regarding near‐term earnings. Additional analysis of conference calls shows that balance sheet, segment, and management earnings forecast disclosures are all associated with more discussion related to these items in the questions‐and‐answers section of conference calls, consistent with analysts playing an information interpretation role with respect to these disclosures.

Prior research reports seemingly conflicting evidence and interpretations concerning the relation between dispersion in analysts' earnings forecasts and stock returns. Diether et al. (2002) and Johnson (2004) find a negative relation between levels of dispersion in analysts' forecasts and future stock returns. Yet, changes in forecast dispersion are negatively associated with contemporaneous stock returns (L'Her and Suret 1996). We demonstrate that levels and changes in dispersion reflect different theoretical constructs. Changes in dispersion primarily reflect changes in information asymmetry whereas levels of dispersion primarily reflect levels of uncertainty. Further, the uncertainty component of dispersion levels reflects idiosyncratic risk that is negatively associated with future stock returns. These findings provide support for Johnson's (2004) explanation that dispersion levels reflect idiosyncratic uncertainty that increases the option value of the firm and generally refute Diether et al.'s (2002) explanation that dispersion levels reflect information asymmetry. In addition, we reconcile L'Her and Suret's (1996) findings with the findings of Johnson (2004). We find that the negative association between changes in dispersion and contemporaneous stock returns is not due to increased uncertainty but rather increased information asymmetry.

The increase in investor diversity over the last 35–40 years prompted us to revisit trading volume reactions to earnings announcements and how these reactions vary with firm size. We argue that this increase in investor diversity would likely increase differences in the precision of pre‐announcement information around earnings announcements, particularly for large firms. This suggests that the role of earnings announcements in resolving investor disagreement, as reflected in trading volume reactions, has increased. Over the 35‐year period 1977–2011, we find a dramatic increase in the magnitude and frequency of volume reactions to earnings announcements, particularly for large firms. The increase in large firms’ trading volume reactions is so pronounced that the relation between volume reactions and firm size has turned positive in recent years, reversing Bamber's ( , ) previously documented negative relation. We provide intuition and empirical evidence that our results are attributable to the resolution of differential prior precision among increasingly diverse investors following large firms.

This study examines the association between firms’ intangible assets and properties of the information contained in analysts’ earnings forecasts. We hypothesize that analysts will supplement firms’ financial information by placing greater relative emphasis on their own private (or idiosyncratic) information when deriving their earnings forecasts for firms with significant intangible assets. Our evidence is consistent with this hypothesis. We find that the consensus in analysts’ forecasts, measured as the correlation in analysts’ forecast errors, is negatively associated with a firm’s level of intangible assets. This result is robust to controlling for analyst uncertainty about a firm’s future earnings, which we also find to be higher for firms with high levels of internally generated (and expensed) intangibles. Given that analyst uncertainty increases and analyst consensus decreases with the level of a firm’s intangible assets, we also expect and find that the degree to which the mean forecast aggregates private information and is more accurate than an individual analyst’s forecast increases with a firm’s intangible assets. Finally, additional analysis reveals that lower levels of analyst consensus are associated with high‐technology manufacturing companies, and that this association is explained by the relatively high R&D expenditures made by these firms. Overall, our results are consistent with financial analysts augmenting the financial reporting systems of firms with higher levels of intangible assets (in terms of contributing to more accurate earnings expectations), particularly R&D‐driven high‐tech manufacturers.

Large earnings surprises and negative earnings surprises represent more egregious errors in analysts' earnings forecasts. We find evidence consistent with our expectation that egregious forecast errors motivate analysts to work harder to develop or acquire relatively more private information in an effort to avoid future forecasting failures. Specifically, we find that after large or negative earnings surprises there is a greater reduction in the error in individual analysts' forecasts of future earnings, and these individual forecasts are based more heavily on individual analysts' private information. This increased reliance on private information reduces the error in the mean forecast of upcoming earnings (even after controlling for the effect of reduced error in individual forecasts). As reliance on private information increases, more of each individual forecast error is idiosyncratic, and thus averaged out in the computation of the mean forecast.

Holthausen and Verrecchia's (1990) and Kim and Verrecchia's (1997) theoretical models predict that private information inferred at the time of an earnings announcement (private event-period information) is associated with greater trading volume. We provide empirical evidence consistent with these theories. Specifically, announcements that increase analysts' private information (as measured by Barron et al.'s [1998] empirical proxies) are associated with increased trading volume, consistent with some investors similarly acquiring private event-period information. In addition, announcements that decrease analysts' consensus are associated with more trading volume. Because consensus declines when private information increases, this finding provides reinforcing evidence that investors trade following earnings announcements because of private information that becomes useful only in conjunction with the information in the announcement and that this information is important enough to spur trading.

This paper presents a model that relates properties of the analysts' information environment of the properties of their forecasts. First, we express forecast dispersion and error in the mean forecast in terms of analyst uncertainty and consensus (that is, the degree to which analysts share a common belief). Second, were reserve the relations to show how uncertainability and consensus cab be measured by combining forecast dispersion, error in the mean forecast, and the number of forecasts. Third, we show that the quality of common and private information available to analysts can be measured using these same observable variables. The relations we present are intuitive and easily applied in empirical studies.