Using Analysts’ Forecasts to Measure Properties of Analysts’ Information Environment

This paper presents a model that relates properties of the analysts\u27 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

Analyst Pessimism and Forecast Timing

In this study, we show that on average relatively pessimistic analysts tend to reveal their earnings forecasts later than other analysts. Further, we find this forecast timing effect explains a substantial proportion of the well-known decrease in consensus analyst forecast optimism over the forecast period prior to earnings announcements, which helps explain why analysts’ longer term earnings forecasts are more optimistically biased than their shorter term forecasts. We extend McNichols and O’Brien’s (1997) and Hayes’ (1998) theory concerning analyst self-selection to argue that analysts with a relatively pessimistic view - compared to other analysts - are more reluctant to issue their earnings forecasts, with the result that they tend to defer revealing their earnings forecasts until later in the forecasting period than other analysts

MD&A quality as measured by the SEC and analysts' earnings forecasts

This study examines the predictive value of management discussion and analysis (MD&A) information. More specifically, it tests the association between properties of analysts' earnings forecasts and MD&A quality, where MD&A quality is measured by the SEC. It is found that high MD&A ratings are associated with less error and less dispersion in analysts' earnings forecasts after controlling for many other expected influences on analysts' forecasts. It is also found that estimated regression coefficients are consistent with MD&A information having a substantial effect on earnings forecasts