Individual-Analyst Characteristics and Forecast Error

The purpose of the study reported here was to investigate how characteristics of analysts affect their forecast errors. Previous research has found positive serial correlation in forecast errors, which can be attributed to underreaction to new information, especially to bad news. The relationship between an analyst's behavior and that analyst's characteristics is not clear, however, because most previous work was based solely on consensus estimates. By using detailed historical data, I found a stronger serial correlation among the herd-to-consensus analysts (that is, the group with a small average distance between their forecasts and the consensus forecast) than among other analysts. Moreover, average distance to consensus itself has a positive serial correlation, and it may be attributed to an analyst's personality (optimistic or pessimistic). I found strong positive serial correlation in the average distance to consensus among the herd-to-consensus analysts. These results show that herd-to-consensus analysts submit earnings estimates that are not only close to the consensus but are also strongly affected by their personalities. The purpose of the study reported in this article was to investigate how an analyst's characteristics affect the analyst's forecast error. Previous literature found positive serial correlation in forecast errors that can be attributed to underreaction to new information, especially to bad news. What is not clear, because most of the papers are based solely on consensus estimates, is the relationship between an individual analyst's behavior and the analyst's personal characteristics. In contrast to previous research, this study focused on each analyst's forecast error (calculated by averaging the errors for each analyst). I investigated the analyst's relative optimism and likely other influential factors, such as previous track record and reputation with customers.First, I reexamined the issue of serial correlation of forecast errors by using each analyst's forecast error. I found that the correlation coefficient is larger for the groups of analysts who had past forecasts that were relatively optimistic and that the average distance to the consensus forecast is small. These results suggest that analysts who are negatively shocked by actual earnings or who “herd” toward the consensus forecast tend to make forecast errors that are positively correlated with previous forecast errors.Next, I investigated serial correlation in the average distance of an analyst's forecast to the consensus forecast—that is, the analyst's relative optimism. I found that the average distance to consensus itself has positive serial correlation that may be attributed to the analyst's personality (optimistic or pessimistic). Furthermore, I found stronger positive serial correlation for the average distance to consensus among the herd-to-consensus analysts. These results indicate that herd-to-consensus analysts report earnings estimates that are not only near the consensus but also are strongly affected by personal characteristics.