Abstract
We constructed forecasts of earnings forecasts using data on 406 firms and forecasts made by 5419 individuals with on average 25 forecasts per individual. We verified previously found predictors, which are the average of the most recent available forecast for each forecaster and the difference between the average and the forecast that this forecaster previously made. We extended the knowledge base by analyzing the unpredictable component of the earnings forecast. We found that for some forecasters the unpredictable component can be used to improve upon the predictable forecast, but we also found that this property is not persistent over time. Hence, a user of the forecasts cannot trust that the forecaster will remain to be of forecasting value. We found that, in general, the larger is the unpredictable component, the larger is the forecast error, while small unpredictable components can lead to gains in forecast accuracy. Based on our results, we formulate the following practical guidelines for investors: (i) for earnings analysts themselves, it seems to be the safest to not make large adjustments to the predictable forecast, unless one is very confident about the additional information; and (ii) for users of earnings forecasts, it seems best to only use those forecasts that do not differ much from their predicted values.
Highlights
Earnings forecasts can provide useful information for investors
A key research subject concerns the drivers of the forecasts of earnings analysts. Such knowledge is relevant as the part that can be predicted from factors that are observable to the end user of the forecast might not be the most interesting part of an earnings forecast
We focused on the within-year yearly earnings forecasts, that is, the forecasts that are produced to forecast the earnings of the current year
Summary
Earnings forecasts can provide useful information for investors. When investors in part rely on such forecasts, it is important to have more insights into how such earnings forecasts are created. A key research subject concerns the drivers of the forecasts of earnings analysts Such knowledge is relevant as the part that can be predicted from factors that are observable to the end user of the forecast might not be the most interesting part of an earnings forecast. It is the unpredictable component of the earnings forecast that amounts to the forecaster’s true added value, based on latent expertise and domain-specific knowledge. In this paperm we answer these questions using appropriate models We applied these models to the earnings forecasts for a large number of firms which constitute the S&P500.
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