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- W3134710273 abstract "We constructed forecasts of earnings forecasts using data on 406 firms and forecasts madeby 5419 individuals with on average 25 forecasts per individual. We verified previously foundpredictors, which are the average of the most recent available forecast for each forecaster and thedifference between the average and the forecast that this forecaster previously made. We extendedthe knowledge base by analyzing the unpredictable component of the earnings forecast.We foundthat for some forecasters the unpredictable component can be used to improve upon the predictableforecast, but we also found that this property is not persistent over time. Hence, a user of the forecastscannot 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 unpredictablecomponents can lead to gains in forecast accuracy. Based on our results, we formulate the followingpractical guidelines for investors: (i) for earnings analysts themselves, it seems to be the safest to notmake large adjustments to the predictable forecast, unless one is very confident about the additionalinformation; and (ii) for users of earnings forecasts, it seems best to only use those forecasts that donot differ much from their predicted values" @default.
- W3134710273 created "2021-03-15" @default.
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- W3134710273 date "2018-07-01" @default.
- W3134710273 modified "2023-09-27" @default.
- W3134710273 title "How Informative are Earnings Forecasts" @default.
- W3134710273 hasPublicationYear "2018" @default.
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