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- W2913665786 abstract "In this study, we examine the relative accuracy of financial analysts' and adaptive time-series earnings forecasts made at the beginning of a fiscal year. We consider IBES consensus forecasts and employ a novel forecasting approach: artificial neural networks. The central question is whether financial analysts efficiently utilize available information and produce forecasts that are more accurate than predictions of statistical models. We identify models that obtain a better forecast accuracy for the greater number of companies and answer the question of which models have a better ability to recognize the sign of future changes in earnings. We find that the relative accuracy of financial analysts' earnings forecasts decreases with the difficulty of the forecasting task as measured by the variation of the change in earnings and the forecast horizon. The evidence presented contributes to the understanding of the formation and value of analysts' predictions." @default.
- W2913665786 created "2019-02-21" @default.
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- W2913665786 date "2006-07-01" @default.
- W2913665786 modified "2023-09-26" @default.
- W2913665786 title "Does Adaptive EPS Forecasting Make Analysts' Forecasts Redundant?" @default.
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