Abstract
Bayesian learning implies decreasing weights on prior beliefs and increasing weights on the accuracy of the analyst's past forecast record, as the number of forecast errors comprising her forecast record (its length) increases. Consistent with this model of investor learning, empirical tests show that investors’ reactions to forecast news are increasing in the product of the accuracy and length of analysts’ forecast records. Moreover, the Bayesian learning predicted by our model is more descriptive of investor reactions than is a static model which predicts that investors’ responses condition only on the prior accuracy of the analyst.
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