Abstract
The average level and cross-sectional variability of fund alphas are estimated from a large sample of mutual funds. This information is incorporated, along with the usual regression estimate of alpha, in a (roughly) precision-weighted average measure of individual fund performance. Substantial “learning across funds” is documented, with significant effects on investment decisions. In a Bayesian framework, this form of learning is inconsistent with the assumption, made in the past literature, of prior independence across funds. Independence can be viewed as an extreme scenario in which the true cross-sectional distribution of alphas is presumed to be known a priori.
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