Investor learning and mutual fund family

• The fund performance evaluation is modeled as a cross-fund learning process.
• Investors conduct learning from the returns of other funds within the fund family.
• The fund performance is attributed to both the fund family and the fund manager.
• The model provides full Bayesian treatment on each of the pricing factors.
• The model presents higher level of shrinkage on cross-fund variance.

In this paper we revisit the cross-fund learning method suggested by Jones and Shanken (2005) and construct a linear hierarchical model to consider the learning across funds within the fund family during the performance evaluation. We provide a full Bayesian treatment on all the factors of the pricing model and allow both the fund family and the individual manager to have dependent prior information regarding funds' alphas. The simulation results suggest that returns from peer funds within the family significantly affect investors' updating on fund alphas since the posterior distribution on fund alphas experiences a faster shrinkage than those reported in the previous literature. The model can also be simulated with specific prior belief on different factors of the pricing model, i.e. fund alphas, betas and factor loadings of each pricing benchmark, to better address the learning issue.