Investor learning and mutual fund family

Zhang, Z.; Ding, L.; Zhou, S.

Abstract

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.

Citation

Zhang, Z., Ding, L., & Zhou, S. (2014). Investor learning and mutual fund family. Journal of Empirical Finance, 26, 171-188. https://doi.org/10.1016/j.jempfin.2013.12.001

• Journal Article Type: Article
• Acceptance Date: Dec 4, 2013
• Online Publication Date: Dec 13, 2013
• Publication Date: Mar 1, 2014
• Deposit Date: Dec 10, 2013
• Publicly Available Date: Apr 24, 2014
• Journal: Journal of Empirical Finance
• Print ISSN: 0927-5398
• Electronic ISSN: 1879-1727
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 26
• Pages: 171-188
• DOI: https://doi.org/10.1016/j.jempfin.2013.12.001
• Keywords: Mutual fund, Performance, Bayesian analysis, Hierarchical model.
• Public URL: https://durham-repository.worktribe.com/output/1446163