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Investor learning and mutual fund family

Investor learning and mutual fund family
Investor learning and mutual fund family
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.
0927-5398
171-188
Zhang, Zhichao
7a646262-463c-4035-b69b-beb5586b3209
Ding, Li
6b3515da-a5c4-4070-9730-265d1e4c4a0d
Zhou, Si
4d888ef9-73bb-4fde-969e-5003c5c0b146
Zhang, Zhichao
7a646262-463c-4035-b69b-beb5586b3209
Ding, Li
6b3515da-a5c4-4070-9730-265d1e4c4a0d
Zhou, Si
4d888ef9-73bb-4fde-969e-5003c5c0b146

Zhang, Zhichao, Ding, Li and Zhou, Si (2014) Investor learning and mutual fund family. Journal of Empirical Finance, 26, 171-188. (doi:10.1016/j.jempfin.2013.12.001).

Record type: Article

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.

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More information

Accepted/In Press date: 4 December 2013
e-pub ahead of print date: 13 December 2013
Published date: March 2014
Organisations: Centre for Digital, Interactive & Data Driven Marketing

Identifiers

Local EPrints ID: 368501
URI: http://eprints.soton.ac.uk/id/eprint/368501
ISSN: 0927-5398
PURE UUID: 19d52ab4-e2ce-43ae-936d-e35c63e204d8
ORCID for Si Zhou: ORCID iD orcid.org/0000-0002-8040-8756

Catalogue record

Date deposited: 02 Sep 2014 16:34
Last modified: 15 Mar 2024 03:51

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Contributors

Author: Zhichao Zhang
Author: Li Ding
Author: Si Zhou ORCID iD

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