Text-Based Mutual Fund Peer Groups

69 Pages Posted: 24 May 2021

See all articles by Simona Abis

Simona Abis

Columbia University - Columbia Business School

Anton Lines

Columbia Business School

Date Written: May 12, 2020

Abstract

The proliferation of mutual fund strategies is a longstanding puzzle in the asset management literature. To gain new insight into this topic, we introduce a method for categorizing funds based on the strategy descriptions in their prospectuses. The resulting Strategy Peer Groups (SPGs), constructed using unsupervised machine learning, capture novel information about the funds and are more detailed than existing style categories. Where the prior literature finds that more unique funds experience greater flows, we find instead that investors prefer funds whose portfolio weights and characteristics are closer to the SPG averages. Investors also favor funds with high SPG-adjusted returns, while different investor clienteles—represented by retail, institutional, and retirement share classes—differ in their allocations across peer groups. Our results are consistent with a mutual fund industry that caters to distinct investor clienteles with heterogeneous marginal rates of substitution, rather than investors with a general preference for variety.

Keywords: Fund Flows, Institutional Demand, Textual Analysis, Machine Learning

JEL Classification: G11, G23, L10

Suggested Citation

Abis, Simona and Lines, Anton, Text-Based Mutual Fund Peer Groups (May 12, 2020). Available at SSRN: https://ssrn.com/abstract=3851861 or http://dx.doi.org/10.2139/ssrn.3851861

Simona Abis

Columbia University - Columbia Business School ( email )

3022 Broadway
New York, NY 10027
United States

HOME PAGE: http://https://www8.gsb.columbia.edu/cbs-directory/detail/sa3518

Anton Lines (Contact Author)

Columbia Business School ( email )

3022 Broadway
New York, NY 10027
United States

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