A New Framework for Analyzing Market Share Dynamics among Fund Families

Abstract

A simple framework decomposes changes in a fund family’s market share into four components. The components are highly relevant for understanding mutual fund market dynamics and evaluating the business performance of fund families. Two components are performance driven, and two are flow driven. Analysis of US market data shows that the “Excess Flows” component, which measures whether fund family flows exceed or lag those of competitors that operate in the same fund categories, has the biggest impact on fund-family market share changes. Major cross-sectional differences characterize how individual families score on each of the components. Fund families can use this framework to provide input for strategic decision making.

Disclosure: The author reports no conflicts of interest.

Editor’s Note

This article was externally reviewed using our double-blind peer-review process. When the article was accepted for publication, the author thanked the reviewers in the acknowledgments for their comments and suggestions. Jian ZHANG and one anonymous reviewer were the reviewers for this article.

Submitted 31 January 2019

Accepted 9 March 2020 by Stephen J. Brown

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Acknowledgments

I would like to thank Executive Editor Stephen Brown, Managing Editor Heidi Raubenheimer, CFA, and Co-Editor Daniel Giamouridis. I am also grateful to Guido Baltussen, Peter Battiste, CFA, Stan Beckers, Ivo Frielink, Fabian Irek, Bart Renner, Noemi Rossini, Willem van der Scheer, CFA, and Marno Verbeek for valuable discussions and assistance.

Notes

¹ See, for example, the Investment Company Institute reports on “Worldwide Regulated Open-End Fund Assets and Flows” (found at https://www.ici.org/research/stats) and the European Fund and Asset Management Association’s quarterly international statistical releases for assets and net flows by region, country, and asset class (https://www.efama.org/statistics/SitePages/International%20Quarterly%20Statistical%20Release.aspx).

² See, for example, Morningstar’s Fund Flows Commentary (https://www.morningstar.com/lp/fund-flows-direct) and Broadridge’s Fund Radar (on the Broadridge Distribution Insight Platform at https://distributioninsight.broadridge.com/).

³ Spiegel and Zhang (2013) indicated that ΔMS can be calculated either by using only funds that were in existence at the start of the period or by using all funds that were in existence at any moment in the period. In this article, I use the second approach.

⁴ When a fund is launched in period t + 1, $TN{A}_{i,t}=0$, which would result in dividing by zero.

⁵ In this article, the term “category” is used.

⁶ In a fund’s first month of existence, the Excess Flows component cannot be expressed in terms of a relative flows differential. Instead, dollar flows versus the market share–equivalent part of the dollar flow of the category must be used (see Appendix B).

⁷ VIX data were obtained from Yahoo Finance.

⁸ Data for aggregate net exchanges were kindly provided by the Investment Company Institute.

⁹ First-quartile fund performance is defined as a fund-family Fund rank greater than 0.75.

¹⁰ This amount is the difference in rank of 0.375 between a top-quartile family (median rank of 0.875) and a median family (rank of 0.500) times the Fund rank coefficient in the EFC model (0.090) times 12 (to annualize), which gives the 0.40 bp of market share gain per year. Performing the regression on annual market share changes produced a similar result. In that case, the coefficient was 0.95, which gives an annual market change impact of 0.36 bp when multiplied by 0.375.

¹¹ Market statistics are based on the sample funds.