Mutual Fund Performance Evaluation And Best Clienteles

Stéphane Chretien

Université Laval

Manel Kammoun

Université du Quebec en Outaouais (UQO)

November 11, 2015

Journal of Financial and Quantitative Analysis (JFQA), Forthcoming


This paper investigates investor disagreement and clientele effects in performance evaluation by developing a measure that considers the best potential clienteles of mutual funds. In an incomplete market under law-of-one-price and no-good-deal conditions, we obtain an upper bound on admissible performance measures that identifies the most favorable alpha. Empirically, we find that a reasonable investor disagreement leads to generally positive performance for the best clienteles. Performance disagreement by investors can be significant enough to change the average evaluation of mutual funds from negative to positive, depending on the clienteles.

Mutual Fund Performance Evaluation And Best Clienteles – Introduction

In today’s mutual fund industry, there are thousands of funds that cater to different investors through their management style and other attributes. In incomplete markets, as investors can disagree about the attractiveness of funds, this catering might be worthwhile in leading the funds to find appropriate clienteles, i.e., the class of investors to whom they are the most valuable.

Recent research examines the effect of investor disagreement and heterogeneity on mutual fund performance evaluation. Studying the issue generally, Ferson and Lin (2014) find that taking into account heterogeneous preferences can lead to large valuation disagreement. In particular, they develop a bound on expected disagreement with a traditional alpha and show that such disagreement can be similar in importance to the widely documented effects of the benchmark choice problem and the statistical imprecision in estimates of alpha. They furthermore provide evidence that investor disagreement and heterogeneity are economically significant in the behavior of fund investors.

Many studies concentrate more specifically on identifying specific clienteles. Glode (2011) argues that mutual funds could be valuable to investors with high marginal utilities in difficult times by providing positive alphas in recessions. Bailey, Kumar, and Ng (2011) document that behavioral biases are factors of investor heterogeneity in the mutual fund industry. Del Guercio and Reuter (2014) find that the retail mutual fund market is formed from two broad clienteles that value funds differently: self-directed investors and investors acting with the help of brokers. In a literature review, Ferson (2010) emphasizes that measuring performance from the point of view of different clienteles is a challenge for future research.

Despite these contributions, the literature has not focused on the valuation that can be the most important for mutual funds, the one from their best potential clienteles. The goal of this paper is to provide additional evidence on investor disagreement and clientele effects in performance evaluation by developing and implementing a measure that considers the best potential clienteles of mutual funds. For our purpose, these clienteles are defined as the class of investors most favorable to a fund in the sense that they value the fund at an upper performance bound in a setup where the market is incomplete. Our “best clientele performance measure” thus not only considers investor disagreement but also focuses on the most worthy clienteles that a mutual fund could target.

We develop this new measure by combining the asset pricing bound literature with the stochastic discount factor (SDF) performance evaluation approach first proposed by Glosten and Jagannathan (1994) and Chen and Knez (1996). Cochrane and Saá-Requejo (2000) propose asset pricing bounds in an incomplete market under the law-of-one-price condition of Hansen and Jagannathan (1991) and a no-good-deal condition that rules out investment opportunities with unreasonably high Sharpe ratios, termed “good deals.” We obtain the best clientele alpha by adapting this approach to performance measurement and focusing on the upper bound.

Using Hansen’s (1982) generalized method of moments (GMM), we estimate the best clientele SDF alphas with monthly returns of 2,786 actively managed U.S. open-ended equity mutual funds from January 1984 to December 2012. Our main results rely on a set of passive portfolios based on ten industry portfolios, although they are similar for style portfolios or the market portfolio. Following the literature, we consider the disagreement generated by allowing a maximum Sharpe ratio (specifying the no-good-deal restriction) equal to the best Sharpe ratio from the passive portfolios plus either half the Sharpe ratio of the market index or its full value.

Empirically, we find that considering investor disagreement and focusing on the best potential clienteles lead to a generally positive performance for mutual funds. For example, with a disagreement that corresponds to an increase in admissible opportunities equivalent to half the market Sharpe ratio, the mean monthly best clientele alpha is equal to 0.236% (t-stat. = 3.35).

Comparatively, the mean alpha when disagreement is ruled out is ?0.179% (t-stat. = ?3.14), a value similar to the findings from standard measures based on representative investors. The spread of 0.415% between these values is comparable to the magnitude of the bound on the average disagreement documented by Ferson and Lin (2014). Accordingly, the proportions of positive and significantly positive alpha estimates increase from 20% to 78% and from 1% to 24%, respectively, when allowing for disagreement. To account for false discoveries, we implement the technique of Barras, Scaillet, and Wermers (2010) and its extension proposed by Ferson and Chen (2015). For the best clienteles, we find that the proportions of skilled funds increase considerably, and the proportions of unskilled funds disappear.

Mutual Fund Performance Evaluation

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