Are specialist funds “special”?

• Published date: 01 June 2019
• Author: Daniel Fricke
• Date: 01 June 2019
• DOI: http://doi.org/10.1111/fima.12257

DOI: 10.1111/fima.12257

ORIGINAL ARTICLE

Are specialist funds “special”?

Daniel Fricke1,2,3,4

1DeutscheBundesbank, Directorate General

FinancialStability, Frankfurt/Main, Germany

2FinancialComputing & Analytics, University

CollegeLondon, London, United Kingdom

3LondonSchool of Economics, Systemic Risk

Centre,London, United Kingdom

4CabDyNComplexity Centre, Saïd Business

School,Oxford University, Oxford, United King-

dom

Correspondence

DanielFricke, Wilhelm-Epstein Str.14, 60431

Frankfurt/Main,Germany.

Email:daniel.fricke@bundesbank.de

Disclaimer:The views expressed in this paper

representthe author's personal opinions and do

notnecessarily reflect the views of the Deutsche

Bundesbankor its staff.

Abstract

In this paper, I explore the relation between portfolio overlap and

performance diversity. Using data on actively managed U.S. equity

mutual funds, I find that the pairwise portfolio overlap between indi-

vidual funds has increased over time and is significant compared to

various randomized benchmarks. These findings motivate the main

question of this paper, namely whether specialist funds (those with

low levels of portfolio overlap with other funds) differ significantly

from funds with high levels of overlap.Here, I find that these special-

ists differ with regard to certain portfolio- and fund-specific charac-

teristics, but they do not appear to outperform other funds.

1INTRODUCTION

Agrowing literature highlights the importance of overlapping portfolios on market dynamics and financial stability (e.g.,

Greenwood, Landier,& Thesmar, 2015; Wagner,2011). In this paper, I take a different perspective on portfolio overlap

by exploring its relation with performance diversity.The basic idea is simple: if two investors hold similar portfolios

(i.e., high levelsof portfolio overlap), their performances should be indistinguishable. Hence, depending on the levels of

portfolio overlap in a givensystem, it may be difficult to detect investors with superior performances.

I investigate this relation for the set of open-ended, actively managed U.S. domestic equity (DE) mutual funds. The

main motivation is illustratedin Figure 1, which shows the “typical” structure of the so-called holdings matrix at a certain

pointin time (here for March 2003). Put simply, the figure shows whether a given mutual fund (rows) holds a given stock

(columns) in its portfolio: this is indicated as a black dot. Given the focus on actively managed funds, I have dropped

index funds and funds with more than 300 stock holdings (closet indexers).The rows/columns in the figure are sorted

according to their number of connections. This reordering shows a “triangular” matrix structure: some mutual funds

hold many stocks intheir portfolios (funds c loserto the top of the figure), whereas others focus on only very few stocks

(closer to the bottom of the figure).1The same is true from the other side: some stocks are held by practicallyall funds

(closer to the left of the figure),and others are held by only a much smaller subset (closer to the right of the figure). The

most interesting feature is that funds with very few connections tend to hold those stocks that are held by all other

funds as well—otherwise there would be more connections in the bottom right part of the figure—which highlights

the portfolio overlap among these mutual funds. (I will introduce concrete measures of portfolio overlap below.)This

c

2018 Financial Management Association International

Financial Management. 2019;48:441–472. wileyonlinelibrary.com/journal/fima 441

442 FRICKE

FIGURE 1 Binary holdings matrix in March 2003. Rows correspond to actively managed domestic equity mutual

funds with at least 3 and at most 300 stock holdings (left y-axis), and columns to stocks. A link between a fund and a

stock exists if the fund holds that particular stock: this is shown as a black dot. Rows and columns are sorted according

to the number of connections. The red line shows the cumulative share of funds'holdings in these stocks relative to

their total holdings (right y-axis) [Color figure can be viewed at wileyonlinelibrary.com]

finding is remarkable given that myfocus is on actively managed funds here, which supposedly aim to outperform both

the market and other actively managed funds. The red line shows the cumulative portfolio share of the stocks shown

on the x-axis relative to the total holdings of the funds in the sample. In line with theabove reasoning, the economically

meaningful investmentsare concentrated on a relatively small number of stocks: for the set of funds shown in Figure 1,

80% (90%) of the total holdings are concentrated in only 358 (623) stocks.2

Takentogether, several questions arise from Figure 1: Is the portfolio overlap between mutual funds significant?

Does portfolio overlap increase or decrease over time? What is the relation between portfolio overlap and perfor-

mance diversity? I tackle these questions in this paper.

Based on data for the period March 2003–December 2014, I quantify the pairwise portfolio overlap between

activelymanaged mutual funds (mainly based on the cosine similarity measure using portfolio weights) for each month.

My main findings are as follows: First, the typical value of the pairwise overlap measure increases over time, such

that funds'portfolios have become more similar. This finding is remarkable given that actively managed funds have

been reported to be particularly affected by increasing levels of competition (e.g., due to the rise of passive invest-

ment strategies (Malkiel, 2013), which should have a negative impact on funds'portfolio overlapbecause (increased)

competition may provide incentives to construct innovativeinvestment portfolios (e.g., Aghion, Bloom, Blundell, Grif-

fith, & Howitt, 2005; Sun, Wang, & Zheng, 2012). This, however,appears not to be the case for the set of mutual funds

under study in this paper. Second, I assess the significance of the observed portfolio overlapby evaluating the hypo-

thetical overlap that would arise if I disregarded funds'preferences for certain stocks (but fixedseveral basic portfolio

characteristics, including the observed distribution of portfolio weights). In all cases, I find that the observedportfolio

overlap significantly exceeds these hypothetical benchmark values, indicating that the observedportfolio overlap is

significant relative to these benchmarks. Third, I shed light on the relation between portfolio overlapand performance

diversity. In line with the factor structure of portfolio returns, I find that evenmodest levels of portfolio overlap can

imply substantial return correlations—that is, low levels of performance diversity.Last, I explore this finding in more

detail by identifying specialist funds (those with low levelsof portfolio overlap with all other funds) and testing whether

these differ significantly from funds with high levels of overlap.Here, I find that specialists differ with regard to certain

portfolio- and fund-specific characteristics (e.g., in terms of their total net assets [TNAs]) but they do not appear to

outperform other funds. Thus, specialists are not that special.

My paper is related to three streams of literature. First, I already mentioned the growing literature on portfolio

overlap and market stability.3In essence, much of this literaturepredicts that higher levels of portfolio overlap can

destabilize the system as a whole, but so far,little is known empirically about realistic levels of portfolio overlap. I quan-

tify the portfolio overlap among a specific set of mutual funds, and the methodology can be generally applied to other

kinds of financial institutions as well.

FRICKE 443

Second, the paper adds to the empirical literature on investor performance. The broad consensus is that detect-

ing investors with superior performance is difficult and many actively managed mutual funds even generate returns

(after fees and expenses) that are significantly lower than those of passiveindex-based strategies (e.g., Barras, Scaillet,

& Wermers, 2010; Berk & Green, 2004; Carhart, 1997; Cremers & Petajisto, 2009; Grinblatt & Titman, 1989; Jensen,

1968; Kacperczyk, Sialm, & Zheng, 2005, 2007). These observations have been linked with the rise of passive invest-

ment strategies over the last decade, which puts additional pressure on active fund managers to come up with novel

strategies to outperform the market.From this perspective, my findings are important in at least two ways: on the one

hand, I find no evidence of an increase in the level of diversity of actively managed funds'asset portfolios. In other

words, despite the increased competition within the mutual fund sector,there is no apparent tendency for innovation

in funds'investment strategies.On the other hand, the fact that even those funds with the most “special” portfolios do

not significantly outperform other funds implies that low levels of performance diversity can be in line with relatively

low levels of portfolio overlap. By purely looking at the cross-section of investment portfolios, policy makers,regula-

tors, and investors mayget a false sense of (portfolio) diversity.

Last, mypaper is related to the literature on herding among professional investors.4Conceptually, herding and port-

folio overlap are strongly related (as illustrated,among others, by Coval & Stafford, 2007): on the one hand, herding is

concernedwith correlated trading in and out of the same assets (flow perspective). On the other hand, portfolio overlap

results from the herding dynamics of investors (stock perspective).To the best of my knowledge, most of the literature

has focused on the flow perspective, whereas I am explicitlyinterested in the stock perspective.

The remainder is structured as follows: Section 2 reviews the relevant literature. Section 3 defines measures of

portfolio overlap. Section 4 introduces the data set and contains the empirical analyses. Section 5 concludes.

2LITERATURE REVIEW

Let me briefly review the relevant literature that sheds light on the equilibrium set of portfolios shown in the cross-

section of Figure 1. Which assets an investor wants to hold (security selection) remains the key aspect of investing.

Given estimates of expectedreturns and covariances, modern portfolio theory produces the optimal portfolio weights

for the relevant assets of interest. In the absence of heterogeneity and/or frictions, the capital asset pricing model

(CAPM) suggests that in equilibrium the only portfolio that will be held is the market portfolio. In other words, in a

CAPM world, all investorswill hold exactly the same diversified portfolio and thus portfolio overlap should be maximal.

Existing empirical evidence (including Figure 1), however,suggests that many investors deviate from these theoretical

predictions and tend to hold much more concentratedportfolios. Several theoretical contributions show why investors

might indeed rationally choose different diversification levelsin the presence of frictions/heterogeneity.5

Taking as given that investors might differ in their diversification levels does not tell us which securities they

will actually select. In a similar fashion, the literature on (institutional) herding establishes different reasons for why

investors might rationally prefer highly correlated performances with others (e.g., information cascades (Banerjee,

1992), correlated information (Shleifer & Summers, 1990), and incentive constraints. For example,it has been shown

that when managers'remuneration depends on their performance relative to their peers, they will rationally choose

highly correlated performances (e.g., Chevalier & Ellison, 1999; Rajan, 2005; Scharfstein & Stein, 1990). Hence, return

correlations should be high if such career concerns are relevant for managers competing in the same job market—

something that seems reasonable for the set of professional mutual fund managers.

Inmost of the above literature, the representative investor's optimization problem is typically absent of price effects

dueto asset liquidations. When a large fund faces unexpected redemptions, it will have to liquidate some of its assets on

the market(Coval & Stafford, 2007). In an otherwise standard portfolio optimization framework, Caccioli, Still, Marsili,

and Kondor (2013) allow investors to takeinto account their own impact on asset prices (in case of liquidation). Thus,

investors'trade-offasset's risk-adjusted return and its illiquidity against each other and, ceteris paribus, by taking mar-

ket impact into account will reduce the exposure to illiquid assets.6Similarly,Lo, Petrov, and Wierzbicki (2003) con-

struct a three-dimensional mean-variance-liquidity frontier, showing that portfolios close to each other on the mean-

variance frontier can differ dramatically in terms of their liquidity.