The Party Structure of Mutual Funds∗
Ryan Bubb New York University
Emiliano Catan New York University
ABSTRACT. We show that a parsimonious spatial model with two dimensions can explain the bulkof mutual fund voting. We estimate the model using a comprehensive dataset of the 5,332,353 votescast on 33,262 proposals from 3,844 portfolio companies from 2010 to 2015 by 3,617 mutual fundsthat in total hold almost 80% of mutual fund industry assets. The two dimensions of funds’ cor-porate governance preferences reflect the role of the two leading proxy advisors. Mutual funds areclustered into three ‘parties’—the Managerialist Party, the Shareholder Intervention Party, and theShareholder Veto Party—that follow distinctive philosophies of corporate governance and share-holders’ role. We use our methodology in turn to construct measures of the individual distributionsof preferences of public companies’ shareholder bases. Our preference measures for mutual fundsand for public companies’ shareholder bases generate a range of insights about the broader systemof corporate governance.
∗We are grateful to Andrew Eggers, Marcel Kahan, Ed Rock, and seminar participants at Oxford University andNew York University for helpful comments and discussions. We thank Nicolas Duque-Franco and Stephanie Thomasfor very able research assistance. Bubb: [email protected]. Catan: [email protected]. First draft: Feb. 14, 2018.This draft: February 16, 2018.
1. INTRODUCTION
To understand corporate governance in the United States, one must understand the voting behavior
of mutual funds. Mutual funds have grown to hold about one-third of publicly traded stock and
are subject to legal duties to vote that stock in the interest of the funds’ investors. Recent years
have also seen a shift in the asset management industry away from actively managed funds to
index funds, as well as increasing concentration of assets in the largest mutual fund families. The
resulting changes in the ownership of public companies have raised concerns about its effects on
corporate governance. Azar, Schmalz, and Tecu (2017), for example, provides evidence that the
growth in common ownership by mutual funds of multiple competitors has had anticompetitive
effects.
In tandem with the growth of mutual funds as corporate shareholders, corporate law and prac-
tice have evolved to elevate the role of the shareholder franchise. Shareholder votes today play an
important role in setting issuer-level corporate governance policies, including through the use of
shareholder proposals to spur governance reforms, and have become an important tool used by in-
stitutional investors to discipline corporate management. Recent legal changes that have enhanced
the role of voting include the Dodd-Frank Act’s requirement that public companies hold regular
advisory votes on executive compensation and the widespread move to majority voting rules for
director elections. Finally, the rise of activist investor campaigns that culminate in a shareholder
vote have made votes by mutual funds increasingly pivotal.
But despite these trends that have made mutual funds central players in corporate governance,
we know relatively little about their behavior as company owners. In this paper we develop the
first systematic account of the structure of mutual fund preferences over corporate governance as
expressed by the proxy votes they cast in their portfolio companies. We do so by analyzing a large
dataset of 5,332,353 votes cast by 3,617 mutual funds from 311 fund families on 33,262 proposals
from 3,844 portfolio companies over 2010 - 2015. The dataset includes the votes by mutual funds
that in aggregate account for almost 80% of assets held by mutual funds. That is, the sample we
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analyze is close to constituting the population of votes cast by mutual funds over the period.
Our first finding is that mutual fund preferences over corporate governance can be well rep-
resented as positions along two latent dimensions. Shareholders vote on a wide range of issues:
Should Sandra Peterson be reelected to the board of Microsoft in 2014? Should Ford declassify
its board in 2010? Should Ron Burkle’s director nominees be placed on Barnes & Noble’s board
instead of reelecting the incumbent directors? Should director elections at Procter & Gamble be
subject to a majority vote rule or a plurality rule? In this sense, the overarching “issue space”
of these preferences is highly multidimensional. But we hypothesize that these preferences and
attitudes can be organized or represented as positions along a small number of latent dimensions.
Echoing studies of the preferences of legislators expressed through votes in Congress (Poole and
Rosenthal, 2007), we show that a parsimonious spatial model with just two dimensions can explain
the bulk of mutual fund voting. Those two main dimensions, in turn, reflect the role of the two
leading proxy advisors. The relatively low dimensionality we estimate reflects linkages between
issues in the high-dimension issue space as part of a “belief system” (Converse, 1964) of mutual
funds and proxy advisors about corporate governance. As in politics, relatively few fundamental
dimensions underlie mutual fund corporate governance preferences.
We show furthermore that mutual fund preferences are not distributed unimodally across this
preference space but rather are clustered into three groups of funds with similar preferences, much
like political parties in similar studies of Congressional voting. In particular, there are three mutual
fund “parties,” which we label (1) the Managerialist Party, (2) the Shareholder Intervention Party,
and (3) the Shareholder Veto Party. Members of the Managerialist Party—which include the “Big
Three” passive managers BlackRock, Vanguard, and State Street—follow corporate management’s
recommendations at much higher rates than members of the other two parties. We refer to the latter
two parties as the “shareholder rights” parties; each follows a distinctive philosophy of corporate
governance and the role of shareholders. The Shareholder Intervention Party supports shareholder
proposals and proxy contests much more often than the Shareholder Veto Party. These votes are
forms of proactive shareholder engagement and entail shareholders attempting to intervene in the
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company’s corporate affairs—hence the name “Shareholder Intervention Party.” In contrast, the
Shareholder Veto Party opposes management proposals at a substantially higher rate than mem-
bers of the Shareholder Intervention Party. These proposals entail corporate management asking
shareholders to ratify some management decision—hence the name “Shareholder Veto Party.” One
might expect shareholders’ willingness to intervene proactively and willingness to veto manage-
ment proposals to be positively related, but we show that across the two shareholder rights parties,
they are negatively related. The two parties thus represent distinctive visions of shareholders’ role
in corporate governance. To our knowledge we are the first to recognize and document these two
competing philosophies that drive institutional shareholders’ voting behavior.
We then investigate the factors that shape mutual funds’ preferences by regressing our prefer-
ence measures on fund characteristics that are likely to influence their incentives. Index funds, and
families that specialize in indexing, are more managerialist than actively managed funds. Even
more interestingly, we also find that funds that follow a growth stock strategy are more manageri-
alist than value funds. This is consistent with the idea that the investment thesis for most growth
stocks includes belief in the company’s current management. We also show that larger funds and
fund families are more managerialist.
Finally, we combine our estimates of the preferences of the mutual funds together with data
about mutual funds’ portfolios to construct, for each portfolio company and every year between
2002 and 2016, a share-weighted measure of the distribution of the preferences of the mutual fund
shareholders of the company as of the relevant year. We provide summary descriptions of those
distributions and characterize their cross-sectional and time-series features. We find that both the
dispersion across companies in the mean preferences of their shareholder bases and the degree
of shareholder rights orientation of companies’ shareholder bases are decreasing in companies’
market capitalization. We also find evidence that growth stock companies (i.e., companies with
lower book-to-market ratios) have systematically more managerialist mutual funds as their share-
holders. Turning to time trends, we show that public companies’ shareholder bases have become
systematically more managerialist over time (especially after 2007) along both of our dimensions
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of shareholder preferences. In work in progress, Bubb and Catan (2018), we extend the method-
ology developed in this paper to characterize the preferences of companies’ broader shareholder
base—including non-mutual fund shareholders—and study more generally the determinants and
consequences of those preferences.
Our main contribution is to systematically measure and characterize the corporate governance
preferences of mutual funds and of the shareholder bases of issuers and to use those measures to
generate new insights about the system of corporate governance more broadly. Importantly, we
start from a statistical model that is devoid of any institutional or other assumptions about the
structure of this preference space—from the perspective of our preference estimation methodol-
ogy, there is nothing unique about ISS, Glass Lewis and management as voters—and we recover
the result that the first and second most significant latent dimensions along which mutual funds’
preferences differ track the corporate governance philosophies of the two main proxy advisors.
Our methodology enables us to construct a parsimonious measure of fund-level preferences and,
in turn, an issuer-level measure of the distributions of preferences of the mutual fund shareholder
bases across public companies.
We hope the “spatial map” of mutual fund corporate governance preferences we provide will
serve as a useful field guide to scholars and practitioners of corporate governance, helping to
reveal important patterns and trends. In addition to descriptive insights, the shareholder preference
measures we introduce to the literature might also enable new quantitative tests of theories and
hypotheses in corporate governance. We consider our results on the determinants of mutual fund
preferences and on the cross-sectional patterns in the preferences of public companies’ shareholder
bases to be an initial proof of concept in that regard.
Our overall findings are broadly consistent with a burgeoning literature on the voting behav-
ior of institutional investors (Matvos and Ostrovsky, 2010; Choi, Fisch, and Kahan, 2013; Appel,
Gormley, and Keim, 2016; Brav, Jiang, and Li, 2017) and the influential role of proxy advisors
(Choi, Fisch, and Kahan, 2010; Ertimur, Ferri, and Oesch, 2013; Iliev and Lowry, 2014; Malenko
and Shen, 2016). In contemporaneous and independent work, Bolton, Li, Ravina, and Rosenthal
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(2018) also estimate a spatial model of voting by institutional investors. Their preference estima-
tion methodology and data are different from ours. In particular, they estimate a 1-dimensional
model using W-NOMINATE, a tool developed in political science to study Congressional voting.
They take the mutual fund family as the “voter” and include “votes” of 211 mutual fund families
in their analysis sample. In contrast, we use 3,617 individual mutual funds (from 311 fund fam-
ilies) as the unit of analysis because, legally, funds and not families vote shares, and in several
major fund families (e.g., Fidelity) there are substantial differences in voting across funds within
the family. They include votes from a single fiscal year, and exclude director election proposals,
yielding only 3,318 proposals in their analysis sample, an order of magnitude fewer than in our
sample. They also do not consider the amount of assets owned by each family when characterizing
the distribution of preferences.
As a combined result of these differences they reach quite different conclusions from ours. In
contrast to our finding of a party structure in mutual fund voting, Bolton, Li, Ravina, and Rosenthal
(2018) emphasize that their distribution of ideal points is close to unimodal, distinguishing it from
the bimodal distribution of preferences in Congress that follows political parties. According to
their 1-dimensional map, Glass-Lewis sits near the mode of the distribution, next to Vanguard and
BlackRock. This is probably due in part to their exclusion of director election proposals, resulting
in their model not picking up some of the characteristic behavior of the Shareholder Veto Party
that distinguishes it from the Managerialist Party.
More fundamentally, Bolton, Li, Ravina, and Rosenthal (2018) interpret their estimated mu-
tual fund preference space in terms of investors’ degree of social orientation versus profit-seeking,
writing: “The left represents relatively socially-oriented investors, while the right represents Man-
agement recommendations and exclusively profit-oriented investors” (p. 16). Our approach results
in a very different understanding of the preference space. We show that two latent dimensions un-
derlie heterogeneity in preferences among the profit-seeking investors that hold the vast majority
of mutual fund assets, and that those two dimensions measure the extent to which funds adopt two
competing modes of shareholder engagement vis-a-vis management.
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Finally, they do not relate their preference measure to fund characteristics or construct measures
of the preferences of the shareholder bases across public companies, as we do. An important
strength of Bolton, Li, Ravina, and Rosenthal (2018) relative to this paper, however, is that they
include data on the votes of 40 public pension funds and place them in the same 1-dimensional
preference space as mutual funds.
The plan of the paper is as follows. In Section 2 we lay out our formal spatial model of discrete
choice and our empirical methodology. In Section 3 we describe our data before turning in Section
4 to describing our estimates of the corporate governance preferences of mutual funds. Section 5
examines the behavior of the three distinct mutual fund parties we identify. In Section 6 we use
our preference measures to test various theories about the determinants of mutal funds’ corporate
governance preferences. In Section 7 we use our mutual fund preference measures to construct
measures of the preferences of public companies’ shareholder bases and examine patterns and
trends in those preferences. Section 8 concludes.
2. A SPATIAL MODEL OF MUTUAL FUND PREFERENCES
Corporate shareholders such as mutual funds vote on a range of issues, including in the election
of directors and on various corporate governance policy issues. We suppose that mutual funds’
preferences over such votes can be represented using a standard spatial model of discrete choice
in which the deterministic component of a mutual fund’s utility from each proposal outcome is a
decreasing function of the distance between the mutual fund’s ideal point in some preference space
and the location of that proposal outcome in that space.
2.1. Setup. Suppose in particular that there are n mutual funds voting on m shareholder propos-
als. Each proposal j = 1, ...,m represents a choice between a “Yes” outcome and a “No” outcome,
Ojy, Ojn ∈ Rd, where d denotes the number of dimensions of the preference space. Each mutual
fund i = 1, ..., n has an “ideal point,” xi ∈ Rd, in that same space. Utility for mutual fund i given
the outcome of proposal j is:
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Ui(Ojy) = −h(‖xi −Ojy‖) + εijy, (1)
and,
Ui(Ojn) = −h(‖xi −Ojn‖) + εijn, (2)
where h(·) is a strictly increasing loss function, and εijy and εijn are random shocks. Fund i’s vote
on proposal j is then given by,
yij = 1⇔ Ui(Ojy) > Ui(Ojn). (3)
2.2. Empirical framework. There are many different empirical models for estimating spatial
models of this form from discrete choice data. The most influential in political science is the
NOMINATE family of parametric models (Poole and Rosenthal, 1991, 2007). Other models in-
clude Poole (2000)’s nonparametric optimal classification method and Bayesian approaches (Clin-
ton, Jackman, and Rivers, 2004). Finally, Heckman and Snyder (1997) develop a linear probability
model approach to estimating a spatial model of preferences over discrete choices in settings in
which the analyst does not observe the attributes of the choices that drive decisions.
We adopt the linear framework of Heckman and Snyder (1997) and estimate their model using
principal components analysis (PCA). Let Y be the n x m matrix of votes of n funds on m pro-
posals. Using the singular value decomposition (SVD) of Y we can write Y = ULA′, where the
“weight matrix” A contains the eigenvectors of Y ′Y , L contains the squareroots of the eigenvalues
of Y ′Y , and U is a scaled version of the principal component “scores” of the funds Z = UL. To
estimate a d-dimensional spatial model, we take the first d columns of Z as the coordinates of the
ideal points xi of the funds.
Let ak denote the k-th column of the weight matrix A. By definition, each principal component
zk = a′kY is calculated by choosing the coefficient vector ak in order to maximize the sample
variance of zk, subject to the normalization constraint a′1a1 = 1 and the requirement that zk be un-
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correlated with the prior principal components. This is one sense in which PCA can be understood
as a data reduction technique. As we discuss in more detail below, the analysis sample covers
votes on 33,262 proposals. Each of those proposals is an outcome variable, the votes on which
contain information about the corporate governance preferences of mutual funds. But in their full
unreduced glory, the sheer number of variables swamps attempts to characterize systematic differ-
ences in preferences across mutual funds. PCA finds the linear transformation of the original data
matrix Y of m variables (votes on proposals) into d < m variables (principal components) that are
uncorrelated and retain the maximal amount of the variation in the original data as is possible.
PCA can also be understood from a prediction standpoint. Starting from the SVD, Y = ULA′,
this can be written element-by-element as, yij =∑r
k=1 uikl1/2k ajk, where r is the rank of Y . Define:
ydij =d∑
k=1
uikl1/2k ajk, (4)
which is an approximation of yij using only the first d principal components. It can be shown that
ydij minimizes,
n∑i=1
m∑j=1
(ydij − yij)2, (5)
where ydij is any rank d approximation of yij .
2.3. Missing data. A challenge to performing PCA posed by our data is that the vote matrix is
sparse. Because most mutual funds own only a small fraction of public companies, each mutual
fund votes on only a small fraction of proposals in the sample. In particular, 95.6% of the cells
in the vote matrix are empty. Let oij = 1 if yij is observed and = 0 otherwise. PCA can be
generalized to this setting by redefining our cost function in (5) as:
n∑i=1
m∑j=1
oij(ydij − yij)2, (6)
that is, the squared prediction errors over the observed votes. There is no analytical solution to the
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problem of choosing the rank d approximation that minimizes this cost function; instead, iterative
numerical learning techniques must be used. We follow the iterative impuation method analyzed
in Ilin and Raiko (2010). It proceeds by first imputing the missing values in Y using the row-wise
means of Y to calculate a complete data matrix Yc. The principal components of this complete
data matrix are then calculated and used to reimpute the missing votes as ydij =∑d
k=1 uikl1/2k ajk.
The principal components of this new complete data matrix using those imputed values are then
calculated, and the algorithm iterates between imputation and principal components calculation
until convergence.
3. DATA
Our mutual fund voting data is from ISS Voting Analytics, which is drawn from public filings
by mutal funds on Form N-PX. Our sample period is from 2010 - 2015. For a proposal to be
included in the analysis sample, we require that at least 20 mutual funds vote on it and that 8% of
mutual funds that vote on it be in the minority. The reason is that highly lopsided votes contain
little information about the relative preferences of mutual funds. To see the intuition, consider the
extreme case of a unanimous vote—unanimous votes contain no information about mutual funds’
relative preferences. Thus we require that there be at least a minimal amount of controversy among
mutual funds about a proposal for the proposal to be included. This excludes many uncontested
director election votes for which the nominee received overwhelming votes in favor, as well as
other routine votes, like the bulk of votes to ratify the company’s auditors. Finally, for a fund to be
included in the sample, it must have voted on at least 200 sample proposals.
The resulting analysis sample covers votes on 33,262 proposals from 3,844 portfolio compa-
nies. Table 1 provides counts of proposal types in each year. The prefixes “MP” and “SP” in
the proposal categories refer to management proposals and shareholder proposals, respectively.
Proposals to elect directors nominated by management are by far the most common type of pro-
posal. The second most frequent proposal category is management proposals related to executive
compensation, the bulk of which are say-on-pay proposals or proposals to approve or amend the
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company’s stock compensation plan. Shareholder proposals are less numerous and mostly focused
on corporate governance issues rather than corporate social responsibility. Table 2 further breaks
down the shareholder proposals on corporate governance into more specific corporate governance
issue categories.
The analysis sample includes 3,617 mutual funds from 311 fund families. We also include as
“voters” in the data matrix rows for management, ISS, and Glass Lewis based on their respective
recommendations. This enables us to place these actors in the same preference space as the mutual
funds, which aids in interpretation of the model. Including these three actors as voters in the data
matrix has a negligible effect on our estimates; all results are robust to excluding them.
For fund characteristics, we merge in data from CRSP’s mutual fund database, using ticker,
fund name, and family name as well as data from EDGAR to link the two datasets. Table 3
compares the CRSP population of mutual funds from 2010 - 2015 holding U.S. common stock to
the CRSP funds that we were able to match to a fund in the ISS Voting Analytics data that was
included in our analysis sample. The merged sample includes about half of the CRSP population
in each year, and almost 80% of the value of U.S. common stock held by mutual funds in CRSP.
Tables 4 and 5 provide summary statistics for the CRSP population and for our merged sample,
respectively. They show that the funds in the merged sample are larger than funds in the CRSP
population of funds and moreover that index funds are disproportionately represented.
With 3,620 voters (3,617 funds plus management, ISS, and Glass Lewis) and 33,262 proposals,
there are a total of 120,408,440 potential votes in the analysis sample and therefore cells in our
data matrix defined by one row for each voter and one column for each proposal. The median
fund, however, owns a total of only 552 unique portfolio companies over the sample period, and
as a consequence there are only 5,332,353 votes in the analysis sample. In other words, 95.6% of
the cells in the data matrix are empty.
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4. THE DIMENSIONS OF MUTUAL FUND PREFERENCE
4.1. The number of dimensions. An initial question is how many dimensions of mutual fund
preference are needed to provide a good model of mutual fund preferences. The eigenvalues of
each principal component provide one perspective on the issue. The eigenvalue of the k-th princi-
pal component measures the variance in the voting data along that dimension. Figure 1 plots the
eigenvalues of the first thirty principal components. Note that starting with the third component,
the plot becomes linear. A widely used rule-of-thumb is to include the principal components up to
the first component in the linear portion of the plot (Joliffe, 2002, pp. 116-117). Here, that is the
third component. But while the first two components have fairly straightforward interpretations,
as discussed below, the third principal component has no obvious interpretation. In what follows,
we thus focus on the first two dimensions as a parsimonious model of mutual fund preference.
Table 6 provides the classification percentage (CP) and average proportional reduction in error
(APRE) for models using 1 - 10 dimensions. The CP is simply the percentage of votes that the
model predicts correctly, where a predicted value ydij > 0.5 is taken to predict a “Yes” vote, and
ydij < 0.5 is a predicted “No” vote. APRE measures the reduction in error the model achieves in
classifying votes relative to a simple benchmark model of predicting that all funds vote with the
majority on the proposal.1 A two-dimensional model performs well, correctly classifying 87% of
the votes, with an APRE of 49%.
4.2. The interpretation of the dimensions. We turn now to the interpretation of the dimensions
of mutual fund preference. Figure 2 shows the estimated preferences of mutual funds, with funds’
scores on the first dimension on the horizontal axis and their scores on the second dimension on
the vertical axis. Each dot is a mutual fund. Also depicted with triangles are the preferences at
the mutual fund family level, calculated as the average of the family’s funds’ preferences, for a
subset of the mutual fund families in the data. For the mutual fund families labeled in the figure,
1For each proposal, the proportional reduction in error (PRE) is equal to Number Minority Votes−Number Classification ErrorsNumber Minority Votes .
The APRE sums over all of the proposals:∑m
j=1 Number Minority Votesj−Number Classification Errorsj∑mj=1 Number Minority Votesj
.
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we also color code the family average and each of the family’s mutual funds, to give some sense
of the dispersion within fund family. We also show the univariate histograms of funds’ locations
on dimensions 1 and 2 in Figure 3.
Note first the locations of management, ISS, and Glass Lewis. Management is in the lower left
of the figure, with low scores on both dimensions, ISS is in the lower right, and Glass Lewis is in
the upper left. These locations reflect our basic interpretation of the two dimensions. Dimension 1
tracks the extent to which mutual funds vote against management along the lines that ISS generally
recommends. Similarly, dimension 2 measures the extent to which mutual funds vote against
management along the lines that Glass Lewis recommends.
To further corroborate this interpretation, we turn to how different types of proposals load on
each of these two dimensions. The estimated dimension 1 coefficients for the proposals (denoted
a1) indicate each proposal’s contribution to a fund’s score on dimension 1—a “Yes” vote on a
proposal adds that proposal’s a1 coefficient to the fund’s score on dimension 1. Hence examin-
ing the pattern of coefficients across proposals can help interpret the substantive content of the
dimensions. The top panel of Figure 4 plots the histograms of the a1 coefficients among proposals
supported by management (all management proposals as well as a few shareholder proposals) and
among proposals opposed by management (most shareholder proposals). While the a1 coefficients
on proposals opposed by management are overwhelmingly positively signed, similar fractions of
proposals supported by management have positive and negative coefficients.
The magnitudes of the a1 coefficients are further broken down in the top panel of Figure 5,
which plots the fraction of proposals in each proposal category that are “Extremely Positive,”
“Non-Extreme,” and “Extremely Negative.” We define as “extreme” coefficients that have an ab-
solute value in the top quartile of the distribution of a1 coefficients’ absolute values. While most
of the extreme coefficients for management proposals are negative, there are a substantial number
that are positive, and vice-versa for shareholder proposals.
Once we further break down the proposals by ISS’s recommendation, the interpretation of
dimension 1 comes into focus. The top panel of Figure 6 gives the distribution of extreme a1
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coefficients by proposal category among proposals that ISS recommends against, and the bottom
panel for those ISS supports. Virtually all of the extremely negative (positive) coefficients are on
proposals ISS opposes (supports). Interestingly, the one exception to this is for proxy contests, for
which there are a few extreme coefficients that go against the ISS recommendation.
This analysis establishes that the first dimension of mutual fund preference measures the degree
to which funds vote in line with ISS’s recommendations. This interpretation is also reflected in the
location of ISS on the first dimension. ISS’s position is determined just like any other voter in
the model by multiplying its “votes” (i.e., recommendations) by the coefficient matrix. As shown
in the histogram of funds’ scores on dimension 1 in Figure 3, ISS is on the extreme positive side
of dimension 1. Moreover, the distribution is bimodal: there are a substantial number of funds
aligned closely with ISS in the right mode and a substantial number of funds in the left mode on
the opposite side of dimension 1.
Turning to the second dimension, the bottom panels of Figures 4 and 5 show the distribution
of a2 coefficients by management recommendation and proposal category, respectively, and Figure
7 further breaks down the extreme coefficients by Glass Lewis recommendation. We are ham-
pered by the fact that we have data on Glass Lewis’s recommendations for only 15,328 out of
33,262 total proposals. But Glass Lewis’s recommendation generally organizes the a2 coefficients
well. Virtually all of the extremely negative coefficients are on proposals Glass Lewis opposes
and management supports. Similarly, almost all of the extremely positive coefficients are on pro-
posals Glass Lewis supports and management opposes. Furthermore, in the histogram of mutual
funds’ scores on dimension 2 in Figure 3, Glass Lewis is on the extreme positive side, opposite
management on the extreme negative side.
In sum, our spatial model shows that mutual fund preferences can be well-represented as posi-
tions along two latent dimensions, one that tracks the recommendations of ISS and one that tracks
Glass Lewis’s views. It is noteworthy that these two dimensions are orthogonal. That is, while
one might imagine that management, ISS, and Glass Lewis sit on a single dimension that ranges
from an extreme managerialist view on one end to an extreme shareholder rights view on the other,
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with ISS and Glass Lewis ordered according to the intensity of their shareholder rights views, a
better representation of mutual fund preferences is that ISS and Glass Lewis’s views reflect two
orthogonal dimensions of shareholder preference. A fund can be extreme on dimension 1 without
being extreme on dimension 2, and vice-versa.
The fact that dimensions 1 and 2 of mutual fund preferences track the views of the two leading
proxy advisors could reflect either or both of two different phenomena: (1) the proxy advisors’
recommendations could have a causal effect on mutual fund choice; and (2) the proxy advisors’
recommendations might track existing patterns in mutual fund preference. In either case, a remain-
ing question is what the substantive content of the two dimensions are in terms of specific views on
corporate governance. Comparing the top and bottom panels of Figure 5, the patterns of loadings
for the two dimensions are broadly similar, generally loading negatively on management proposals
and positively on shareholder proposals. In this sense, both dimensions measure a vision of “share-
holder rights,” and the patterns of coefficients reveal that, for both dimensions, the higher a fund
scores, the more the fund opposes management and supports shareholder proposals. We explore
in the next section the differences between the visions of corporate governance and shareholders’
role captured by these two dimensions by examining how clusters of funds that are extreme on just
one of the dimensions vote their shares.
4.3. Stability over time. So far we have used voting data from the entire sample period 2010 -
2015 to estimate a single ideal point for each fund. A natural question is whether the structure of
preferences we estimate is stable over the period. To investigate this, we divide the sample into
three two-year cohorts and estimate the spatial model separately for each cohort. The resulting
fund ideal points for each cohort are plotted in Figure 8. The basic configuration of preferences,
including the locations of management, Glass Lewis, and ISS, is remarkably stable over time.
In each of the cohorts, the first and second dimensions measure the same tendencies to vote in
accordance with the corporate governance philosophies of the two major proxy advisors. The
locations of major fund families are stable over time as well, showing that the latent dimensions
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of corporate governance preferences that we estimate are stable, and giving us more confidence in
our results from the pooled sample.
Comparing the 2010 - 2011 cohort to the 2014 - 2015 cohorts, however, reveals modest but
potentially important shifts of two of the largest fund families. BlackRock starts out the sample
period aligned with management on dimension 1 but in the middle of funds’ distribution on di-
mension 2. But by 2014 - 2015, BlackRock had fallen to the far negative side of dimension 2 and
is located at the heart of the cluster of funds located near management. BlackRock’s corporate
governance philosophy has thus evolved from a significant willingness to oppose management in
accordance with the view of shareholder rights measured on dimension 2 to a more strictly man-
agerialist approach by the end. In contrast, State Street has steadily drifted along dimension 1
away from management, becoming more shareholder rights oriented over time. Neither of these
two shifts is dramatic, but given the sizes of the two fund families, they are noteworthy.
5. THE PARTY STRUCTURE OF MUTUAL FUNDS
One might expect mutual funds’ preferences to be distributed unimodally, and perhaps normally,
across their two-dimensional preference space. But the contour plot of the density of mutual fund
preferences (weighted by the TNA of each fund) in Figure 9 reveals that there are in fact three dis-
tinct modes, or clusters, of mutual funds, located near management, ISS, and Glass Lewis. Mutual
funds’ voting behavior thus reveals the existence of three distinct mutual fund “parties”: groups of
funds that have similar preferences about corporate governance and generally vote together. For
reasons we explain below, we label these three parties (1) the Managerialist Party, (2) the Share-
holder Intervention (SI) Party, and (3) the Shareholder Veto (SV) Party, respectively. We refer to
the latter two parties as the “shareholder rights” parties. We depict our definitions of these three
parties in Figure 10 by superimposing on the scatter plot of fund preferences circles that define
each party’s membership. We define the Managerialist Party as funds within a distance of 30 from
the center of the cluster of mutual funds in the lower-left. The SI Party and SV Party are defined
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as funds within a distance of 30 from the locations of ISS and Glass Lewis, respectively.2
5.1. Party coherence. Table 7 provides measures of the degree to which party members vote
together. For each proposal, we calculate the outcome voted for by a majority of each party’s
members. We then report the fraction of party members’ votes in each proposal category that
were cast in the opposite direction of the party’s majority. The column labeled “All” reports the
corresponding fractions for all mutual funds considered as a single party, which serves as a useful
benchmark. Considering mutual funds as a whole, 25% of mutual fund votes in the sample are cast
in the opposite way from how a majority of mutual funds voted on the proposal. In contrast, for all
proposals, only 16% of the votes of Managerialist Party members were cast against the majority of
the party, and the corresponding figures for the SI Party and SV Party are 4% and 8%, respectively.
Hence the two shareholder rights parties exhibit substantially more coherence in their voting than
the Managerialist Party, a result consistent with those parties having a clear focal point in their
proxy advisor’s recommendations to coordinate their votes. The degree of coherence varies across
proposal types. For example, the proposal type with the highest fraction of SV Party members
voting in their party’s minority is shareholder proposals on corporate governance, at 17%. The
proposal types with the highest fraction of SI Party members voting in their party’s minority, in
contrast, are management proposals related to mergers and acquisitions and shareholder social
proposals.
5.2. Party corporate governance philosophies. Consider now the substantive visions of corpo-
rate governance that animate each of the three parties. Table 8 provides the fraction of “Yes” votes
cast by each party by proposal category. In interpreting these figures, it is important to keep in
mind that for a proposal to make it into the sample, it must pass our “lopsidedness” threshold by
having at least 8% of the mutual funds voting on the proposal vote in the minority among mu-
tual funds. Relative to the entire population of proposals, this sample selection criterion generally
drives down support for management proposals and drives up support for shareholder proposals.
2Our characterizations of party voting behavior below are all robust to using smaller radii to define these parties.
16
As befits its name, mutual funds in the Managerialist Party support management proposals, and
oppose shareholder proposals, at far higher rates than do members of the other two parties. To be
clear, Managerialist Party members do not always support management. Across all management
proposals, members of the Managerialist Party vote in favor 82% of the time, and similarly they
vote in favor of shareholder proposals (and hence generally against management’s wishes) 19% of
the time. In comparison, these rates across all mutual funds are 70% and 47%, respectively.
The two shareholder rights parties, by contrast, vote against management at much higher rates.
Each shareholder rights party represents a distinctive vision of corporate governance and share-
holders’ role. The two key comparisons that capture their basic difference is (1) between their
overall rates of support for management proposals; and (2) between their overall rates of support
for shareholder proposals. The SI Party supports shareholder proposals at a rate of 84%, com-
pared to only 50% for the SV Party. These shareholder proposals generally entail shareholders
attempting to affirmatively intervene in the corporate affairs of the company by demanding that the
company’s board of directors adopt changes to corporate policies and practices, hence the name
“Shareholder Intervention Party.” In contrast, the SV Party supports management proposals at a
rate of only 50%, compared to 62% support for the SI Party. Put differently, the SV Party opposes
management proposals at a rate of 50%, which is 32% more often than the SI Party’s rate of 38%.
Management proposals generally entail corporate management asking for shareholders to ratify
some decision by the board, such as management’s pay practices, an increase in authorized com-
mon stock, a management nominee for director, and the like. A vote against these proposals is thus
a sort of “veto” by the shareholder of a management initiated decision, hence the name “Share-
holder Veto Party” for the shareholder rights party that tries to veto management at relatively high
rates.
This basic distinction between the two shareholder rights parties is reinforced by examination
of specific subcategories of management proposals and shareholder proposals. For all but two
categories of management proposals, the SV Party supports them at lower rates than the SI Party,
and the two exceptions prove the rule. One exception is management nominees for director in
17
a proxy contest in which a shareholder dissident has nominated a competitive slate of directors.
Such proxy contests are perhaps the most interventionist shareholder actions possible, and hence
it is consistent with our conceptual distinction between the two sharehoder rights parties that the
SI Party opposes the management nominees (and supports the shareholder nominees) at greater
rates than the SV Party. Similarly, the other exception is management proposals on mergers and
acquisitions. These are among the most consequential for the corporation, since failure to receive
shareholder ratification generally means a major transformative transaction cannot go forward.
Votes against these are thus arguably more “interventionist” than other categories of management
proposals, like voting against management’s say-on-pay proposal. Similarly, for every subcategory
of shareholder proposal, the SI Party supports the proposals at greater rates than does the SV Party.
Table 9 further breaks down the shareholder proposals on corporate governance into specific types
of proposals. In 8 out of the 9 corporate governance proposal types with a substantial difference
between the two parties, the SI Party supports the proposals at a greater rate than does the SV Party.
The two shareholder rights parties thus represent distinctive philosophies of shareholders’ role
in corporate governance. The Shareholder Veto Party focuses on monitoring the management of the
corporation and critically considering management’s proposed courses of actions, voting against
them when the shareholder views them as problematic. In contrast, the Shareholder Intervention
Party focuses instead on actively intervening in corporate affairs by demanding reforms to basic
corporate governance rules and supporting proxy contests for corporate control. Importantly, while
one might expect shareholders’ willingness to affirmatively intervene and their inclination to veto
management’s proposed actions and policies to be positively related, we find that the SV Party
intervenes less than the SI Party, while the SI Party vetos less than the SV Party. So among
members of the two shareholder rights parties, these two modes of shareholder engagement are
negatively related.
These two competing visions of corporate governance are in turn reflected in the recommen-
dations of ISS and Glass Lewis. Recall that the SI Party is centered on ISS’s preference location
and the SV Party is centered on Glass Lewis’s preference location. The extent to which these two
18
competing visions of corporate governance among institutional investors predate ISS and Glass
Lewis, or instead were brought about by the recommendations of ISS and Glass Lewis, remains
an open question. In either case, one theory that might explain why ISS and Glass Lewis follow
these two distinctive approaches is based on product differentiation. ISS and Glass Lewis compete
in the market for proxy advice. Given some preexisting cleavages among institutional investors
in their corporate governance philosophies, competition between the two may have led to a prod-
uct differentiation and market segmentation that has resulted in the reinforcement or even creation
of the two shareholder rights parties and their distinctive approaches to superintending corporate
management.
Finally, note that there are a few funds in the preference plot in Figure 2 that score highly on
both dimensions of fund preference. These are funds that often vote against management when ei-
ther ISS or Glass Lewis recommends against management. We have labeled the fund families with
average scores in the upper right of the preference space—note that they are socially responsible
fund management companies, like Domini and Calvert. Our framework shows that these socially
responsible fund families are extreme in their shareholder rights orientation, as expressed through
their votes.
5.3. Characteristics and sizes of the parties’ memberships. A final set of questions about the
party structure of mutual funds concerns the characteristics of their memberships and their size in
terms of total net assets. Table 10 provides average characteristics across the three parties. The
primary contrast is between the Managerialist Party on the one hand and the two shareholder rights
parties on the other. Funds in the Managerialist Party are more heavily indexed, far larger by assets,
come from larger fund families, and charge substantially lower expense ratios. Another intriguing
difference is that funds in the Managerialist Party are more heavily weighted toward growth stock
strategies than value stock strategies in comparison to the two shareholder rights parties. We return
to this pattern in Sections 6 and 7 below. The final row in the table gives the fraction of TNA in
the mutual fund industry held by funds in each of the parties as of 2015. The Managerialist Party
19
is much larger than the others, at 68% of mutual fund industry TNA, followed by the SI Party at
11% and the SV Party at 5%
5.4. Mapping the Votes on Specific Proposals. To build further intuition about the window into
mutual fund voting preferences our framework provides and to illustrate how it works, in this
section we discuss four specific proposals using our spatial model to explain the patterns in funds’
votes. Figures 11 - 14 depict the spatial maps of votes for four different proposals. In each figure,
we plot the “Yes” and “No” votes cast by each fund at the location of the fund’s ideal point, using
different marker styles to identify “Yes” and “No” votes according to whether the model correctly
predicted the fund’s vote. Each point is labeled using the first two letters of the fund’s family name,
to aid in identifying funds (e.g., Vanguard fund votes are labeled “Va”).
5.4.1. Dissident nominee for director at DuPont. The first proposal vote plotted in Figure 11 is
for a candidate for director at DuPont nominated by the activist hedge fund Trian Partners in a
2015 proxy contest. Trian had nominated four candidates for DuPont’s twelve member board,
but despite winning the backing of most non-index institutions, its candidates narrowly lost to
management’s nominees when the “Big Three” indexers, Vanguard, BlackRock, and State Street,
all sided with management. In our data, 72% of funds (ignoring the number of shares each held)
voted in favor of the Trian nominee. Following the outcome of the vote, it was reported that had
any of the Big Three backed Trian, its nominees would have won.3
Examining the vote plot for this proposal, it is clear that the cutting line that separates the
model’s predictions of “Yes” versus “No” cuts diagonally through the two clusters in the lower left
of the diagram between the incorrectly predicted “Yes” and “No” votes. As illustrated in the figure,
Trian won the support of not only the bulk of the Shareholder Intervention Party, but also most of
the Shareholder Veto Party and a substantial fraction of the upper-right half of the Managerialist
Party. Interestingly, the model predicted that State Street funds would vote “Yes”–they are among
the cloud of incorrectly predicted “No” votes in the upper-right portion of the Managerialist Party.
3See Reuters, “DuPont wins board proxy fight against activist investor Peltz,” May 13, 2015.
20
The model suggests that this was indeed a close call, with State Street in particular the key swing
vote.
Overall the model predicts correctly 78% of the votes on this proposal and achieves a propor-
tional reduction in error (PRE) of 24%. So while the model certainly helps organize and explain
the voting patterns on the proposal, it performs less well in predicting votes in this proxy contest
than it does on average. (Recall the overall percentage of votes classified correctly across the entire
sample of proposals is 87%, with an APRE of 49%.)
5.4.2. Shareholder proposal on executive compensation at Waste Management, Inc. The share-
holder proposal at issue in the vote plotted in Figure 12 was submitted by International Brotherhood
of Teamsters General Fund at the 2015 annual shareholder meeting of Waste Management, Inc. It
requested that the board of directors adopt a policy under which it would limit the acceleration of
the vesting of equity awards for Waste Management executives, noting that under the company’s
current practices its CEO would receive accelerated vesting of stock worth $18.2 million upon a
change of control. 44% of funds in our data voted for the proposal.
The vote plot reveals that this proposal attracted the votes of the Shareholder Intervention Party,
but not those of the Shareholder Veto Party or Managerialist Party. Interestingly, State Street again
bucked our model’s predictions: the cloud of incorrectly predicted “Yes” votes on the right side
of the Managerialist Party are State Street funds. But overall, the model performs very well in
predicting funds’ votes on this proposal, correctly predicting 91% of the votes and achieving a
PRE of 78%.
5.4.3. Management say-on-pay proposal at PolyOne Corporation. In Figure 13 is plotted a say-
on-pay proposal at PolyOne Corporation from its 2015 annual shareholder meeting. 83% of funds
in our data voted for the proposal. As predicted by the model, the proposal attracted the support
of most of both the Managerialist Party and the Shareholder Intervention Party, and was opposed
by most of the Shareholder Veto Party. Overall the model correctly predicts 91% of the votes and
achieves a PRE of 47%
21
5.4.4. Uncontested director election at Avnet, Inc. Finally, in Figure 14 is plotted an uncontested
director election at Avnet Inc. from 2011. 85% of funds in our data voted for the proposal. Again,
the model organizes funds’ votes well, with the SV Party opposed but the Managerialist Party and
the SI Party in favor. The cutting line separates the incorrectly predicted “Yes” and “No” votes
toward the upper left of the preference space, and the model mispredicts only the funds that are
close to that cutting line. Overall the model correctly predicts 94% of the votes and achieves a PRE
of 61%.
6. THE DETERMINANTS OF MUTUAL FUND PREFERENCE
What factors shape mutual funds’ preferences and underlie the heterogeneity we have documented?
6.1. Theory. As a theoretical matter, mutual fund managers’ incentives to become informed and
exercise the funds’ voting rights in a meaningful way are relatively weak. One reason is the classic
collective action problem that besets shareholder voting generally. Because (1) shareholders bear
all of the costs of becoming informed but share the benefits of informed voting with other share-
holders; and (2) the probability that any public company shareholder’s votes are pivotal is so small,
shareholders have little incentive to invest in voting.
By aggregating share capital, mutual funds might seem to mitigate these problems. But these
intermediaries are subject to their own agency problem vis-a-vis the ultimate beneficial owners who
invest in them (Rock, 1991; Black, 1992). In particular, any improvement in portfolio company
value produced by a mutual fund exerting effort to vote in an informed manner is shared with all
of the mutual fund competitors who hold the company’s stock. For index funds, this means that
costly voting effort increases their costs without improving their returns relative to competitors.
One caveat to this conclusion is that, since index funds (unlike actively managed funds) cannot
sell when they are displeased with management, their inability to exit might lead them to resort
more to “voice.” But even so, the basic logic above that effective voice is costly without producing
competitive benefits would seem, as a theoretical matter, overpowering.
22
In contrast, the managers of actively managed funds face somewhat stronger voting incentives
than those of index funds (Kahan and Rock, 2007; Shapiro-Lund, 2017). An actively managed
fund manager can improve the fund’s return relative to competitors by taking actions that increase
the value of a stock in which the fund is overweight relative to the market. Moreover, the process
of investment selection might generate information that is useful for voting.
Relatedly, we hypothesize that different approaches to security selection might result in dif-
ferent views about corporate governance. Active managers who follow growth stock strategies
attempt to identify companies whose rich market valuation is more than justified by their growth
prospects. That suggests that the fund manager will believe in the management of the company,
leading the fund manager toward a view of corporate governance generally in line with the Man-
agerialist Party’s view (i.e., to score low on each dimension). In contrast, a value stock strategy
might include investing in stocks whose undervaluation is driven in part by mistakes of the current
management, and that investing philosophy might lead naturally to a view of corporate governance
in line with the views of one of the shareholder rights parties.
The size of a mutual fund and of a fund family might also shape their corporate governance
preferences. In particular, larger funds and fund families benefit from economies of scale in voting
and moreover have a higher probability of being pivotal voters. The largest fund families often
own more than 5% stakes in large public companies, making them potentially crucial swing vot-
ers (Fichtner, Heemskerk, and Garcia-Bernardo, 2017). How this translates into their corporate
governance preferences is not obvious. We offer two competing hypotheses. First, it may be that
the path of least resistance (i.e., the lowest cost approach to voting) is to simply generally follow
management’s recommendations. Under this account, economies of scale in voting might make it
cost effective to invest in information that could lead the fund to vote against management, at least
for the actively managed funds that might receive a competitive benefit from votes that increase
firm value. In this case, we might expect large funds and families to have higher scores (i.e., to
be less managerialist) than smaller funds. But note that this hypothesis is at odds with the basic
comparisons of fund characteristics across the three parties in Table 10.
23
Alternatively, the path of least resistance for small funds might be to simply follow one of the
two proxy advisors. Economies of scale might thus result in large funds and fund families moving
away from simply outsourcing voting decisions to proxy advisors. Indeed, Iliev and Lowry (2014)
show that, among actively managed funds, larger funds and larger fund families are less likely to
vote in line with ISS’s recommendations. Under this view, larger funds and larger fund families
will score lower on dimensions 1 and 2.
Finally, consider the relationship between funds’ expense ratios and their preferences. One
natural perspective is that because it is costly to become informed about the issues being voted on,
the funds that do so will have higher expense ratios. But theoretically, this leads to the same set of
ambiguous predictions for their preferences as does fund size—it turns on what the “path of least
resistance” for fund voting is.
6.2. Empirical approach. Ultimately whether variation in incentives faced by fund managers
shapes their corporate governance preferences as reflected in how they vote portfolio company
shares is an empirical question. We view one contribution of this paper as the creation of parsi-
monious measures of shareholder preference that allow for testing various theories and hypotheses
in corporate governance, like the ones considered above. In this section we illustrate the potential
value of our measures of shareholder preference by using them to investigate whether institutional
investors’ incentives matter for their voting behavior.
The univariate comparisons between characteristics of members of the three mutual fund par-
ties in Table 10 discussed above suggest that fund and family size and indexing are associated with
mutual fund corporate governance preferences. But because these fund characteristics covary—for
example, index funds are on average much larger than actively managed funds—multiple regres-
sion can help tease out partial correlations that are more informative about the underlying process
of preference formation.
We report regressions of mutual funds’ scores on dimensions 1 and 2 on fund-level and family-
level characteristics in Tables 11 and 12, respectively, with standard errors clustered at the family
24
level. Because ultimately we are trying to characterize mutual funds’ votes, and those votes are a
direct linear function of assets (since dollars buy shares which equal votes), we use weighted OLS
regressions, weighting each fund observation by its TNA.
Column (1) of Tables 11 and 12 reports a regression of funds’ score on dimension 1 on mea-
sures of the funds’ / families’ management style (index, growth, or value) and quintiles of TNA.
Column (2) adds a measure of the fund’s / family’s expense ratio to the regression, which is miss-
ing for about half of sample funds, and columns (3) and (4) repeat the two regressions using funds’
score on dimension 2 as the dependent variable.
6.3. Results.
6.3.1. Fund management style. There is strong evidence that indexing leads to lower scores on
the two dimensions. Focusing on the full sample regressions in columns (1) and (3) of Table 11, the
coefficient on Index fund is negative for both dimensions but statistically significant only for Score
2. The models using family-level covariates in Table 12 show that indexing has a large and statis-
tically significant negative relationship with both Score 1 and Score 2. Indeed, the absolute value
of the coefficients on fraction of family assets indexed are much larger than for the coefficients on
Index fund in the fund-level covariate regressions. This suggests that the role of indexing in shap-
ing mutual fund preferences is largely a family-level, as opposed to fund-level, phenomenon. This
makes economic sense: for families that specialize in index funds, like Vanguard, BlackRock, and
State Street, there would be little reason to delegate voting decisions down to the fund manager
level. The activities of index fund managers would generate little useful information for voting
the funds’ shares. Similarly, for families that specialize in active management, while it may make
sense to allow some individual managers for active funds to direct their funds’ votes separately
from the fund family as a whole, the voting of shares held by the family’s index funds is likely to
be centralized and therefore follow the general approach of the fund family.
There is also evidence for our hypothesis that growth stock investment strategies lead to more
managerialist voting behavior relative to value stock investment strategies. The coefficient on
25
Growth fund is below the coefficient on Value fund for both Score 1 and Score 2 in Table 11, but
this difference is statistically signficant only for Score 2 (F-test p-value = .007). The results using
family-level measures of growth versus value strategies in Table 12 are similar, with the difference
between the two coefficients statistically significant only for Score 2 (F-test p-value = 0.041).
6.3.2. Fund and family size. There is strong evidence that fund size is negatively related to scores
on both dimensions. The coefficients on quintiles of fund TNA in Table 11 are monotonically
decreasing in fund size for both dimensions. Importantly, note that this is controlling for being an
index fund versus an actively managed fund. Big funds are more managerialist.
The relationship between family size and preferences is more complicated. The coefficients in
column (1) of Table 12 show an initially increasing relationship between Score 1 and family TNA
up to the 3rd quintile of TNA. The coefficients then fall thereafter, with the coefficient on the 5th
quintile statistically significantly different from the coefficient on the 4th quintile. The pattern is
similar for Score 2 in column (3).
6.3.3. Expense ratios. Finally, the coefficients on expense ratio reported in columns (2) and (4)
of the two tables are consistently positive but only statistically significant in the fund-level model
for Score 2. Note again that this is controlling for being an index fund, which have lower expense
ratios than actively managed funds.
7. THE PREFERENCES OF PUBLIC COMPANIES’ MUTUAL FUND
SHAREHOLDER BASES
We have documented systematic variation in mutual funds’ preferences and developed a simple
taxonomy of mutual fund corporate governance preferences, organizing them into three “parties.”
We turn now to companies’ shareholder bases. Each public company has a particular set of mutual
fund and other shareholders and hence a particular distribution of corporate governance prefer-
ences among its shareholder base. Because mutual funds’ portfolios vary, the distribution of mu-
26
tual fund preferences varies across public companies’ shareholder bases. In this section we use
our preference measures, combined with data on mutual funds’ portfolios, to construct issuer-level
measures of the preferences of these shareholder bases. We then use measures to describe sys-
tematic patterns across issuers in their shareholders’ preferences. An important caveat is that our
estimated distributions of shareholder preferences pertain only to the mutual fund shareholders. In
work in progress (Bubb and Catan, 2018) we extend the methodology developed in this paper to
characterize the preferences of companies’ broader shareholder base—including non-mutual fund
shareholders—and study the determinants and consequences of those preferences. Our hope is that
our measures of the preferences of public companies’ shareholder bases will be a useful addition
to the corporate governance literature and enable the testing of a range of theories and hypotheses
about the system of corporate governance.
7.1. Methodology. For each year-end between 2002 and 2016, we construct the distribution of
the preferences of mutual fund shareholders of each portfolio company as follows. Starting from
the Thomson Reuters Mutual Fund Holdings database, we identify, for the last quarter of the year,
the portfolio owned by each fund (identified through the fund’s fundno) during that quarter.4 Each
portfolio contains a list of each of the securities owned by the fund, together with an indication of
the number of such securities that the fund owned. After excluding securities that do not corre-
spond to equity securities in operating companies,5 for each public company held by mutual funds
in the sample in each year we construct the distribution of preferences across shares owned by mu-
tual funds. To characterize trends and patterns across these distributions, we focus on the average
share’s preference among shares in the company owned by mutual funds for each company-year
as a simple summary measure. To be specific, we calculate the weighted average of the scores of
mutual fund shareholders of the company on each dimension, weighting by the number of shares
owned by each mutal fund. We will refer to these measures of average preference as “company
4Our algorithm for estimating mutual fund preferences identifies mutual funds through their CRSP crsp_portno.We link a Thomson Reuters’ fundno with its corresponding crsp_portno as of the relevant date using WRDS’MFLINKS table.
5For example, a mutual fund might hold an interest in an exchange traded fund.
27
shareholder scores.”6 Note that because our preference estimates are static—a single ideal point
is estimated for each mutual fund based on the votes they cast from 2010 - 2015—the variation
over time in the company-level distributions of mutual fund preference is all driven by changes in
mutual funds’ portfolios over time.
With this procedure, we are able to construct estimates of the distribution of mutual fund share-
holder preference scores of company shares owned by mutal funds for a total of 44,286 company-
year’s. Table 13 provides for each year basic information about the set of companies for which
we were able to estimate mutual fund shareholder preference distributions. At the beginning of
the sample in 2002, we can estimate preference distributions for 2,827 public companies, and the
number rises to 3,023 by the end of the sample period in 2016. The average percentage of shares
of these companies that are owned by mutual funds rises from 19% in 2002 to 26% by 2016, con-
sistent with the growth of the mutual fund industry over this period. In contrast, the mutual funds
for which we have preference estimates own only 11% of the sample companies’ shares in 2002,
rising to 23% by 2016. Our preference measures’ coverage of mutual fund shareholders improves
over time because we estimated mutual funds’ preferences using voting data from 2010 - 2015,
and turnover in the fund industry means that there are funds in the earlier years that do not appear
in our voting data. The average number of mutual fund shareholders with estimated ideal points
for sample companies increases from 59 in 2002 to 156 in 2009 before falling to 122 by 2016. The
fall may be due to increasing concentration in the fund industry over this period.
7.2. Results. As a preliminary matter, it is useful to simply compare the distribution of company
shareholder scores to the distribution of mutual funds’ scores on dimensions 1 and 2. Figures 16
and 17 plot the company shareholder scores as of 2002 and 2016, respectively, over a contour plot
of the (unweighted) density of the mutual fund scores reported in Section 4. Company shareholder
scores are relatively less disperse than their fund-level counterparts. This is intutive, as a company’s
shareholder score is simply a convex combination of the scores of the funds that owned shares in
6For the sake of brevity, the fact that (1) the measures pertain only to the preferences of the mutual fund sharehold-ers and (2) that the measure summarizes the distribution of the preferences of those shareholders through the mean ofthat distribution will be implied.
28
the company. Nonetheless, there is a substantial amount of dispersion in the company shareholder
scores. In short: not all public companies have the same distribution of preferences among their
shareholders. This is not surprising, but to our knowledge we are the first to document the degree
of dispersion across companies in the preferences of their shareholder bases.
Company shareholder scores have several interesting cross-sectional features. First, as shown
in Figure 15, mean company shareholder scores, and the dispersion of those scores across compa-
nies, are decreasing in companies’ market capitalization. Both of these features are likely due to the
fact that index funds (which tend to have more managerialist preferences) own a larger proportion
of the shares of larger companies. The figure reveals, however, that there remains considerable vari-
ation in company shareholder scores even among the largest companies by market-capitalization.
Second, as we discussed in Section 6, there are reasons to expect investors that follow different
stock-picking strategies to also follow systematically different approaches to corporate governance.
In particular, the investment thesis for growth stocks generally entails expectations of future growth
that more than justifies the stocks’ rich valuation, and it is natural to think those expectations
correlate with faith in management and hence more managerialist voting behavior. To investigate
this, Figure 18 plots the average company shareholder score in each year by quintile of book-
to-market ratio, which is generally used to distinguish growth stocks from value stocks.7 For
almost every year, there is a monotonically-increasing relationship between the book-to-market
ratio and the average company shareholder score. This suggests that, on average, the mutual fund
shareholders of growth stock companies are systematically more managerialist than the mutual
fund shareholders of value stock companies.
We confirm this pattern using regression analysis reported in Table 14. The regressions in
columns (1) and (3) show that, controlling for year fixed effects, firms with higher book-to-market
ratios have systematically higher company shareholder scores.8 In columns (2) and (4) we ver-
ify the robustness of this finding by using dummies for quintiles 2-5 of the yearly distribution of
7We construct the book-to-market ratio following the algorithm described in Davis, Fama, and French (2000).8Since the distribution of the book-to-market ratio features a small number of extreme outliers, for the sake of
regression analysis we winsorize that ratio at the 1% and 99% values of the distribution.
29
book-to-market (quintile one is the omitted category).9 Moving from a firm in the bottom quintile
of book-to-market to a firm in the top quintile of book-to-market is associated with an average
increase of 5.49 (respectively, 4.86) units in company shareholder score on dimension 1 (respec-
tively, dimension 2). These changes amount to about one third and almost 40 per cent of the
standard deviation of the company shareholder score on dimensions 1 and 2, respectively.
We turn now to time trends in the preferences of public companies’ shareholder bases. The pe-
riod from 2002 - 2016 was marked by a shift of assets from actively managed funds to index funds
and increasing concentration in the fund industry. Figure 19 shows the resulting trends in average
company shareholder scores for sample companies in this period. After a steep drop in average
company shareholder scores on both dimensions between 2007 and 2008, both averages continue
to drop (albeit at a less steep rate) over 2008-2016. Between 2007 and 2016, the average company
shareholder score on dimension 1 drops from -3.87 to -12.87, while the corresponding average on
dimension 2 drops from -5.48 to -14.03. These drops amount to 40 percent and 60 percent of one
standard deviation in the distribution of the company shareholder scores for dimension 1 and 2,
respectively, as of 2007.
To gain further insight into how the distributions of company shareholder scores evolved in
this period, Figure 20 plots estimates of the density of company shareholder scores in 2002, 2007,
2012, and 2016. For both dimensions, the distributions become substantially less right-skewed and
more symmetric over the sample period, losing mass to the right of the mode (in line with the drop
in the evolution of the cross-company averages reported in Figure 19). In addition, the modes of
the distributions also become systematically higher over time. Finally, in the case of dimension 2,
the entire distribution shifts systematically to the left over time.
Figure 21 breaks down the aggregate time trend in company shareholder scores by company
size category. For each year we partition the sample into four market-capitalization buckets using
as cutoffs the 90th, 70th, and 30th percentiles of the distribution of market capitalization as of the
9For the specifications estimated in columns (2) and (4), for every pair of adjacent book-to-market quintiles, anF-test rejects the null hypothesis that the average company shareholder score in one quintile is the same as in theadjacent one (in every case, the p-values for the test are smaller than 0.01).
30
relevant year.10 For almost every year there is a monotonically decreasing relationship between
company size and the company shareholder scores on both dimensions. While the average com-
pany shareholder scores on dimension 1 across micro-caps and small-caps dropped quite steeply
during the 2007-2016 period, the corresponding scores for mid-caps and large-caps did not change
meaningfully. By comparison, for all groups of firms, company shareholder scores on dimension
2 dropped, on average, over 2008-2017. In fact, the average magnitude of the drop in the score
on dimension 2 experienced by micro-, small-, and mid-cap firms is comparable across the entire
period (while the time-series for the large-caps is hump-shaped).
8. CONCLUSION
In this paper we have systematically characterized the corporate governance preferences of mutual
funds. We show that a spatial model with just two latent dimensions of preference provides a highly
predictive model of mutual fund voting behavior. Those two dimensions in turn reflect the role of
the two leading proxy advisors and measure two different philosophies of corporate governance
and shareholders’ role.
Our parsimonious measures of mutual funds’ corporate governance preferences generate a
number of descriptive insights about the broader system of corporate governance and moreover
enable the quantitative testing of various hypotheses. In particular, we show that mutual funds
are clustered into three parties, a Managerialist Party and two shareholder rights parties. The two
shareholder rights parties follow two distinctive modes of shareholder engagement. The Share-
holder Intervention Party supports efforts to actively intervene in corporate affairs, including by
supporting shareholder-initiated reforms to basic corporate governance rules and activist investor
proxy contests for board seats. In contrast, the Shareholder Veto Party focuses on monitoring
corporate management and vetoing management’s proposed courses of actions when they raise
10For example, as of the end of 2016, the smallest Small-Cap company has a market value of equity of approximately$365M, the smallest Mid-Cap company has a market value of equity of approximately is $2566M, and smallest Large-Cap company has a market value of equity of approximately $11500M. In unreported analysis, we corroborate that thetime-series for our coverage variable is similar across market-cap buckets.
31
concerns. We show furthermore that funds’ preferences are shaped by fund characteristics that
influence their incentives. Index funds, growth stock funds, and larger funds are all more manage-
rialist than actively managed funds, value stock funds, and smaller funds, respectively.
We use our measures of mutual funds’ preferences to construct measures of the preferences
of public companies’ mutual fund shareholder bases. We show that, with the growth of large
passive management fund families, the shareholder bases of public companies have become more
managerialist over time. Moreover, we show that the shareholders of growth stock companies are
more managerialist than shareholders of value stock companies, consistent with the hypothesis that
the investment thesis for most growth stocks includes belief in the company’s current management.
We hope the introduction of our measures of mutual fund corporate governance preferences to the
literature will enable other researchers to test quantitatively a range of theories and hypotheses
about corporate governance.
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33
APPENDIX
Table 1: Proposal Categories by Meeting Year
2010 2011 2012 2013 2014 2015 totalMP-Compensation 729 1360 1075 1108 1077 761 6110MP-Corporate Finance 114 91 75 76 55 51 462MP-Corporate Governance 43 35 30 45 33 63 249MP-Elect Director (Contested) 9 54 45 64 95 91 358MP-Elect Directors 4029 3891 4050 3498 3463 3018 21949MP-Merger / Acquisition Related 102 113 91 170 149 99 724MP-Other 119 75 64 60 63 35 416MP-Ratify Auditors 40 54 47 38 36 42 257SP-Corporate Governance 207 188 182 172 181 251 1181SP-Elect / Remove Directors (Contested) 6 47 45 103 71 66 338SP-Executive/Director Compensation 108 33 61 82 64 70 418SP-Other 42 9 13 18 8 6 96SP-Social Proposal 94 102 91 121 159 137 704
Table 2: Corporate Governance Shareholder Proposal Categories by Meeting Year
2010 2011 2012 2013 2014 2015 totalSP-Proxy Access 0 0 6 7 10 84 107SP-Independent Chairman/Lead Director 40 28 55 59 63 62 307SP-Written Consent 18 33 19 28 28 36 162SP-Special Meetings 45 30 17 11 14 21 138SP-Declassify Board 16 4 4 9 7 4 44SP-Reduce Supermajority Requirements 14 14 16 18 9 9 80SP-Majority Vote for Directors 33 36 24 25 17 2 137SP-Cumulative Voting 20 27 20 2 6 2 77SP-Eliminate Dual Class Shares 0 4 4 4 3 6 21SP-Subject Pill to Shareholder Approval/ 3 0 5 2 4 2 16SP-Other Corporate Governance Proposals 18 12 12 7 20 23 92
34
Table 3: CRSP Coverage
Year Number ofCRSP Funds
Number ofMerged Funds
Fraction TNA CRSP TNA Merged Fraction
2010 5,763 3,224 0.56 5,252,852 4,080,021 0.782011 5,970 3,326 0.56 4,990,577 3,890,089 0.782012 5,833 3,357 0.58 5,586,311 4,356,507 0.782013 6,024 3,342 0.55 7,652,467 5,992,014 0.782014 6,207 3,312 0.53 8,603,009 6,725,080 0.782015 6,420 3,237 0.50 8,551,900 6,723,619 0.79Notes: Number of CRSP Funds is the number of unique crsp_portno’s in CRSP in the respective year that holdU.S. common stock. Number of Merged Funds is the number of such crsp_portno’s that were merged with afundid from ISS Voting Analytics for which we estimated an ideal point. TNA CRSP is the sum of total netassets invested in common stock for crsp_portno’s in CRSP that hold U.S. cmmon stock. TNA Merged is thesum of total net assets invested in common stock for crsp_portno’s in CRSP that were merged with a fundidfrom ISS Voting Analytics for which we estimated an ideal point. All TNA in $ millions.
35
Table 4: CRSP Summary Statistics
N Mean St. Dev. 25th %-tile median 75th %-tileTNA ($ millions) 8143 882 5284 17 98 458Index fund 8143 0.12 0.33 0 0 0Growth fund 8143 0.23 0.42 0 0 0Value fund 8143 0.11 0.31 0 0 0Exp. ratio (bps) 3478 104 84 65 101 134Number funds in family 8143 95 107 20 70 123Family TNA ($ millions) 8143 98954 229798 2122 18894 59342Frac. family indexed 8143 0.16 0.28 0 0.021 0.15Frac. family growth 8143 0.26 0.22 0.083 0.24 0.39Frac. family value 8143 0.12 0.15 0.017 0.08 0.16Family avg. exp. ratio (bps) 7930 70 62 37 67 98Notes: Sample is all crsp_portno’s that appear in the CRSP Mutual Fund dataset from 2010 - 2015 that helddomestic equity. Time-varying variables are created by averaging over the years the fund is in the sample.TNA is total net assets in common stock. Index fund, Growth fund, and Value fund are indicators for funds thatCRSP identifies as "pure" index funds, growth funds, and value funds, respectively. Exp. ratio is the expenseratio of the fund. Family TNA is total net assets in domestic common stock held by mutual funds in the fund’sfamily. Frac. family indexed / growth / value are the fractions of common stock held by funds in the fund’sfamily that are in funds in the respective category. Family avg. exp. ratio is the average expense ratio acrossfunds in the fund’s family that own domestic equity, weighted by TNA of each fund. Number funds in familyis the number of crsp_portno’s in CRSP for the management company that manages the fund.
Table 5: Merged Sample Summary Statistics
N Mean St. Dev. 25th %-tile median 75th %-tileTNA ($ millions) 3611 1519 7394 67 258 857Index fund 3611 0.17 0.38 0 0 0Growth fund 3611 0.27 0.44 0 0 1Value fund 3611 0.15 0.35 0 0 0Exp. ratio (bps) 1761 98 46 65 97 127Number funds in family 3611 115 113 40 85 132Family TNA ($ millions) 3611 128937 259684 9009 29643 80759Frac. family indexed 3611 0.19 0.29 0 0.058 0.17Frac. family growth 3611 0.28 0.21 0.13 0.28 0.39Frac. family value 3611 0.12 0.13 0.046 0.091 0.17Family avg. exp. ratio (bps) 3574 65 38 37 66 93Number of votes 3611 1452 2592 377 641 1326Notes: Sample is all crsp_portno’s that appear in the CRSP Mutual Fund dataset from 2010 - 2015 that weremerged with a fundid from ISS Voting Analytics for which we estimated an ideal point. Common variables areas defined in notes to Table 4. Number of votes is the number of proposals voted on by the fund used to estimateits ideal point.
36
Table 6: Goodness of Fit by Number of Dimensions
# of dims CP APRE1 0.84 0.362 0.87 0.493 0.90 0.584 0.91 0.655 0.93 0.706 0.94 0.747 0.94 0.778 0.95 0.819 0.96 0.83
10 0.96 0.85
Table 7: Fraction of Votes Cast in the Party’s Minority
proptype All Man. Party SI Party SV PartyMP-Compensation 0.24 0.14 0.05 0.04MP-Corporate Finance 0.22 0.24 0.04 0.09MP-Corporate Governance 0.30 0.22 0.06 0.08MP-Elect Director (Contested) 0.29 0.29 0.04 0.09MP-Elect Directors 0.23 0.16 0.03 0.08MP-Merger / Acquisition Related 0.15 0.18 0.10 0.02MP-Other 0.17 0.25 0.03 0.02MP-Ratify Auditors 0.13 0.05 0.01 0.12SP-Corporate Governance 0.32 0.19 0.06 0.17SP-Elect / Remove Directors (Contested) 0.28 0.28 0.06 0.08SP-Executive/Director Compensation 0.37 0.10 0.05 0.05SP-Other 0.33 0.16 0.07 0.07SP-Social Proposal 0.37 0.08 0.13 0.08All 0.25 0.16 0.04 0.08Notes: For each party we determine for each proposal the majority vote among members of the party.We then calculate the fraction of party member votes that are cast against the party’s majority.
37
Table 8: Fraction of Yes Votes by Proposal Category, Party
All Man. Party SI Party SV PartyMP-Compensation 0.68 0.83 0.59 0.46MP-Corporate Finance 0.68 0.72 0.67 0.52MP-Corporate Governance 0.57 0.72 0.44 0.42MP-Elect Director (Contested) 0.40 0.61 0.20 0.59MP-Elect Directors 0.72 0.83 0.64 0.52MP-Merger / Acquisition Related 0.83 0.80 0.81 0.92MP-Other 0.21 0.33 0.08 0.07MP-Ratify Auditors 0.85 0.95 0.93 0.16All MP 0.70 0.82 0.62 0.50SP-Corporate Governance 0.53 0.26 0.82 0.70SP-Elect / Remove Directors (Contested) 0.54 0.36 0.71 0.36SP-Executive/Director Compensation 0.44 0.10 0.94 0.34SP-Other 0.42 0.18 0.84 0.26SP-Social Proposal 0.39 0.09 0.83 0.31All SP 0.47 0.19 0.84 0.50
Table 9: Fraction of Yes Votes by Corporate Governance Proposal Category, Party
All Man. Party SI Party SV PartySP-Proxy Access 0.69 0.46 0.92 0.64SP-Independent Chairman/Lead Director 0.35 0.13 0.55 0.64SP-Written Consent 0.55 0.15 0.91 0.89SP-Special Meetings 0.57 0.23 0.96 0.61SP-Declassify Board 0.89 0.83 0.96 0.81SP-Reduce Supermajority Requirements 0.83 0.68 0.94 0.94SP-Executive/Director Compensation 0.41 0.09 0.94 0.26SP-Majority Vote for Directors 0.71 0.42 0.96 0.97SP-Cumulative Voting 0.36 0.10 0.85 0.25SP-Eliminate Dual Class Shares 0.88 0.75 0.99 0.95SP-Subject Pill to Shareholder Approval/Redeem Pill 0.85 0.70 0.98 0.99SP-Other Corporate Governance Proposals 0.43 0.14 0.84 0.48
38
Table 10: Fund Characteristics by Party
Man. Party SI Party SV PartyFraction Index 0.34 0.13 0.15
TNA ($ millions) 2815 599 1064Family TNA ($ millions) 274326 34284 66865
Expense Ratio (bps) 45 79 80Fraction Growth 0.31 0.26 0.17
Fraction Value 0.1 0.15 0.22Fraction MF TNA in Party 2015 0.68 0.11 0.054Notes: Index is the fraction of funds (by assets) in the party that are index funds; TNAis the average total net assets in domestic equity of funds in the party; Family TNA isthe average total net assets of the fund family of the funds in the party; Expense Ratiois the weighted average expense ratio of funds in the party in basis points (weightedby the fund’s TNA). Fraction Growth and Fraction Value are the fraction of funds (byassets) in the party that are growth funds and value funds, respectively.
Table 11: The Determinants of Mutual Fund Preference: Fund-level Covariates
Score 1 Score 2
(1) (2) (3) (4)
Index fund −8.638 −11.698 −16.955∗∗ 2.211(7.046) (15.275) (7.281) (4.820)
Growth fund −3.504 −10.159∗∗ −5.215∗ −6.492∗
(2.921) (4.232) (3.007) (3.323)Value fund −1.224 −1.028 7.008∗∗ 8.832∗∗
(3.467) (5.080) (2.916) (3.447)Exp. ratio (bps) 0.079 0.232∗∗∗
(0.144) (0.089)2nd quintile TNA −5.797 −2.174 −1.054 2.560
(5.816) (6.769) (2.338) (3.716)3rd quintile TNA −7.518 0.399 −3.787 1.819
(6.143) (8.073) (2.622) (4.111)4th quintile TNA −13.353∗∗ −8.415 −6.719∗∗ −3.234
(6.325) (8.924) (3.302) (4.718)5th quintile TNA −26.448∗∗∗ −19.461∗ −17.697∗∗∗ −11.834∗∗
(6.872) (10.107) (4.628) (4.621)Constant 11.582∗ 2.765 7.435∗∗ −24.117∗∗
(6.862) (21.417) (3.523) (12.228)
Observations 3,611 1,761 3,611 1,761R2 0.055 0.125 0.122 0.252
Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01Regression is weighted using the funds’ TNA.
Standard errors are clustered at the fund family level.
39
Table 12: The Determinants of Mutual Fund Preference: Family-level Covariates
Score 1 Score 2
(1) (2) (3) (4)
Frac. family indexed −30.453∗∗ −31.009∗ −32.280∗∗ −23.459∗∗
(12.117) (16.848) (12.882) (10.740)Frac. family growth −14.291 −15.723 −7.470 −4.376
(17.421) (18.308) (14.161) (14.097)Frac. family value −7.513 −8.459 52.887∗ 54.031∗∗
(27.289) (28.296) (27.666) (27.356)Family avg. exp. ratio (bps) 0.002 0.158
(0.133) (0.102)2nd quintile family TNA 7.145 7.325 −5.552 −4.856
(9.052) (9.216) (9.010) (9.385)3rd quintile family TNA 20.100∗∗ 21.930∗∗ −3.542 0.965
(9.048) (9.643) (8.071) (8.744)4th quintile family TNA 9.531 9.649 −7.723 −3.462
(9.580) (10.441) (8.205) (9.179)5th quintile family TNA −7.566 −7.223 −15.715∗∗ −6.085
(8.763) (11.529) (7.935) (9.943)Constant 2.354 2.630 5.790 −13.957
(9.140) (20.031) (9.091) (15.331)
Observations 3,611 3,574 3,611 3,574R2 0.120 0.122 0.215 0.234
Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01Regression is weighted using the funds’ TNA.
Standard errors are clustered at the fund family level.
40
Table 13: Summary of Company Shareholder Base Preference Sample
Year
NumberofCompanies
Avg. %SharesOwned byMFs
Avg. % SharesOwned by MFswith Ideal Points
Avg. Number ofFundShareholderswith Ideal Points
2002 2,827 19 11 59
2003 3,161 20 11 60
2004 3,268 22 13 73
2005 3,265 21 14 75
2006 3,310 22 14 79
2007 3,158 24 16 104
2008 2,571 28 21 153
2009 2,744 27 22 157
2010 2,782 27 23 149
2011 2,637 27 23 152
2012 2,683 27 23 147
2013 2,842 27 23 136
2014 2,995 26 22 124
2015 3,020 25 22 117
2016 3,023 26 23 122
41
Table 14: Company shareholder scores: Growth vs Value companies
Company Shareholder Score 1 Company Shareholder Score 2
(1) (2) (3) (4)
Book-to-Market 5.043∗∗∗ 3.841∗∗∗
(0.385) (0.385)2nd Quintile Book-to-Market −0.078 1.183∗∗∗
(0.322) (0.235)3rd Quintile Book-to-Market 1.021∗∗∗ 2.436∗∗∗
(0.358) (0.260)4th Quintile Book-to-Market 3.343∗∗∗ 3.878∗∗∗
(0.399) (0.280)5th Quintile Book-to-Market 5.486∗∗∗ 4.862∗∗∗
(0.473) (0.329)Constant −9.910∗∗∗ −8.625∗∗∗ −9.663∗∗∗ −9.668∗∗∗
(0.429) (0.430) (0.429) (0.276)
Year Fixed effects Yes Yes Yes YesObservations 41,360 41,360 41,360 41,360R2 0.061 0.062 0.095 0.099
Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01Standard errors are clustered at the company level
42
0
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Figure 1: Scree Plot
43
Columbia FundsCharles Schwab Pax World Manag
Calvert Investm
Domini Social I
Green Century C
American Funds
Vanguard Group
Institutional S
Fidelity Manage
T Rowe Price As
William Blair &
BlackRock IncState Street Ba
Glass Lewis
Management−50
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Dimension 1
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Figure 2: Dimension 1 vs. Dimension 2
44
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45
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Figure 4: Histograms of a1 and a2 by Management Recommendation
46
0.00
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proptype
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LoadingDim1
Extremely Positive
Non−Extreme
Extremely Negative
LoadingDim2
Extremely Positive
Non−Extreme
Extremely Negative
Figure 5: Signs of a1 and a2 by Proposal Category
47
ISS Supports
ISS Opposes
MP
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Fin
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proptype
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Figure 6: Sign of a1 by Proposal Category, ISS Rec.
48
GL Supports
GL Opposes
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Figure 7: Sign of a2 by Proposal Category, GL Rec.
49
Columbia Funds
Charles Schwab Pax World Manag
Calvert Investm
Domini Social I
American Funds
Vanguard Group
Institutional S
Fidelity Manage
T Rowe Price AsWilliam Blair &
BlackRock IncState Street Ba
Glass Lewis
Management
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2010 − 2011
Columbia FundsCharles Schwab
Pax World Manag
Calvert Investm
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Vanguard Group
Institutional S
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BlackRock Inc
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Glass Lewis
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Columbia FundsCharles Schwab
Pax World Manag
Calvert Investm
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Green Century C
American Funds
Vanguard Group
Institutional S
Fidelity ManageT Rowe Price As
William Blair &BlackRock Inc
State Street Ba
Glass Lewis
Management−20
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Figure 8: Preference Estimates Over Time50
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Figure 9: Density of Mutual Fund Preferences
51
Shareholder Intervention Party
Managerialist Party
Shareholder Veto Party
−50
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score1
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Figure 10: The Parties
52
Ma
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Figure 11: Dissident Nominee for Director at DuPont
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Figure 12: A Shareholder Proposal on Exec. Comp. at Waste Management, Inc.
53
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Figure 13: A Say-on-Pay Proposal at PolyOne Corporation
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Figure 14: An Uncontested Director Election at Avnet, Inc.
54
−30
0
30
60
4 6 8 10 12
log(Market Value of Equity)
Com
pany
Sha
ehol
der
Sco
re 1
−50
0
50
100
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Com
pany
Sha
reho
lder
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re 2
Figure 15: Company shareholder scores vs. market capitalization (2012)
55
−50
0
50
−40 0 40 80
Dimension 1
Dim
ensi
on 2
Figure 16: Company shareholder scores (2002) and density of mutual fund scores
−50
0
50
−40 0 40 80
Dimension 1
Dim
ensi
on 2
Figure 17: Company shareholder scores (2016) and density of mutual fund scores
56
−15
−10
−5
0
1 2 3 4 5
Sco
reDimension 1
−16
−12
−8
−4
1 2 3 4 5
BE/ME quintile
Sco
re
Dimension 2
year20022005
20072009
20112013
2016
Figure 18: Average company mean shareholder scores by Book-to-Market quintiles - selectedyears
57
−12
−9
−6
2004 2008 2012 2016
year
Sco
re
Dimension Dimension 1 Dimension 2
Figure 19: Yearly average of company shareholder score
0.00
0.01
0.02
0.03
0.04
0.05
−60 −30 0 30 60
Dimension 1
dens
ity
0.00
0.02
0.04
0.06
−50 0 50 100
Dimension 2
dens
ity
year 2002 2007 2012 2016
Figure 20: Company shareholder score distributions: evolution over time
58
−10
0
2004 2008 2012 2016
year
Sco
re
Dimension 1
−15
−10
−5
0
2004 2008 2012 2016
year
Dimension 2
cap.bucket Large−Cap Mid−Cap Small−Cap Micro−Cap
Figure 21: Average company-level score by market-cap category
59
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