Performance Persistence in Institutional Investment Management
Performance Persistence in Institutional Investment
Jeﬀrey A. Busse Amit Goyal Sunil Wahal∗
Using new, survivorship-bias free data, we examine the persistence in perfor-
mance of 6,260 institutional portfolios managed by 1,475 investment managers
between 1991 and 2004. Persistence in domestic equity portfolios is signiﬁcant
and economically large up to one year after portfolio formation. Unlike retail mu-
tual funds, this persistence is entirely in winner portfolios. Similar patterns are
evident in international equity portfolios, but persistence in ﬁxed income portfo-
lios lasts up to three years. Better performing portfolios are more likely to oﬀer
incentive fees and most-favored-nation clauses but also charge higher fees. Fee
size is insuﬃcient to eliminate excess returns. Top performers draw an inﬂux of
assets from plan sponsors, and in the year following such inﬂows, alphas sharply
decline. Overall, the results are consistent with the supply-side quantity-based
equilibrating process modeled by Berk and Green (2004).
Busse is from the Goizueta Business School, Emory University, email: Jeﬀ Busse@bus.emory.edu;
Goyal is from the Goizueta Business School, Emory University, email: Amit Goyal@bus.emory.edu; and
Wahal is from the WP Carey School of Business, Arizona State University, email: Sunil.Wahal@asu.edu.
We are indebted to Margaret Tobiasen at Informa Investment Solutions, and to Jim Minnick and
Frithjof van Zyp at eVestment Alliance for graciously providing data. We thank Byoung-Hyoun Hwang,
George Benston, Narasimhan Jegadeesh, and seminar particpants at Emory University, UCLA and the
University of Oregon for helpful suggestions.
Performance persistence in delegated investment management represents a signiﬁcant
challenge to eﬃcient markets. Academic opinion on whether persistence exists is Bayesian
– we update our priors based on the most recent evidence incorporating either new data
or improved measurement technology. Although Jensen’s (1968) original examination of
mutual funds concludes that funds do not produce abnormal performance, later stud-
ies provide compelling evidence that relative performance persists over both short and
long horizons (see, for example, Grinblatt and Titman (1992), Elton, Gruber, Das,
and Hlavka (1993), Hendricks, Patel, and Zeckhauser (1993), Goetzmann and Ibbotson
(1994), Brown and Goetzmann (1995), Elton, Gruber, and Blake (1996), and Wermers
(1999)). More recently, Carhart (1997) shows that accounting for momentum in indi-
vidual stock returns eliminates almost all evidence of persistence among mutual funds.1
However, Bollen and Busse (2005) ﬁnd that performance persists over short quarterly
holding periods, even after controlling for momentum.
The attention given to persistence in retail mutual fund performance is entirely war-
ranted. The data are good, and this form of delegated asset management provides
millions of investors access to readily-built portfolios. As a result, at the end of 2004,
there were 7,101 equity, bond, and hybrid mutual funds responsible for investing $6.1
trillion in assets (Investment Company Institute (2004)). However, there is an equally
large arm of delegated investment management that receives much less attention but is
no less important. At the end of 2004, over 50,000 plan sponsors (public and private
retirement plans, endowments, foundations, and multi-employer unions) allocated over
$6 trillion in assets to about 1,500 institutional asset managers (Money Market Direc-
tory, 2004). In this paper, we comprehensively examine the performance persistence of
portfolios managed by institutional investment management ﬁrms.2
Institutional asset management ﬁrms draw ﬁxed amounts of capital (referred to as
“mandates”) from public and private deﬁned beneﬁt retirement plans, endowments,
foundations, unions, and trusts. Although these mandates span a variety of asset classes,
including domestic equity, ﬁxed income, international equity, real estate securities, and
One exception is the continued strong underperformance of the worst performing funds. Berk and
Xu (2004) show that this persistence occurs in both the current and prior year. They argue that
the unwillingness of investors to withdraw capital from these funds causes this remaining pocket of
Persistence in another largely unexplored form of delegated asset management, hedge funds, is
studied by Jagannathan and Novikov (2005), among others. They estimate persistence in hedge returns
to be approximately 1 percent per year.
alternative assets, we restrict our attention to the ﬁrst three. To our knowledge, we
are the ﬁrst to examine persistence in asset classes beyond domestic equity. Invest-
ment styles within these asset classes run the gamut in terms of size and growth-value
gradations for equity portfolios, and credit and maturity dimensions for ﬁxed income
portfolios. Investment style identiﬁcation is important to plan sponsors because it al-
lows plan sponsors to make better-informed asset allocation decisions. Conveniently, it
also provides us with self-identiﬁed natural benchmarks to judge returns.
Our data are drawn from two independent sources: Informa Investment Solutions
(IIS) and eVestment Alliance. Both ﬁrms collect self-reported returns and other infor-
mation from investment management ﬁrms and provide data, services, and consulting to
plan sponsors, investment consultants, and investment management ﬁrms. IIS provides
a longer time series of returns and has better cross-sectional coverage; it provides quar-
terly returns for 6,260 portfolios managed by 1,475 institutional asset management ﬁrms
from 1979 to 2004. Since these data suﬀer from survivorship bias prior to 1991, we de-
rive our estimates from the post-1991 sample period. In addition to returns, eVestment
provides detailed information about fee arrangements, schedules, and whether portfolios
are closed to new investors. We use these data to add texture to our persistence analysis
and sharpen inferences with respect to net of fee performance.
We investigate persistence over both short and long horizons. We form deciles using
one-year raw returns as well as benchmark-adjusted returns, and then evaluate the
future excess performance of these deciles. Using raw returns to form deciles mirrors
the typical method employed in mutual fund studies, and allows us to compare our
results with those in such papers. Using benchmark-adjusted returns is consistent with
performance evaluation methods used by plan sponsors, and also aﬀords us the ability to
show benchmark-adjusted returns and information ratios in the post portfolio formation
periods. We estimate alphas using both unconditional and conditional three- and four-
One quarter after deciles are formed based on raw returns, mean decile alphas are
positive, large, and statistically signﬁcant in the majority of winner portfolios. For in-
stance, the one-quarter Fama and French (1993) three-factor alpha from the extreme
winner decile is 2.88 percent per quarter with a t-statistic of 3.77. Accounting for mo-
mentum using the Carhart (1997) procedure reduces the point estimate to 1.07 percent
per quarter with a t-statistic of 1.85. Using a factor model that includes an aggregate
bond index, the term spread, and the default spread, unconditional one-quarter alphas
for the winner ﬁxed income decile vary from 1.03 percent to 1.53 percent depending
on the speciﬁcation. Consistent with Christopherson, Ferson and Glassman (1998), we
ﬁnd that conditioning information materially inﬂuences, but does not reduce, estimates
of alphas for ﬁxed income portfolios. The alphas of the winner international equity
decile vary from 2.04 percent to 2.67 percent per quarter. When we form deciles based
on benchmark-adjusted returns, the alphas shrink somewhat but remain economically
large and statistically signﬁcant.
Performance continues to persist over longer horizons. For domestic equity portfolios,
persistence continues up to one year after portfolio formation; the quarterly alpha of the
extreme winner decile from a three-factor model over a one-year evaluation period is
1.44 percent. Interestingly, after the ﬁrst year, domestic equity alphas are statistically
indistinguishable from zero. For ﬁxed income portfolios, the ﬁrst year alpha for the
extreme winner decile is 0.62 percent. In the second and third year thereafter, the
alpha shrinks marginally (to 0.60 and 0.51 percent respectively) and retains statistical
signiﬁcance. International equity portfolios show a pattern similar to domestic equity.
One year after decile formation, the alpha for the extreme winner decile is 2.76 percent,
but is indistinguishable from zero in the second and third year.
Our persistence results are based on returns that are gross of fees (but net of trading
costs). Investment management fees could potentially wipe out any gains to plan spon-
sors from chasing prior winners. To determine if that is the case, we use fee schedules
from our second data source (eVestment Alliance). These schedules provide a pro forma
fee that typically declines with the size of the mandate supplied by the plan sponsor
(as in a step function). The marginal fees for each breakpoint vary across asset classes,
investment styles, and individual ﬁrms. We ﬁnd that annual fees are a fraction of the
alphas of the extreme winner portfolios and insuﬃcient to eliminate excess returns. For
example, using the most aggressive fee schedule for domestic equity portfolios, a $10
million mandate in small cap growth portfolios has an annual fee of 1 percent. For these
portfolios, the annual alpha is 5.76 percent (a quarterly alpha of 1.44 multiplied by 4),
which is an order of magnitude higher than fees. Moreover, the schedules themselves
represent an upper limit on actual fees charged by investment managers; individual fee
arrangements between plan sponsors and investment managers typically include rebates,
some of which can be quite large.
Beyond the sheer magnitude of fees, there is other interesting heterogeneity in fee
arrangements. Two types of arrangements are more common among better performing
portfolios: performance-based fees and most-favored nation (MFN) clauses. The former
typically involve low or no minimum fees, and link actual fees to beating a prescribed
benchmark. In domestic equity portfolios, almost 55 percent of portfolios in the extreme
winner decile oﬀer performance-based fees, compared with 47 percent for the extreme
loser decile. By comparison, the use of incentive fees is less common in mutual funds.
Elton, Gruber and Blake (2003) report that only 108 funds oﬀered incentive fees in 1999
(see also Golec and Starks (2004)). MFN clauses require that the investment manager
match fees for comparable mandates and plan sponsors. For example, if plan sponsor
A negotiates a fee of x basis points, and plan sponsor B subsequently negotiates a fee
of x-5 basis points for a similar sized mandate with similar restrictions, the investment
management ﬁrm is obliged to reduce A’s fees by 5 basis points. Again, investment
managers with portfolios in the extreme winner decile are more likely to oﬀer MFN
provisions than in the extreme loser decile (38 versus 33 percent).
There is considerable economic and practical signiﬁcance to persistence in institu-
tional portfolios. From a practical perspective, if performance persists net of fees, then
plan sponsors could beneﬁt from picking winners. Interestingly, and in stark contrast
to retail mutual funds, predictability in institutional portfolios arises entirely from the
winner portfolios. Since loser portfolios cannot be shorted, the upshot of this asymme-
try is that picking winners is a feasible strategy for plan sponsors. Consistent with this
implication, Goyal and Wahal (2005) show that plan sponsors hire investment managers
that have provided superior historical returns. The economic impact of chasing past
winners is that capital inﬂows should follow returns. Del Guercio and Tkac (2002) and
Heisler, Knittel, Neumann, and Stewart (2004) show that the ﬂow-performance relation
for institutional asset managers is linear, unlike the convex relation for mutual funds
(Ippolito (1992), Sirri and Tufano (1998), and others). If there are diseconomies of scale
in investment management, such inﬂows should cause future alphas to deteriorate.
We investigate the relation between performance and ﬂows across portfolios in each
of the three asset classes. For domestic equity portfolios, capital ﬂows one year after
portfolio formation increase monotonically from loser to winner deciles. The spread in
ﬂows between the extreme winner and loser deciles is as much as 36 percent of total
net assets. In dollar terms, the average portfolio in the loser decile loses $27 million of
assets while the average portfolio in the winner decile gains $132 million. Monotonicity
is also evident for ﬁxed income and international equity portfolios. Winner deciles have
considerably higher inﬂows than loser deciles; the extreme winner-loser spread in ﬂows
is 16 percent for ﬁxed income and 24 percent for international equity.
These large capital inﬂows appear to have severe consequences for future perfor-
mance, at least for domestic and international equity portfolios. In domestic equity
portfolios, the winner decile that had a Fama-French alpha of 1.44 percent per quarter
in the ﬁrst post-ranking year (with an accompanying inﬂow of 31.80 percent of assets),
has an alpha of 0.28 percent (statistically insigniﬁcant) in the second post-ranking year.
In international equity portfolios, the alpha for the extreme winner decile deteriorates
from 2.76 percent per quarter in the ﬁrst post-ranking year to 0.36 percent in the second
year and -0.35 percent in the third year. Persistence and ﬂows in ﬁxed income portfolios
remain an enigma: alphas in winner deciles remain high up to two years, and some-
times three years, after decile formation. Moreover, ﬂows do not appear to diminish
signiﬁcantly over time.
Even for equity portfolios, making the causal leap that ﬂows drive down alphas is
a diﬃcult, if not impossible, task. To do so, one would need to generate an estimate
of capacity as well as diseconomies originating from investment ideas and/or execution
costs – a task that is impossible without proprietary data. We can, however, make
some progress on the issue at a more aggregate level. We do so by taking our extreme
winner portfolio at the end of the ﬁrst year and double sorting it with respect to total
assets and capital ﬂows. Thus, we split the extreme winner portfolio into four groups.
If ﬂows drive down alphas for portfolios that are closer to capacity, then the subsequent
decline in alpha should be larger in the high total assets and high ﬂow intersection. The
advantage of this approach is that it holds pre-inﬂow alpha approximately constant and
allows us to focus on alpha changes. We ﬁnd that the decline in alpha is indeed higher for
the high-assets/high-ﬂow group than for the low-assets/low-ﬂow group. For example,
in the domestic equity asset class using a four-factor model, the decline in alpha is -
0.53 percent for the former group and -0.21 percent for the latter. While this certainly
does not nail the causal issue, it does reinforce the idea that ﬂows and performance
are endogenous. This notion is central to papers that seek to understand the ﬂow-
performance relation (Lynch and Musto (2003), Berk and Green (2004), and others).
It is this very endogeneity that, when combined with diseconomies of scale, produces
the quantity-based equilibrating mechanism that is at the heart of Berk and Green
(2004). Our results show that such equilibration is not instantaneous – some persistence
is necessary to draw the ﬂows that subsequently extinguish persistence. In retail mutual
funds, redemptions and capital inﬂows are rapid. In contrast, in an institutional setting,
capital ﬂows are sticky. As a result, the equilibration process appears to take one to two
Mechanisms exist to control asset ﬂows that are endogenous to investment man-
agers. For instance, investment managers that recognize potential diseconomies and
understand the ﬂow-performance relation could simply refuse capital inﬂows by shut-
ting winner portfolios to new investors. In retail mutual funds, Bris, Gulen, Kadiyala,
and Rau (2005) report that over a 10 year period, 143 equity funds that delivered posi-
tive excess returns subsequently closed to new investors. In our sample, 208 out of 2,881
domestic equity portfolios are closed to new investors, a rate that is higher than that
for retail mutual funds. More interestingly, portfolios that are closed to new investors
have an average benchmark-adjusted quarterly return of 1.15 percent, compared to 0.65
percent for all portfolios. Thus, it appears that some investment management ﬁrms close
portfolios to new investors due to concerns about diseconomies.3 Another way to con-
trol ﬂows is through fees. Although we can only observe fee schedules (not actual fees),
we can determine if portfolios in the winner decile charge more than those in the loser
decile. Our data provide some weak evidence that this is indeed the case: for similar
sized mandates, marginal fees are higher for better performing portfolios. To the extent
that better-performing portfolios are likely to oﬀer smaller rebates than portfolios in the
loser decile, the spread in fees may in fact be larger than we can detect.
Our paper builds on the small but growing literature in institutional asset manage-
ment. The progenitors in this area are Lakonishok, Shleifer, and Vishny (1992), who
examine the performance of equity-only portfolios managed by 341 investment manage-
ment ﬁrms between 1983 and 1989. Lakonishok et al. (1992) ﬁnd that performance is
poor on average, and although there is some evidence of persistence, they conclude that
survival bias and a short time series prevent them from drawing a robust conclusion.
Coggin, Fabozzi, and Rahman (1993) also focus on equity portfolios and ﬁnd that in-
vestment managers have limited skill in selecting stocks. Del Guercio and Tkac (2002)
and Heisler, Knittel, Neumann, and Stewart (2004) examine the relation between ﬂow
of funds and performance and conclude that plan sponsors withdraw funds from poorly
performing investment managers. They also ﬁnd that ﬂows are positively related to
Jensen’s alpha but negatively related to tracking error. Goyal and Wahal (2005) exam-
ine the selection and termination of investment managers by plan sponsors and ﬁnd that
investment management ﬁrms are hired after superior performance and, generally, but
not exclusively, ﬁred after poor performance. Post-hiring excess returns are zero, rather
than positive, as one would expect if performance persists. However, the diﬀerence be-
tween their results and ours is largely due to horizon: hiring decisions are conditioned
on three-year returns, at which point persistence has died out. Ferson and Khang (2002)
use portfolio weights to infer persistence, and Tonks (2005) examines the performance of
For example, Aronson+Johson+Ortiz, a prominent investment management ﬁrm, recently stopped
accepting new capital into its large cap value portfolio precisely because of such worries.
UK pension fund managers between 1983 and 1997. Both ﬁnd some evidence of excess
performance. Perhaps the closest study to ours is Christopherson, Ferson, and Glass-
man (1998), who study persistence among 185 equity-only investment managers between
1979 and 1990. Although their sample suﬀers from survival bias, using a conditional
approach, they ﬁnd some evidence of persistence, particularly among poorly performing
Our paper proceeds as follows. Section 2 discusses our data and methodology. Section
3 presents results. Section 4 concludes.
2 Data and Methodology
As mentioned earlier, we obtain data from two independent data providers: Informa In-
vestment Solutions (IIS) and eVestment Alliance. Both ﬁrms provide data, services, and
consulting to plan sponsors, investment consultants, and investment managers. Since
there are diﬀerences in composition of each of the databases, we describe them in detail
below, noting issues that are particularly relevant for our results.
IIS provides quarterly returns of portfolios managed by investment management ﬁrms
from 1979 to 2004. Panel A of Table 1 presents some basic descriptive statistics of the
IIS database. Prior to 1991, this database only contains “live” portfolios. Subsequently,
data gathering policies were revised such that investment management ﬁrms that exit
the universe due to closures, mergers, and bankruptcies were retained in the database.
Thus, data over the 1979-1990 sample period suﬀers from survivorship bias, while the
returns thereafter are free of such problems. We therefore report separate statistics
for the two subperiods, 1979-1990 and 1991-2004. Not surprisingly, both the total and
average number of ﬁrms (and portfolios) per year are much higher in the second part
of the sample period. In general, coverage of the database is fairly comprehensive;
we cross-check the number of ﬁrms with data contained in the Mercer Performance
Analytic database and ﬁnd that our database coverage is slightly better. Our coverage
also corresponds favorably to that found in publications such as the Money Market
Using the structural break in survivorship in our sample (1979-1990 and 1991-2004), we later show
that the magnitude of the survivorship can be quite large. For example, the survivorship-biased alpha
computed using Fama-French procedures in the extreme loser portfolio is 0.82 percent per quarter versus
-1.19 percent in the non-survivorship-biased period, a spread of 2 percent per quanter.
Directory of Investment Advisors. As expected, the attrition rate of portfolios between
1979 and 1990 is zero. Carhart (1997) reports that one-third of all retail mutual funds
disappear over a 31 year period, which corresponds to about 3 percent per year. Attrition
in our non-survivorship biased sample period (1991-2004) is slightly higher and varies
from 3.2 to 3.6 percent per year.
Several features of the data are important for understanding the results. First, since
investment management ﬁrms typically manage more than one portfolio, the database
contains returns for each portfolio. For example, Aronson+Johnson+Ortiz, an invest-
ment management ﬁrm with over $22 billion in assets, manages 10 portfolios in a variety
of capitalizations and value strategies. The returns in our database correspond to each
of these 10 portfolios, and our unit of analysis is each portfolio return. Second, the
database contains “composite” returns provided by the investment management ﬁrm.
The individual returns earned by each plan sponsor client (account) may deviate from
these composite returns for a variety of reasons. For example, a public deﬁned beneﬁt
plan may ask an investment management ﬁrm to eliminate “sin” stocks from its portfo-
lio. Such restrictions cause deviations from composite returns, but these deviations are
typically quite small. Therefore, composite returns are representative of actual earned
returns. Third, the returns are net of trading costs but gross of investment management
The database also contains two other critical pieces of information: style assignments
and assets at the end of the year. For domestic and international equity portfolios, each
portfolio is associated with a primary style and a market capitalization. Twenty-nine
primary equity styles and four market capitalization categories exist. The majority of
the data reside in value, growth, and core-diversiﬁed styles. The market capitalization
categories include micro (<$500 million), small ($500 million–$2 billion), medium ($2
billion–$7 billion), and large (>$7 billion). Geographic parameters are not available
for international equity portfolios (e.g. EAFE or EAFE excluding Japan). Twenty-
eight primary ﬁxed income styles exist, but again, most of the data reside in just a
few categories, including core, maturity controlled, government, and high yield. Fixed
income maturity breakpoints are 1, 3, and 7 years. Unlike returns, total assets in each
portfolio are only recorded at the end of the year. Moreover, asset information is only
available for approximately 30 percent of investment management ﬁrms.
The database contains both active and passive portfolios, but since our interest is in
the performance persistence of active managers, we remove all passive portfolios from
the sample. We break statistics down by the three major asset classes - domestic equity
(including all size and value-growth intersections), ﬁxed income (domestic ﬁxed income
portfolios containing corporate and/or government debt securities), and international
equity (including global portfolios). This is in contrast to Lakonishok et al. (1992), Del
Guercio and Tkac (2002), Heisler et al. (2004), and Christopherson et al. (1998), all of
whom focus exclusively on domestic equity portfolios.
Our secondary data source, eVestment Alliance, provides quarterly composite re-
turns, fee information, and an identiﬁer that tags portfolios that are closed to new
investors. Unlike IIS, the names of investment management ﬁrms are not hidden. Panel
B of Table 1 provides descriptive statistics for the eVestment data. The time series cov-
erage is shorter, starting in 1991. The cross-sectional coverage is also smaller than the
IIS database. For example, the IIS database covers 1,137 domestic equity investment
management ﬁrms and 3,381 portfolios between 1991 and 2004. In contrast, eVestment
provides data on 805 ﬁrms and 2,682 portfolios. The attrition rate is approximately 1
percent, substantially smaller than for the IIS database.5 Because of these diﬀerences,
we generate estimates of persistence from the IIS database, and use eVestment data to
provide a better understanding of fees and portfolio closures.
2.2 Methodological Approach
Our empirical approach to measuring persistence follows the mutual fund literature
with some minor adjustments to accommodate certain facets of institutional investment
management. For instance, we follow Carhart (1997) and form deciles based on raw
returns during a ranking period and examine returns over a subsequent evaluation period.
However, we also form deciles based on benchmark-adjusted returns for two reasons.
First, plan sponsors frequently focus on benchmark-adjusted returns, at least in part
because expected returns from benchmarks are useful for thinking about broader asset
allocation decisions in the context of contributions and retirement withdrawls. Second,
sorting on raw returns could cause portfolios that follow certain types of investment
styles to systematically fall into winner and loser deciles. For instance, small cap value
portfolios may fall into winner deciles in some periods, not because these portfolios
delivered abnormal returns, but because this asset class generated large returns over
that period. Although alpha estimates from three- and four-factor models adjusts for
these characteristics in the post-ranking period, they do not inﬂuence decile formation if
We note, however, that a direct comparison of the attrition rates is not possible because the cross-
sectional coverage is also smaller; it could be that attrition rates in the ﬁrms not sampled are higher.
assignments are based on raw returns. By contrast, using benchmark-adjusted returns
to form deciles circumvents this problem.
Beginning at the end of 1979, we sort portfolios into deciles based on the prior annual
raw or benchmark-adjusted return.6 We then compute the equal-weighted return for each
decile over the following one-quarter. As we expand our analysis to examine persistence
over longer horizons, we compute this return over appropriate future intervals (for one-
year results, we compute the equally-weighted return over quarters 1 through 4, and so
forth). We then roll forward, producing a non-overlapping set of post-ranking quarterly
returns. Concatenating the evaluation period quarterly returns results in a time series of
post-ranking returns for each portfolio; we generate estimates of abnormal performance
from these time-series.
We assess post-ranking abnormal performance by regressing the post-ranking returns
on K factors as follows:
rp,t = αp + βp,k fk,t + p,t , (1)
where r is the return on portfolio p, and fk is the k th factor return. For domestic equity
portfolios we use the Fama and French (1993) three-factor model with market, size, and
book-to-market factors. Since Carhart (1997) shows that incorporating individual stock
momentum (Jegadeesh and Titman (1993)) removes most of the persistence evident in
mutual funds, we also estimate models that include a momentum factor. We obtain these
four factors from Ken French’s web site. For ﬁxed income portfolios, we again follow
Fama and French (1993) and estimate a three-factor model with the Lehman Brothers
Aggregate Bond Index return, a Term Spread Return computed as the diﬀerence between
the long-term government bond return and the T-bill return, and a Default Spread
Return computed as the diﬀerence between the corporate bond return and the long-
term government bond return. We obtain aggregate bond index returns from Mercer
Performance Analytics, available from 1981 onwards. We obtain the default and term
spread returns from Ibbotson Associates, available from 1979 onwards. For international
equity portfolios, we employ an international version of the three-factor model. We
obain the international market return and book-to-market factor from Ken French. We
We sort based on prior one-year returns for two reasons. First, this choice is consistent with that of
the mutual fund literature and allows us to directly compare estimates of alpha. Second, longer ranking
periods are more likely associated with large diﬀerences in fund size, and, given the evidence in Chen,
Hong, Huang, and Kubik (2004), we might not expect performance to persist across large variations in
fund size (due to diseconomies).
compute the international size factor as the diﬀerence between the S&P/Citigroup PMI
World index return and the S&P/Citigroup EMI World index return, both of which
exclude the United States (see http://www.globalindices.standardandpoors.com).
Christopherson et al. (1998) argue that unconditional performance measures are
inappropriate for two reasons. First, they note that sophisticated plan sponsors pre-
sumably condition their expectations based on the state of the economy. Second, to the
extent that plan sponsors employ dynamic trading strategies that react to changes in
market conditions, unconditional performance indicators may be biased. They advocate
and show that conditional performance measures can improve inferences. We follow their
prescription and estimate conditional models in addition to the unconditional models
described above. We estimate the conditional models as:
C 0 l
rp,t = αp + βp,k + βp,k Zl,t−1 fk,t + p,t , (2)
where the Z’s are L conditioning variables.
We use four conditioning variables in our analysis. We obtain the 3-month T-bill
rate from the economic research database at the Federal Reserve Bank at St. Louis.
We compute the default yield spread as the diﬀerence between BAA- and AAA- rated
corporate bonds using the same database. We obtain the dividend-price ratio, computed
as the logarithm of the 12-month sum of dividends on the S&P 500 index divided by
the logarithm of the index level, from Standard & Poor’s. Finally, we compute the term
yield spread as the diﬀerence between the long term yield on government bonds and the
T-bill yield, using data from Ibbotson Associates.
3.1 Short-term Persistence
Table 2 shows estimates of one-quarter evaluation period alphas where raw returns deter-
mine decile assignment. The table reports results for both unconditional and conditional
methods. The t-statistic reported next to the alpha estimate for each decile is based on
the standard error of the regression coeﬃcient. At the bottom of each column we report
a Spearman correlation coeﬃcient.
Panel A of Table 2 shows results for domestic equity. Although we do not report
each of the individual R from the regressions, the average R is 0.91 (0.94) for the
unconditional three-factor (four-factor) model. The corresponding average R for the
conditional models are greater, 0.95 and 0.97 respectively. In general, the R are large
and comparable to those in prior studies (Fama and French (1993) and Carhart (1997)).
Three facts immediately stand out. First, the eﬀect of survivorship bias is quite
large. For instance, the alpha of the ﬁrst decile (extreme losers) using the three-factor
model over the survivor-biased 1979-1990 sample period is 0.82 percent per quarter with
a t-statistic of 2.11. The corresponding alpha in the non-survivorship-biased period of
1991-2004 is -1.19 percent, a diﬀerential of 2.01 percent. For the four-factor model, the
diﬀerential is 1.74 percent per quarter. Another way to see the eﬀects of survivorship
bias is to examine the spread between the extreme winner and extreme loser decile.
Based on the unconditional three-factor model, this spread is 1.21 percent per quarter
in the survivorship biased sample and 4.07 percent per quarter in the unbiased sample.
Second, there is virtually no persistence among the worst performers. Concentrating
on the survivor-bias free sample period, the alphas of loser deciles are statistically in-
distinguishable from zero. This is in stark contrast to the evidence in mutual funds,
where persistence is strongest in the extreme loser decile. Third, persistence in deciles
7 through 10 (and sometimes in decile 6) is statistically signiﬁcant. The alphas across
these deciles increase monotonically, with alphas in the extreme winner deciles ranging
from 1.07 percent per quarter to 3.33 percent per quarter. Again, in contrast to the
evidence for retail mutual funds, the addition of momentum to the factor model reduces
but does not eliminate persistence; between 1991 and 2004, the unconditional (condi-
tional) alpha drops from 2.88 percent (3.33 percent) per quarter to 1.07 percent (1.62
percent), but remains highly statistically signiﬁcant. Even by conservative standards,
the coeﬃcients are economically large, generating annualized abnormal returns greater
than 4 percent, which compares favorably to retail mutual funds.
Panel B of Table 2 provides estimates of one-quarter alphas for ﬁxed income port-
folios. The average R of the unconditional regressions is substantially lower than for
domestic equity portfolios, 0.79, but improves to 0.91 for the conditional regressions.
This increase in R is perhaps not surprising since some of the conditioning variables
capture the eﬀects of variations in yields in ﬁxed income securities. Since conditioning
information is especially relevant for this asset class, we focus our attention on estimates
generated by conditional models. Because factor information is not available prior to
1981, we present results for 1981-1990 and 1991-2004 subperiods.
Evidence of persistence in the loser deciles does not exist in either subperiod. How-
ever, the winner deciles persist considerably. In conditional models estimated for the
1991-2004 subperiod, alphas are positive, increase monotonically, and are statistically
signiﬁcant for deciles 6 through 10. For the extreme winner decile, the alpha from the
unconditional (conditional) model is 1.03 percent (1.53 percent) per quarter with a t-
statistic of 3.76 (4.68). These abnormal returns are economically large, particularly for
ﬁxed income portfolios.
Panel C of Table 2 shows one-quarter alphas for international equity portfolios es-
timated using three-factor models. Since we only have factor data from 1989 onwards,
and since very few international equity portfolios exist prior to 1991, we calculate al-
phas for the non-survivor-biased sample from 1991 onwards. Again, the performance
of loser deciles does not persist. Winner deciles, by contrast, continue to show large
positive alphas. For example, for deciles 9 and 10, unconditional and conditional alphas
range from a low of 2.04 to a high of 2.67 percent per quarter, and three out of four are
In addition to forming deciles based on raw returns, we also form deciles based on
benchmark-adjusted returns. Our returns data come with style assignments and self
designated benchmarks for domestic equity and ﬁxed income portfolios. For domes-
tic equity, benchmarks are based on size and value-growth grids. For ﬁxed income,
benchmarks are based on credit and maturity terms. We use benchmark-adjusted an-
nual returns to form deciles and estimate alphas in the subsequent quarter using the
same methods as before. Unfortunately, benchmark designations are not available for
international equity portfolios.
In Table 3, we ignore the survivorship-biased sample period and present results us-
ing benchmark-adjusted returns for 1991-2004. Compared to the earlier results, some
changes are evident in the results for domestic equity portfolios. First, the point esti-
mates of alphas for loser deciles are larger than those generated by raw returns sorts, but
remain statistically insigiﬁcant. Thus, although portfolios in loser deciles do not perform
as poorly, no evidence of persistence or reversal continues to exist among losers. Second,
the alphas of portfolios in winner deciles are smaller by approximately 0.5 percent per
quarter. In the extreme winner decile, the alpha from an unconditional three-factor
model drops from 2.88 percent per quarter (based on the raw return sort) to 2.03 per-
cent per quarter. In one case, the alpha loses statistical signﬁcance (the unconditional
four-factor model based on benchmark-adjusted returns). Despite these diﬀerences, the
general pattern of results remains the same – loser deciles do not persist and extreme
winner deciles do.
For ﬁxed income portfolios, ranking on benchmark-adjusted returns produces a sim-
ilar set of results, at least for conditional alpha estimates. Portfolios in extreme loser
deciles show no predictability. However, deciles 4 through 10 show positive and statisti-
cally signiﬁcant alphas. For deciles 4 through 9, alphas range from 0.10 to 0.31 percent,
and the alpha for the extreme winner decile is 0.80 percent per quarter.
3.2 Longer-term Persistence
Signiﬁcant persistence in performance one quarter after portfolio formation is certainly
economically meaningful. However, plan sponsors typically do not deploy capital in a
portfolio for one quarter, since the transaction costs from exiting a portfolio after one
quarter and entering a new one are large and potentially prohibitive. Even if plan spon-
sors employ transition management ﬁrms that seek to minimize such costs, the frictions
are simply too large to justify a performance-chasing strategy. In addition, adverse rep-
utation eﬀects likely exist from trading in and out of institutional portfolios. If excess
returns remain high for a suﬃcient period of time after portfolio formation, however,
then plan sponsors may be able to exploit performance persistence. Accordingly, in this
section, we examine persistence in institutional portfolios over longer horizons.
Methodologically, we follow the same procedure as before, with some minor adjust-
ments. We roll forward annually and calculate post-ranking quarterly returns for three
years following portfolio formation. Thus, we compute the alpha for the ﬁrst year from
the beginning of year 1 to the end of year 1, we compute the alpha for the second year
from the beginning of year 2 to the end of year 2, and so forth. For domestic equity and
ﬁxed income, we assign deciles using benchmark-adjusted returns. Since benchmarks are
unavailable for international equity portfolios, we assign their deciles using raw returns.
Also, since conditioning information clearly aﬀects inference for ﬁxed income portfo-
lios, we estimate conditional ﬁxed income alphas; alphas for domestic and international
equity portfolios are unconditional.
We do not show results for two- and three-quarter evaluation periods so that we do
not overwhelm the reader with too many results.7 Instead, we simply show alphas for
each decile in years 1, 2, and 3 after portfolio formation.
The alphas for winner portfolios over two- and three-quarter holding periods are generally large
and statistically signiﬁcant.
Panel A of Table 4 shows alphas for domestic equity. Using a three-factor model,
the performance of winner deciles persists. For example, deciles 9 and 10 have alphas
of 0.56 (t-statistic=2.51) and 1.44 (t-statistic=3.34) percent respectively. However, the
four-factor model alphas for these portfolios shrink to 0.21 and 0.47 percent respectively
and lose their statistical signiﬁcance. In the second year, alphas sharply reverse: the loser
decile alphas are actually positive (1.04 and 0.80 percent) and statistically signiﬁcant,
whereas the winner decile alphas are indistinguishable from zero using both three- and
four-factor models. What appears to be reversal in the loser decile is most likely driven
by greater attrition in that decile. The attrition rate in the extreme loser decile is 7.6
percent in the ﬁrst year, 5.3 percent in the second year, and 5.6 percent in the third
year. In comparison, corresponding attrition rates in the extreme winner decile are 1.9
percent, 2.7 percent, and 2.8 percent respecitvely. Since we do not assign a “delisting”
return to the portfolios that exit a decile, the returns of loser deciles are positively biased.
Three years after portfolio formation, almost all alphas have deteriorated to zero.
Alphas in ﬁxed income deciles appear to follow a U-shape in the ﬁrst year (Panel B
of of Table 4) – alphas are greatest in the extreme winner and loser deciles and weaker in
intermediate deciles. Again, reversal in the extreme loser decile is most likely driven by
higher attrition rates among loser portfolios and the fact that we do not assign a delisting
return to exiting portfolios (the cumulative attrition rate over a three year period is 11.9
percent for the extreme loser decile and 7.4 percent for the extreme winner decile). In the
second year, the alphas of the extreme deciles are dampened and by the third year, the
alpha of the extreme loser decile is indistinguishable from zero. In all cases, the extreme
winner decile has the highest alpha and it is economically quite large. For instance, the
alpha is 0.62 percent per quarter in the ﬁrst year and 0.58 percent per quarter in the
In winner deciles of international equity portfolios (Panel C of of Table 4), alphas
are 1.98 and 2.76 percent per quarter for deciles 9 and 10 in the ﬁrst year. These alphas
decline sharply in the following years. The decile 10 alpha drops from 2.76 percent in
the ﬁrst year to 0.97 percent in the second year, and 0.48 percent in the third year.
Interestingly, alphas for the loser deciles climb over time, although the t-statistic of the
extreme loser decile never achieves signiﬁcance.
In general, persistence appears to last up to one year in equity portfolios (both
domestic and international). The ﬁxed income results, however, are puzzling to the
extent that persistence continues up to three years after decile formation. It is possible
that the composition of the extreme deciles is unusual and causes this pattern. We
examine whether any particular types of investment styles load in these deciles. To do so,
we calculate the percentage of each decile’s observations associated with each investment
style, and then subtract this percentage from the unconditional mean across deciles. The
nine styles are: money market, municipal, core, short-term, intermediate-term, long-
term, mortgages, convertibles, and high-yield. This procedure produces a deviation
for each decile and style, which sums to zero within a decile. We ﬁnd that deciles
1 and 10 have the largest loadings on high yield portfolios.8 Since the default spread
variable that we use is the diﬀerence between corporate and government bond returns, it
likely poorly adjusts for high yield securities. Consequently, the persistence results that
we observe are likely caused by imprecise factor adjustments. To help with inference,
in addition to computing alphas based on factor models, we also compute benchmark-
adusted returns and information ratios for each decile over the three-year period following
decile assignment. Our hope is that benchmarks more precisely account for high yield
securities in the high yield portfolios. Naturally, we sacriﬁce the regression approach
and instead compute simple benchmark-adjusted returns and information ratios:
rt = rp,t − rb,t (3)
IR = e
where rp is the return on portfolio p, rb is the return on the corresponding benchmark,
r e is the excess return, and IR is the information ratio.
Table 5 shows these returns and information ratios each year for domestic equity
(Panel A) and ﬁxed income portfolios (Panel B). The domestic equity results mirror
those from the regression approach - persistence lasts up to one year in the winner
deciles and disappears thereafter. For ﬁxed income portfolios, persistence only appears
in the intermediate deciles during the ﬁrst year, which represents an important departure
from the alphas presented in Table 4, and is consistent with our suspicion that the ﬁxed
income factor model inadequately handles high yield portfolios.
Overall, performance persists at least up to the end of the ﬁrst year after portfolio
formation for domestic and international equity portfolios. These excess returns revert
to zero thereafter. Persistence in ﬁxed income lasts longer in some speciﬁcations, and in
many ways remains enigmatic.
Decile 1 (10) shows postive deviation of 5.3 percent (18.3 percent) from the unconditional mean.
Additionally, the intermediate deciles all have negative loadings on high yield investment styles. We
also check loadings of various investment styles in domestic equity and ﬁnd no substantial deviations.
3.3 Persistence and Flows
The evidence thus far suggests that the performance of institutional portfolios persists
over short- to intermediate-term horizons. Excess returns are generally positive up to
one year after portfolio formation and revert to zero thereafter. Although the magnitude
of the excess returns and the reversals varies across asset classes, the general pattern
appears widespread. Prior performance is an important screen used by plan sponsors in
selecting investment management ﬁrms and allocating capital. Thus, new capital likely
ﬂows into extreme winner portfolios and ﬂows out of extreme loser portfolios. If there are
decreasing returns to scale in investment management, then capital ﬂows could account
for the patterns in persistence that we observe.
We measure percentage asset ﬂows, Cfp,t , for each portfolio p during the year t as:
Ap,t − Ap,t−1 × (1 + rp,t)
Cfp,t = , (5)
where Ap,t measures the dollar amount of assets in portfolio p at the end of year t, and
rp,t is the gross return on portfolio p during the year (not quarter) t. Measurement of
ﬂows in this manner (as fractional ﬂows) is analagous to that typically employed in the
mutual fund literature. We truncate ﬂows from below by 1 so that small asset values in
the denominator do not produce large outlier ﬂows that could distort our results.
Two important considerations exist in measuring institutional portfolio ﬂows. First,
total assets for each portfolio are only available on an annual basis, whereas returns
are available quarterly. This mismatch could potentially induce discreteness into the
ﬂow-performance relation. In practice, however, it unlikely makes much of a diﬀerence,
because investment management ﬁrms largely gain or lose assets when they are hired or
ﬁred by plan sponsors. Since such selection and termination decisions are in themselves
discrete, no artiﬁcial discreteness is induced in the ﬂow data. It is also worth noting
that this capital ﬂow process is quite diﬀerent from retail mutual funds, where purchases
and redemptions take place daily. Second, total asset data are available for a smaller
sample than that of returns. To ensure that we assign portfolios to the correct decile, we
assign deciles based on all portfolios that report returns, and then calculate the mean
ﬂow for each decile portfolio with available data. This procedure could in and of itself
cause selection bias, an issue that we address in section 3.4.
Table 6 shows average capital ﬂows into domestic equity, ﬁxed income, and interna-
tional equity portfolios in each decile 1, 2, and 3 years following decile formation. In
domestic equity, a monotonic relation exists between ﬂows in year 1 and decile ranking
based on the prior year return. In year 1, decile 10 receives a ﬂow of almost 32 percent
of assets whereas decile 1 loses almost 4 percent of assets, a spread of 36 percent. Since
the average portfolio in decile 1 (10) has total assets of $466 million ($420 million), this
implies a loss (gain) of $27 million ($132 million). In the following year (year 2), the
alphas revert. For winner portfolios, the three- (four-) factor alpha declines from 1.44
percent (0.47) per quarter in year 1 to 0.09 percent (-0.22 percent) in year 2. Interest-
ingly, the loser decile three- (four-) factor alpha goes from 0.05 percent (0.40 percent)
in year 1 to 1.04 percent (0.80 percent) in year 2. Portfolios in the intermediate deciles
follow similar patterns: deciles with the highest capital ﬂows have the lowest alphas
during the next year.
In ﬁxed income, alphas persist over longer horizons. Capital ﬂows are smaller in
percentange terms than domestic equity, but the spread between winner and loser deciles
remain. Perhaps even more interesting is the fact that changes in ﬂows over time are
not as dramatic. For instance, in the extreme winner decile, the change in ﬂow from
year 1 to year 2 is only 4 percentage points, less than half the 9 percentage points for
The ﬂow patterns for international equity portfolios are more similar to those for
domestic equity. Alphas in the ﬁrst year are quite high (the extreme winner decile
alpha is 2.76 percent) and followed by extremely high capital ﬂows (29 percent). The
year after these capital inﬂows, decile 10 alpha shrinks to 0.97 percent, following which
capital inﬂows decline to 23 percent. Again, similar patterns exist in other deciles.
Despite the barrage of statistics above, the general pattern that emerges for domestic
and international equity portfolios is straightforward. Performance lasts up to a year
after portfolio formation. Among winners, this persistence elicits capial inﬂows, and
excess returns subequently revert. It is appropriate at this point to examine our re-
sults in the context of the assumptions and implications of Berk and Green (2004). In
Berk and Green’s model, performance does not persist, even though there is diﬀerential
ability across fund managers. Investors rationally respond to past performance. Capital
ﬂows into superior performers, which in conjunction with assumed diseconomies of scale,
causes future excess returns to disappear. We observe persistence. We cannot directly
measure diseconomies of scale, although some evidence indicates it exists (Perold and
Salomon (1991)). We also observe ﬂows that appear to follow performance after which
excess returns disappear.
The individual moving parts of the evidence are consistent with ﬂows aﬀecting future
persistence. To establish a clear causal link, one would need to know the capacity
of an existing portfolio and then the impact of ﬂows on future returns. That is an
impossible task without proprietary data. We can, however, make a modest attempt in
that direction. To do so, we separate the extreme winner decile into four groups at the
end of the ﬁrst year based on total assets and the degree of capital ﬂows. Eﬀectively, we
do a double-sort on the winner portfolio using assets and ﬂows. Our hope is that total
assets provide a crude measure of capacity, and that if diseconomies of scale reduce future
alphas, then changes in alpha should be larger for big portfoliso that experience larger
inﬂows. We ﬁnd that the decline in alpha is indeed greater for the high-assets/highﬂow
group than for the low-assets/low-ﬂow group. In domestic equity, using a four-factor
model, the decline in alpha is -0.53 percent for the former and -0.21 percent for the
latter. This evidence certainly suggests a link between ﬂows and future performance.
At a minimum, it provides circumstantial support for the quantity-based equilibrating
process that is at the heart of Berk and Green (2004). The key diﬀerence is that in our
setting it takes one year, and in some cases two, for the equilibrating mechanism to take
eﬀect, most likely because capital ﬂows in the institutional arena are sticky (particularly
when compared to retail mutual funds).
3.4 Fee Arrangements
We base our results thus far entirely on returns that are net of trading costs but gross
of fees. The possibility exists that investment management fees eliminate the post-
ranking period excess returns. Cross-sectional variation in fees could also be so large
that it swamps alphas in winner portfolios; in other words, average fees may not be high
enough to eliminate excess returns across all portfolios, but only in the extreme winner
deciles. In this section, we analyze fees charged by investment management ﬁrms for
As noted earlier, to study fees we employ data from eVestment Alliance. This ﬁrm
provides composite quarterly returns and fee information. The proto-typical fee struc-
ture is such that management fees decline as a step function of the size of the mandate
assigned to the investment management ﬁrm by the plan sponsor. Although variation
undoubtedly exists in the breakpoints, eVestment collects marginal fee schedules using
“standardized” breakpoints. Speciﬁcally, each investment manager identiﬁes fees for
$10, $25, $50, $75, and $100 million mandates. These marginal fees are based on fee
schedules; actual fees are individually negotiated between investment managers and plan
sponsors. Such individual negotiations involve rebates to the marginal fees as well as
other structural fee arrangements (e.g. performance linkages). To our knowledge, no
available database details individual fee arrangements. As a result, we regard our analy-
sis as exploratory in nature and designed only to address issues pertaining to persistence.
While our data are new and unique, they are not rich enough to provide a comprehensive
understanding of actual fee arrangements in institutional investment management.
Table 7 provides descriptive information on fee arrangements. In Panel A, we report
the percentage of portfolios that oﬀer performance-based fee clauses in contracts across
each asset class and decile.9 Performance-based fees often have no minimum and link
actual fees to performance above a prescribed benchmark. For domestic equity and
ﬁxed income portfolios, a substantial variation exists in the percentage of portfolios that
oﬀer performance-based fees across the deciles. In domestic equity, for example, only 47
percent of the portfolios in the loser decile oﬀer performance-based fees while almost 55
percent in the winner decile oﬀer such an arrangement. The corresponding percentages
for ﬁxed income are 38 percent for decile 1 and 46 percent for decile 10. Little variation
exists in international equity portfolios.
Panel B of Table 7 shows the percentage of portfolios that oﬀer most-favored nation
(MFN) clauses by asset class and decile. MFN provisions typically state that the invest-
ment manager will charge the plan sponsor a fee that is the lowest of that charged to
similar mandates from other comparable plan sponsors. If properly enforced, an MFN
clause beneﬁts incumbent plan sponsors in the sense that the investment manager is
required to match lower fees provided to a new plan sponsors.10 Again, some evidence
suggests that portfolios in winner deciles have greater propensity to oﬀer MFN clauses
compared to portfolios in loser deciles. The spread between extreme winner-loser deciles
for domestic equity, ﬁxed income, and international equity are 5, 4, and 3 percentage
Table 8 shows the distribution of annual fees across deciles for domestic equity, ﬁxed
income, and international equity (Panels A, B and C respectively). For each decile,
we show the average marginal fee (in basis points) in each breakpoint described above.
The sum of the “Yes” and “No” columns does not add up to 100 percent because this information
is missing for a small number of portfolios.
As with most such contracts and clauses, many of the beneﬁts are dependent on the details of the
contract and its enforcement. For instance, the investment management ﬁrm and plan sponsor might
reasonably disagree on whether mandates from two-plan sponsors are comparable because of size or
speciﬁc portfolio restrictions (e.g. no sin stocks or use of directed brokerage).
Since fees vary widely across investment styles within domestic equity, we show four
major intersections of the size and growth-value grid: large cap growth, large cap value,
small cap growth, and small cap value. Similarly, for ﬁxed income, we only show fees
for four styles: municipal, high yield, intermediate term, and mortage-backed securities.
The results in Table 8 display several important elements. First, the magnitude
of the fees are such that they are unlikely to inﬂuence our inferences with regard to
persistence; the fees are simply not large enough to eliminate the alphas. Take domestic
equity, for example. The largest fee reported in the table is 100 basis points for the
extreme winner decile corresponding to the smallest ($10 million) mandate in small cap
growth. The quarterly alpha one year after decile formation using a three-factor model
was 1.44 percent, implying an annual alpha of almost 6 percent. Even if one were to
use the four-factor alpha of 0.47 percent (which is statistically insigniﬁcant), removing
maximum fees still leaves an annual alpha of 1 percent. The results for ﬁxed income and
international equity are substantially stronger. The quarterly alpha one year after decile
formation for the extreme winner decile was 0.62 percent, implying an annual alpha of
2.4 percent. The maximum annual fee (from the high yield investment style) is 59 basis
points. The corresponding quarterly alpha for international equity was 2.76 percent (9.5
percent annually), where the maximum annual fee is 88 basis points.
We have been quite conservative in assessing the impact of fees on two accounts.
First, fees are based on reported schedules. Actual fees involve signiﬁcant rebates and
are likely to be substantially lower. Second, we have deliberatedly cherry-picked the
largest fees in an asset class to shrink alphas. Despite this conservatism, investment
management fees are clearly not large enough to eliminate the alphas discovered in the
One might legitimately ask whether better-performing portfolios charge more in fees
than worse-performing portfolios. An examination of the variation in fees across deciles
in each of the panels shows evidence that this is indeed the case. Reported fees are
consistently higher for portfolios in decile 10 than in decile 1; the diﬀerentials are modest
in domestic equity and ﬁxed income, but larger in international equity. To the extent
that investment managers are likely to oﬀer larger rebates for worse performing portfolios
and less likely to oﬀer large rebates for better performing portfolios, the diﬀerences may
in fact be larger than can be estimated using these data.
3.5 Alternative Mechanisms to Control Flows
Portfolio managers cognizant of diseconomies of scale and wishing to preserve their
reputation for providing consistent excess gross returns may prefer to restrict asset ﬂows
rather than suﬀer declines in alpha. Several possible mechanisms could be used to achieve
this result. One obvious mechanism is price. Fee increases could be used to control asset
ﬂows, preserving gross performance and persistence. In retail mutual funds, fees and
loads vary widely, aﬀecting redemptions and capital inﬂows (see Nanda, Narayanan, and
Warther (2000) for a model in which heterogeneous fees appear endogenously). To the
extent that better-performing investment managers charge higher fees, there is some
circumstantial evidence that fees are used to control ﬂows. However, as noted earlier,
actual fee arrangements between institutional investment management ﬁrms and plan
sponsors are private. As a result, we cannot detect fee increases or cleanly observe the
use of heterogenous negotiated fees to discourage asset inﬂows from particular types of
(perhaps short-term) plan sponsors.
Another, more direct, way to control asset ﬂows is to simply stop accepting new
money in winner portfolios. Bris, Gulen, Kadiyala, and Rau (2005) ﬁnd that 143 re-
tail equity mutual funds closed to new investors over a 10-year period, and document
that these funds delivered positive excess returns prior to closing. We are anecdotally
aware of some investment advisors that have followed this approach in the institutional
marketplace. For example, returning to our example of Aronson+Johnson+Ortiz, this
investment management ﬁrm closed its ﬂagship large cap value portfolio to new institu-
tional portfolios under the belief that it could not continue to generate superior returns
with a larger asset base.
Data from eVestment tags portfolios that have been closed to new investors, and we
can use these data to see if this mechanism is employed by institutional investment man-
agers. Out of 5,122 portfolios, eVestment reports that 277 are closed to new investors,
a rate that appears higher than that for mutual funds. The majority of these closures
(270) are in domestic and international equity portfolios (208 and 62 respectively, out
of 2,881 and 910 portfolios in each of these asset classes). Only 7 out of 1,331 ﬁxed in-
come portfolios are identiﬁed as closed. More interestingly, the returns are substantially
higher for closed portfolios than portfolios that are open to new investors. The average
quarterly raw return for closed portfolios are 4.9 percent, 3.2 percent, and 3.8 percent
respectively for domestic equity, ﬁxed income, and international equity respectively. The
corresponding returns for portfolios open to new investors are lower in each case: 3.9,
1.9, and 3.0 percent respectively. These diﬀerentials are also evident in benchmark-
adjusted returns, which can be computed for domestic equity and ﬁxed income only.
The average quarterly benchmark-adjusted return in closed domestic equity portfolios is
1.15 percent, compared to 0.65 to open portfolios. The comparable and corresponding
returns for ﬁxed income portfolios are 1.05 and 0.13 percent respectively.11
3.6 Robustness and Other Issues
Our results could be inﬂuenced by backﬁll bias, similar to that observed in hedge fund
databases (Liang (2000)). Speciﬁcally, it could be that only portfolios that have been
successful over some period of time enter the database. To determine how this bias may
aﬀect our results, we follow Jagannathan and Novikov (2005), and eliminate the ﬁrst 2
years of returns for each portfolio in our sample, and then re-estimate our regressions.
The results of this exercise are not reported in the paper, but our basic estimates of
persistence are almost identical.
Another possibility is that a selection bias exists in the investment management ﬁrms
that report total assets under management per portfolio. Although ex ante the source
of such a bias is hard to identify, it is a possibility that could cloud our inferences. In
a perfect world, we would be able to observe characteristics of ﬁrms that report assets
and those that do not, and then examine whether their ﬂow-performance relations diﬀer.
Unfortunately, this is not possible, particularly since we do not have ﬁrm identities. We
can, however, examine whether our persistence results diﬀer for the subsample of ﬁrms
that include both assets and returns data. We replicate our results for this subsample
and ﬁnd that the alpha estimates do not diﬀer materially from those reported in Table 4,
and in some cases they are even larger.
Since the database used to generate estimates of fees diﬀers from the data on which
we base persistence estimates, the two databases may not be comparable. To examine
this possibility, we estimate one-year alphas (equivalent to those reported in Table 4).
The alpha estimates from eVestment data are very similar to those estimated using the
larger sample obtained from IIS.
As shown earlier, although our data is survivorship-bias free, greater attrition exists
Ideally, we would like to know the date on which a portfolio was closed to new investors so that we
can compute returns before and after closure. Unfortunately, our data do not include closure dates. If
investment management ﬁrms do in fact avoid diseconomies by closing portfolios, then our computed
return diﬀerentials are downward biased.
in the loser decile portfolios than in the winner deciles. Since portfolios are not assigned a
delisting return, this implies that post-ranking returns in loser deciles are biased upward.
If we were to somehow assign a delisting return, this might generate persistence (and
negative alphas) in the loser decile. We choose not to do so for two reasons. First, there is
no obvious choice of delisting return. Second, and more importantly, since institutional
portfolios cannot be shorted, there is little practical advantage in doing so.
Finally, a reconciliation of our results with those reported in Goyal and Wahal (2005)
are in order. They report that one, two, and three-year excess returns of investment
management ﬁrms prior to being hired by plan sponsors are signiﬁcantly positive. Post-
hiring excess returns are statistically indistinguishable from zero. On the surface, this
may seem at odds with our evidence of persistence. However, the key lies in horizons.
Hiring decisions reported by Goyal and Wahal occur after three years of superior per-
formance. Our evidence of persistence is that after two years (the portfolio formation
year and the subsequent evaluation year), reversals start to occur. Consequently, these
two sets of results suggest that deciles based on three-year return rankings (rather than
one-year return rankings) should not persist. To conﬁrm this, we re-estimate the results
in Table 4 after forming deciles based on three-year returns. This exercise (results not
tabulated) shows that performance does not persist when deciles are formed in this way.
In this paper, we examine the persistence in performance of 6,260 portfolios managed by
1,475 investment management ﬁrms between 1991 and 2004. These portfolios provide
exposure to domestic equity, ﬁxed income, and international equity asset classes to
public and private deﬁned beneﬁt retirement plans, endowments, foundations, multi-
employer unions, and trusts. A large number of active investment styles and strategies
are included; for equity strategies, all size and growth-value gradations are represented,
and all maturity and credit risk dimensions are included in ﬁxed income portfolios. To
our knowledge, we are the ﬁrst to examine persistence beyond the traditionally studied
domestic equity funds.
We form deciles using raw and benchmark-adjusted returns over one year and ex-
amine the persistence in performance thereafter using a variety of three- and four-factor
models. The factor models do a good job of explaining the return series, but we ﬁnd
signiﬁcant excess returns in the post-decile formation period. Unlike retail mutual funds,
however, these excess returns are concentrated entirely in winner deciles. The magni-
tudes of the alphas themselves are economically large. Moreover, typical investment fees
are not large enough to eliminate net-of-fee excess returns.
From a practical perspective, our results suggest that the widespread practice of
hiring investment managers that have delivered superior returns is both rational and
potentially proﬁtable. Indeed, the organizational structure of institutional investment
management and, in particular, the use of consultants to pick investment managers is
conducive to this eﬀort. However, the persistence that is the source of potential gains
for plan sponsors is its very own death knell: we ﬁnd that portfolios in the winner deciles
draw an inﬂux of capital from plan sponsors, and in the year following this capital inﬂow,
the excess returns disappear. Berk and Green (2004) argue that diseconomies of scale
in investment management, combined with capital ﬂows following superior performance,
could generate this result. Our results are consistent with their model and highlight the
transmission mechanism by which this may take place. Particularly noteworthy is the
fact that, within the institutional money management arena, it takes time for asset ﬂows
to eliminate persistence.
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Table 1: Descriptive Statistics
This table presents descriptive statistics on the sample of institutional investment management ﬁrms
and their portfolios. Statistics are presented for the survivorship biased sample period of 1979 to 1990
and survivorship bias free sample period of 1991 to 2004. Firm and portfolio size is in millions of dollars.
The attrition rate is calculated by summing the number of portfolios that disappear from the database
during a year and scaling by the total number of portfolios at the beginning of the year.
Domestic Domestic International
Equity Fixed Income Equity
Panel A: Data Source: IIS
Total # Firms 670 397 144
Total # Portfolios 1123 742 331
Avg. # Firms per year 359 204 57
Avg. # Portfolios per year 516 309 108
Avg. Size of Firm 823 1439 1188
Avg. Size of Portfolio 510 797 553
Attrition Rate 0.00 0.00 0.00
Total # Firms 1137 602 330
Total # Portfolios 3381 1683 1196
Avg. # Firms per year 873 470 236
Avg. # Portfolios per year 2146 1192 746
Avg. Size of Firm 2530 3764 3648
Avg. Size of Portfolio 981 1427 1121
Attrition Rate 3.08 3.18 3.48
Panel B: Data Source: eVestment Alliance
Total # Firms 805 356 237
Total # Portfolios 2682 1290 821
Avg. # Firms per year 630 292 169
Avg. # Portfolios per year 1657 914 491
Attrition Rate 1.01 1.02 1.21
Table 2: Post-Ranking One-quarter Alphas with Deciles formed using Raw Returns
This table lists the post-ranking alphas for deciles of funds sorted according to the raw return during the ranking period of one year. The portfolio
deciles are rebalanced at the end of every quarter and are held for one post-ranking quarter. Unconditional alphas are calculated from the factor
rp,t = αU +
p βp,k fk,t + p,t ,
while the conditional alphas are calculated from the factor model
rp,t = αC +
p βp,k + βp,k Zl,t−1 fk,t + p,t ,
where f are K factors and Z’s are L instruments. The list of instruments includes T-bill, dividend price ratio, term spread and default spread. The
factors for domestic equity are the three Fama and French (1993) factors and the Carhart (1997) momentum factor. The factors for domestic ﬁxed
income are the Lehman Brothers Aggregate Bond Index returns, Term Spread Return, and Default Spread Return. The factors for international
equity are the international versions of Fama and French (1993) factors. We report alphas for the survivorship biased sample period of 1979 to 1990
and survivorship bias free sample period of 1991 to 2004. All alphas are in percent per quarter and t-statistics are reported in parentheses next to
alphas. Decile 1 contains the worst performing portfolio and decile 10 contains the best performing portfolios.
Panel A: Domestic Equity
1979–1990 1991–2004 1979–1990 1991–2004
Decile FF Carhart FF Carhart FF Carhart FF Carhart
1 0.82 (2.11) 1.59 (4.16) -1.19 (-1.64) -0.17 (-0.23) 0.38 (0.86) 0.72 (1.56) -1.13 (-1.62) -0.37 (-0.52)
2 0.26 (1.01) 0.68 (2.55) -0.81 (-1.56) -0.13 (-0.25) -0.00 (-0.00) 0.34 (0.92) -0.78 (-1.55) -0.08 (-0.16)
3 0.58 (2.77) 0.70 (2.89) -0.62 (-1.69) -0.12 (-0.34) 0.40 (1.48) 0.49 (1.42) -0.54 (-1.47) -0.06 (-0.16)
4 0.64 (3.56) 0.61 (2.92) -0.35 (-1.29) -0.06 (-0.23) 0.32 (1.53) 0.37 (1.47) -0.32 (-1.22) 0.07 (0.25)
5 0.74 (3.85) 0.65 (2.89) -0.01 (-0.03) 0.15 (0.76) 0.52 (2.66) 0.59 (2.53) 0.03 (0.23) 0.25 (1.54)
6 0.70 (4.07) 0.50 (2.61) 0.18 (1.30) 0.16 (1.08) 0.72 (3.33) 0.62 (2.55) 0.22 (1.84) 0.28 (2.01)
7 1.10 (5.29) 0.74 (3.45) 0.67 (3.47) 0.44 (2.22) 0.89 (3.81) 0.79 (3.78) 0.74 (3.75) 0.57 (2.79)
8 1.09 (4.81) 0.74 (3.07) 0.97 (3.52) 0.47 (1.86) 0.79 (3.57) 0.59 (2.64) 1.05 (3.69) 0.70 (2.61)
9 1.37 (4.34) 0.69 (2.31) 1.51 (3.70) 0.66 (1.89) 1.13 (2.93) 0.90 (2.56) 1.65 (3.98) 0.91 (2.36)
10 2.03 (4.41) 1.11 (2.46) 2.88 (3.77) 1.07 (1.85) 1.61 (2.85) 1.23 (2.18) 3.33 (4.13) 1.62 (2.59)
SpCorr 0.79 (0.01) 0.13 (0.73) 1.00 (0.00) 1.00 (0.00) 0.93 (0.00) 0.65 (0.05) 1.00 (0.00) 1.00 (0.00)
Table 3: Post-Ranking One-quarter Alphas with Deciles formed using
This table lists the post-ranking alphas for deciles of funds sorted according to the benchmark-adjusted
return during the ranking period of one year. The portfolio deciles are rebalanced at the end of every
quarter and are held for one post-ranking quarter. Unconditional alphas are calculated from the factor
rp,t = αU +
p βp,k fk,t + p,t ,
while the conditional alphas are calculated from the factor model
rp,t = αC +
βp,k + l
βp,k Zl,t−1 fk,t + p,t ,
where f are K factors and Z’s are L instruments. The list of instruments includes T-bill, dividend
price ratio, term spread and default spread. The factors for domestic equity are the three Fama and
French (1993) factors and the Carhart (1997) momentum factor. The factors for domestic ﬁxed income
are the Lehman Brothers Aggregate Bond Index returns, Term Spread Return, and Default Spread
Return. We report alphas only for survivorship bias free sample period of 1991 to 2004. All alphas are
in percent per quarter and t-statistics are reported in parentheses next to alphas. Decile 1 contains the
worst performing portfolio and decile 10 contains the best performing portfolios.
Domestic Equity Domestic Fixed Income
Unconditional Conditional Unconditional Conditional
Decile FF Carhart FF Carhart FF FF
1 -0.08 (-0.21) 0.62 (1.63) -0.05 (-0.17) 0.34 (1.00) 0.35 (2.20) 0.19 (1.18)
2 -0.08 (-0.25) 0.43 (1.40) 0.01 (0.05) 0.50 (2.19) 0.19 (2.77) 0.08 (1.01)
3 -0.03 (-0.11) 0.25 (0.95) 0.05 (0.25) 0.33 (1.77) 0.11 (2.66) 0.06 (1.24)
4 0.05 (0.24) 0.25 (1.13) 0.16 (1.11) 0.42 (2.94) 0.11 (3.21) 0.10 (2.33)
5 0.08 (0.45) 0.13 (0.69) 0.16 (1.27) 0.29 (2.19) 0.09 (2.24) 0.11 (3.00)
6 0.09 (0.60) 0.11 (0.69) 0.15 (1.35) 0.26 (1.95) 0.10 (2.30) 0.16 (3.90)
7 0.28 (1.82) 0.10 (0.62) 0.32 (2.33) 0.21 (1.29) 0.11 (2.64) 0.16 (4.55)
8 0.30 (1.65) 0.12 (0.61) 0.35 (2.18) 0.24 (1.31) 0.14 (2.64) 0.26 (5.30)
9 0.61 (2.26) -0.05 (-0.28) 0.76 (3.23) 0.23 (1.21) 0.15 (2.13) 0.31 (4.81)
10 2.03 (3.56) 0.51 (1.42) 2.39 (4.47) 1.07 (2.94) 0.70 (2.58) 0.80 (4.31)
SpCorr 1.00 (0.00) -0.52 (0.13) 0.95 (0.00) -0.36 (0.31) 0.03 (0.95) 0.73 (0.02)
Table 4: Post-Ranking One- to Three-Year Alphas
This table lists the post-ranking alphas for deciles of funds sorted according to the benchmark-adjusted
return (for domestic equity in Panel A and domestic ﬁxed income in Panel B) or raw return (for
international equity in Panel C) during the ranking period of one year. The portfolio deciles are
rebalanced at the end of every year and are held for one to three post-ranking years. Unconditional
alphas are calculated from the factor model
rp,t = αU +
p βp,k fk,t + p,t ,
while the conditional alphas are calculated from the factor model
rp,t = αC +
βp,k + l
βp,k Zl,t−1 fk,t + p,t ,
where f are K factors and Z’s are L instruments. The list of instruments includes T-bill, dividend price
ratio, term spread and default spread. The factors for domestic equity are the three Fama and French
(1993) factors and the Carhart (1997) momentum factor. The factors for domestic ﬁxed income are the
Lehman Brothers Aggregate Bond Index returns, Term Spread Return, and Default Spread Return.
The factors for international equity are the international versions of Fama and French (1993) factors.
We report unconditional alphas for domestic equity and international equity and conditional alphas for
domestic ﬁxed income. The sample period is 1991 to 2004. All alphas are in percent per quarter and
t-statistics are reported in parentheses next to alphas. Decile 1 contains the worst performing portfolio
and decile 10 contains the best performing portfolios.
Panel A: Domestic Equity (Unconditional alphas)
First-Year Second-Year Third-Year
Decile FF Carhart FF Carhart FF Carhart
1 0.05 (0.15) 0.40 (1.30) 1.04 (3.54) 0.80 (2.55) 0.73 (2.38) 0.31 (1.00)
2 0.16 (0.56) 0.24 (0.77) 0.56 (2.82) 0.39 (1.83) 0.40 (2.12) 0.20 (1.01)
3 0.15 (0.64) 0.21 (0.80) 0.38 (2.08) 0.27 (1.37) 0.14 (0.98) 0.03 (0.20)
4 0.06 (0.31) 0.19 (0.86) 0.46 (2.37) 0.47 (2.15) 0.13 (0.82) 0.18 (1.02)
5 0.12 (0.77) 0.17 (0.98) 0.06 (0.38) 0.11 (0.57) 0.17 (0.96) 0.23 (1.16)
6 0.12 (0.77) 0.15 (0.83) 0.08 (0.54) 0.15 (0.85) 0.11 (0.67) 0.18 (1.01)
7 0.16 (0.95) 0.20 (1.07) 0.09 (0.53) 0.21 (1.19) 0.18 (1.24) 0.18 (1.08)
8 0.32 (1.74) 0.16 (0.82) 0.17 (0.98) 0.32 (1.70) 0.09 (0.48) 0.12 (0.54)
9 0.56 (2.51) 0.21 (0.98) 0.05 (0.17) 0.13 (0.43) 0.32 (1.13) 0.22 (0.70)
10 1.44 (3.34) 0.47 (1.36) 0.09 (0.23) -0.22 (-0.53) 0.23 (0.58) -0.04 (-0.09)
SpCorr 0.64 (0.05) 0.49 (0.15) 1.00 (0.00) -0.52 (0.13) 0.81 (0.01) -0.18 (0.63)
Table 5: Post-Ranking One- to Three-Year Benchmark-Adjusted Returns
This table shows descriptives on the post-ranking benchmark-adjusted returns for deciles of funds sorted
according to the benchmark-adjusted return for domestic equity in Panel A and domestic ﬁxed income
in Panel B during the ranking period of one year. The portfolio deciles are rebalanced at the end of
every year and are held for one to three post-ranking years. The sample period of 1991 to 2004. Mean
returns are listed under the columns ‘Mean’ while Information Ratios are listed under the column ‘IR.’
Means signiﬁcant at the 99% level are indicated by three stars, signiﬁcant at the 95% level are indicated
by two stars, and signiﬁcant at the 90% level are indicated by one star. Decile 1 contains the worst
performing portfolio and decile 10 contains the best performing portfolios.
Panel A: Domestic Equity
First-Year Second-Year Third-Year
Decile Mean IR Mean IR Mean IR
1 0.12 0.04 0.96 0.23 0.80∗ 0.28
2 0.20 0.09 0.63∗ 0.25 0.50 0.24
3 0.20 0.14 0.43 0.21 0.34 0.20
4 0.30∗ 0.24 0.43∗ 0.28 0.38∗ 0.25
5 0.34∗∗ 0.35 0.38∗ 0.26 0.49∗∗ 0.32
6 0.47∗∗∗ 0.43 0.32∗∗ 0.30 0.40∗∗ 0.33
7 0.52∗∗∗ 0.45 0.33∗∗ 0.36 0.35∗∗ 0.30
8 0.65∗∗∗ 0.55 0.46∗∗∗ 0.59 0.27∗ 0.30
9 0.86∗∗∗ 0.54 0.49∗∗∗ 0.44 0.44∗ 0.28
10 1.55∗∗∗ 0.40 0.71∗∗∗ 0.41 0.70∗ 0.26
Panel B: Domestic Fixed Income
First-Year Second-Year Third-Year
Decile Mean IR Mean IR Mean IR
1 0.30∗ 0.23 -0.18 -0.11 0.03 0.03
2 0.11 0.18 -0.09 -0.12 -0.03 -0.03
3 0.10∗∗ 0.29 -0.03 -0.10 0.08∗∗ 0.31
4 0.08∗∗∗ 0.42 0.03 0.13 0.05∗∗ 0.35
5 0.05∗∗∗ 0.50 0.05∗∗∗ 0.45 0.05∗∗∗ 0.41
6 0.06∗∗ 0.29 0.06∗∗ 0.32 0.04∗ 0.27
7 0.05 0.20 0.09∗∗ 0.35 0.07∗∗ 0.37
8 0.02 0.04 0.09∗∗ 0.29 0.06 0.19
9 -0.04 -0.05 0.22∗∗∗ 0.43 0.05 0.08
10 0.11 0.08 0.50∗∗∗ 0.54 0.08 0.07