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Empirical evidence from a database free of survivorship bias shows that the excess return patterns of long-only industry-level momentum strategies are highly correlated with active fund returns in the growth and the core domains, especially since publication of the momentum effect phenomenon in 1993. The best-performing managers are more strongly similar than the poorest-performing managers, who have low correlation with momentum. Investment performance of momentum strategies at the industry level is competitive, or between the top 10% and top 25% of funds in each period. The source and the persistence of these patterns compared to optimal asset allocation are cause for speculation.
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here is an extensive literature on
predicting stock returns on the basis
of past performance. De Bondt and
Thaler [1985, 1987] argue that port-
folios o
f prior losers perform better than port-
folios of winners over the next three to five
years, in an analysis of New York Stock Exchange
common stocks over 1926–1982. Lehmann
[1990] illustrates short-term return reversals of
winner and loser stocks even after taking bid-ask
spreads and transaction costs into account.
Jegadeesh and Titman [1993] report the
momentum of stock returns over an interme-
diate term (3 to 12 months). They construct
relative-strength portfolios, or zero-investment
portfolios formed by buying winners and selling
losers defined in terms of their returns over the
past one to four quarters and varying holding
periods from one to four quarters. They show
that these portfolios yield positive returns from
1965 to 1989. Jegadeesh [1990] also shows stock
return reversion for relatively short periods. He
demonstrates significantly negative first-order
serial correlation in monthly stock return data
over 1929–1982.
Many other authors document that money
managers can take advantage of a momentum
effect. For example, Grinblatt, Titman, and Wer-
mers [1995] report that almost three-quarters
equity funds track momentum. Carhart [1997]
argues that a one-year momentum effect
explains the “hot hands” effect of mutual funds,
while individual funds that follow momentum
strategies do not turn in superior performance.
Badrinath and Wahal [2002] suggest that insti-
tutional money managers act as momentum
traders when they buy stocks. Nofsinger and
Sias [1999]; Sias, Starks, and Titman [2001];
Sapp and Tiwari [2004], and Sias [2004] doc-
ument similar results.
Yet most previous research focuses on
exploring the behavioral characteristics of
active funds, rather than providing actual equity
selection rules that can generate performance
similar to the funds.
Our primary purpose is to reveal an
investment strategy that can replicate the per-
formance patterns of so-called good active
funds, besides investigating the relation of the
active funds and the momentum strategy.
We focus mainly on identifying trading
strategies that institutional money managers
adopt in selecting equities and that thus can
be used as the hedging vehicle for active funds.
Active managers consistently use momentum
selection rules at the industry level, especially
in growth and core domains. The similarity
between an industry-level momentum strategy
and actively managed funds strengthens as
time passes, and funds that perform well tend
to be more strongly similar. We also find that
individual funds can benefit by following a
momentum strategy.
To see this, we construct a variant of the
relative-strength portfolio at the industry level
and compare its return pattern to active fund
Active Equity Managers
in the U.S.:
Do the Best
Follow Momentum Strategies?
is a professor of Operations
Research & Financial Engi-
neering at Princeton Uni-
versity in Princeton, NJ.
is a Ph.D. candidate in the
Operations Research &
Financial Engineering
Department at Princeton
University in Princeton, NJ.
Copyright © 2008
return patterns. We focus on industry-level data because
they can capture fund managers’ efforts with regard to
diversification better than a stock-level momentum
strategy, which tends to be volatile and thus represent a
poor choice for managers. While an industry-level
momentum strategy can take advantage of the profitability
of the momentum effect, it is less volatile than stock-level
strategies; a diversification effect embedded within each
industry makes it a good candidate for portfolio con-
struction rules for active funds.
An industry-level momentum strategy moreover yields
more robust performance. While stock-level momentum is
highly affected by idiosyncratic factors, industry-level
momentum is driven mainly by common economic factors.
This makes it robust to parameters such as evaluation periods
or holding periods. For evaluation purposes, we use long-
only momentum strategies.
We collect for empirical analysis monthly returns
of active managed funds from the eVestment database.
The sample period is 20 years long, from 1987 through
2006. Each fund of the 2,546 in the data set is classified
into one of 12 styles—large, mid, mid-small, or small-
cap, with growth, and value breakouts. Fund managers
in a specific style are asked to outperform the benchmark
index while constructing their portfolio using only stocks
within or similar to the index. Exhibit 1 shows the fund
styles and the benchmark indexes.
As the main source of investment in active funds in
the database comes from long-term institutional investors,
shorting is limited, if not prohibited altogether. To elim-
inate the survivorship bias that Malkiel [1995] argues to
be a critical factor, the database is constructed to include
all closed funds. And as we are interested more in the
investment patterns of active funds than their performance,
all tests are conducted using before-fees returns.
One advantage of analyzing a style- and size-segmented
database is that we can effectively eliminate size effects at
the data collecting stage. Active funds might achieve per-
sistent investment performance simply by over- or under-
weighting smaller stocks in their portfolios. Size effects
might result in biased estimates of funds’ relative perfor-
mance such as excess returns, tracking errors, and infor-
mation ratios, so size should be taken into account in
analysis of the results.
In our case, fund managers in a particular style class
are limited to constructing portfolios only with equities
corresponding to their benchmark index, so sizes of the
constituent stocks should remain relatively similar to that
of the benchmark over time. For instance, a large-cap value
fund manager is asked to outperform the Russell 1000
Value index by constructing the portfolio using large-cap
and value-oriented stocks, and a small-cap growth fund
should be constructed with stocks whose positions on the
style map are similar to the Russell 2000 Growth index.
Because funds in the database are long-only investors,
industry-level momentum portfolios are constructed as a
long-only version of the zero-cost relative-strength port-
folio in Jegadeesh and Titman [1993]. That is, only past
winner industries are included in the portfolios. We adopt
monthly returns of level-4 industries defined by Data-
stream services (38 industries + Datastream U.S. market
index). Note that each industry represents a cap-weighted
index of firms within a classification.
The recipe for the momentum portfolios can be
described as follows:
1. Choose winner industries (top 10%: 4 industries)
with equal weights based on their past 3-, 6-, 9-, and
12-month return—possibly up to 16 industries.
2. If an industry is seen more than once, put more
weight on it accordingly.
3. Hold the chosen industries for the predetermined
holding period.
4. To reduce the timing bias, we form the portfolio
using overlapping time windows.
Style Classification, Benchmark Indexes,
and Number of Funds
Copyright © 2008
5. Holding periods are 3, 6, 9, and 12 months.
6. Repeat the five steps.
In Mulvey and Kim [2007] we study strategies of
this type. They have been implemented in mutual funds
such as the “Rydex Sector Rotation Fund” [2006] and
in exchange-traded funds such as Merrill Lynch’s Ele-
ments Spectrum Large-Cap U.S. Sector Momentum
Exhibit 2 describes the performance of the momen-
tum portfolios and the benchmark index (the Datastream
U.S. market index).
Our empirical tests examine several issues regarding
similarities of active funds and momentum strategies:
1. To see the relation of active funds and momentum
strategies, we compare performance patterns of
“average” active funds and momentum portfolios.
2. Funds are divided according to performance, and
the average funds that represent funds within each
performance group are also compared to momentum
portfolios in order to evaluate whether the best funds
are more likely to exploit momentum effects.
To test whether following momentum rules can yield
better performance at the individual fund level, we
compare the investment performance of active fund
groups based on similarities with the momentum
portfolios. We also measure the relative performance
of the momentum portfolio to rank it among the
active funds.
Comparing Average Funds
to Momentum Portfolios
The “average” fund is an imaginary fund that per-
forms the same as an investment in the whole universe or
all funds of a specific style. For instance, the return of the
average fund of the whole universe for a specific month
is simply the average return across all funds.
In order to extract the market-free relation between
active funds and momentum, we compare
excess return
, instead of using a total return series. Excess returns
are calculated by subtracting returns of the corresponding
benchmark index from fund or strategy returns.
For example, the excess returns of a large-cap core
fund are obtained by subtracting the returns of the Rus-
sell 1000 index from the returns of the corresponding
average fund. For momentum strategies, we use the equa-
tion, Strategy Return–Datastream Index Return.
Exhibit 3 presents the results of analyzing correla-
tion between the excess returns of momentum and average
funds for the entire 20-year sample period (1987–2006,
Panel A) and five-year subperiods (Panels B–E). A three-
month holding period is used for the momentum strategy.
In other words, the industries with the best 3-, 6-, 9-,
and 12 month performance are chosen and held for three
months. Then, new industries are selected according to
their past performance, and the process is repeated again.
Each panel has 20 entries corresponding to correlations
for specific fund styles.
We can relate several meaningful observations. First,
the excess return series of the average fund for the whole
universe has a correlation of 0.345 with the industry-level
momentum strategy. Considering that correlations are
Performance of Momentum Strategies 1987–2006
Copyright © 2008
free of the market fluctuation, active funds indeed share
similar return patterns with momentum.
Second, growth-oriented funds are more strongly
similar (
ρ = 0.400 for all growth-oriented funds) than value-
oriented ones (
ρ = 0.029 for all value-oriented funds). Also,
core-oriented funds are meaningfully correlated (
ρ = 0.194
for all core-oriented funds), although not as strongly as
growth-oriented funds. This may be because of differences
in risk aversion among different styles. The more risk-averse
value-oriented funds may be more likely to follow conser-
vative strategies. Growth-oriented funds may take more
risks, so they might be more likely to exploit momentum
effects. Also, core-oriented funds lie between value and
growth funds in terms of risk aversion, so their interme-
diate correlations may be explainable in that context.
Next, large-cap funds are more strongly correlated
with the momentum strategy (
ρ = 0.468 for all large-cap
funds) than smaller-cap ones (
ρ = –0.013 ~ 0.314). This
does not necessarily mean that large-cap funds are more
strongly related to a momentum strategy, however. As
Datastream industry indexes are cap-weighted and cover the
whole U.S. stock market, the industry-level momentum
strategies used for the tests are inevitably skewed toward
large-cap funds. Thus, there may be a size bias within
strategy settings, so we might find stronger results among
smaller-cap funds, if momentum strategies were constructed
using industries corresponding with the managers’ uni-
verse such as mid-cap, small cap, and so on. Further analysis
is warranted in this respect.
Finally, all these observations are valid for all five-
year subsample periods except the first one (1987–1991).
Correlations moreover tend to strengthen as time passes.
In the first five-year period (1987–1991), there is little
evidence that active funds could use a momentum strategy
as a hedging vehicle; the correlation is merely –0.05. As
time passes, though, correlations rise up to 0.545 for
2002–2006. The excess return series of the average large-
cap fund in particular has a correlation of 0.702 in
1997–2001. After 1992, correlations range between 0.534
and 0.628, compared to only 0.041 in the first period.
Consider that the key research on the momentum strategy
(Jegadeesh and Titman) was published in 1993.
Are the “Best” More Likely to Follow
a Momentum Strategy?
Our empirical tests results indicate that average funds
have strong similarities with momentum portfolios, espe-
cially large-cap growth and core funds. The goal is to find
a strategy that active fund managers use or a strategy that
can mimic those funds’ performance patterns. When we
see that the momentum strategy can play such a role, it
is natural to ask whether correlations vary depending the
performance of the funds. We thus explore how similar
the representative funds in core and growth groups are to
momentum strategies.
To this end, we construct another set of fund
groups with different investment performance. In other
words, funds in each of the large core and the large
growth domains are divided into four groups according
to performance, and average funds are constructed as
Average fund is constructed to perform the same as the investment in the
whole universe or all funds in a specific style. A three-month holding period
is employed for the momentum strategy.
Excess Return Correlations of Active Funds
and Momentum Strategy
Copyright © 2008
equal-weighted portfolios of the particular fund groups.
We use excess returns, information ratios, and risk-adjusted
returns as performance measures. For instance, the return
series of the representative fund among the best large
growth funds in terms of risk-adjusted returns is the
average of fund return series whose risk-adjusted returns
fall in the first quartile during the given sample period.
Now a tricky problem arises. Because the dataset
includes all closed funds to eliminate survivorship bias,
sample periods differ by funds. That is, some funds begin
reporting in 1987, say, and disappear from the database after
1996, while some others are introduced to the database
after 1997. Under these circumstances, it is inappropriate
simply to compare funds’ performance, for their time
windows may differ.
To overcome this complication, we divide the
sample period into ten two-year subsample periods, and
comparisons are carried out only with funds whose
data are available for 20 months or more for each
long subperiods. In this manner, we can
reduce the bias from the difference in the timing as well
as from ignoring too many funds.
Exhibit 4 presents the results, divided into correla-
tions between the excess returns of momentum strategies
and average funds representing performance groups in
large-cap growth and large-cap core. Results are shown
Average fund in a specific performance group performs the same as investment in the equal-weighted portfolio of all funds in the corresponding performance group.
1st quartile performs best. A three-month holding period is employed for the momentum strategy.
*Significant at 90% confidence level.
**Significant at 95% confidence level.
***Significant at 99% confidence level.
Correlation Analysis for Ranked Active Fund Groups
Copyright © 2008
for excess returns, information ratios, and risk-adjusted
returns in descending order from left to right. A three-
month holding period is used.
The most intriguing finding in Exhibit 4 is that cor-
relations tend to rise as performance improves. For
instance, after 1993, correlations of the best large growth
funds and the momentum portfolio range from 0.37 to
0.88 (with statistical significance), while values for the
poorest are much lower and without statistical signifi-
cance except in 2001–2002. Similar patterns are seen for
large growth funds in Panel B.
As Menkhoff and Schmidt [2005] state in their
survey, “aggressive” funds are more likely to adopt
momentum strategies. Thus, it is intuitive to expect less
agreement with a conclusion that the better the perfor-
mance, the stronger the similarity, when performance is
adjusted for risk. In fact, results for two risk-adjusted per-
formance measures (the IR and risk-adjusted returns)
indicate that such a relation still strongly holds, which
would indicate that momentum rules can improve invest-
ment performance even after taking risk into account.
Results of ANOVA tests on investment performance in fund groups divided by similarity with the momentum strategy. All tests conducted for 10 two-year sub-
sample periods in the 20-year long sample period (1987~2006).
Comparison of Investment Performance for Funds with Different Correlation Levels with Momentum Portfolio
Copyright © 2008
Can Individual Funds Benefit
from Following Momentum Rules?
So far we have examined the relation of active funds
to momentum at the portfolio, not individual level. Such
approaches can provide insights for investment in funds,
but they do not directly show whether each individual
fund can benefit from following a momentum rule. To
see whether a momentum rule produces better perfor-
mance, we first rank funds on the basis of their similari-
ties to momentum strategies. Correlations between excess
returns of each individual fund and the momentum port-
folio are estimated, and the results are sorted by correla-
tion. Then, three performance measures are calculated
for each fund.
We conduct one-way analysis of variance tests of
fund groups divided by correlation level to see whether
different levels of similarities yield different performance.
To effectively eliminate timing bias, we divide the 20-year
sample period into ten two-year subsample periods, and
run tests only for funds with data available for 20-months
or more for each subperiod.
Exhibit 5 illustrates results of the ANOVA tests.
Exhibit 6 shows results of the correlation analysis between
fund performance and similarities to momentum in order
to depict the direction of the relation. These results show
that funds whose return patterns are similar to momentum
tend to perform better than funds with lower correlation.
Most of the test results indicate that funds highly correlated
with momentum outperform funds with lower
with 99% statistical significance for the last eight years
(1999–2006), which implies that an individual fund in
growth and core domains can improve its performance by
following industry-level momentum selection rules.
It would be interesting to see what might have hap-
pened with investment performance if a fund manager
had adopted an industry-level momentum strategy for
the entire sample period. The problem is that the bench-
mark for the momentum strategy covers the whole U.S.
stock market, while actual funds are restricted to choosing
stocks in their target benchmark indexes.
Thus, we consider only funds within the large-cap
core domain for a comparison, because the Russell 1000
index, the benchmark for the large core universe, has an
almost identical return pattern to the Datastream index.
The reason is that both indexes are market value weighted,
so they are skewed toward large stocks, which will make
the difference from smaller stocks almost negligible. Fur-
thermore, the momentum strategy portfolio is composed
of market value-weighted industry indexes, which natu-
rally makes it skewed toward large-cap stocks. Also, we
use risk-adjusted returns as the performance measure for
evaluation purposes in order to reduce the bias of the dif-
ferent benchmarks.
The test results in Exhibit 7 indicate that the risk-
adjusted return of the industry-level momentum
strategy (5.1% per year) is in between the return of the
top 10% (6.7% per year) and top 25% funds (4.2% per
Test statistics represent (correlation of a fund with the momentum strategy in terms of excess returns, information ratio, and alpha).
*Significant at 90% confidence level.
**Significant at 95% confidence level.
***Significant at 99% confidence level.
Comparison of Investment Performance and Momentum Similarity
Copyright © 2008
Note that the top funds are chosen every two
years as in the previous subsections. In order to beat the
momentum strategy by duplicating the performance of
the top 10%
funds in Exhibit 7, one would have to be
lucky enough to pick two-year subperiod winners ten
consecutive times.
Institutional money managers, especially in large
growth and the large core domains, are momentum traders.
When we compare a long-only industry-level momentum
strategy to these active funds, we find that performance pat-
terns have been very similar, especially since 1993. The
similarity becomes stronger as time passes, and as funds per-
form better. The evidence is that active managers can
improve their performance by adopting momentum rules.
The authors thank Randy Cusick and Jon Mossman for
their assistance. They generously provided the active fund data
as well as helpful comments.
Results using net-of-fees data produce almost identical
Tests with 6-, 9-, and 12-month holding periods yield
that similar results.
Tests conducted with funds with 50% (12-month) and
full data availability (24-month) for each of the subsample
periods yield similar conclusions. Analysis of the entire sample
period as well as four five-year-long subsample periods yields
results similar to those in Exhibit 4.
Results are similar for different holding periods (6, 9,
and 12 months).
Surprisingly, even large-cap value funds show a signifi-
cant similarity with the momentum strategy after 1997.
Results of tests for funds with 50% (12 months) and full
data availability (24 months) yield similar conclusions.
The top funds in Exhibit 7 are chosen on the basis of
risk-adjusted returns. Similar results are obtained when we use
other performance measures.
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... Note that chapters 2 to 5 are based on Kim and Mulvey (2009), Mulvey, et al. (2009a), Mulvey, et al. (2009b, and Mulvey and Kim (2008a), respectively. Institutional investors employing asset allocation manage their portfolios by controlling their positions on broad asset classes while delegating micro management within each asset class to the hired fund managers. ...
... Kacperczyk, et al. (2005) show that mutual funds with high industry concentration outperform well diversified funds. Also, Mulvey and Kim (2008a) report that active equity funds whose return patterns are similar to industry-level momentum strategy tend to show good performance 15 . Since managers are generally compensated by short-to mid-term performance, some may be willing to construct industry-concentrated portfolios hoping a short-term performance boost up at the cost of long term risks. ...
... A good investment manager is one who is able to react quickly to changes in the market. He is able to understand and follow the market momentum (the measure of the rate of rise and fall in stock price) (Mulvey & Kim 2008). Good managers learn to follow and take advantage of momentum effect, since it is reported by Grinblatt, Titman, & Wermers (1995) that about three-quarters of equity funds track momentum. ...
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It is well documented that past winning stocks continue to outperform past losing stocks in numerous equity markets. However, existing Australian evidence on the momentum effect is contradictory and limited, partly due to differences in empirical designs, sample periods and stock populations. We assess the momentum profitability over the most eligible stocks which are constituents of the S&P/ASX200 index. These stocks represent the principal equity investment universe for institutional investors and managed funds due to their sufficient size and liquidity which make the momentum trading strategies practical and implementable. By incorporating the short-selling ban during the global financial crisis, we find evidence of return persistence. The momentum effect is most pronounced amongst winning stocks for longer holding periods. Upon further exploration we find that neither an industry-driven momentum effect nor common risk factors can fully account for the momentum effect.
In view of the evidence of significant earnings and revenue drifts following firm announcement, this study examines the profitability and its behavior of revenue momentum strategy in conjunction with the previously documented price momentum and earnings momentum strategies. Several interesting and new results emerge from our tests. We first provide new evidence of significant revenue momentum profit and confirm the price and earnings momentum profits. Next, the comparison tests indicate that price momentum generates profit largest in size and then earnings momentum and revenue momentum, whereas none is found to dominate among these three strategies. This latter result implicates that each measure, being prior returns, earnings surprise or revenue surprise, offers investors unique firm-specific information to some extent. More interestingly, the momentum strategies based on multivariate sorts further indicate that the profitability of one momentum strategy (e.g., price momentum) depends on another (e.g., revenue momentum). That is, investors tend to evaluate these information jointly while react to them inefficiently, leading to significantly more improved profit from combined momentum strategies. In particular, a combined momentum strategy utilizing all three measures is found to yield a monthly return as high as 1.57%.
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Predictable variation in equity returns might reflect either (1) predictable changes in expected returns or (2) market inefficiency and stock price “overreaction.” These explanations can be distinguished by examining returns over short time intervals since systematic changes in fundamental valuation over intervals like a week should not occur in efficient markets. The evidence suggests that the “winners” and “losers” one week experience sizeable return reversals the next week in a way that reflects apparent arbitrage profits which persist after corrections for bid-ask spreads and plausible transactions costs. This probably reflects inefficiency in the market for liquidity around large price changes.
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A questionnaire survey has found that most fund managers rely on the strategies of buy-&-hold, momentum and contrarian trading. These strategies are typically applied mutually. Their use is rooted in the attributes and beliefs of the respective fund managers: buy-&-hold traders are fundamentally oriented, risk averse and are less (over)confident than others. Momentum traders appear as the least risk-averse professionals, going aggressively with the trend. Contrarian traders, however, show signs of overconfidence and peculiar risk aversion, both indicating difficulties in successful strategy implementation. The behavioural patterns revealed are not easily reconciled with efficient markets.
Institutional investors' demand for a security this quarter is positively correlated with their demand for the security last quarter. We attribute this to institutional investors following each other into and out of the same securities ("herding") and institutional investors following their own lag trades. Although institutional investors are "momentum" traders, little of their herding results from momentum trading. Moreover, institutional demand is more strongly related to lag institutional demand than lag returns. Results are most consistent with the hypothesis that institutions herd as a result of inferring information from each other's trades.
Recent studies document a strong positive relation between quarterly and annual changes in institutional ownership and returns measured over the same period. The source of this positive correlation could arise from institutional investors' intra-period positive feedback trading, institutions forecasting intra-period price changes, or from price pressure caused by institutional trades. Price pressure can in turn arise for inventory/liquidity reasons, or because market participants infer information from institutional trades. Our results suggest that the price impact of institutional trading is primarily responsible for the documented positive covariance between quarterly changes in institutional ownership and quarterly returns. Moreover, our analyses suggest this price pressure results from information revealed through institutional trading.
Institutional investors' demand for a security this quarter is positively correlated with their demand for the security last quarter. We attribute this to institutional investors following each other into and out of the same securities (“herding”) and institutional investors following their own lag trades. Although institutional investors are “momentum” traders, little of their herding results from momentum trading. Moreover, institutional demand is more strongly related to lag institutional demand than lag returns. Results are most consistent with the hypothesis that institutions herd as a result of inferring information from each other's trades.
Several recent studies suggest that equity mutual fund managers achieve superior returns and that considerable persistence in performance exists. This study utilizes a unique data set including returns from all equity mutual funds existing each year. These data enables the author to more precisely examine performance and the extent of survivorship bias. In the aggregate, funds have underperformed benchmark portfolios both after management expenses and even gross of expenses. Survivorship bias appears to be more important than other studies have estimated. Moreover, while considerable performance persistence existed during the 1970s, there was no consistency in fund returns during the 1980s. Copyright 1995 by American Finance Association.
This paper documents that strategies that buy stocks that have performed well in the past and sell stocks that hav e performed poorly in the past generate significant positive returns o ver three- to twelve-month holding periods. The authors find that the profitability of these strategies are not due to their systematic risk or to delay ed stock price reactions to common factors. However, part of the abnorm al returns generated in the first year after portfolio formation dissipates in the following two years. A similar pattern of returns around the earnings announcements of past winners and losers is also documented. Copyright 1993 by American Finance Association.