Strategies with high active share have garnered much attention from institutional investors following the release of Martijn Cremers and Antti Petajisto’s research paper that introduced the concept. In this paper we isolate the impact of active share on performance by focusing on “product pairs,” which are two portfolios that share many characteristics (same management team, basic philosophy, research platform, etc.) but have different degrees of concentration (concentrated vs. diversified), which translates fairly directly to a difference in active share. We ran several analyses using product pairs identified in Callan’s database in order to better understand— and quantify—the performance differences between concentrated and diversified products managed by the same team. Our analysis reveals the inherent difficulty of identifying reliable predictors of excess return across strategies and over time. High active share may be worthy of consideration as a screening variable, but it is clearly only one of potentially dozens of factors that might influence the magnitude and direction of the excess return for any given strategy over time. Author Gregory C. Allen is Callan’s President and Director of Research. He oversees Callan’s Fund Sponsor Consulting, Trust Advisory, and multiple other firm-wide research groups. Greg is a member of Callan’s Management, Alternatives Review, and Client Policy Review Committees. He is also a member of the Investment Committee, which has oversight responsibility for all of Callan’s discretionary multi-manager solutions.

1. Knowledge. Experience. Integrity.
In a 2007 paper,Antti Petajisto and Martijn Cremers1
—researchers from the Yale School of Management—
introduced a holdings-based portfolio metric called “active share,” which measures the difference in com-
position between a portfolio and its benchmark. In their widely read paper, they used active share to
evaluate a broad universe of registered U.S. equity mutual funds over the period from 1980 through 2003.
Some interesting findings include:
1. Active share has been fairly consistent over time across funds, meaning that high active share funds
(those that are quite different from their benchmark) tend to maintain their high active share.
2. Funds with higher active share tend to be smaller in terms of assets under management (AUM), and
have higher expense ratios.
CALLAN
INVESTMENTS
INSTITUTE
Research
April 2015
Active Share and Product Pairs Analysis
Strategies with high active share have garnered much attention from institutional investors following
the release of Martijn Cremers and Antti Petajisto’s research paper that introduced the concept.
In this paper we isolate the impact of active share on performance by focusing on “product pairs,”
which are two portfolios that share many characteristics (same management team, basic philosophy,
research platform, etc.) but have different degrees of concentration (concentrated vs. diversified),
which translates fairly directly to a difference in active share.
We ran several analyses using product pairs identified in Callan’s database in order to better under-
stand—and quantify—the performance differences between concentrated and diversified products
managed by the same team. Our analysis reveals the inherent difficulty of identifying reliable predic-
tors of excess return across strategies and over time. High active share may be worthy of consid-
eration as a screening variable, but it is clearly only one of potentially dozens of factors that might
influence the magnitude and direction of the excess return for any given strategy over time.
1 Martijn Cremers and Antti Petajisto. “How Active Is Your Manager? A New Measure that Predicts Performance,” AFA 2007 Chicago
Meetings Paper; EFA. 2007 Ljubljana Meetings Paper; Yale ICF Working Paper No. 06-14, 2007, Available at SSRN: http://ssrn.com/
abstract=891719.

2. 2
3. The funds with the highest active share exhibited some skill, outperforming their benchmarks (on
average) by just over 1% net of fees, while the lowest active share funds underperformed their
benchmarks.
4. Higher active share across funds is not perfectly correlated with higher tracking error. This is due to
the fact that high active share generated through stock picking does not necessarily result in big fac-
tor or sector bets in risk-controlled portfolios.
5. Tracking error in the absence of active share was not rewarded, indicating that funds that focused on
factor bets rather than stock picking tended to have zero to negative skill (on average) after fees.
6. The impact of high active share on performance was more pronounced for funds with smaller AUM.
The analysis was comprehensive and the primary findings of the paper appear to be well-supported and
statistically significant. The reaction to these findings by the institutional investment community has been
notable, with investors taking a greater interest in higher active share strategies.
Product Pairs
In this paper we expand on the work of Petajisto and Cremers to isolate the impact of active share on per-
formance by focusing on “product pairs.” Product pairs are portfolios that are managed by the same team
or individual, are based on the same basic philosophy, are supported by the same research platform, and
are managed against the same benchmark. The fundamental difference between the two portfolios within
a product pair is their degree of concentration, which translates fairly directly to a difference in active
share.
Managers of product pairs indicate the primary distinction between the two portfolios is the level of “con-
viction” in the underlying holdings. The more concentrated portfolio is typically characterized as the “best
ideas” or “high-conviction” portfolio. The less concentrated portfolio is typically described as the “diversi-
fied” or “risk-controlled” portfolio. We run several analyses on product pairs in Callan’s manager database
to better understand the differences in the composition and performance of these two types of portfolios
(managed by the same teams over the same time periods). This may provide further insight into the poten-
tial usefulness of higher active share strategies in the context of institutional multi-manager portfolios.
The Dataset
Finding legitimate product pairs portfolios proved to be more difficult than initially expected. Quantitative
techniques and database queries were unreliable due to the large number of false positives that they
returned. In the end, approximately 80% of the pairs used in this analysis were sourced qualitatively by
specialists in Callan’s Global Manager Research group. The dataset is limited to U.S. equity portfolios
and includes a total of 74 pairs (148 portfolios) spanning a broad range of styles. The breakdown of the
dataset by style is shown in Exhibit 1.
Product pairs are
portfolios that are man-
aged by the same team or
individual, are based on
the same basic philoso-
phy, are supported by the
same research platform,
and are managed against
the same benchmark. The
fundamental difference
between the two portfolios
within a product pair is
their degree of concentra-
tion, which translates fairly
directly to a difference in
active share.

3. 3Knowledge. Experience. Integrity.
Our analysis considered the period from December 31, 1989, through December 31, 2013. Only a hand-
ful of products had track records spanning the entire period. Every effort was made to source all product
pairs within Callan’s Total Equity Database. Anecdotal input from our manager research specialists, how-
ever, indicates that there are likely a number of instances where managers have a concentrated version
of their strategy but do not submit data due to a perceived lack of institutional interest or relatively poor
performance.2
Assets Under Management
Exhibit 2 illustrates the growth in aggregate assets under management across the entire dataset since
1990. Total assets peaked at the end of 2006 at approximately $500 billion.
Exhibit 1
Distribution of Product
Pairs by Style
Large Cap Value
24%
Large Cap Growth
43%
Mid Cap 5%
Large Cap Core
15%
Small Cap 13%
Exhibit 2
Total Asset Growth by
Product Type
Diversified versus
Concentrated
$0
$50
$100
$150
$200
$250
$300
$350
$400
Diversified AUM Concentrated AUM
90 1312111009080706050403020100999897969594939291
Billions
2 This hints at a potential self-selection bias in the dataset that might exaggerate the relative performance of the concentrated strate-
gies. No attempt was made to quantify its impact.
Source: Callan
Source: Callan

4. 4
Diversified products have clearly been more successful in gathering (and retaining) assets than their
more concentrated counterparts. Exhibit 3 shows the ratio of the median AUM of the concentrated prod-
ucts relative to the median AUM of the diversified products, illustrating the relative success of the diversi-
fied products in gaining market share.
The product pairs universe’s asset growth and composition illustrate an important point to consider when
employing concentrated strategies: Economic viability risk has generally been higher for concentrated
products. The data highlights the fact that concentrated products have not been as successful at raising
assets, and have been more prone to asset losses during periods of underperformance compared with
their diversified counterparts. Many of the concentrated products in this set of pairs would have had a dif-
ficult time surviving without the revenue from the diversified product to support the portfolio management,
marketing, and client service teams.
Concentration
Relative concentration varied widely across product pairs. Exhibit 4 illustrates the distribution of average
number of holdings across the concentrated and diversified populations.
Exhibit 4
Distribution of Average
Number of Holdings
Diversified versus
Concentrated
0%
10%
20%
30%
40%
50%
60%
120 +100 to 11980 to 9960 to 7940 to 5920 to 390 to 19
17%
0%0%0%
12%
51%51%51%
16%
37%37%37%
3%
28%28%28%
1%
8%8%8%
0%
4%4%4%
0%
4%4%4%
Diversified Concentrated
Average Number of Holdings
Exhibit 3
Total Asset Percentage
Breakdown by Product
Type
Diversified versus
Concentrated
0%
20%
40%
60%
80%
100%
Median Diversified AUM Median Concentrated AUM
93 1312111009080706050403020100999897969594 14
Source: Callan
Source: Callan

5. 5Knowledge. Experience. Integrity.
• Concentrated portfolios average number of holdings: 36 (within a range of 7 to 103). The majority of
these portfolios held between 10 and 39 securities.
• Diversified portfolios average number of holdings: 66 (within a range of 17 to 201). The majority of
these portfolios held between 40 and 79 securities.
On average, across the whole dataset, the concentrated portfolios held roughly half the number of securi-
ties that their corresponding diversified counterparts held. In the most extreme case of relative risk, the
concentrated product held an average of 25% (20 stocks versus 81) of the number of securities that were
held in the diversified version. On the other end of the spectrum there were a fairly large number of prod-
ucts (25% of the population) that took on only a marginal amount of additional risk in their concentrated
products, holding 60% to 80% of the number of names held in the diversified versions over time.
Active Share
Active share is an alternative (and arguably more robust) way to measure relative concentration. Active
share takes into account portfolio differences that may not be reflected in the securities count but manifest
themselves in terms of sector or factor bets. Active share compares the underlying holdings of a portfolio
with those of a relevant index. It measures the percentage of the portfolio (by market value) that is the
same as the index. An index fund employing full replication will have 0% active share relative to its index.3
A portfolio with no overlapping securities with the index will have 100% active share.
In this exercise we calculated active share for every product for every quarter that holdings were avail-
able. The appropriate Russell style index was used for each product pair.4
Exhibit 5 demonstrates that the concentrated products generally had higher active share relative to the
index: almost half of them (45%) registered values above 90%. Concentrated products had an average
active share of 85%, while the diversified products had an average active share of 70%. None of the prod-
ucts in our sample had active share below 50%, indicating that overall these teams tended to manage
0%
10%
20%
30%
40%
50%
90% +80% to 89%70% to 79%60% to 69%50% to 59%
0%
12%12%12%
3%
16%16%16% 16%
25%25%25%
29%29%29%29%
45%
19%19%19%
Diversified Concentrated
Active Share Levels
Exhibit 5
Distribution of Active
Share
Diversified and
Concentrated Portfolios
versus Relevant Index
3 Technically this is only true if the index fund uses full replication. If stratified sampling is used it will present itself as an active share
value greater than zero.
4 Small cap products were compared against the Russell 2000, Russell 2000 Growth, or Russell 2000 Value Indices. Large cap
products were compared against the Russell 1000, Russell 1000 Growth, or Russell 1000 Value Indices.
Source: Callan

6. 6
relatively active strategies (even in the diversified versions of their products). Interestingly, almost 20% of
the “diversified” products had active share values above 90%.
We also calculated the active share of the concentrated portfolio relative to its diversified counterpart.
This provided some insight into how much relative risk the team was willing to take in their high-conviction
portfolio relative to their diversified product. Exhibit 6 shows that the distribution was fairly normal across
the dataset, with a median value of approximately 50%.
However, this distribution masks the variability that these relative active share values demonstrated over
time. While roughly 30% of the products had a disciplined and stable relative risk relationship (maintaining
an average relative active share that was within 10% of their minimum and maximum quarterly values),
the other 70% demonstrated a much higher level of variability. This suggests that it was not uncommon
among these product pairs for the relative risk of the two products to change over time in response to
market factors or business conditions.
In addition to a high degree of variability, we noted a pervasive trend of portfolio management teams
reducing the relative risk of their concentrated products over time.5
More than half of the concentrated
products in the dataset showed a decrease in active share (relative to their diversified counterparts) of
over 10% from the first two years to the last two years of their lifecycle. In many cases the relative active
share of the concentrated product was reduced by more than half over its lifecycle. By contrast, only 6%
of the products showed an increase in active share of greater than 10% when comparing the first and last
two years of their lifecycle.
0%
10%
20%
30%
40%
50%
>80%60% to 79%40% to 59%20% to 39%<20%
3%3%3%
27%27%27%
47%47%47%
20%20%20%
3%3%3%
Relative Active Share
Exhibit 6
Distribution of Active
Share
Concentrated Product
versus Diversified
Counterpart
5 The evolution of active share over the product lifecycle was measured by comparing the average active share of the concentrated
product relative to the diversified product in the first two years of its history and in the last two years. For products that survived until
the end of the measurement period, the last two-year period ended March 31, 2014.
Source: Callan

7. 7Knowledge. Experience. Integrity.
The frequency with which relative risk was reduced across this dataset hints at the difficulty of maintain-
ing a high-conviction portfolio across full market cycles. For much of the period between 2008 and 2013,
stock prices were profoundly influenced by global macro trends. This meant that short- and medium-term
relative performance across stocks had little correlation with their underlying fundamentals. Stock picking
was generally not rewarded during this period, and active managers with concentrated portfolios ran a
particularly high risk of underperformance. This dataset indicates that over half of the teams reacted to
this environment by (arguably prudently) reducing the relative risk of their concentrated portfolios.
Based on the starting point and lengths of the track records for these products, the vast majority of man-
agers developed their concentrated products after the launch of the diversified version. Furthermore, the
concentrated products had a higher incidence of cessation of data submission, which suggests they were
terminated. Overall, the track records for the concentrated products were roughly 20% shorter than those
of their diversified counterparts; we observed only two instances where the concentrated product had a
longer track record than its diversified counterpart.
Finally, while a somewhat small point, it was common to see very high relative active share in the first
quarter or last quarter of a product’s lifecycle. This is likely explained by the process by which a portfolio
goes from cash to fully invested and vice versa.
Performance
Our primary objective at the outset of this exercise was to better understand (and to quantify) the perfor-
mance differences between concentrated and diversified products managed by the same team. Petajisto
and Cremers’ work suggests that, on average across a large dataset of mutual funds, high active share
has conferred some type of structural advantage that has resulted in superior performance. Evaluating
this premise across product pairs seems like a reasonable way of isolating the impact of concentration
while reducing the number of exogenous factors (different teams, different styles, different market envi-
ronments, different distribution strategies, different organizational structures, etc.) that can impact relative
performance.
To measure the performance differences between the concentrated and diversified products, we con-
structed a simple T-test that compared the quarterly excess returns of the concentrated products and the
diversified products over all of the quarters for which there was return data for both products.6
Exhibit 7
provides descriptive statistics for all three series used in the analysis: the concentrated product excess
returns versus benchmark; the diversified product excess returns versus benchmark; and the difference
series (concentrated excess returns minus diversified excess returns for each product pair).
6 Excess returns were calculated relative to the appropriate Russell index for each product pair.
The frequency with which
relative risk was reduced
across this dataset hints at
the difficulty of maintaining
a high-conviction portfolio
across full market cycles.

8. 8
We draw a couple of interesting observations from this simple analysis. First, the mean values for the
The Concentrated Excess Return series and the Diversified Excess Return series are positive and their
associated t-statistics are quite high. This indicates that, on average over the measurement period, the
teams managing these strategies generated statistically significant positive excess returns versus their
benchmarks for both of their products. This suggests that collectively this group possessed some insight
and/or skill which they successfully applied to the management of both portfolios.
The second observation is that the mean value for the difference series (Concentrated minus Diversified)
is positive and the t-statistic is also quite high. This indicates that, on average over the measurement
period, the excess return of the concentrated products relative to their diversified counterpart was positive
and statistically significant. If the 95th percentile confidence level is a conservative estimate of expected
excess return, you might reasonably expect a concentrated product to outperform its diversified counter-
part by 11 basis points per quarter or 45 basis points per year, on average, over this period. This finding
seems to support some of the conclusions from the Petajisto and Cremers work.
Notably, this analysis does not indicate that higher concentration will always result in better performance.
A reasonable interpretation might be that higher concentration (within reason) in the presence of skill is
likely to result in greater outperformance. Had this group generated negative average excess returns
across both portfolios over the measurement period, it is not clear whether the concentrated strategies
would have outperformed their diversified counterparts.7
7 As a practical matter, it is not clear that you could undertake this type of analysis on a set of pairs that had collectively underper-
formed their benchmarks since it is very unusual for a manager to launch a concentrated version of a diversified portfolio that has
underperformed.
Descriptive Statistics*
Concentrated
Excess Return
Diversified
Excess Return
Concentrated
minus Diversified
Mean 0.60 0.39 0.22
Standard Deviation 4.49 3.32 3.23
Median 0.22 0.18 0.11
Standard Error 0.08 0.06 0.06
t-statistic** 7.46 6.48 3.74
95th percentile confidence level*** 0.16 0.12 0.11
Observations 3,111
Exhibit 7
Quarterly Excess Return
Statistics
* Statistics were calculated based on quarterly return series. Means and standard deviations have not been annualized.
** t-statistics above 2.5 generally indicate that the mean for the series is statistically significantly different from zero.
*** Data indicates with 95% probability that the mean of the series is above this number.
Source: Callan
T-statistics are helpful in
comparing two popula-
tion means. A t-statistic
above 2.5 generally indi-
cates that the mean for
the series is statistically
different from zero.

9. 9Knowledge. Experience. Integrity.
Active Share as a Predictor of Excess Return
The work of Petajisto and Cremers has resulted in an increased interest in concentrated portfolios, and
high active share has become a common screening criterion in sourcing and selecting strategies. This
trend begs the question: Has high active share been a good predictor of positive excess return over time?
Exhibit 8 presents the results of a simple regression we ran to answer this question. We regressed the
entire quarterly excess return series used in this analysis (both concentrated and diversified products)
against three other independent variables that might explain excess returns of actively managed equity
strategies over time. The explanatory variables included:
1. Active Share: the active share for each product at the end of the preceding quarter
2. Small Cap: the excess return of the Russell 2000 relative to the Russell 1000 during the quarter8
3. Active Value: the average excess return across the products in Callan’s Large Cap Value universe
relative to the Russell 1000 Value Index during the quarter
4. Active Growth: the average excess return across the products in Callan’s Large Cap Growth universe
relative to the Russell 1000 Growth Index during the quarter9
Each of the explanatory variables used in the regression had a positive coefficient and a high t-statistic.
This leads to the conclusion that they all had some statistically significant explanatory power of excess
returns. Of the four, however, only active share can be known in advance, making it a potentially reason-
able screening variable for sourcing active equity strategies. The fact that the coefficient for active share
was positive and significant implies that, all else being equal, you might want to screen for higher active
share strategies in order to generate potentially higher excess returns.
Exhibit 8
Excess Returns Across
All Strategies Over Time
Independent Variables Coefficients Standard Error t-Statistics
Intercept 0.00 n/a n/a
Active Share 0.40 0.06 6.68
Small Cap 0.05 0.01 4.11
Active Value 0.30 0.05 6.02
Active Growth 0.78 0.04 21.04
Regression Statistics
Multiple R 0.3073
R-Squared 0.0945
Adjusted R-Squared 0.0938
Standard Error 3.5584
Observations 5,650
Note: the dependent variable is a quarterly excess return series for all strategies (diversified and concentrated).
Source: Callan
8 Equity strategies often have a small cap bias relative to the benchmark due to their tendency to equal weight securities in their
portfolios. This variable is meant to proxy periods when small cap was in favor.
9 The last two variables are meant to proxy periods when active management generally outperforms that are not explained by the
small cap effect.

10. 10
The second table in Exhibit 8 contains the adjusted R-squared value for the regression, which is important
in interpreting these results. The value of 0.0938 implies that the four independent variables explained
only about 10% of the variation of excess returns across this cross-sectional time series. When active
share is used as the only explanatory variable in a simpler regression, the adjusted R-squared value
drops to less than 2%. This implies that most of the variability of excess returns across this sample was
explained by something other than the active share of the portfolio.
It is also important to keep in mind when interpreting these results that this population of strategies is
unique in that the managers exhibited above-average skill over the measurement period across both their
diversified and concentrated products. It is not clear that the positive relationship between high active
share and high excess return would prevail in a broader population of strategies or over a different time
period where the average excess return is closer to zero.
These results illustrate the inherent difficulty of identifying reliable predictors of excess return across
strategies and over time. Excess returns are highly variable and inherently unpredictable by nature; they
are influenced by factors ranging from market anomalies to product-specific organizational or strategy
changes. While high active share may be worthy of consideration as a screening variable, it is clearly only
one of potentially dozens of factors that might influence the magnitude and direction of the excess return
for any given strategy over time.
Risk
Our previous discussion of assets under management illustrated the fact that concentrated strategies
have had a more difficult time raising and retaining assets than their diversified counterparts. An analysis
of risk using tracking error and maximum drawdown analysis can provide some insight into why this was
true in spite of the fact that the concentrated products (as a group) produced better performance over the
measurement period.
Exhibit 9
Range of Annualized
Tracking Error
Product Pairs versus
Traditional Large Cap
Products
0%
3%
6%
9%
12%
15%
Concentrated Diversified Large Cap Growth Large Cap Value
Products Products Products Products
10th Percentile 13.33 9.50 6.71 5.36
25th Percentile 8.52 7.34 4.54 4.24
Median 6.68 4.91 3.96 3.42
75th Percentile 5.28 3.77 3.00 2.61
90th Percentile 4.42 2.62 2.41 2.05
AnnualizedTrackingError
Source: Callan

11. 11Knowledge. Experience. Integrity.
Tracking error measures the level of variability of excess returns for a strategy relative to its benchmark.
Higher tracking error products can experience sharp or sustained periods of underperformance (or out-
performance) relative to the benchmark. As expected, the concentrated products in this analysis gener-
ally exhibited higher tracking error than their diversified counterparts. Exhibit 9 contrasts the annualized
levels of tracking error generated by the concentrated and diversified products with those of traditional
large cap equity products within Callan’s Large Cap Growth and Large Cap Value styles.
These tracking error comparisons support our previous conclusion that this sample of managers is rela-
tively active. Even the diversified products within this product set generated higher levels of tracking
error (median 4.91) than the typical Large Cap Growth (3.96) or Large Cap Value (3.42) products. Within
product pairs, the concentrated products generated approximately 45% higher tracking error than their
diversified counterparts, with only seven cases where the concentrated product generated less tracking
error than its diversified counterpart. This tracking error analysis points out the fact that the extra return
generated by the concentrated strategies typically came at the expense of a more “exciting” ride.
While tracking error measures the variability of excess returns, it does not necessarily tell you their direc-
tion. Maximum relative drawdown analysis allows you to measure how bad things would have been if you
happened to choose the absolute worst entry point to invest in a strategy. It is a measure of the largest
cumulative (not annualized) underperformance of a product relative to its benchmark over the full length
of its track record. Oftentimes a large magnitude drawdown can lead to watch list status or, in extreme
cases, termination.
Exhibit 10 reveals the range of maximum relative drawdown for the two product types within the product
pairs. (Relative drawdown is calculated for each product in the dataset relative to its appropriate benchmark.)
Exhibit 10
Range of Maximum
Relative Drawdown
Concentrated versus
Diversified
0%
10%
20%
30%
40%
Concentrated Diversified
Products Products
90th Percentile 28.4% 30.5%
75th Percentile 20.9% 22.2%
Median 12.8% 14.8%
25th Percentile 9.4% 9.1%
10th Percentile 6.4% 4.8%
MaximumRelativeDrawdown
Source: Callan

12. 12
Exhibit 10 highlights a somewhat unexpected finding: the distributions are remarkably similar, and the
diversified products actually demonstrate slightly larger median and worst-case (90th percentile) relative
drawdowns. Even more surprisingly, in exactly 50% of the cases the concentrated products experienced
smaller maximum relative drawdowns than their diversified counterparts. Given that the concentrated
products exhibited tracking error was 45% higher than their diversified counterpart on average, it would
have been reasonable to expect the maximum drawdown for the concentrated products to be consistently
higher. These results indicate that higher tracking error does not always translate into greater cumulative
underperformance in worst-case outcomes.
Conclusions
Like most industry studies that have set out to explain active management, our dataset was subject to
biases including a small sample size, self-selection bias, short and disparate track records, an over-
representation of growth products relative to other styles of management, and dependence on a unique
historical period. In short, it was no different than any other study that has been conducted over the last
thirty years that has attempted to examine historical track records to better understand and predict the
behavior of active portfolio managers. Thus we do not draw any general conclusions that can be sup-
ported with a high degree of confidence, but rather we can take away several observations about concen-
trated products relative to their diversified counterparts.
1. In the presence of skill, evidence suggests that concentration can lead to superior long-term results.
Most managers employ the “live to fight another day” philosophy of devoting a portion of the portfolio
to risk control and diversification. This allows them to survive during the periods when their approach
is out of favor so that they can put up the long-term (albeit slightly watered down) results that lead to
success.
2. In the grand scheme of variables that explain excess return, concentration is important but likely
explains less than 5% of the total picture. More concentrated portfolios will generate relative returns
of greater magnitude, but the sign of these excess returns remains in question. Survivorship and
self-selection biases are pervasive in any study of managed portfolios, and it’s reasonable to argue
that their impact is directly proportional to the relative volatility of the products being considered.
Survivorship bias could easily explain the results in both this study and the ones conducted by
Petajisto and Cremers.
3. Concentrated products are generally subject to greater business risk. Sustained or sharp periods of
underperformance virtually guarantee their elimination from the vast majority of standard institutional
manager search exercises. Depending on the entry point of existing clients, they can also lead to
terminations. These periods can make it very difficult to sustain the resources required to generate
their promised long-run outperformance.

13. 13Knowledge. Experience. Integrity.
4. The efficacy of a concentrated approach can be significantly impacted by general market conditions.
In certain market environments, diversification is your friend no matter how good a stock-picker you
might be. When macro conditions drive prices up (or down) for factor exposures that have nothing
to do with stock picking (e.g., yield and energy), concentrated portfolios can pay a particularly steep
price in terms of relative performance. While that may be an acceptable price to pay for some inves-
tors with a longer-term view, it can spell the end for a concentrated product in the wrong part of its
overall business cycle.
5. The definition of a concentrated portfolio varies widely across asset managers and time periods.
Number of holdings, sector weights, factor exposures, tracking error, and active share are all measures
designed to help quantify the level of relative concentration. While they are correlated, each measure
provides a different perspective on the question. All of them are useful in providing a mosaic that leads
to better understanding of a strategy, but one measure alone cannot capture the entire picture.

14. 14
Author
Gregory C. Allen is Callan’s President and Director of Research. He oversees Callan’s
Fund Sponsor Consulting, Trust Advisory, and multiple other firm-wide research groups.
Greg is a member of Callan’s Management, Alternatives Review, and Client Policy Review
Committees. He is also a member of the Investment Committee, which has oversight
responsibility for all of Callan’s discretionary multi-manager solutions.
Greg joined Callan in 1988 as an analyst in the Capital Markets Research Group. In 1993, he took over
the Operations and Capital Markets Research groups. He has managed the development of many proj-
ects for Callan, including the firm’s performance measurement service, the website and web-based per-
formance reporting capabilities, and Callan’s PEP for WindowsTM
software. In 2000, he became Manager
of Specialty Consulting (now Director of Research) and in 2007 was promoted to President. Greg is a
shareholder of the firm.
Greg earned an MS in Applied Economics and a BA in Economics (Honors, Phi Beta Kappa) from the
University of California at Santa Cruz. He is a frequent speaker and writer on investment-related topics.
His work has been published multiple times in the Journal of Portfolio Management, including “Does Size
Matter?” published in 2007.
Certain information herein has been compiled by Callan and is based on information provided by a variety of sources believed to
be reliable for which Callan has not necessarily verified the accuracy or completeness of or updated. This report is for informational
purposes only and should not be construed as legal or tax advice on any matter. Any investment decision you make on the basis of
this report is your sole responsibility. You should consult with legal and tax advisers before applying any of this information to your
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affiliation or endorsement of such product, service or entity by Callan. Past performance is no guarantee of future results. This report
may consist of statements of opinion, which are made as of the date they are expressed and are not statements of fact. The Callan
Investments Institute (the “Institute”) is, and will be, the sole owner and copyright holder of all material prepared or developed by the
Institute. No party has the right to reproduce, revise, resell, disseminate externally, disseminate to subsidiaries or parents, or post on
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About Callan
Callan was founded as an employee-owned investment consulting firm in 1973. Ever since, we have
empowered institutional clients with creative, customized investment solutions that are uniquely backed
by proprietary research, exclusive data, ongoing education and decision support. Today, Callan advises
on $2 trillion in total assets, which makes us among the largest independently owned investment con-
sulting firms in the U.S. We use a client-focused consulting model to serve public and private pension
plan sponsors, endowments, foundations, operating funds, smaller investment consulting firms, invest-
ment managers, and financial intermediaries. For more information, please visit www.callan.com.
About the Callan Investments Institute
The Callan Investments Institute, established in 1980, is a source of continuing education for those in
the institutional investment community. The Institute conducts conferences and workshops and provides
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