active share and product pairs analysis

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

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 PairsIn 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 DatasetFinding 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.

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 ManagementExhibit 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 Growth43%

Mid Cap 5%

Large Cap Core15%

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

Bill

ions

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

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.

ConcentrationRelative 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


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


0%

20%

40%

60%

80%

100%

Median Diversified AUM Median Concentrated AUM

93 1312111009080706050403020100999897969594 14

Source: Callan

Source: Callan


• 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

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


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.

PerformanceOur 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

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.


Active Share as a Predictor of Excess ReturnThe 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

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.

RiskOur 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

Ann

ualiz

ed T

rack

ing

Err

or

Source: Callan


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%

Max

imum

Rel

ativ

e D

raw

dow

n

Source: Callan

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.

ConclusionsLike 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.


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

AuthorGregory 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 particular situation. Reference in this report to any product, service or entity should not be construed as a recommendation, approval, 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 internal web sites any part of any material prepared or developed by the Institute, without the Institute’s permission. Institute clients only have the right to utilize such material internally in their business.

If you have any questions or comments, please email [email protected].

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About the Callan Investments InstituteThe 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

published research, surveys, and newsletters. The Institute strives to present the most timely and relevant

research and education available so our clients and our associates stay abreast of important trends in the

investments industry.

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active share and product pairs analysis

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