Improved Beta? - IndexUniverse.com

Improved Beta? 

Noël Amenc, Felix Goltz and Lionel Martellini 

The Evolution Of Equal Weighting 

Pradeep Velvadapu 

Expert Weighting: A Roundtable Discussion 

With Robert Arnott, Gus Sauter, Scott Ebner, Srikant Dash and more 

Core Vs. Blend 

David Blanchett 

Plus Blitzer on classifications, Wall on benchmarking microfinance and Crigger going bananas

www.journalofindexes.com 

Vol. 14 No. 1 

features 


by Noël Amenc, Felix Goltz 

and Lionel Martellini ........................ 10 

Comparing index weighting methodologies. 

The Evolution Of Equal Weighting 

by Pradeep Velvadapu ..................... 20 

What happens when you equal-weight sectors?. 

The Weight Debate 

with Robert Arnott, Gus Sauter, Scott Ebner, 

Srikant Dash, Frank Nielsen and Chris Woods . . 30 

Experts opine on the intricacies of weighting schemes. 


by David Blanchett ........................ 36 

Do “core” indexes outperform “blend” indexes? 

Benchmarking Microfinance Equity Investments 

by Peter Wall ............................ 40 

How to construct an index of microfinance institutions. 

Sorting And Digging 

by David Blitzer .......................... 50 

The art of dividing the market into useful pieces. 

Better Beta By Bananas 

by Lara Crigger ........................... 64 

The grueling quest for the ultimate weighting scheme. 

news 

Indexes Regain Edge Over Active . . . . . . . . . . . . 52 

TD Ameritrade Offers Commission-Free ETFs . . 52 

MIT Launches Own Inflation Indexes . . . . . . . . . 52 

Wave Of ETF Closures Announced In Sept., Oct. . 52 

Indexing Developments . . . . . . . . . . . . . . . . . . . 53 

Around The World Of ETFs . . . . . . . . . . . . . . . . 56 

Back To The Futures ..................... 58 

Know Your Options ...................... 58 

On The Move ........................... 58 

10 

20 

data 

Selected Major Indexes . . . . . . . . . . . . . . . . . . . 59 

Returns Of Largest U.S. Index Mutual Funds . . . 60 

U.S. Market Overview In Style . . . . . . . . . . . . . . 61 

U.S. Industry Review ..................... 62 

Exchange-Traded Funds Corner . . . . . . . . . . . . . 63 

36 

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Contributors 

Noël Amenc 

Noël Amenc is a professor of finance and director of development at EDHEC 

Business School, where he heads the EDHEC-Risk Institute. Amenc has a master’s 

degree in economics and a Ph.D. in finance and has conducted active 

research in the fields of quantitative equity management, portfolio performance 

analysis and active asset allocation. He is a member of the editorial 

board of the Journal of Portfolio Management, associate editor of the Journal of 

Alternative Investments and a member of the scientific advisory council of the 

AMF (French financial regulatory authority). 

David Blanchett 

David Blanchett is the director of Consulting and Investment Research for the 

Retirement Plan Consulting Group at Unified Trust Company in Lexington, Ky. 

He is primarily responsible for helping 401(k) advisers with fiduciary, compliance, 

operational and investment issues relating to Unified Trust’s retirement 

plan services. Blanchett has published over 30 papers in various industry 

journals. He is a CFA charterholder and recently completed his MBA at the 

University of Chicago Booth School of Business. 

David Blitzer 

David Blitzer is managing director and chairman of the Standard & Poor’s 

Index Committee. He has overall responsibility for security selection for S&P’s 

indices and index analysis and management. Blitzer previously served as chief 

economist for S&P and corporate economist at The McGraw-Hill Companies, 

S&P’s parent corporation. A graduate of Cornell University with a B.S. in engineering, 

he received his M.A. in economics from Georgetown University and 

his Ph.D. in economics from Columbia University. 

Lara Crigger 

Lara Crigger is an associate editor at Index Publications. She oversees editorial 

content for Hard Assets Investor, and provides research and content for Index 

Universe, Hard Assets Investor, Journal of Indexes and the Exchange-Traded Funds 

Report. Prior to joining Index Publications, Crigger was a freelance journalist 

specializing in finance and technology. She holds a B.S. in physics from the 

Rochester Institute of Technology. 

Felix Goltz 

Felix Goltz is head of Applied Research at EDHEC-Risk Institute. He conducts 

research in empirical finance, asset allocation and performance analysis, with a 

focus on indexing strategies. Goltz’s research has been published in international 

academic and practitioner journals and handbooks, and he is a frequent speaker 

at industry conferences. Goltz obtained a Ph.D. in finance from the Université de 

Nice Sophia Antipolis after studying economics and business administration at 

the University of Bayreuth and EDHEC Business School. 

Pradeep Velvadapu 

Pradeep Velvadapu is a senior research analyst for the index business at 

Russell Investments, based in Seattle. As part of the research and innovation 

group, he is responsible for conducting research and analysis necessary for 

the development of new index and investable products, and the maintenance 

and enhancement of existing products. Velvadapu joined Russell in 2006 as a 

member of the research team responsible for the development and construction 

of the Russell Global indexes, launched in January 2007. 

Peter Wall 

Peter Wall is founder and CEO of Wall’s Street Advisor Services, a consultancy 

specializing in innovations in financial information services. He has over 30 

years’ experience in developing and marketing financial information services, 

with International Finance Corporation, FTSE International and most recently, 

Microfinance Information Exchange Inc. In these capacities, Wall has also been 

active in many index and international financial reporting standards-setting 

activities. A member of the CFA Institute, he is based in Falls Church, Va. 

2 

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Editor’s Note 

Considering Alternatives 

Jim Wiandt 

Editor 

With beta becoming the new alpha, it seems apt that index weighting schemes are 

again coming into focus. Despite Rob Arnott and others pitching alternatively 

weighted indexes as effectively better beta, they’ve always struck me as an effort 

to try to squeeze some alpha in over the market cap … more akin to enhanced indexing 

than “better beta,” and certainly some bet against where the market holds its money. 

Now, to some extent, the numbers are in. Alternative weighting strategies have been 

on the market for a few years now, and they’ve been stress-tested in some very stressful 

environments. We thought it was time to take a good, hard look at how they’ve fared. 

That’s all the more important considering that many more alternative strategies are coming 

down the pike, including new equal-weighted ETFs providing exposure to all your 

favorite indexes. In this issue, you’ll get some insight into how these types of funds and 

indexes work. 

Right on point is a survey from Noël Amenc, Felix Goltz and Lionel Martellini firmly planting 

the flag that the alternatively weighted indexes outperform, in many respects, their capweighted 

counterparts. There is plenty of data in the piece for you to peruse and ponder. 

Pradeep Velvadapu weighs in with a piece looking at equal sector weighting … a 

strategy that has particularly fared well on the bust side of boom/bust bubbles like late 

’90s tech and 2008 financials ... both extremely interesting case study time periods for 

alternatively weighted indexes. Compare, contrast, reflect. 

Next up is an old-school roundtable from some of the leading players on the alternative 

and cap side of the weighting equation, including the thoughts of Gus Sauter, Rob 

Arnott, Scott Ebner, Srikant Dash, Frank Nielsen and Chris Woods. The hardcore JoI fans 

out there will LOVE this Q&A. 

Rounding out the issue is a look at “core vs. blend” portfolios by the always engaging 

David Blanchett, a great piece on the challenge of benchmarking microfinance by Peter 

Wall, David Blitzer making the assertion that if you’re in the markets, you’re a sorter or a 

digger, and Lara Crigger with a back-page jolly that sort of pans … me. 

Happy investing! These are exciting times around here and in our industry. 

Jim Wiandt 

Editor 

8 


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A comparison of index weighting schemes 

By Noël Amenc, Felix Goltz and Lionel Martellini 

10 


In recent years, many alternatives to cap-weighted equity 

indexes have been launched. These indexes are constructed 

using other weighting schemes that are supposed to 

improve on capitalization weighting and thus provide investors 

with “improved beta.” The objective of this paper is to 

analyze the performance of a set of such indexes. The results 

suggest that the improved beta approaches provide benefits 

compared to the standard cap-weighted indexes. Moreover, 

the weighting schemes achieve very different objectives, 

making good on their promise to alleviate specific problems 

inherent to cap-weighting. 

Cap-weighted equity indexes have come to dominate 

the market for equity index products. Standard & Poor’s 

introduced its first cap-weighted stock index in 1923. Such 

indexes were intended to provide information on the market’s 

mood and direction, and often serve as a bellwether for 

the economy. The leading economic indicator computed by 

the Conference Board, for example, has such a stock market 

index as one of its components. Stock market indexes have 

also become a popular underlying for derivatives contracts. 

In 1982, the Chicago Mercantile Exchange introduced futures 

contracts on the S&P 500 Index and, one year later, the 

Chicago Board of Options Exchange listed options on the 

same index. The predominance of cap-weighting in equity 

index construction is closely linked to these uses. Arguably, 

reflecting the performance of stocks in proportion to their 

market capitalization allows a good representation of market 

movements. And, for the kind of short-term trading needed 

to replicate derivatives contracts, the liquidity inherent to 

cap-weighting is an advantage. 

However, investors do not use equity indexes only to 

obtain information and for short-term trading. Today capweighted 

indexes have become an integral part of the investment 

process of long-term investors such as pension funds, 

endowments and insurance companies. The choice of an 

index will have a critical impact both on asset allocation and 

performance measurement. In particular, by creating a set of 

rules for selection of the asset universe, the weighting scheme 

of the selected assets, and periodic rebalancing, a particular 

index construction method will direct the risk exposures and 

performance of related passive investment vehicles and of 

active mandates managed with reference to the index. 

To be useful in the investment process, an index must be 

more than a reliable indicator of short-term market movements. 

Bailey, Richards and Tierney [1990] and Bailey [1992] point out 

that a chosen benchmark needs to be unambiguous, investable, 

measurable, appropriate, reflective of the investor’s current 

investment views and specified in advance. These criteria may 

of course be fulfilled by construction methods other than cap 

weighting, leaving room for different weighting schemes. 

Such alternatives have been developed in response to 

critiques of capitalization weighting. About 20 years ago, 

papers by Ferson, Kandel and Stambaugh [1987], Haugen 

and Baker [1991] and Grinold [1992] presented convincing 

empirical evidence that cap-weighted indexes provide an 

inefficient risk/return trade-off. In addition, Ranaldo and 

Häberle [2007] point out that many capitalization-weighted 

indexes may be perceived as active investment strategies. In 

pursuit of a more representative weighting scheme, recently 

launched indexes have proposed to weight stocks by company 

characteristics such as earnings or book value (Arnott, Hsu 

and Moore [2005]; Siegel, Schwartz and Siracusano [2007]). 

Other indexes weight stocks to achieve the highest risk/ 

reward efficiency (Amenc et al. [2010]) or the lowest possible 

portfolio volatility (Nielsen and Aylursubramanian [2008]). 

Other approaches have focused on constructing maximum 

diversification benchmarks (Choueifaty and Coignard [2008]) 

or equal-risk contribution benchmarks (Maillard, Roncalli and 

Teiletche [2010]). Rather than exploiting public information 

based on accounting data or risk/return computations, equalweighted 

indexes simply attribute an identical weight to all 

constituents (Dash and Loggie [2008]). 

As a consequence of these developments, investors now 

have a wide range of weighting schemes at their disposal. A 

natural question is to ask how these schemes compare. In 

particular, these indexes have very different objectives, ranging 

from minimizing risk to improving the representation of 

the economy through a stock market index. They also use 

very different types of information to attribute weights— 

including risk/return data or accounting data—and even 

ignore certain information, as in the case of equal weighting. 

A detailed comparison will help investors decide which 

of these alternatives are most useful to them. This paper 

does such a comparison for U.S. equity indexes that use the 

different weighting schemes. Among the aforementioned 

approaches, our analysis focuses on those approaches that 

have given rise to indexes published by major index providers. 

In particular, we focus on the four following weighting 

schemes that have been used by the main index providers to 

propose alternatives to market-cap-weighted indexes: efficient 

indexes (FTSE), fundamental indexes (FTSE), minimumvolatility 

indexes (MSCI) and equal-weighted indexes (S&P). 

Our performance analysis roughly covers the past decade, for 

which data on a variety of indexes is available. 

Available Weighting Schemes 

Investors may choose to use indexes for information acquisition, 

for performance measurement or as an investment 

vehicle. One of the key attractions of equity indexing is the 

transparency such indexes provide. Indexes are also sought for 

their purported ability to represent the equity market. Finally, 

but perhaps most importantly, equity indexes are supposed to 

provide investors an attractive risk/reward ratio so that they 

can give useful guidance on investment choices. 

There are a variety of systematic weighting approaches— 

other than weighting the stocks by their market capitalization—that 

may fulfill the criteria of representativity and 

efficiency. It turns out, however, that the approaches usually 

have a focus that is on one of the two aspects. In addition, they 

exploit very different sources of information to reach their 

objectives. Cap-weighted indexes and fundamental indexes 

are mainly concerned with representativity by weighting 

stocks by company characteristics, either market capitalization 

or accounting characteristics. Minimum-volatility indexes 

and efficient indexes, on the other hand, exploit information 

in the returns data of constituent stocks, concerning either 

www.journalofindexes.com January / February 2011 

11

volatility and correlation (for minimum-volatility indexes) or 

volatility, correlation and expected returns (for efficient indexation). 

Equal-weighted indexes are the extreme, in the sense 

that they do not exploit any stock-specific information. Their 

weights can be computed simply from the number of constituent 

stocks in the index without any further knowledge of any 

characteristics of these stocks. 

Indexes Focusing On Representativity 

A focus on representativity naturally leads to indexes that 

allocate greater weights to larger companies than to smaller 

companies. There are, however, two main approaches to 

measuring the size of a company. The cap-weighting scheme 

assumes that market capitalization is the best measure for 

the size of a company, whereas the characteristic-based 

weighting scheme proposes to use more fundamental measures 

of company size. 

Cap-weighted indexes use a stock’s market capitalization 

(i.e., the number of outstanding shares times the share price) 

as the weighting criterion. Each stock’s weight in the index 

will correspond to its share of the overall market capitalization 

of all stocks in the index. Such market-cap weighting is 

supposed to provide an accurate representation of the stock 

market, as more valuable firms will make up a larger share 

of the index. This weighting scheme is often justified by the 

capital asset pricint model (CAPM) and its central conclusion 

that the cap-weighted portfolio of all available assets in the 

economy will be mean-variance efficient. The major difference 

of cap-weighted equity indexes from this theoretical 

market portfolio is that the available indexes omit most of 

the assets available in the economy, as dividends on stocks 

account for only a small share of wealth compared to income 

and benefits derived from labor, nonlisted businesses, social 

security and private housing. 

The cap-weighted indexes we include in our empirical 

analysis are the S&P 500 Index and the Russell 1000 Index, 

which are made up of the largest stocks by market capitalization. 

We chose these indexes as they are the most widely 

used by investors. Rather than being weighted by market 

capitalization, stocks are actually weighted by their freefloat; 

i.e., only the market capitalization available to nonstrategic 

investors is accounted for. This is the standard practice 

in cap-weighted construction of equity indexes. 

Fundamental-weighted indexes attempt to be more representative 

than cap-weighted indexes by introducing a different 

measure of firm size. Rather than being proportional 

to the stock’s market capitalization, the weights will be 

proportional to an accounting-based measure of size, such as 

the company’s earnings or its book value. Such indexes thus 

provide a better representation of stocks by their size as far 

as size can be captured by accounting measures. 

The idea behind such indexes is not to optimize the risk/ 

reward trade-off but to have measures of firm size more reliable 

than market capitalization. Such indexes may, however, 

improve returns compared to cap-weighted indexes, since— 

compared to capitalization weighting—they will favor stocks 

with low price earnings ratios or high book-to-market ratios. 

In our empirical analysis, we use the FTSE RAFI 1000 Index. 

This index weights and selects the 1,000 largest stocks by a 

set of four characteristics measured over the past five years: 

dividends paid, book value, cash flow and company sales. 

Indexes Focusing On Efficiency 

Portfolio theory suggests that when indexes are used as 

investment benchmarks, the focus should be not on representativity 

but on achieving the highest possible risk/reward 

ratio. As mentioned in the introduction, one of the key shortcomings 

of the cap-weighted indexes is inefficiency resulting 

from poor diversification. Several indexes have attempted to 

improve this dimension, while exploiting different approaches 

to building better diversified portfolios. 

Equal-weighted indexes simply attribute the same weight 

to each of their constituents. These indexes may provide a 

fair representation of the stock market, as their return for 

a given day actually shows the average return of all stocks 

on this day. More importantly, perhaps, equal weighting 

may lead to favorable risk/reward properties, since it is a 

straightforward way to construct well-diversified portfolios. 

This diversification rule, known as naive diversification, 

has its origins in a recommendation, known as the onethird 

rule, more than 1,500 years old. This rule cited in 

the Talmud recommends dividing one’s wealth into equal 

shares of land, business and cash. In the case of stock 

market indexes, it is called the 1/N rule. It recommends 

giving an equal weight to each of the N index constituents. 

However, equal weighting is an extreme view, since it also 

means that we agree to give up deriving any form of useful 

information for weighting constituents, and that information 

on market capitalization or other measures used to 

derive index weights is entirely useless. 

An equal-weighted version of the S&P 500, which we use 

in our empirical analysis, is computed by Standard & Poor’s. 

This index is based on the same 500 constituents as the 

cap-weighted version of the index but it attributes an equal 

weight to each constituent. Each quarter, the weight of each 

constituent is set to 0.2 percent. 

Minimum-volatility weighting is an initial approach to building 

scientifically rather than naively diversified portfolios. 

The focus here is on finding the constituent weights that lead 

to the lowest possible portfolio volatility. To construct such 

an index, one need only estimate volatilities and correlations 

of index constituent stocks. No knowledge of expected 

returns is necessary. 

Such a portfolio would be representative of the tangency 

portfolio if expected returns were identical for all stocks. It 

may thus represent the opportunity set for mean-variance 

investors in the absence of any information on differences in 

expected returns across stocks. In terms of risk/reward efficiency, 

the index focuses on lowering risk, without addressing 

its expected return properties. 

The empirical analyses below draw on data for the MSCI 

Minimum Volatility Index of U.S. stocks. This index is based on 

all constituents in the MSCI US index. The Barra portfolio optimizer 

is then used to find the minimum-volatility weights. 

Efficient-weighted indexes are based on portfolio optimization. 

They aim to improve the Sharpe ratio compared to 

12 


cap-weighted indexes by weighting stocks by their impact on 

portfolio risk and reward. It can be argued that these indexes 

provide higher welfare for investors in the mean-variance 

space and could thus be seen as better representing the 

equity risk premium accessible in the stock market. 

Efficient indexes focus directly on risk/reward properties. 

Their greater efficiency thus results from the construction 

method, as long as robust parameter estimates for expected 

returns, correlations and volatilities are used. These indexes 

are different from cap-weighted, equal-weighted or fundamentally 

weighted indexes in that, to create an optimal portfolio, 

they weight constituents by quantitative information 

on expected returns, correlations and volatilities. 

In our empirical analysis, we use the FTSE EDHEC-Risk 

Efficient Index for the U.S. market. The index weights stocks 

to maximize the index’s Sharpe ratio. To obtain parameter 

estimates for the stocks’ return comovements, an equity 

factor model is used to estimate common return drivers. 

To estimate the stocks’ expected returns, downside risk is 

measured for each stock. The stock’s riskiness is then used 

as an indicator of its expected returns. Portfolio optimization 

is conducted on all mid- and large-cap stocks in the FTSE U.S. 

index, which is made up of approximately 700 stocks. 

Comparing Risk And Return 

Over the period for which data is available, we first 

assess the return and risk properties of all the indexes. We 

compute average returns, various risk measures and riskadjusted 

return ratios. Our concern is mainly with the four 

noncap-weighted indexes and their comparison with two 

cap-weighted indexes, the S&P 500 and the Russell 1000. 

The paper looks at the period from January 8, 1999 to 

January 1, 2010. This period is chosen so as to have official 

track records as published by the index providers for all 

four of the noncap-weighted indexes. While it would be 

possible in principle to construct a longer data set with simulated 

returns to these indexes by applying the weighting 

scheme to a data set of individual stocks in the U.S., such 

an approach of simulating returns comes with the challenge 

of being able to account for all elements of the construction 

methodology of the respective index. Implementation of 

each approach, which may include various constraints and 

detailed protocols for rebalancing procedures, can have a 

significant impact on returns. To give a true and fair view of 

each method, we prefer to use official track records rather 

than making a potentially imperfect attempt of replicating 

the various methodologies. 

Figure 1 shows that all four alternatives to cap weighting 

achieve higher returns over this period. While the capweighted 

indexes return only about 1 percent per year over 

this period, the efficient index, fundamental index and equalweighted 

index have annual returns in excess of 5 percent. 

With a return of 2.5 percent per year, the minimum-volatility 

index yields more than the cap-weighted indexes, but considerably 

less than the other noncap-weighted methods. 

In terms of risk, Figure 1 reports the standard deviation 

Figure 1 

Performance Statistics 

This table shows summary statistics for different equity indexes. The construction principles of the indexes are more fully described in the text. 

The statistics are based on 11 years of weekly data from 1/8/99-1/1/10. All statistics are annualized, and performance ratios that involve the average 

returns are based on the geometric average, which reliably reflects multiple holding period returns for investors. A Cornish-Fisher expansion 

was used to compute a value-at-Risk estimate that takes into account the mean, volatility, skewness and excess kurtosis of index returns. 

Efficient 

Index 

Noncap-Weighted 

Minimum 

Volatility 

Sources: Data from Thomson Reuters and Bloomberg; EDHEC-Risk Institute computations. 

Fundamental 

Index 

S&P 500 

Equal-Weighted 

Cap-Weighted 

S&P 500 Russell 1000 

Average return (geometric) 6.4% 2.5% 5.3% 5.7% 0.9% 1.3% 

Standard deviation 19.4% 16.6% 20.5% 21.4% 19.8% 20.0% 

Semi-deviation (below zero) 13.9% 12.2% 14.5% 15.2% 14.3% 14.4% 

Tracking error (w.r.t. S&P 500) 5.8% 6.6% 6.5% 6.5% 0.0% 1.3% 

Beta (w.r.t. S&P 500) 0.94 0.80 0.99 1.03 1.00 1.01 

Sharpe ratio 0.18 -0.03 0.11 0.13 -0.10 -0.08 

Sortino ratio 0.46 0.20 0.36 0.38 0.06 0.09 

Information ratio 0.95 0.24 0.67 0.75 NA 0.32 

Treynor ratio 0.04 -0.01 0.02 0.03 -0.02 -0.02 

95% Value-at-Risk 4.4% 3.9% 4.4% 4.8% 4.5% 4.6% 

99% Value-at-Risk 11.0% 10.3% 11.9% 11.3% 10.5% 10.6% 

Skewness -0.62 -0.83 -0.39 -0.46 -0.50 -0.49 

Kurtosis 9.44 10.89 10.20 8.43 8.33 8.35 


13

Figure 2 

Risk/Reward Difference Compared To S&P 500 

This table, based on 11 years of weekly data, from 1/8/99-1/1/10, shows differences in average returns, in volatility, and in Sharpe ratios between 

each index and the cap-weighted S&P 500 and the associated p-values. All differences are computed from annualized statistics, and the average 

returns are a geometric average. The p-values for differences are computed using a paired t-test for the average returns, a Fisher test for the volatility 

and a Jobson-Korkie test for the Sharpe ratio. Differences that are significantly different from zero at the 5% level are indicated in bold. 



Efficient Index Minimum Volatility Fundamental Index 

S&P 500 


Diff. in average 5.5% 1.6% 4.4% 4.9% 

p-value 0.3% 61.6% 2.5% 1.0% 

Diff. in volatility -0.4% -3.2% 0.8% 1.6% 

p-value 64.3% 0.0% 36.1% 5.8% 

Diff. in Sharpe ratio 0.28 0.08 0.22 0.23 

p-value 0.2% 53.6% 2.7% 1.0% 

and downside risk (semi-deviation below zero) for all indexes. 

In addition, the table shows the tracking error volatility and 

the beta with respect to the cap-weighted S&P 500. The minimum 

volatility index stands out, as it, unsurprisingly, has the 

lowest volatility of all indexes. The volatility of the efficient 

index is slightly lower than that of the cap-weighted indexes, 

whereas that of the fundamental and equal-weighted indexes 

is slightly higher. The minimum volatility index has a much 

lower beta with respect to the S&P 500 than any of the other 

noncap-weighted indexes. This result suggests that minimizing 

volatility leads to a portfolio that loads up on the more 

defensive low-beta stocks. Equal weighting and fundamental 

weighting lead to a beta almost identical to one, whereas 

efficient indexation, with a beta of 0.94, falls somewhat in 

between these two groups. 

Interestingly, efficient indexation has the lowest tracking 

error volatility of the noncap-weighted indexes, suggesting 

that its outperformance of the S&P 500 over this period is 

more stable than that of the other three alternatives. 

Figure 1 also shows risk-adjusted performance measures— 

the Sharpe, Sortino, information and Treynor ratios—for all 

indexes in this study. The Sharpe ratio reflects the indexes’ risk/ 

reward efficiency by adjusting excess returns over the risk-free 

interest rate by the volatility incurred by the index. The Sharpe 

ratio of all but one of the noncap-weighted indexes is positive 

and overall is clearly more attractive for each of the noncapweighted 

indexes than for the cap-weighted indexes, which 

have negative Sharpe ratios over this period. The only noncapweighted 

index with a negative Sharpe ratio is the minimumvolatility 

index. We can see here that reducing volatility does 

not necessarily result in attractive risk/reward efficiency, as the 

index has lower volatility than its noncap-weighted rivals and 

markedly lower returns. Its risk/reward profile, however, is still 

more attractive than that of the cap-weighted indexes. The efficient 

index has the highest Sharpe ratio of all indexes, reflecting 

that, compared to the cap-weighted S&P 500, it increases 

expected returns and lowers volatility. 

The results for the Sortino ratio, which uses downside risk 

instead of volatility to adjust for risk, and for the Treynor 

ratio, which uses the market beta to adjust for risk, are similar 

to the results for the Sharpe ratio. 

Figure 1 shows the indexes’ information ratios computed 

with respect to the S&P 500 cap-weighted index. This statistic 

reflects the average return difference with the S&P 500 capweight 

index when it is adjusted for the incurred tracking 

error. We see that all noncap-weighted indexes have rather 

high information ratios, meaning that they outperform the 

cap-weighted S&P 500 fairly consistently. 

The minimum-volatility index, which effectively generates 

the lowest volatility of all indexes, also has the highest 

kurtosis, i.e., the highest indicator of fat tails in the returns 

distribution. This suggests that the focus on decreasing 

average risk (volatility) comes at the cost of an increase in 

tail risk, a result that has been put forward in the finance 

literature (Andersen, Simonetti and Sornette [2000]). 

Although the noncap-weighted indexes appear attractive 

overall in terms of risk and reward statistics, it is 

interesting to ask whether these differences are actually 

statistically significant. In fact, it would be possible that one 

would observe positive differences over a particular time 

period, even if the “true” performance of the underlying 

strategies were not better than that of the cap-weighting 

scheme. A statistical significance test allows us to assess 

whether we would be likely to observe such differences in 

the data if there were no true difference with respect to 

the cap-weighted S&P 500 Index. Figure 2 shows the results 

of significance tests for these three summary performance 

measures. All differences are computed with respect to the 

S&P 500 cap-weighted index, and results that are significant 

at the 5 percent level are indicated in bold. 

The results in Figure 2 show that three of the noncapweighted 

indexes have significantly higher average returns 

than the cap-weighted S&P 500, but only the minimumvolatility 

index has significantly lower volatility. In terms 

14 


of risk/reward efficiency, three of the noncap-weighted 

indexes have significantly higher Sharpe ratios than the 

cap-weighted S&P 500. 

It is useful to give some intuition on the statistical significance 

levels shown in Figure 2. For the Sharpe ratio, the 

efficient index has the most significant increase over the capweighted 

S&P 500, with a probability value of 0.2 percent. 

What this means is that, if there were no true difference 

in the Sharpe ratio with the cap-weighted S&P 500 index, 

we would expect to observe a difference as pronounced as 

the one we observed (i.e., 0.28) about one out of every 500 

times we look at a data set of 11 years. The probability values 

for the fundamental index and the equal-weighted index are 

on the order of 1 to 3 percent; hence it would also be highly 

unlikely to obtain such results if there were no true difference. 

To summarize the results that are statistically significant, 

we can conclude that—compared to the cap-weighted 

S&P 500—minimum-volatility indexes have lower volatility 

and the other noncap-weighted indexes have higher average 

returns and higher Sharpe ratios. 

Factor Exposures 

The analysis of risk and return measures yields insights 

into how the indexes behave, which is obviously important 

information for investors. However, it is also interesting to 

analyze where the return properties come from. The noncapweighted 

indexes may take on exposures to common risk 

factors that are well-known in academic finance literature, 

such as value, momentum and small-cap exposures. 

Since the indexes are broadly diversified across constituent 

stocks, one may in fact expect that the risk and return 

properties are largely driven by such factor exposures, leaving 

only a small fraction of returns that are completely specific 

to the method of index design. From the investor’s perspective, 

such risk exposures matter. They are often implicit 

results of portfolio construction, but investors want to know 

how exposed they are to certain factors. 

In this section, we use factor models to analyze exposure 

to the equity factors commonly used in academic literature. 

The standard factor models in the empirical finance 

literature use a market risk factor, which is made up of all 

stocks that are traded on the U.S. market and additional 

factors that are long/short portfolios, rebalanced monthly, 

of stocks that have been selected from this universe for 

their style characteristics. In our empirical analysis, we use 

the standard four-factor model (Carhart [1997]), in which 

we regress the returns in excess of the risk-free rate of the 

indexes on the excess returns of the cap-weighted index 

of all stocks listed on the NYSE, NYSE Amex and Nasdaq, 

on the Fama-French [1992] factors for the value and smallcap 

premium, and on an additional factor representing a 

momentum strategy. In terms of the Fama-French factors, 

the value factor is a portfolio that is long high book-to-price 

stocks (value stocks) and short low book-to-price stocks 

(growth stocks), while the small-cap factor is a portfolio 

that is long low market-cap stocks (small stocks) and short 

high market-cap stocks (large stocks). The momentum factor 

goes long in stocks with high recent returns (winners) 

and short in stocks with low recent returns (losers). Figure 

3 shows the results of these four-factor regressions. 

When the commonly used equity risk factors are adjusted 

for, only the efficient index and the equal-weighted index 

Figure 3 

Factor Exposures 

This table, based on 11 years of weekly data, from 1/8/99-1/1/10, shows results for factor regressions of the excess returns of various 

equity indexes on the market factor, value factor, small-cap factor and momentum factor. The table shows R-squares, regression coefficients 

and the p-values associated with the null hypothesis that the regression coefficients are zero. Coefficients that are significantly 

different from zero at the 5% level are indicated in bold. 

Efficient 

Index 


Minimum 

Volatility 

Fundamental 

Index 

S&P 500 


Cap-Weighted 


Adj. R-squared 0.95 0.91 0.96 0.96 0.99 0.99 

Ann. Alpha 2.45% -0.41% 1.70% 2.44% -0.37% -0.17% 

p-value 5% 75% 12% 6% 53% 71% 

Market Beta 0.90 0.80 0.94 0.97 0.97 0.98 

p-value 0% 0% 0% 0% 0% 0% 

Small-Cap Beta -0.02 -0.27 -0.12 0.01 -0.21 -0.15 

p-value 47% 0% 0% 73% 0% 0% 

Value Beta 0.32 0.23 0.41 0.30 0.00 -0.01 

p-value 0% 0% 0% 0% 99% 39% 

Momentum Beta -0.05 0.06 -0.08 -0.13 -0.02 -0.02 

p-value 2% 0% 0% 0% 7% 1% 



15

show marginal statistical significance for alpha if one considers 

the 5 percent level the threshold. Outperformance of the 

cap-weighted total market index is nevertheless economically 

significant for three of the noncap-weighted indexes (annual 

outperformance on the order of 2 percent). 

We now turn to a detailed discussion of estimated factor 

exposures reported in Figure 3. 

Market Exposure 

The results for the beta with the broad cap-weighted 

market proxy used here are qualitatively similar to what was 

obtained in Figure 2 when regressing the various indexes on 

the S&P 500 as a single market factor. The results confirm the 

low beta nature of the minimum-volatility index, whereas the 

other indexes have beta reasonably close to 1. 

Value/Growth Exposure 

It should first be noted that any reasonable definition 

of style neutrality would lead to the conclusion that equal 

weighting should be free of a value or growth bias. In 

fact, since no information whatsoever on valuation enters 

the determination of weights in an equal-weighted index, 

it is difficult to imagine that such an index would imply 

any choices in terms of value or growth exposure. That 

the equal-weighted index shows a value bias with respect 

to the cap-weighted index in Figure 3 indicates only the 

relativity of the reference point, and one could actually 

argue that the cap-weighted index has a growth bias relative 

to the equal-weighted reference. If we take the equalweighted 

index as a reference for value/growth neutrality, 

the direct implication is that indexes whose factor exposure 

is not markedly different from that of the equal-weighted 

index do not include any value biases. 

The results in Figure 3 show that only the fundamental 

index has a value exposure that is substantially greater 

than that of the equal-weighted index. Other noncapweighted 

indexes such as efficient indexation and minimum 

volatility have value exposures that are comparable 

to that of equal weighting. These results are consistent 

with economic intuition: the fundamental index method 

derives constituent weights from company characteristics 

such as earnings. Kaplan [2008] has shown that weighting 

stocks by their earnings actually corresponds to an 

adjustment of the market-cap weight for the earnings 

yield of each stock relative to the earnings yield of the 

cap-weighted index. Thus, stocks with a high earnings 

yield will be mechanically overweighted, whereas stocks 

with a low earnings yield will be underweighted for their 

capitalization weight. Similar arguments can be made concerning 

the additional company characteristics used in the 

index method; notably dividends, book value and sales: 

Stocks with a high-dividend yield, high book-to-market 

ratio and high sales-to-price ratio will be overweighted 

compared with their capitalization weight. As these are 

typical ratios used in value strategies, it is not surprising 

to find a substantial exposure to the value factor. Other 

weighting schemes considered in this analysis do not use 

fundamental information, and therefore do not lead to 

such a mechanical value bias. Compared with the equalweighted 

reference, neither the efficient index nor the 

minimum-volatility index displays any value bias. 

Small-Cap Exposure 

If one were to use an equal-weighted index that included 

a broad set of stocks covering all ranges of market capitalization, 

one would ascertain a small-cap bias, as most of the 

stocks traded on an exchange are small- or even micro-cap 

stocks. However, the equal-weighted index used here is composed 

of S&P 500 constituents, i.e., large-cap stocks—the 

reason for which the small-cap exposure in Figure 3 is insignificantly 

different from zero for the equal-weighted index. 

Moreover, the indexes studied here are made up of large-cap 

stocks, so none of these indexes should show any meaningful 

bias toward small caps. 

Interestingly, the minimum-volatility index and the 

fundamental index show the opposite of a small-cap bias. 

Figure 3 shows a statistically significant negative exposure 

to the small-cap factor for these two indexes, as well as 

for the two cap-weighted indexes. This can be explained 

by the fact that the cap-weighted market factor in these 

models is not the narrow S&P 500 but the much broader 

index reflecting the entire stock universe of all NYSE, NYSE 

Amex and Nasdaq stocks. Compared to this market factor, 

the returns of cap-weighted and fundamentally weighted 

indexes are driven largely by the largest stocks. This can 

be explained by the fact that their constituents represent 

only the largest stocks and their weighting mechanism 

depends on company size. The minimum-volatility index 

also has a significantly negative exposure to small caps, 

which perhaps can be explained by a concentration in 

low beta stocks that leads to a bias towards defensive 

industries such as telecoms and utilities, which are usually 

dominated by a few large companies. In contrast, the efficient 

and equal-weighted indexes have insignificant exposures 

to the small-cap factor. They appear to be the least 

biased toward large-cap stocks by virtue of their weighting 

mechanism, even though their constituent universe is 

also made up of the largest and most liquid stocks. 

Momentum Exposure 

Of all the noncap-weighted indexes, momentum exposure 

is most negative for equal-weighted indexes. After all, 

compared to cap-weighting, equal-weighting mechanically 

rebalances away from stocks that increase in price. This 

mechanical rebalancing is also inherent to the fundamental 

index, which has the next most pronounced negative 

momentum beta. Momentum exposure is less pronounced 

for the efficient index and the minimum-volatility index. 

This can perhaps be explained by the fact that the efficient 

index and minimum-volatility index are rebalanced based 

on currently available quantitative information on risk and 

returns of the constituent stocks, so they do not necessarily 

have a mechanical rebalancing effect built into their 

weighting mechanism. 

In addition to the factor exposures, Figure 3 contains 

interesting results concerning the closeness-of-fit of the fac- 

16 


Figure 4 

Appendix: Performance Statistics For Subperiods 

This table shows summary statistics for different equity indexes. The construction principles of the indexes are more fully described in the text. 

The statistics are based on weekly data. All statistics are annualized, and performance ratios that involve the average returns are based on the 

geometric average, which reliably reflects multiple holding period returns for investors. A Cornish-Fisher expansion was used to compute a valueat-risk 

estimate that takes into account the mean, volatility, skewness and excess kurtosis of index returns. 

Efficient 

Index 

Minimum 

Volatility 


Fundamental 

Index 

S&P 500 



Average (geometric) 7.8% 1.7% 6.5% 7.3% -0.1% 0.2% 

Std. Dev. 16.5% 15.2% 17.2% 18.7% 19.3% 19.4% 


Tracking error 7.3% 8.0% 7.3% 7.5% 0.0% 1.6% 

Beta with S&P 500 0.79 0.73 0.83 0.90 1.00 1.00 

Sharpe ratio 0.28 -0.10 0.19 0.22 -0.17 -0.15 

Sortino ratio 0.67 0.15 0.54 0.55 -0.01 0.02 


Treynor ratio 0.06 -0.02 0.04 0.05 -0.03 -0.03 

95% Value-at-Risk 3.9% 3.6% 4.0% 4.4% 4.5% 4.5% 

99% Value-at-Risk 7.2% 6.7% 7.6% 7.8% 7.9% 8.0% 

Skewness -0.69 -0.50 -0.49 -0.54 -0.34 -0.35 

Kurtosis 5.49 5.49 5.74 4.98 4.92 4.98 

Efficient 

Index 


Minimum 

Volatility 

First subperiod (from 1/8/99-7/2/04) 

Second subperiod (from 7/9/04-1/1/10) 


Fundamental 

Index 

S&P 500 


Cap-Weighted 

Cap-Weighted 


Average (geometric) 5.0% 3.3% 4.0% 4.2% 1.9% 2.3% 

Std. Dev. 21.9% 17.9% 23.4% 23.8% 20.3% 20.6% 


Tracking error 3.7% 4.9% 5.7% 5.2% 0.0% 0.9% 

Beta with S&P 500 1.07 0.86 1.13 1.16 1.00 1.01 

Sharpe ratio 0.10 0.03 0.06 0.06 -0.04 -0.02 

Sortino ratio 0.32 0.25 0.24 0.25 0.13 0.16 


Treynor ratio 0.02 0.01 0.01 0.01 -0.01 0.00 

95% VaR (Cornish Fisher) 4.9% 4.1% 5.0% 5.2% 4.6% 4.6% 

99% VaR (Cornish Fisher) 12.7% 12.6% 13.7% 13.0% 12.7% 12.8% 

Skewness -0.55 -1.02 -0.33 -0.40 -0.64 -0.61 

Kurtosis 9.81 13.33 10.41 9.14 11.10 10.99 

tor models. The R-squared for these regressions indicates 

how well the factor model fits the observed returns of the 

respective index, with an R-squared of 1 indicating a perfect 

fit, meaning that the index would be perfectly replicable 

with the equity factors. For the cap-weighted indexes, the 

results in Figure 3 show that the factor model suffices to get 

an R-squared close to 1. This is not the case for the noncapweighted 

indexes. They contain some return variation that 

cannot be explained by the factors. The R-squared reaches 

levels on the order of 0.95, leaving 5 percent of the return 

variation unexplained by exposure to common factors. This 

unexplained part can be linked to the unique index construction 

method of each of the noncap-weighted indexes. 

Conclusion 

The analysis provided in this paper clearly shows that the 

noncap-weighted indexes beat the standard cap-weighted 

indexes such as the S&P 500 and the Russell 1000 in terms of 


17

Figure 5 

Appendix: Risk/Reward Difference Compared To S&P 500 For Subperiods 

This table shows differences in average returns, in volatility and in Sharpe ratios between each index and the cap-weighted S&P 500 and the associated 

p-values. All statistics are computed from weekly returns data. All differences are computed from annualized statistics and the average returns 

are a geometric average. The p-values for differences are computed using a paired t-test for the average returns, a Fisher test for the volatility and 

a Jobson-Korkie test for the Sharpe ratio. Differences that are significantly different from zero at the 5% level are indicated in bold. 



S&P 500 



p-value 2.1% 74.2% 5.1% 2.7% 

Diff. in volatility -2.8% -4.1% -2.1% -0.6% 

p-value 0.8% 0.0% 5.3% 62.4% 


p-value 0.9% 75.1% 3.6% 2.5% 


First subperiod (from 1/8/99-7/2/04) 

Second subperiod (from 7/9/04-1/1/10) 



S&P 500 



p-value 3.6% 67.3% 25.3% 18.0% 

Diff. in volatility 1.7% -2.4% 3.2% 3.5% 

p-value 18.2% 3.6% 1.4% 0.7% 


p-value 2.9% 54.7% 23.5% 12.2% 

risk-adjusted performance over the 11 years for which data is 

available for all indexes. 

Moreover, the “improved beta” strategies achieve the 

main objectives—which vary widely from one noncapweighted 

index to another—that they have set for themselves. 

The efficient index, whose aim is higher risk/reward 

efficiency, does indeed obtain the highest Sharpe ratio of 

all the indexes. The minimum-volatility index obtains the 

lowest volatility. Equal-weighted indexes and fundamental 

indexes obtain higher average returns as a result of their 

mechanical rebalancing feature. The differences among 

the indexes are also reflected in quite different factor 

exposures. Minimum volatility, as it favors low beta stocks, 

clearly has the lowest market beta of all noncap-weighted 

indexes, while fundamental weighting leads to the highest 

value exposure, and equal weighting to the most pronounced 

anti-momentum exposure. 

In sum, efficient indexes, the newest of the strategies 

analyzed in this paper, not only obtain the highest Sharpe 

ratios in the period analyzed in this paper, but also appear 

to have the most diversified factor exposures. Their volatility 

is lower than that of equal-weighted and fundamental-weighted 

indexes, and since they are not subject to the 

same tilt toward low beta stocks, their average returns 

are higher than those of minimum-volatility indexes. 

Efficient indexes, which are an attempt to provide an 

investable proxy for the tangency portfolio from modern 

portfolio theory, are thus a significant new addition to the 

improved beta toolbox. 

That the four weighting schemes have rather different 

risk and return properties also suggests that different 

investors may choose different alternatives, depending on 

which characteristic they value most. Moreover, combining 

these alternatives may add even more value, as different 

return properties may allow an investor who wants to 

move away from capitalization weighting to diversify his 

exposure to noncap-weighted schemes. This issue is left 

for further research. 

Appendix 

Summary Statistics And Differences With Capitalization 

Weighting In Subperiods 

Figures 4 and 5 contain an analysis of results for the two subperiods 

of the sample. On the whole, the results per subperiod 

confirm the conclusions drawn for the full period. The results 

for the full period can thus be considered quite robust. 

Additional Description Of Data Sources 

For the noncap-weighted indexes, the data for MSCI and 

S&P EW is obtained from Datastream. The information on 

18 


fundamental indexes is obtained from the data described in 

Amenc, Goltz and Le Sourd [2009] and updated with returns 

from Datastream on the FTSE RAFI 1000 Index. The data on 

the efficient U.S. index is obtained from the data in Amenc et 

al. [2010] and updated using the FTSE EDHEC-Risk Efficient 

Index for the US from Datastream. 

The risk-free rate is taken from the daily risk-free rate 

data in the Fama-French factors provided by Kenneth French. 

It is converted into weekly returns from daily returns. This 

rate reflects a one-month Treasury bill index. The average 

risk-free rate is 2.91 percent per annum. The market factor 

in the factor regressions is taken from Kenneth French; this 

is the value-weighted CRSP total market index (NYSE, NYSE 

Amex and Nasdaq stocks). 

References 

Amenc, N., F. Goltz and V. Le Sourd, 2009. “The performance of characteristics-based indexes.” European Financial Management, vol. 15 pp 241-278. 

Amenc, N., F. Goltz, P. Retkowsky, and L. Martellini. 2010. “Efficient indexation: An alternative to cap-weighted indexes.” Working paper. 

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19

The Evolution Of 

Equal Weighting 

Adding a sector approach 

By Pradeep Velvadapu 

20 


Over the past few years, market observers have seen 

increased interest in indexes that use “alternative” 

weighting methodologies. While market-cap 

weighting is an excellent choice for benchmarks for active 

management and for the basis of investable products such 

as passive mutual funds, exchange-traded funds (ETFs), 

futures and options, investor interest in alternative index 

weighting methodologies, such as equal weighting and fundamental 

weighting, is growing. 

In this paper we provide an overview of some of these 

alternatives and a broader analysis of equal-weighted 

indexes. We first describe cap-weighted, fundamentalweighted 

and equal-weighted indexes and compare their 

characteristics. We then discuss in greater detail the conventional 

equal-weighted index structure and review a new 

alternative to this method: the sector equal-weighted index 

methodology. We compare and contrast the conventional 

equal-weighted methodology to the sector equal-weighted 

indexes and illustrate the benefits that a sector equalweighted 

index can provide. We then extend this analysis 

to the international indexes and analyze whether the characteristics 

observed in the domestic market persist. 

Based on our simulations, we find that: 

VËËÖ?ËÝj~Í~ËMßËWÄÍÍÖjÍÄ^ËÝjËÄ¬jË?aËÍÁ?Äparent, 

introduces sector risk into an index. 

VËË.jWÍÁËjÖ?Ýj~ÍjaËajÞjÄË¬ÁÜajaË?ËMjÍÍjÁË?MÄlute 

return with lower volatility for the time period 

tested compared to traditional equal-weighted and capweighted 

indexes. 

VËË0jÄjËÁjÄÖÍÄË?ÁjËWÄÄÍjÍË?WÁÄÄËÍjËajÄÍWË?Á~j 

cap, mid-cap and small-cap spectrum and the global 

developed and emerging regions. 

Figure 1 

Weight 

70% 

60% 

50% 

40% 

30% 

20% 

10% 

Russell 1000 Weight By Size Deciles as of 1/1/2010 

0% 

Source: Russell Investments 

1 2 3 4 5 6 7 8 9 10 

N Cap Weight 

Decile 

N Equal Weight 

Capitalization-Weighted Indexes 

Market-cap weighting remains the most popular 

approach to index construction. Cap-weighting is an 

objective, practical and theoretically grounded weighting 

scheme: objective, in that market values represent the 

market’s assessment of the relative values of firms; practical, 

given that a portfolio automatically adjusts its constituent 

weights as market prices move, resulting in fewer 

rebalancing trades; and theoretically grounded, drawn as 

it is from Harry Markowitz’s capital asset pricing model. 

The capital asset pricing model (CAPM) and the efficient 

market hypothesis (EMH) provide the theoretical bases for 

cap-weighted indexes. The efficient market hypothesis 1 

states that asset prices are rationally priced by all investors, 

who have equal access to relevant information. Thus, the 

price of a security (and by inference, its market capitalization) 

reflects its true value, based upon all available information 

at any given point in time. 

The CAPM designates the aggregation of all assets as 

being the “market portfolio” and states that the market 

portfolio is efficient, i.e., that it has the highest level 

of expected return for its level of risk. According to the 

CAPM, the market portfolio (which holds risk-free assets in 

some proportion) is the only risky asset portfolio investors 

need to invest in. 

While there is justifiable debate on the validity of the 

CAPM, the theory establishes the market portfolio as an 

important baseline methodology. The assumption that capweighted 

indexes measure the market portfolio has led to 

their being considered the best proxies for measuring the 

Figure 2 

Sector (as Trading of 1/1/2010) 

Symbol 

Russell 1000 

Cap Weighted 

Russell 3-Month 1000 Average Constituent 

Daily Equal Volume Weight 

Russell 1000 

Sector Equal Weight 

Consumer Discretionary 12.0% 16.9% 11.1% 

Consumer Staples 9.2% 5.6% 11.1% 

Energy 11.4% 7.9% 11.1% 

Financial Services 15.4% 19.7% 11.1% 

Health Care 12.5% 10.0% 11.1% 

Materials & Processing 4.3% 7.4% 11.1% 

Producer Durables 10.6% 13.1% 11.1% 


Comparison Of Sector Weights, As of 1/1/2010 

Technology 17.9% 11.8% 11.1% 

Utilities 6.7% 7.6% 11.1% 


21

Figure 3 

1 

0.8 

0.6 

0.4 

0.2 

0 

Figure 4 

Volatility Of The Russell 2000 Index 

Sector 1998–2000 2007–2009 

Consumer Discretionary 6.56 9.56 

Consumer Staples 4.55 5.12 

Energy 13.87 12.70 

Financial Services 5.03 7.64 

Health Care 13.30 6.66 

Materials & Processing 6.33 9.03 

Producer Durables 7.12 8.09 

Technology 13.57 8.13 

Utilities 5.42 4.97 


Correlations Of Russell 2000 SEW and 

Russell 2000 SEW-MC With Russell 2000 

Jun 30 

2000 

Jun 30 

2002 

Jun 30 

2004 

Source: Russell Investments, 6/30/1999-6/30/2010. 

Figure 5 

Jun 30 

2006 

Jun 30 

2008 

Jun 30 

2010 

N Russell 2000 Sector Equal Weight, Constituents Market Cap Weighted 

N Russell 2000 Sector Equal Weight, Constituents Equal Weighted 

Tracking Error And Turnover In Sector Equal Weight Indexes 

Reweighting Frequency 

Russell 1000 Sector Equal Weight 

Tracking Error 3 

Turnover (average 

per annum) 4 

Daily 0.00 177.67% 

Monthly 1.03 39.09% 

Quarterly 1.76 26.15% 

Semiannually 2.32 21.33% 

Annually 4.60 16.08% 

Russell 2000 Sector Equal Weight 

Daily 0.00 253.75% 

Monthly 1.51 55.77% 

Quarterly 2.48 37.80% 

Semiannually 3.34 33.44% 

Annually 6.54 26.65% 


performance of a market and of active managers, and it 

forms the theoretical basis for passive index investing (see 

Christopherson, Cariño, Ferson [2009]). 

Over and above the theoretical basis, weighting the 

constituents of an index by their market capitalization has 

many practical advantages that appeal to passive investors. 

Cap-weighted indexes are the only indexes to represent a 

buy-and-hold strategy and to provide broad market representation 

at a very low cost within the replicated portfolio. 

Because they do not require frequent rebalancing, 

cap-weighted indexes help to keep transaction costs low 

within the replicated portfolio. Most cap-weighted indexes 

also have an objective and transparent methodology that is 

simple to understand, construct and track. 

Critics of cap-weighted indexes point to the fact that 

basing index constituents’ weights on their market capitalization 

results in the largest securities having the biggest 

weights in the index, so much so that the contribution of 

smaller-capitalization securities can be minimal. Passive 

investment based on cap weighting captures the return of 

the benchmark. But if investors believe markets are not efficient, 

they must believe it is possible to earn excess return 

over the benchmark. 

While market-cap weighting is an excellent choice for 

passive investable products, there is growing interest in 

alternative index weighting methodologies. Two leading 

alternative approaches are fundamental-weighted indexes 

and equal-weighted indexes. 

Fundamental-Weighted Indexes 

Fundamental indexes present an investment strategy 

based on the premise that market prices cannot reliably 

represent the underlying value of a company, because markets 

are not efficient. Fundamental indexes weight stocks 

by business metrics such as sales, cash flows, book value, 

dividends and buybacks (the so-called “Main Street” factors), 

rather than by the float-adjusted market capitalization 

2 of securities. 

By weighting stocks on the basis of subjective factors, 

an index is inherently placing a bet on which companies 

have greater performance potential than others (see, for 

example, Blitz and Swinkels). This passive investment strategy 

has been seen to provide a higher return with lower 

volatility relative to cap weighting, over sample periods 

(see Arnott [2005]). 

Since fundamental indexes weight securities by a firm’s 

economic fundamentals, critics argue that this creates a 

value bias within the index. Companies that invest heavily in 

future growth will tend to have low earnings and therefore 

have a smaller weight in a fundamental index. This creates 

a scenario wherein fundamental indexes overweight value 

stocks and underweight growth stocks. 

Fundamental indexes also have higher turnover than capweighted 

indexes. 

Equal-Weighted Indexes 

Equal-weighting does not consider any information 

about the stocks within an index; the only relevant infor- 

22 


mation is the number of stocks in the index. The basis of 

the equal-weighting approach is to assign a weight of 1/N 

to each security in an index, where N is the number of 

securities in the index. 

Assume, for example, that you “know” which security is 

going to be the best performer in the Russell 1000 Index. 

If you want the best return, you will hold only that security. 

If you diversify away from that future best-performing 

security by purchasing any other securities, you will not 

achieve the best possible return. But since most investors 

know they don’t have infallible foresight, they diversify 

in order to make sure they don’t own only the worstperforming 

security. 

Similar motivations for diversification have been noticed 

in some active managers’ portfolios. Some active managers, 

in an attempt to diversify across their holdings, tend not to 

weight their portfolios by market capitalization. As Fabozzi 

[1998] notes, “… managers tend to not capitalization-weight 

their portfolios for a variety of reasons. The most often cited 

reason is related to the manager’s aversion to putting too 

much money in any one basket (such as IBM)—they want 

stock name diversification.” 

While market-capitalization-weighted indexes provide 

diversification benefits by providing exposure to every 

security in the index, equal-weighting provides equal exposure 

to every company in the index. Figure 1 presents the 

weights represented by the Russell 1000 cap weight and 

the Russell 1000 constituent equal weight indexes, grouped 

by deciles of company size. The largest 10 percent of companies 

account for 58 percent of the overall weight in the 

Russell 1000 cap weight index, compared with just 10 percent 

of the overall weight in the Russell 1000 equal weight 

index. Equal-weight indexes provide equal exposure to 

every size decile of the index, while smaller-capitalization 

securities have very small weights in a cap-weighted index. 

The Russell Equal Weight Indexes 

The conventional approach to the construction of equalweighted 

indexes, however, brings with it issues such as 

inherent sector biases, potential capacity constraints/liquidity 

concerns, high turnover and rebalancing issues. 

Sector Biases 

An index with equal weights across all constituents (constituent 

equal weighting: CEW) will allocate significantly 

higher weights to some sectors than to others, embedding 

sector bias into the index. 

An alternative approach is to equal-weight sectors 

within an index. Figure 2 compares the sector weights 

of the Russell 1000 cap-weighted index, the Russell 1000 

CEW index and the Russell 1000 SEW index as of January 

1, 2010. The technology, financial services and health care 

segments dominate the market-cap-weighted index, while 

financial services, producer durables and consumer discretionary 

sectors dominate the conventional equal-weighted 

variant, accounting for more than 50 percent of the index. 

The SEW approach evens this out, placing equal weights 

on each segment of the market. 

Figure 6 

80 

70 

60 

50 

40 

30 

20 

10 

Figure 7 

40 

35 

30 

25 

20 

15 

10 

5 

0 

Jun 30 

1978 

Growth Of A Dollar For Russell 2000 SEW, CEW 

And Cap-Weight Indexes 

Jun 30 

1982 

Jun 30 

1986 

Jun 30 

1990 

Jun 30 

1994 

N Russell 2000 Constituent Equal Weight 

N Russell 2000 Cap Weight 


Figure 8 

0 

Jun 30 

1978 

30 

25 

20 

15 

10 

5 

0 

Growth Of A Dollar For Russell 1000 SEW, CEW 

And Cap-Weight Indexes 

Jun 30 

1982 

Jun 30 

1984 

Jun 30 

1986 

Jun 30 

1988 

Jun 30 

1990 

Jun 30 

1992 

Jun 30 

1994 




Jun 30 

1996 

Jun 30 

1998 

Jun 30 

2002 

Jun 30 

2006 

N Russell 2000 Sector Equal Weight 

Volatility Of The Russell 1000 Cap Weight, 

SEW And CEW Indexes 




Jun 30 

1998 

Jun 30 

2000 

Jun 30 

2002 

Jun 30 

2004 

Jun 30 

2006 


Jun 30 

2010 

Jun 30 

2008 


Jun 30 

2010 


23

Russell’s sector equal-weighted indexes allocate an equal 

portion of the portfolio to each of the nine Russell sectors, 

and then equal-weight the constituents within each sector. 

Why Equal-Weight Constituents Within The Sector? 

Equal-weighting the constituents within a sector assures 

that a few large companies will not drive the performance of 

the sector. This may also add diversification benefits similar 

to those outlined above for equally weighted sectors. To 

test this, we simulate a portfolio that is equal-weighted at 

the sector level, but the constituents within each sector are 

weighted according to their float-adjusted market capitalization 

(SEW-MC). Rolling 36-month correlations between the 

SEW-MC, SEW and the cap-weighted Russell 2000 Index are 

presented in Figure 3. 

The results are surprising. The correlation between the 

SEW index and the Russell 2000 falls dramatically during the 

technology boom-and-bust period, dropping as low as 60 

percent on August 2001 and maintaining a correlation of 90 

percent to 95 percent after May 2003. The correlation of the 

SEW-MC index to the Russell 2000 stays above 95 percent 

through the whole period. 

We do not see a big drop in correlations during the 

2008-2009 financial crisis, and a closer look at the sector 

volatility of the Russell 2000 explains why. We calculate the 

volatility of the sector returns of the Russell 2000 index for 

two periods, 1998 to 2000 and 2007 to 2009. For the 1998- 

2000 period, volatility is high in the energy, health care and 

technology sectors, while the other sectors have relatively 

low volatility. For the 2007-2009 period, volatility is spread 

out across all sectors, with just the energy sector comparatively 

higher than the other sectors (see Figure 4). This 

suggests that during periods where volatility is increasing 

within a few sectors, the SEW index could potentially provide 

diversification benefits when used in conjunction with 

cap-weighted indexes. We note that equal-weighting the 

constituents within sectors adds attractive diversification 

features to the index, and that simply equal-weighting the 

sectors does not replicate an SEW index. 

Figure 9 

35 

30 

25 

20 

15 

10 

5 

0 

Volatility Of The Russell 2000 Cap Weight, 

SEW And CEW Indexes 

Jun 30 

1984 

Jun 30 

1988 

Jun 30 

1992 



Jun 30 

1996 


Jun 30 

2000 

Jun 30 

2004 

Jun 30 

2008 


Capacity Constraints 

Equal-weighted indexes have been criticized for the 

capacity constraints potentially posed by constituents that 

are too small to take an equal position. Other concerns focus 

on high turnover caused by frequent rebalancing. These 

problems can be eliminated or mitigated in the construction 

and maintenance of the equal-weighted index. 

To address one of the main criticisms of equal-weighted 

indexes—that an investor can take a significant position 

within a smaller-capitalization security, posing a liquidity 

risk—the Russell Sector Equal Weight Index methodology 

applies a screen prior to the construction of each index, which 

is designed to remove securities that could have difficulty 

assuming their required positions in a particular security. 

This screen has an insignificant impact on the returns of 

the index. For the period 1996 to 2010, the Russell 1000 SEW 

index with a capacity screen applied had an annualized tracking 

error of only 28 basis points to the same index with no 

capacity screen applied. For the small-cap Russell 2000 SEW 

index, the annualized tracking error was 38 basis points. 

Turnover 

While portfolio turnover can be a concern with equalweighted 

indexes, it is helpful to remember that much of this 

turnover can be attributed to the reconstitution of the associated 

cap-weighted index. The average quarterly turnover on 

the Russell 1000 Sector Equal Weight Index was 6.3 percent 

for the July 1996 to June 2010 period. However, when we 

remove the July period of each year (when reconstitution of 

the cap-weighted Russell 1000 Index occurs), the average turnover 

becomes 4.2 percent. This suggests that the reconstitution 

of the underlying index can have a significant influence on 

the turnover observed in the equal-weighted index. Even when 

the reconstitution period for the underlying index is taken into 

account, the portfolio turnover observed in the Russell 1000 

Sector Equal Weight Index is comparable to those of equalweighted 

indexes currently in the marketplace and certainly 

lower than the turnover observed in active funds. 

Additional measures can be taken in the daily maintenance 

of the index to avert turnover in the index between 

reweighting periods, which might otherwise be caused by 

corporate actions affecting the constituents. For example, 

not applying month-end share adjustments and not increasing 

a company’s weight when a company acquires another 

company from different size tiers can reduce turnover 

between reweighting periods. 

Rebalancing Frequency 

Sector equal-weighted indexes assign a weight to each 

security at each rebalancing period. However, between rebalancing 

periods, the weights of each security will deviate from 

this target weight due to daily changes in price. A perfect 

implementation of the sector equal-weight strategy would 

require sectors and constituents to rebalance daily. Daily 

rebalancing would be impractical to implement as a passive 

strategy; it would require selling and buying constituents’ 

shares daily to keep the weights constant, and thus bring 

about high transaction costs for replicated portfolios. 

24 


Thus, a trade-off is required between representation of 

the strategy and turnover. To keep portfolio turnover down, 

we reweight less frequently but incur some tracking error to 

a hypothetical portfolio that is reweighted daily. To test the 

impact of different reweighting periods, we simulate sector 

equal-weighted strategy indexes based on different reweighting 

time frames—daily, monthly, quarterly, semiannually and 

annually. Intuitively, we should see tracking error maximized 

for the scenario with the longest reweighting period and 

turnover maximized for the shortest reweighting period. 

We measure the tracking error of each reweighting time 

frame against the theoretical perfect implementation of daily 

reweighting. We also calculate the turnover of the portfolios 

for each of the different reweighting time frames. These 

results are presented in Figure 5 for the Russell 1000 and 

Russell 2000 sector equal-weighted indexes. 

The results are as expected. When the frequency of 

reweighting increases, so does the turnover of the portfolio. 

However, as the frequency is increased, the tracking error of 

the portfolio decreases. A daily reweighted large-cap portfolio 

has turnover of more than 175 percent. The turnover is more 

pronounced for the daily reweighted small-cap portfolio, 

where it is more than 250 percent. Turnover decreases dramatically 

for both the large-cap and small-cap portfolios when 

the reweighting is done on a monthly basis, becoming less 

than a quarter of that of the daily reweighted portfolio. The 

annually reweighted large-cap portfolio has the least turnover 

(16 percent), but also the highest tracking error (4.6 percent). 

The Russell Equal Weight Indexes: Performance 5 

In this section, we examine the investment performance 

of quarterly rebalanced cap-weighted SEW and CEW index 

Figure 10 

Trading 

Symbol 

Value of a Dollar 

Volatility 3-Month (annualized) 

Average 

Daily Volume 

January 1979-June 2010 

Sharpe Ratio 

Russell 1000 $27.25 15.68 0.1182 

Russell 1000 CEW $44.86 17.86 0.1354 

Russell 1000 SEW $59.19 17.20 0.1535 

Russell 2000 $27.03 19.96 0.1039 

Russell 2000 CEW $17.02 21.47 0.0807 

Russell 2000 SEW $26.92 20.43 0.1025 

Russell 3000 $27.06 15.81 0.1172 

Russell 3000 CEW $24.98 19.89 0.1001 

Russell 3000 SEW $36.28 18.90 0.1207 


Russell 1000 $2.23 16.44 0.0657 

Russell 1000 CEW $3.49 19.62 0.1091 

Russell 1000 SEW $4.22 18.44 0.1332 

Russell 2000 $2.33 20.99 0.0677 

Russell 2000 CEW $2.42 23.58 0.0702 

Russell 2000 SEW $3.03 22.47 0.0904 

Russell 3000 $2.22 16.52 0.0651 

Russell 3000 CEW $2.76 21.87 0.0827 

Russell 3000 SEW $3.42 20.58 0.1049 


Russell 1000 $0.97 16.38 –0.0216 

Russell 1000 CEW $1.64 20.94 0.0717 

Russell 1000 SEW $1.87 19.52 0.0932 

Russell 2000 $1.43 20.90 0.0517 

Russell 2000 CEW $1.73 25.26 0.0768 

Russell 2000 SEW $1.90 23.89 0.0892 

Russell 3000 $0.99 16.58 –0.0154 

Russell 3000 CEW $1.71 23.55 0.0759 

Russell 3000 SEW $1.90 22.01 0.0916 


Sharpe Ratios For The Russell 1000, Russell 2000 And Russell 3000 Indexes From 1979-2010, 1996-2010 And 2001-2010 


25

structures in the U.S. large- and small-cap equity markets 

and in global developed and emerging markets. Generally we 

find that the Russell Equal Weight indexes display superior 

returns with lower levels of volatility when compared to capweight 

and CEW indexes over our sample periods. 

Performance Of U.S. Indexes 

Figure 6 displays the performance of the Russell 1000 SEW, 

CEW and cap-weight indexes from December 1978 to June 

2010. The cumulative return of the SEW index was almost 

double that of the cap-weighted Russell 1000 index from 

January 1, 1979 to June 2010 and 35 percent more than that of 

the CEW index. Since 1979, the cap-weighted indexes outperformed 

the equal-weighted indexes for a meaningful period of 

time just once, during the tech bubble of the late 1990s. 

Previous research has suggested that during periods of 

strong bull markets, equal-weighted indexes can underperform 

cap-weighted indexes (Dash and Loggie [2008]). 

However, an extension to this might be necessary. While it 

may be true that equal-weighting underperformed during 

this period, we should note that from 1998 to 2000, the market 

was sector-led. Mega-cap technology stocks contributed 

significantly to the return of the cap-weighted indexes during 

this period. The weight of the technology sector in the 

Russell 1000 cap-weighted index increased significantly over 

this time period, going from 11.5 percent on January 1, 1998 

to a high of over 30 percent on July 3, 2000. Comparatively, 

the weight of the technology sector in the Russell 1000 SEW 

index was consistently reset to 11.1 percent throughout this 

sample time period. It is fair to conclude that the outperformance 

of the sector during this period led to the outperformance 

of the cap-weight indexes over the equal-weighted 

indexes. We also note that having such a large weight in 

the technology sector of the Russell 1000 Index hurt performance 

after the bubble burst, with the equal-weighted 

indexes faring better than the cap-weighted indexes during 

the technology sector downturn in 2001 and 2002. 

Cumulative returns are presented for the cap-weighted SEW 

and CEW Russell 2000 indexes from 1979 to 2010 in Figure 

7. The Russell 2000 cap-weighted index outperforms both 

the SEW and CEW indexes for much of the 1980s and 1990s, 

and does so handily during the tech bubble of the late ’90s. 

However, after the year 2000, the SEW index outperformed the 

cap-weighted Russell 2000. The Russell 2000 SEW index outperformed 

the respective CEW index over this time period. We 

note that the value of an investment in the CEW index would 

not have been higher than that of a like investment in the sector 

equal-weighted index for any significant period of time. 

Volatility 

The volatility of the Russell 1000 and Russell 2000—as 

measured by the annualized rolling three-year standard 

deviation of monthly returns—in the cap weight, SEW and 

CEW indexes is displayed in Figures 8 and 9. 

The Russell 1000 cap-weighted index has lower volatility 

than both of the respective equal-weighted indexes for the 

entire period, except in the late 1990s, when the volatility 

of the cap-weighted indexes exceeds the level of the other 

two indexes, going from a low of 8.0 percent at the end of 

April 1996 to a high of 19.5 percent on April 2001. The SEW 

Figure 11 

Regression Results For The Russell 1000 

Trading 

Symbol 

t Stat 

1/1/1979-12/31/1990 Russell 1000 

R Square 0.9884 

P-value 

Intercept –0.6780 –13.8474 0.0000 

Russell 3000 0.9723 86.2907 0.0000 

Russell Midcap - Russell Top 200 0.4730 18.4476 0.0000 

Russell 1000 Value - Russell 1000 Growth –0.0467 –1.9097 0.0582 

1/1/1991-12/31/2000 Russell 1000 


Intercept –0.3346 –3.4871 0.0007 

Russell 3000 1.0321 41.3417 0.0000 


Russell 1000 Value - Russell 1000 Growth 0.2269 8.2533 0.0000 

1/1/2001-6/30/2010 Russell 1000 


Intercept 0.1024 1.0637 0.2898 

Russell 3000 1.0473 49.2877 0.0000 




26 


index consistently has lower volatility than the CEW index. 

The Russell CEW 1000 index has significantly higher volatility 

than the other two indexes from 2002 to 2005, deviating 

as much as 3 percent from the SEW index. 

In contrast, as we show in Figure 9, the Russell 2000 capweight 

index does not consistently display lower volatility 

than the Russell 2000 SEW. The SEW index shows lower 

volatility than the cap-weighted indexes from 1982 to 1993 

and during the technology boom-and-bust period, while the 

cap-weighted indexes have lower volatility for much of the 

current decade. There is no significant period of time during 

which the CEW index has lower volatility than the capweighted 

index or the SEW index. 

We observed similar return and risk characteristics when 

we tested the SEW and CEW methodologies based on the 

Russell Global indexes. The SEW index consistently outperformed 

the CEW index, with lower volatility. 

Risk-Adjusted Returns 

The Russell SEW indexes have outperformed capweighted 

indexes over the entire period under review 

(1/1/1979-6/30/2010) in the domestic U.S. indexes and in 

international markets. However, the outperformance was 

accompanied by higher volatility. 

We present Sharpe ratios over three time periods for the 

Russell 1000, 2000 and 3000 Indexes for the cap-weighted, 

SEW and CEW indexes (see Figure 10). With the exception of 

the Russell 2000, over the longest sample time period (1979 

to June 2010), the risk-adjusted returns for the SEW were 

higher than for the cap-weighted and CEW indexes. 

For the period 1996 to 2010, which includes the outperformance 

of cap-weighted indexes during the technology 

boom, absolute returns and Sharpe ratios are higher 

for the SEW indexes. 

Where Is the Outperformance Coming From? 

To analyze where the outperformance of the SEW indexes 

is coming from and to gain additional insight, we perform a 

Fama-French three-factor regression analysis and calculate 

contribution to returns by sector and size. 

Fama-French Factor Regression 

We regress the returns of the Russell 1000 and the Russell 

2000 SEW indexes using the Fama-French three-factor model 

of market, size and style for each decade on the Russell 1000 

and Russell 2000 cap-weighted indexes. The dependent variable 

is the excess return of the respective SEW index, minus 

the risk-free rate. The small-capitalization factor (SmB) for 

the Russell 1000 SEW index is measured as the excess return 

of the Russell Midcap Index over the Russell Top 200 Index, 

and for the Russell 2000 SEW index as the excess return of 

the Russell 2000 over the Russell 1000. The high minus low 

(HmL) style factor is taken as the value minus growth of the 

respective cap-weighted indexes. The regression results are 

presented in Figures 11 and 12. 

When adjusted for size and style factors, the equalweighted 

indexes exhibit an alpha of 0.10 percent and 0.13 

percent per month for the Russell 1000 and Russell 2000 

SEW indexes, respectively, in the last decade, though these 

are not statistically significant. For the 1980s and 1990s, a 

Figure 12 

Regression Results For The Russell 2000 

Trading 

Symbol 

t Stat 

1/1/1979-12/31/1990 Russell 2000 


P-value 

Intercept –0.7324 –8.1698 0.0000 

Russell 3000 0.9001 41.2856 0.0000 

Russell 2000 - Russell 1000 0.9637 28.0772 0.0000 


1/1/1991-12/31/2000 Russell 2000 


Intercept –0.5357 –3.8859 0.0002 

Russell 3000 1.0376 26.4475 0.0000 


Russell 2000 Value - Russell 2000 Growth 0.1765 4.5246 0.0000 

1/1/2001-6/30/2010 Russell 2000 


Intercept 0.1345 0.8866 0.3772 

Russell 3000 1.1150 31.8928 0.0000 





27

negative alpha is observed for both the Russell 1000 and 

Russell 2000 SEW indexes and is statistically significant. 

The regression results provide strong empirical support 

for assertion that equal-weighted indexes tilt toward 

smaller-capitalization securities to a statistically significant 

level. For each decade, the SmB coefficient is more than 0.4 

for the Russell 1000 SEW index and more than 0.9 for the 

Russell 2000 SEW index. Thus, the Russell 1000 SEW index 

is influenced by mid-cap securities, and the Russell 2000 

SEW index is strongly influenced by securities at the middle 

to bottom of the Russell 2000 benchmark. 

What is even more interesting is the shifting influence of 

growth and value factors from one decade to the next for both 

the Russell 1000 and Russell 2000 SEW indexes. The indexes 

seem to exhibit a growth tilt for much of the 1980s, a “value” 

tilt for the 1990s and then a growth tilt for the 2000s. 

Contribution To Return By Sector 

In Figure 13, we compare cumulative contribution to return 

by sector from July 1996 to December 1999 and January 2000 

to June 2010 between the Russell cap-weighted and SEW 

indexes. Not surprisingly, the technology, consumer discretionary 

and financial services sectors contribute almost 66 percent 

of the total return. While all of the sectors in the Russell 

SEW 1000 index had a positive return for the period 1996 to 

1999, the contribution from seven of the nine sectors underperformed 

the sector returns of the cap-weighted index. 

We see similar results for the Russell 2000, with the technology 

and financial services sector driving the returns of the 

cap-weighted index. Although the Russell 2000 SEW index has 

five of the nine sectors contributing higher returns than those 

of the cap-weighted index, they are not high enough to overcome 

the low contributions from the technology and financial 

services sectors. Thus, the cap-weighted index outperformed 

the Russell 2000 SEW index for the 1996-1999 time period. 

As we previously observed, the outperformance is reversed 

within the last decade, with the SEW indexes outperforming the 

cap-weighted indexes significantly. Every sector in the Russell 

1000 SEW has a positive return and a higher contribution than 

the cap-weighted indexes. The energy sector has the highest 

contribution, providing 25.4 percent of the total return for 

the SEW indexes. Comparatively, within the Russell 1000 capweighted 

index, the energy sector contributes only 1.7 percent, 

with the consumer staples sector providing the largest contribution 

at 3.7 percent. The technology sector has a large negative 

contribution of 13.9 percent in the Russell 1000 cap-weighted 

index, resulting in the index having a negative performance of 

10.9 percent for the 2000-2010 time period. 

Again, we see similar results in the Russell 2000 SEW index 

from 2000 to 2010. The energy sector provides the largest 

contribution, at 26.8 percent, while the utilities sector has 

the only negative contribution. The financial services sector 

performs the best within the Russell 2000 cap-weighted 

index, with a contribution of 22.7 percent, larger than that 

of the Russell 2000 SEW index. 

There are some interesting results we should consider from 

the sector contributions, particularly the large deviation in 

the technology sector from 2000 to 2010 for both the Russell 

1000 and the Russell 2000. The differences in contribution to 

return cannot entirely be attributed to the sector weight differences 

or the different rebalance schemes. We provide the 

contribution to return by deciles in Figure 14 for the Russell 

1000 cap-weighted index. The large negative contribution of 

-22.58 percent from the top 10 percent of the largest companies 

in the index played a significant role in the index achieving 

a negative return for this time period. 

The Fama-French regression analysis showed the large 

influence of smaller-capitalization securities in the SEW 

index. We surmise from these results that the outperformance 

of smaller-capitalization securities and the underperformance 

of large-cap companies led to the SEW index 

outperformance from 2000 to 2010. This suggests that when 

larger-cap companies outperform smaller-cap companies, the 

SEW index might underperform a cap-weighted index. 

Figure 13 

Contribution To Return By Sector 

Trading 

Symbol 

Russell 1000 

7/1/1996-12/31/1999 1/1/2000-6/30/2010 

Russell 2000 Russell 1000 Russell 2000 

Cap 

Weight 

SEW 

Cap 

Weight 

SEW 

Cap 

Weight 

SEW 

Cap 

Weight 

Consumer Discretionary 14.4% 7.2% 5.5% 2.8% –1.5% 8.8% 7.3% 7.8% 

Consumer Staples 8.5% 7.3% 1.7% 4.2% 3.7% 15.5% 2.1% 13.3% 

Energy 5.8% 6.2% 0.5% 1.8% 1.7% 25.4% 4.4% 26.8% 

Financial Services 23.7% 11.3% 12.4% 5.2% 1.0% 13.1% 22.7% 14.5% 

Health Care 13.9% 7.0% 1.5% 0.0% 1.9% 17.6% 7.3% 11.7% 

Materials & Processing 3.7% 4.8% –0.4% 0.2% 0.6% 12.7% 7.7% 13.6% 

Producer Durables 11.4% 8.5% 5.5% 5.6% –0.6% 12.9% 3.3% 11.6% 

Technology 31.7% 18.3% 19.2% 12.2% –13.9% 3.2% –15.7% 0.2% 


Utilities 12.4% 10.5% 6.3% 9.5% –3.8% 1.5% –0.7% –7.7% 

SEW 

28 


Figure 14 

Contribution To Return By Company Size Deciles 

Trading 

Symbol 

Average 

Weight 

Russell 1000 

7/01/1996-12/31/1999 12/31/1999-6/30/2010 

Total Return 

Contribution 

to Return 

Average 

Weight 

Total Return 

Contribution 

to Return 

MC Decile 1 54.35 139.41 72.30 52.98 –30.91 –22.58 

MC Decile 2 15.00 110.15 16.68 10.77 6.08 0.59 

MC Decile 3 8.16 78.40 7.41 7.38 4.12 0.39 

MC Decile 4 5.99 158.36 8.23 5.56 73.16 2.88 

MC Decile 5 4.24 68.49 3.32 4.25 37.34 1.67 

MC Decile 6 3.42 37.14 2.06 3.10 52.56 1.44 

MC Decile 7 2.73 45.97 1.85 2.64 82.04 1.50 

MC Decile 8 2.45 39.90 1.75 1.83 73.31 1.06 

MC Decile 9 1.64 86.30 1.43 1.87 77.39 1.18 

MC Decile 10 1.47 40.78 0.76 5.27 41.31 2.42 

[N/A] 0.56 106.83 0.64 4.34 –19.16 0.55 

Source: FactSet 

Conclusion 

Equal-weighting by constituents, while simple, introduces 

sector risk into an index exposure. We find that the sector 

equal-weighted indexes provided a better absolute return 

with lower volatility for the time period tested. On the 

basis of simulated returns, we find that equal-weighting by 

sector provided better risk-adjusted returns than a constituent 

equal-weighted index and the respective cap-weighted 

index over our sample periods. These results are consistent 

across the domestic large-cap and small-cap spectrum and 

the global developed and emerging markets, although that 

analysis was not included in the results. The analysis of the 

performance attribution and regression shows that the sector 

equal-weight index is strongly influenced by small-capitalization 

securities in the index, suggesting that the sector 

equal-weighted index might underperform a cap-weighted 

index when larger-capitalization securities outperform smaller-capitalization 

companies. 

Endnotes 

1 There are three forms of the EMH: The weak form asserts that all past information is fully reflected in the price of a security. The semi-strong form asserts that all publicly available 

information is fully reflected in the price of a security. The strong form asserts that all information is reflected in the price of a security. 

2 The float-adjusted market capitalization of a stock is the total market capitalization net of closely held shares that are not freely available to the public. 

3 Tracking error is calculated as the standard deviation of monthly excess return over the daily reweighted portfolio multiplied by the square root of 12. 

4 Turnover is the average turnover (calculated as the minimum of the addition and deletion percentage of the portfolio) per time frame multiplied by the number of times the 

portfolio is reweighted per annum. 

5 The returns of the Sector Equal Weight and the Constituent Equal Weight indexes are simulated and the constituents are not screened for capacity constraints. 

References 

Ankrim, Ernie, and Jill Johnson. 2000. “Global Equity Portfolio Diversification: Is It Still a Valid Investment Strategy?” Russell Research Commentary (November). 

Arnott, Robert D., Vitali Kalesnik, Paul Moghtader and Craig Scholl. 2010. “Beyond Cap Weight: The empirical evidence for a diversified beta.” Journal of Indexes 

(January/February). 

Arnott, Robert D., and John M. West. 2006. “Fundamental Indexes: Current and Future Applications.” in A Guide to Exchange Traded Funds and Indexing Innovations— 

Fifth Anniversary Issue. 

Arnott, Robert D., Jason Hsu and Philip Moore. “Fundamental Indexation.” 2005. Financial Analysts Journal (March/April). 

Blitz, David, and Laurens Swinkels. 2008. “Fundamental indexation: an active value strategy in disguise.” SSRN (August). 

Carino, David R., and Thomas H. Goodwin. “The Measurement of Excess Return.” 2001. Russell Research Commentary (November). 

Christopherson, Jon, David R. Carino and Wayne E. Ferson. 2009. “Portfolio Performance Measurement and Benchmarking.” McGraw-Hill Finance & Investing. 

Dash, Srikant, and Keith Loggie. 2008. “Equal Weight Indexes: Five Years Later.” S&P Indices (April). 

Dash, Srikant, and Liyu Zeng. 2010. “Equal Weight Indexes: Seven Years Later.” S&P Indices (July). 

“Equal Weighted Indexing—A Critique of S&P’s Study.” 2008. Advisor Perspectives newsletter (May). 

Fabozzi Frank J. 1998. “Active Equity Portfolio Management” Wiley. 

Fama, Eugene F., and Kenneth R. French. 1992. “The Cross-Section of Expected Stock Returns.” Journal of Finance. Vol. 47, No. 2 (June), pp. 427–465. 

Pope, Brad. 2009. “Insights on Market Capitalization and Fundamental-Weighted Indexes.” BlackRock Institutional Trust Company, N.A. 

Treynor, Jack. 2005. “Why Market-Valuation-Indifferent Indexing Works.” Financial Analysts Journal (September/October). 

Warren, Geoff, and Don Ezra. 2010. “When should investors consider an alternative to passive investing?” Russell Research (January). 


29

The Weight Debate 

A roundtable 

30 


Alternative weighting strategies burst onto the market a number 

of years ago and attracted huge swaths of attention. There 

were media gatherings, fund launches aplenty and op-eds in the 

Wall Street Journal. 

Where do things stand now that the strategies have been live 

for some time and have gone through a number of vigorous market 

cycles? The Journal of Indexes gathered some of the leading 

thinkers in the weighting space to discuss where things stand on 

the cusp of 2011. 

Gus Sauter, Chief Investment Officer, 

Vanguard Group 

JoI: What have the last three years taught us 

about the potential risks and potential benefits 

of alternative weighting strategies? 

Sauter: I don’t think a three-year time period is enough to really 

judge anything. When you’re looking at alternative weights, 

the big question is whether or not you’re gaining factor exposure, 

or factor exposure plus alpha—and you can’t determine 

whether there’s alpha in a three-year time period. 

JoI: Cap weighting is “the market,” but is it how we should be 

thinking about investing? 

Sauter: There are two components of return from the market. 

One is the various factor returns you can gain exposure 

to, and the other would be value added above and beyond 

that, or alpha. The factor returns are basically market-capweighted 

returns, and the big question is: Can you actually 

add value above and beyond that? I think it’s very, very difficult 

to add value above and beyond that, and I don’t think 

it’s possible using simple heuristics. 

My view is that these various alternative weighting 

schemes really are just giving you factor exposure that you 

can gain through cap weighting. 

You can look at these various alternative constructs through 

many different lenses. You can look at them mathematically; 

you can look at them performancewise. And when I do that I 

keep coming up with the same answer: These [indexes] typically 

have a smaller-cap bias and a value bias. We certainly know 

that, if you go back over the last 50, 80, 100 years, small-cap 

and value have outperformed. It would be hard to construct 

an index that has a tilt towards those segments of the marketplace 

and not have that index or that benchmark outperform 

the S&P 500, which is usually what people are comparing 

these things to. But I’d say it’s a wholly unfair comparison and 

that really the right comparison is against a mid-cap value, or 

perhaps small-cap value, cap-weighted index. And there you’ll 

find that they perform very similarly. 

JOI: Alternatively weighted indexes have been available for some 

time. Why haven’t more end-users begun using them? 

Sauter: I think—like with anything—there should be some 

skepticism. The promoters of these indexes have made 

pretty big promises, and I would hope that investors would 

be skeptical of that. Usually when you make pretty grandiose 

claims, it’s probably too good to be true. 

JoI: Why is cap weighting so entrenched? What is its appeal to 

investors? 

Sauter: I think there are two reasons. Cap weighting has been 

the dominant form of indexing or investing—and that’s No. 1. 

Theory would tell us the appropriate weight for a portfolio is 

cap weighting, if you believe in efficient markets. Personally, I 

don’t. But even if you don’t believe in efficient markets, I still 

think cap-weighted indexing makes sense, and that’s because 

of the argument that outperformance is a zero-sum game. 

We know that, in aggregate, investors can only get the 

market rate of return, and unfortunately they can’t even get 

that, because of costs. So a majority of investors will underperform 

the market. That proposal really doesn’t require any 

assumptions—that’s just truisms. 

For that reason, we have a good deal of confidence that capweighted 

indexing would provide a relatively attractive rate of 

return, relative to what active investors can obtain. The other 

proposals for weightings don’t have a theoretical underpinning, 

and they don’t have a simple rational explanation either. 

JoI: What makes a weighting methodology superior? Is it just 

performance? 

Sauter: Performance, which does include risk as well: Riskadjusted 

returns are really the most important aspect of 

investing. I think simplicity is important as well; certainly 

cap-weighted indexing is as simple a construct as you can 

devise. Transparency [is important] as well, and certainly a 

cap-weighted index is the most transparent. 

JOI: In an era of rising correlations, can diversifying your weighting 

methodology have any beneficial effect on portfolio performance 

statistics? 

Sauter: I’d never want to say that diversification isn’t a benefit. 

The problem is: Are you getting additional diversification 

from these alternative weighting schemes? I guess I’d 

say you’re getting other factor exposure. I’d just ask: Do you 

want those factor bets? And if you do, why not just take them 

directly with a cap-weighted index? 

You’re not really diversifying; you’re basically tilting. 

Rob Arnott, Founder and Chairman, 

Research Affiliates LLC 

JOI: What have the last three years taught us 



Arnott: The alternative weighting strategies are as different 

from one another as each of them is from cap weight, so it’s 

very difficult to generalize. I think it’s fair to say that the past 

three years have taught us that the markets’ perspective on 

risks, perspectives on opportunity, and willingness to pay 


31

for perceived growth is constantly in flux, and that strategies 

that contra-trade against the market’s constantly changing 

opinion can capitalize on that. That’s a good thing for most 

of the alternative indexing approaches, because most of 

them do contra-trade. 

The past three years has also seen markets that were profoundly 

hostile to value strategies—and markets that were 

profoundly rewarding of value strategies, especially deep 

value, especially low-quality value. That’s a reminder that 

none of these strategies is going to add value or beat the 

market at all times, because every one of these strategies is 

different from cap weight—and from a cap-weighted-centric 

world view, is therefore active relative to cap weight. 

Each of these strategies has active bets that will at times be 

in favor with the market’s preferences, and at times be out of 

favor. Those would be my takeaways from the last three years. 



Arnott: There’s enormous inertia in our business. Cap-weighted 

indexes have been with us for 50 years. Cap-weighted indexes 

beat the median active manager at least two years out of three 

for 50 years. Cap-weighted indexation is still a minority strategy 

in the marketplace. But I look at the [use] of Fundamental Index 

strategies, which collectively—including RAFI and its imitators— 

grew from $0 to $50 billion in five years. That’s pretty amazing. 

One can view this from a half-empty or half-full perspective. 

The half-empty perspective is well, gosh, stock markets 

alone collectively are over $40 trillion worldwide. So a $50 

billion takeup is barely a 10th of a percent. The half-full 

perspective would be, I’ve never seen any strategy, any new 

product idea, in the investing world garner $50 billion in 

assets in five years. It’s not as if momentum is slowing and 

investors are disheartened. Instead, quite to the contrary, 

we’re finding that recent experience—the latest three years, 

for instance—has ratified the merits of these strategies 

emphatically. And those who were on the fence are one-byone 

coming off the fence, most of them in favor. 

What we’re finding is, in each of four continents— 

Australia, Asia, North America and Europe—at least one 

of the two largest pools of capital on each continent has a 

live Fundamental Index portfolio. Most of these are one- or 

two-billion-dollar “toe in the water” experiments, but if the 

experiments go well, for these organizations to ramp up to 

$50 billion, individually, would be unsurprising. 

I see this as a situation in which our work and pioneering 

and championing the Fundamental Index concept has opened 

the floodgates for a whole raft of alternative indexes—noncap 

indexes, valuation-independent indexes—to gain traction 

and to gain a serious hearing. I see momentum accelerating 

fairly fast, not decelerating. It would be unsurprising to 

me if that $50 billion crosses $100 billion in two years. 

JOI: Does market capitalization accurately represent the value 

of a company? 

Arnott: Absolutely. Cap weighting is the value of a company, if 

you define value as the current market value. The word “value” 

is heavily used in our industry, and it means different things to 

different people. To the classic Graham and Dodd analyst, value 

is the net present value of all future cash flows, and is the true 

fair value of a company, something we can never know. To the 

efficient markets person, value is what a company will fetch 

today, and market cap perfectly captures that. To the consultants 

in our industry and quantitative investors, value sometimes 

means a strategy, a bias towards lower-PE and higheryielding 

companies. If you define it as the true fair value—as if 

somebody was clairvoyant and could see the future—of course 

it’s not, but then again, nobody knows what that number is. 

JOI: Why is cap weighting so entrenched? What is its appeal to 

investors? 

Arnott: The appeal of cap weight is simplicity, cost and 

theoretical backing. Simplicity: What could be simpler than 

weighting companies by their aggregate market value? Cost: 

What could be cheaper than running such a strategy—both 

in terms of direct fees and in terms of indirect costs, because 

turnover is so very light? 

In terms of theoretical appeal, the advent of the capital 

asset pricing model [CAPM] by Bill Sharpe and others in the 

early ’60s, and the subsequent development of the efficient 

markets hypothesis, wound up ratifying cap weight as the 

sensible way to invest. CAPM says if markets are efficient, the 

cap-weighted market is unbeatable on a risk-adjusted basis. 

And efficient markets hypothesis says prices are correct; 

therefore, stock-picking is a waste of time. 

Both of those are really powerful and really intuitively 

appealing theories. But the originators of these ideas—Bill 

Sharpe and Burt Malkiel—are alive today. Both of them would 

cheerfully admit that their theories are approximations of the 

real world, that the real world is more complex than that. 

If markets aren’t efficient, then the inherent pre-eminence 

of cap weight as the only sensible indexing approach suddenly 

disappears and opens the door for a whole array of 

interesting ideas. And so I think to some extent we helped 

to spawn a revolution in the way our industry thinks about 

indexes. If you want a benchmark that looks like the market, 

it’s got to be cap weighted. If you want an index that looks 

like the broad economy, the economy is not cap weighted; 

it’s something closer to a Fundamental Index strategy. 

If you want a strategy that minimizes variance or maximizes 

Sharpe ratio, then a minimum variance or an EDHEC 

approach suddenly becomes dominant and very interesting. 

I think what we’ve seen is that the floodgates are open for a 

whole spectrum of really interesting ideas, and all of a sudden 

they are no longer viewed as quirky, peculiar departments, 

but rather as legitimate alternatives to cap weight. 

JOI: What makes a weighting methodology superior? Is it just 

performance? 

Arnott: No [that’s just one piece. There’s also risk.] If you 

manage the risk right, you have the staying power for any 

return-seeking strategy. 

32 


There can also be the implementation challenges. Some 

of the new index methods have high turnover, or have a relatively 

aggressive reliance on small-cap and illiquid companies. 

That doesn’t make the strategies inferior; it makes them 

more challenging to implement. It amplifies the importance 

of very, very careful implementation. Also, the superiority of 

any strategy on any of these measures is a moving target. 

In any given year, the style bets, if you will, relative to the 

market of any of these strategies, will change. And as they 

change, no single strategy will be the best in all years. In 

individual years, different strategies will be dominant. 



statistics? 

Arnott: Absolutely. High correlation on issue-specific risk merely 

means that the relative values are stickier than they should be, 

relative to the shifting fundamentals, and relative return opportunities 

of the individual companies. I would argue that if correlations 

became extremely high, the benefits of classic, old-fashioned 

Graham and Dodd analysis—comparing fair prices with 

a company’s business potential—could come back into vogue 

and turn out to be a very powerful tool. Sticky prices mean you 

have lots and lots of time to study the individual companies, and 

to gauge which ones are mispriced. It means the mispricing is 

stickier and longer lasting. I view high correlations as something 

that, if anything, enhance the potential of disciplined strategies 

that depart from the cap-weighted market. 

Scott Ebner, Managing Director and 

Global Head of ETF Product Development, 

State Street Global Advisors 




Ebner: Well, if you look at the actual product movement in 

the ETF space, we’ve seen a lot of new products launched that 

are based on alternative indices, but the assets in the business 

continue to be focused on the [more] standard benchmark 

indexes. So I think we’ve learned that while they’re very 

interesting—and I think the investor choice is a good thing— 

the investor demand, especially on the ETF front, for index 

products continues to be focused on core benchmarks. 

JOI: Alternatively weighted indexes have been around for 

some time. Why haven’t more investors and end users begun 

using them? 

Ebner: Standard indexes have a kind of gravitational pull 

that continue to draw their attention. I don’t consider it to 

be a failure in any respect for alternatively weighted indexes 

that the assets and volume in the market continue to be 

concentrated around standard benchmarks. Any time you 

have greater variety, you have greater fragmentation, and in 

the space of alternatively weighted indices or alternatively 

constructed indices, it starts to look a little bit more like 

the actively managed space, where there’s greater degrees 

of fragmentation based on process or brand, or differentiation—so 

you would expect the fragmentation to be higher. 

JOI: Why is cap weighting so entrenched? What is its appeal 

to investors? 

Ebner: It’s a very simple concept, and it’s a concept that has 

shown over time that it’s very hard for active managers to beat. 

Investors a lot of times also are not aware of the weighting 

methodologies behind the indexes. They’re aware of the 

names of the indices they know, and they follow the index 

values. More sophisticated investors are usually more aware 

of the particular methodology of their indices. But some 

of the most widely known indices in the world are not cap 

weighted. The Dow Jones industrial average is one of the 

most widely known, and it’s a tremendously good blue-chip 

index, but it’s not cap weighted. 



statistics? 

Ebner: I think the ability for an alternative weighting mechanism 

to significantly de-correlate with a benchmark index is 

unlikely. Most of the academic research that I’ve seen shows 

that it’s the choice of allocation that is the highest explanatory 

variable for performance outcome. And when you talk about 

alternative weighting screens, you’re typically not adjusting 

that asset allocation space. So you may be picking up marginal 

performance versus the cap-weighted benchmark, but I don’t 

think you’re significantly altering the risk characteristics. 

I don’t think most of the index providers who offer alternatively 

weighted indices think that they’re significantly 

altering the risk characteristics. In fact, they’re looking to 

kind of keep with the investment objectives of the category 

as a whole, but to represent it differently. 

Srikant Dash, Managing Director, S&P 

Indices, Standard & Poor’s 




Dash: I think the last three years have showed us that the lines 

between active and passive are blurring, and there’s a large 

shade of gray in between. In general, a minority of investors 

are truly passive—they believe in passive indexing. A minority 

of investors truly believe in active investing. A majority of 

investors are somewhere in between. Index products that are 

based on strategies or based on different weighting schemes 

seek to address the needs of those investors. 

Being in the middle region between active and passive also 

means that not only do you harness the benefits of both— 

meaning that you can have the diversification and relatively 

low costs of indexing—but you also have some of the factor 


33

isks and active risks in active strategies. It’s become a more 

complex decision for investors [as a result]. 



Dash: The first thing to look for in investing is to look for how 

things are. That’s your frame of reference. And your frame of 

reference doesn’t necessarily dictate how you want to actually 

invest your money. So I think it’s kind of a flawed debate to 

have, if you are trying to correlate the frame of reference with 

investment choice. Despite overwhelming evidence that by 

investing in a passive portfolio that tracks a benchmark over 

long periods of time you do a lot better, the majority of investors 

prefer to do otherwise. That really relates to the distinction 

between the frame of reference and actual investments. 

The investment decision is not just shaped by the frame of 

reference; it’s shaped by asset allocation models and your belief 

system. And a lot of investors don’t want to settle for the market 

average. A lot of investors feel that they can do better than average, 

and that’s why they choose something other than passive. To 

me the debate should be about how we set our frame of reference, 

and how we go about investing our money. And it should 

be set in the broader context of investor psychology. It’s a sterile 

debate to make it about X benchmark versus Y benchmark. 



Dash: We have a slew of alternatively weighted indexes. 

We have had fairly robust growth in our business in alternatively 

weighted indexes. We probably have $1.5 billion to 

$2 billion in our factor-weight indexes, about $5 billion to 

$6 billion in our dividend-weighted indexes and half a billion 

dollars in our liquidity-weighted indexes; probably $7 

billion to $8 billion in our equal weight indexes and benchmark. 

It’s a small part of our business, but it’s a meaningful 

and growing part of our business. 

JOI: Why is cap weighting so entrenched? What is its appeal 

to investors? 

Dash: It’s a frame of reference. At the end of the day, as I said 

before, one needs to figure out the frame of reference and the 

actual investments. The actual investments are influenced by 

a number of factors, investor psychology being one of them. 

[Also] the marketing of financial products earlier: In general, the 

industry markets products where it makes the most money. And 

frankly, nonmarket-cap-weighted products are higher-margin 

products for all of us in the industry, just like actively managed 

funds are higher-margin products for fund providers. 

Again, it’s really important to know the distinction between 

the frame of reference and actual investments. One could have 

a particular frame of reference, but the investments may be 

shaped completely different than the frame of reference. 


performance? 

Dash: Arguing weighting by factor A versus weighting by factor 

B is like arguing my investment strategy is better than 

somebody else’s investment strategy. At the end of the day, 

the investor starts to evaluate these alternative-weighted 

indexes, just like they would evaluate active managers. What is 

the investment philosophy? What are the risk exposures? And 

what are the chances that these exposures pay off? This is a 

little bit like one of those sterile debates. 



statistics? 

Dash: I would run a thousand miles away from anyone who 

is making a case for diversifying a portfolio simply by picking 

the weights within an asset class. Whoever is making that 

case is just selling product. The time-honored and tested way 

of diversifying a portfolio over long periods of time is to give 

exposure to multiple asset classes. It’s clear that the comprehensive 

majority of one’s returns comes from asset allocation 

across asset classes. They don’t come from particular investment 

strategies within an asset class. 

Frank Nielsen, Executive Director and 

Head of Applied and Index Research, MSCI 

JOI: What has the last three years taught us 



Nielsen: I’m not sure if the last three years taught us anything 

different from what we’ve learned in previous years. 

Alternative weighting can mean lots of different things. 

At one extreme is capital or capitalization, which is the 

standard. [The other] extreme would be equal weighted, 

where one would end up with a very heavy weight in small 

caps. And an alternative [to those two] would be what are 

now known as fundamentally weighted indices, where one 

weights individual securities based on the underlying fundamentals; 

[the fundamentally weighted indices] tend to have 

strong value and small-cap biases, and often high dividends. 

Also, in the last couple of years, risk-weighted alternative 

weighting schemes have come up, including minimum variance, 

or minimum volatility indices. 

They all try to capture something different. The cap 

weighted clearly has the largest capacity [for investment]— 

it’s reflecting the market assessment of the value of individual 

securities. This is also extremely cost efficient to implement, 

so it has many advantages. 

With regard to fundamentally weighted indexes, there are 

all kinds of alternative indexes, and how one wants to define 

fundamentals can vary. But I think they all basically have the 

common characteristics of small-cap and value bias. They 

have less capacity, but it’s still pretty good—certainly more 

than an equal-weighted index. 

What we saw during the crisis clearly was that value had 

a terrible period relative to its long-term average. Valuerelated 

companies and indices underperformed the market, 

34 


which in itself didn’t perform well. That really highlighted 

that value-oriented or fundamentally weighted indices are 

not an investment that will consistently and always do better 

than a simple cap-weighted index. 

With these alternative weighting schemes, you are basically 

making a bet on certain risk premia that have been identified 

in academia, as well as in empirical research. Those risk 

premia may go through cycles, or may disappear altogether. 

If you look back in time, there are these cycles where value 

was done very well, and then there are other cycles where 

value hasn’t done well relative to growth companies. 

Finally, the risk-weighted indexes are sort of the most recent 

addition. Their capacity is not as good as that of cap-weighted 

indices, but similar to fundamentally weighted indices. And 

certainly the ones that focus on low risk have a bias towards 

low-risk or low-beta companies. They behave very differently 

depending on the cycle you are in and have consistently lower 

risk than any of the other indices we discussed. They also tend 

to do very well in periods where the markets tank—they did 

extremely well during the financial crisis, as well as when the 

Internet bubble burst. These volatility-weighted indices have 

benefits in down markets, but again, you are betting on low 

volatility, and low volatility stocks will not always do well. 



Nielsen: It’s super-cheap, efficient, low turnover, And there’s 

very little proof that you can consistently outperform it. 

And even though the market is wrong at certain times, it’s 

always difficult to figure out when the market is wrong or 

right. Therefore, many people still believe that whatever the 

market price of a security, that’s the best assessment of the 

company’s value you can get. 

If you don’t agree, then there are all these alternatives. 

Then you can basically buy growth indices, value indices, 

small-cap indices—but you’re basically making an assessment 

that there is a mispricing in the market that you can 

identify and then benefit from. 

JOI: What makes a leading methodology superior? Is it just 

performance? 

Nielsen: I think a strong motivation for the development of 

alternatively weighted indices has been performance. And a 

large chunk of the marketing of the existing ones is driven by 

performance arguments. 

Some try to come up with academic explanations, or 

theoretical explanations, and a lot of them use also empirical 

data. The issue with empirical data is that you’re always 

looking at a relatively short period of time. Even if it’s 20 

years, it may not be sufficient. But I think what many of these 

alternative-weighted indices are really doing is they try to 

capture well-established risk premiums. 

If you believe that this risk premia exists, then it becomes a 

question of how to best get exposure to these risk premia. 



statistics? 

Nielsen: If correlations really increase very significantly, like 

they did during the financial crisis, it doesn’t really make 

much of a difference. Let’s say you were invested in the world 

and you just used different weighting schemes for developed 

markets: It didn’t really make much of a difference if you were 

equal weighted, cap weighted, alternatively weighted. It may 

have made a difference insofar as instead of losing 40 percent, 

you may have lost 45 percent or 38 percent, but it wasn’t 

giving you a 15 percent loss relative to a 40 percent loss. In 

financial crises, usually correlations increase significantly. 

I think for some crises, these low-risk strategies, or 

low-risk alternatively weighted indices really offer a better 

alternative. Your investment is still going down, though. 

Our minimum volatility index in ’08 outperformed the capweighted 

one by roughly 15 percent or so. It still was down, 

but it went down significantly less. But again, it will give you 

also very different performance in big up-markets. It’s not 

always superior by any stretch of imagination, but it’s a specific 

strategy that will help protect you on the downside. 

Chris Woods, Managing Director, 

Governance & Policy, FTSE Group 


about the potential risks, and the potential 

benefits of alternative weighting strategies? 

Woods: I think they’ve taught us that these are a useful 

complement to traditional cap-weighted strategies. They’re 

particularly beneficial in the aftermath of bubbles: They perform 

well when cap-weighted indices are becoming less concentrated, 

but conversely they underperform cap-weighted 

indices when those become more concentrated. 



Woods: In part because cap-weighted indices are deeply 

entrenched, they have stood the test of time and are subject 

to relatively lower transaction costs and rebalancing costs. 

And in part, because I think many potential users still don’t 

understand the rationale behind alternative weighting, and 

are reluctant to move away from the tried and tested until 

there’s a greater groundswell of acceptance. 

JOI: Why is cap weighting so entrenched? What is its appeal to 

investors? 

Woods: These indices are relatively easy to construct, and 

relatively easy to manage investment portfolios against. And 

theoretically they have a degree of backing from the capital 

asset pricing model that suggests that the optimal portfolio 

in a risk/reward sense is the cap-weighted portfolio. 

continued on page 48 


35


Index investing in the middle 

By David M. Blanchett 

36 


While Value and Growth investing styles are typically 

purely defined, the “middle” style tends to be more 

subjective. Most indexes that seek to track the 

middle style are typically “Blend,” whereby they hold stocks 

that are considered both Value and Growth. This approach 

implies that investing in the “middle,” or Core, is not worthwhile 

and that an aggregated Blend approach is enough. In 

order to determine if Core is a worthwhile investing style, 

the historical performance differences of the domestic equity 

Morningstar Large Cap Core, Mid Cap Core and Small Cap Core 

are compared against the performance of the Morningstar 

Blend indexes and the Russell indexes (which are also Blend) 

on a return and market-factor-adjusted basis. The results of 

the analysis suggest that while the outperformance of Core 

over Blend is not statistically significant, Core indexes have 

historically outperformed Blend indexes on a return and 

market-factor-adjusted basis, with slightly less risk. 

Investing In Style 

The concept of investing in styles is not new. In 1934 

Graham and Dodd documented the superior performance 

of strategies that invest in high-dividend yield stocks in the 

U.S. This gave rise to what has become known as the value 

style (although it’s likely had different names through time). 

In 1977 a study published by S. Basu documented that low 

P/E stocks had historically outperformed large-cap stocks in 

the U.S. by a margin that could not be explained by conventional 

measures of risk. Similarly, in 1981 a study published 

in the Journal of Financial Economics by Rolf Banz documented 

that small-cap stocks had historically outperformed large-cap 

stocks in the U.S. by a margin that could not be explained 

by conventional measures of risk. This was followed by a 

number of other papers, perhaps most notably research by 

Fama and French [1992] that indentified risk factors that are 

highly correlated with long-term historical returns, namely 

company size and value orientation. 

The findings of Basu, Banz and other researchers that followed 

gave rise to the concept of the “Style Box” introduced 

by Morningstar in 1993. The equity Style Box is a nine-square 

grid that classifies securities by market capitalization along 

the vertical axis and by value and growth characteristics 

along the horizontal axis and has become perhaps the 

most commonly utilized method of categorizing U.S. equity 

mutual funds. Morningstar’s equity style methodology uses a 

“building block,” holdings-based approach that is consistent 

with Morningstar’s fundamental approach to investment 

research. Style is first determined at the stock level and then 

those attributes are “rolled up” to determine the overall 

investment style of a fund or portfolio. 

The importance of style investing can be witnessed in the 

naming methodology utilized by many U.S. fund companies 

that call funds “Mid-Cap Growth” or “Small Value,” etc., to 

inform the potential investors of the target equity exposure 

of the fund. Style groups, such as those used by Morningstar 

in its fund categories, have become the primary peer groups 

for mutual fund comparison purposes and funds that have 

performed well historically (e.g., are “5 star” funds within 

their respective Morningstar categories, etc.) tend to feature 

such information prominently in sales materials. Morningstar 

even implicitly recognized the importance of rating funds 

within specific styles versus broad groups when it changed 

its Star Rating system in 2002. 


A variety of companies such as Dow Jones, 1 Morningstar, 

MSCI, S&P and Russell have developed indexes that track 

the performance of the various domestic equity styles 

(i.e., the complete style box). While it is impossible to 

directly invest in an index, there are a variety of ways an 

investor can obtain those market exposures, e.g., through 

an index mutual fund or ETF. The methodologies across 

index providers, though, can differ materially. These 

differences can result in varying market exposures and 

varying returns, something that has been documented by 

Israelsen [2007], among others. 

While market capitalization is a relatively straightforward 

way to determine a stock’s relative total value, it 

becomes more complex when considering things like free 

float, whereby the number of shares outstanding are adjusted 

based on those available for trading. However, there is 

considerably more disagreement about what makes a stock 

“Value” or “Growth” than its respective market capitalization, 

which is why providers use varying methodologies to 

make the distinction. For example, the Dow Jones methodology 

uses “six intuitive fundamental factors” to determine 

whether a stock is Value or Growth, and then determines 

the aggregate style score by measuring a stock’s “Euclidian 

distance” from the growth and value seeds. S&P uses three 

factors to measure Growth and four factors to measure 

Value, while Morningstar has a 10-factor model that assigns 

a 50 percent weighting to forward-looking estimates and a 

50 percent weighting to historical values. Once a stock has 

been categorized as Value or Growth, it is then allocated 

to the respective index accordingly, and even here, the 

methodologies can vary. For example, Russell uses a “nonlinear 

probability” method to assign stocks to the Growth 

and Value style indexes. 

For each of the major providers, Value and Growth tend 

to be well-defined, but the “middle” is not. The middle is 

typically identified with “Blend,” which represents that market-capitalization-weighted 

proxy for all the stocks within 

Figure 1 

Value 

Differences In ‘Middle’ Large Portfolios: 

Morningstar Large Core, Russell 1000, And S&P 500 

Morningstar Large Core Russell 1000 S&P 500 

Large Mid Small 

Blend Growth Value Blend Growth Value Blend Growth 

Source: Morningstar 




37

that given size range. In contrast, Morningstar utilizes 

an approach where stocks are assigned to three different 

“buckets”: Growth, Core and Value, with “Core” representing 

stocks that are not identified strongly with either the 

Growth or Value categories. This differs from most providers 

in that the “middle” style is viewed as a distinct investing 

style, not simply the market capitalization combination 

of all the stocks in the style. Morningstar’s Core approach 

results in a much more precisely defined “middle” style, as 

is depicted in Figure 1. Note how the dispersion around the 

centroid, or weighted average size and style exposure of 

the index, is considerably less than the Russell 1000 or S&P 

500, indexes which are both Blend, rather than Core. 

Analysis 

An analysis was performed in order to determine whether 

Core represents a more advantageous method to invest in 

the “middle.” For the analysis, Morningstar’s Core indexes 

are used as the proxy for Core, while Morningstar’s Blend 

indexes and the Russell indexes are used as proxies for 

the Blend approach. Morningstar Core was the only Core 

approach considered for the analysis. While it would be 

possible to synthetically create other Core indexes, such as 

by synthetically creating an S&P Core index based on those 

securities excluded from either the S&P Pure Growth Index 

or S&P Pure Value Index (since these remaining stocks, 

which would be 34 percent of the S&P 500 Index, would be 

considered Core), only Morningstar Core is used because 

it is the only Core strategy currently available as an investment 

(via a family of iShares ETFs). Two definitions of Blend 

are included. The Morningstar Blend indexes were selected 

since they have the most similar underlying construction 

methodology and factors as the Morningstar Core indexes. 

The Russell indexes were selected to represent Blend as 

well since they are the most widely known for benchmarking 

in the investment industry. 

Monthly returns for the respective indexes were obtained 

from Morningstar Direct. Two tests are performed based on 

monthly returns over the longest period of data available at 

the time of the analysis, from July 1, 1997 to May 31, 2010. For 

both tests, the monthly return of the Blend benchmark index 

(either Morningstar Blend or Russell) is subtracted from the 

return of the respective Core index (Morningstar Core) over 

the entire time period. This type of analysis is most similar to 

a two-sample t-test assuming unequal variances. 

The first test compares the raw performance for the two 

Figure 2 

Average Historical Monthly Returns, July 1997 - May 2010 

Morningstar Core Morningstar Blend Russell 

Large Mid Small Large Mid Small Large Mid Small 

Geomean 0.29% 0.51% 0.64% 0.19% 0.47% 0.47% 0.27% 0.56% 0.38% 

Std Dev 4.53% 5.35% 5.96% 4.76% 5.39% 6.11% 4.83% 5.37% 6.19% 

Source: Morningstar Direct 

Figure 3 

Correlations 


Large Cap Mid Cap Small Cap 

MCore MBlend R1000 MCore MBlend RMid MCore MBlend R2000 

MCore 1.00 0.94 0.93 

MBlend 0.94 1.00 0.99 

R1000 0.94 0.99 1.00 

MCore 1.00 0.95 0.92 

MBlend 0.95 1.00 0.99 

RMid 0.93 0.99 1.00 

MCore 1.00 0.98 0.93 

MBlend 0.98 1.00 0.98 

R2000 0.94 0.98 1.00 

Figure 4 

Monthly Returns Test: Morningstar Core vs. Morningstar Blend July 1997 - May 2010 

Raw Return Difference 

Replicating Portfolio Alpha Difference 

Large Mid Small Large Mid Small 

Geomean 0.08% 0.02% 0.15% 0.04% 0.00% 0.11% 

Std Dev 1.60% 1.70% 1.30% 1.34% 1.24% 0.98% 

t-stat 0.62 0.13 1.44 0.36 -0.05 1.42 


38 


strategies. Past research by Blanchett [2010] has noted that 

indexes have varying factor exposures that have a significant 

impact on performance. For the second test, the returns of 

each index are compared against a market-factor-adjusted 

portfolio, and then to each other. The market-factor-adjusted 

portfolio was determined based on a single four-factor (i.e., 

Carhart) regression over the entire period of monthly returns. 

This approach removes any potential tilts an index may have 

that would skew its raw performance (e.g., it has a Small tilt 

and/or Value tilt). All data for the beta factors, as well as the 

risk-free rate, was obtained from Kenneth French’s website. 

For the four-factor regression, the excess return of the 

index (which is defined as the return of the index for the 

month minus the risk-free rate for the month) is regressed 

against a Market Beta factor (defined as the return on the 

market minus the risk-free rate); a Value factor (or HML, 

defined as the return on Value stocks minus the return on 

Growth stocks); a Size factor (or SMB, defined as the return 

on Small stocks minus the return on Big stocks); and a 

Momentum factor (based on the six value-weight portfolios 

formed on size and prior two- to 12-month returns, the average 

return on the two high prior-return portfolios minus the 

average return on the two low prior-return portfolios). The 

four-factor regression equation is: 

R index 

– R f 

= A index 

+ B index 

(R market 

– R f 

) + B SMB 

(SMB) + 

B HML 

(HML) + B MOM 

(Momentum) + E asset 

Where R index 

is the return on the index, R f 

is the risk-free rate, 

A index 

is the alpha of the index, B index 

is the index’s beta with 

respect to the market, R market 

is the return of the market, B SMB 

is 

the index’s beta with respect to the “Large” factor (SMB), B HML 

is the index’s beta with respect to the “Value” factor (HML), 

B MOM 

is the index’s beta with respect to the “Momentum” factor 

(MOM) and E asset 

is the error term. For those readers not 

familiar with the four-factor regression approach, see Fama 

and French [1993] and Carhart [1997]. 

Cremers, Petajisto and Zitzewitz [2008] have noted that the 

standard Fama-French (three-factor) and Carhart (four-factor) 

regression models can produce statistically significant nonzero 

alphas for passive indexes primarily from the disproportionate 

weight the Fama-French factors place on Small Value stocks 

(which have performed well). While Cremers et al. introduce 

regression factors that outperform standard models in their 

paper, the traditional four-factor estimates are used for this 

research, due to their widespread use and acceptance. 

The regression factors for the nine test indexes are 

included in Appendix I. Note that only the Morningstar Core 

indexes had positive intercepts for the each of the three 

indexes (i.e., positive monthly alpha). Morningstar Blend 

had negative intercepts for Large Cap and Small Cap, while 

Russell had a negative intercept for Small Cap that was quite 

large in absolute terms, of -0.24 percent (with a t-statistic of 

-2.81). The R² values are higher for Morningstar Blend and 

Russell than for Morningstar Core. This should not be surprising 

given the factors are constructed using broad marketcap 

indexes (most similar to the Wilshire 5000) that are more 

similar to a Blend approach than a Core approach. 

Figure 5 

Morningstar Large Core Market-Factor-Adjusted Rolling 

Annual Outperformance Vs. Morningstar Large Blend 

Morningstar Core Rolling Risk-Adjusted 

Alpha vs. Morningstar Blend 

10% 

8% 

6% 

4% 

2% 

0% 

-2% 

-4% 

-6% 

-8% 

June 

‘98 


Feb 

‘01 

Results 

The monthly geometric average performance results for 

the test indexes are included in Figure 2, and the correlations 

across capitalization groups are included in Figure 3. 

The Morningstar Core indexes tended to have higher average 

returns than the respective Morningstar Blend and Russell 

indexes over the test period, as well as lower risk, which 

is defined as the standard deviation of monthly returns. 

Not surprisingly, the Morningstar Core indexes were more 

similar to the Morningstar Blend indexes (versus the Russell 

indexes), given their similar construction methodology. Also, 

not surprisingly, the Morningstar Blend indexes were more 

similar to the Russell indexes (versus the Morningstar Core 

indexes), since both represent “Blend” styles of investing. 

Figure 4 contains information regarding the statistical 

significance of the differences in the monthly returns 

for the Morningstar Core indexes versus the Morningstar 

Blend indexes on both a raw return basis and on a marketfactor-adjusted 

basis. The Morningstar Core indexes outperformed 

each of the three Blend indexes on a raw return 

basis, although none of the t-statistics were statistically 

significant. The outperformance of the Morningstar Core 

indexes over the Morningstar Blend indexes is less on a 

market-factor-adjusted basis because the indexes have 

varying “tilts” that have been corrected for. For example, 

the Morningstar Large Core Index is slightly more valueoriented 

than the Morningstar Large Blend Index, which 

contributes positively to its raw return outperformance. 

However, although the Morningstar Core outperformed 

Morningstar Blend on a factor-adjusted basis for all three 

capitalization groups, the outperformance wasn’t significant 

at the 5 percent level. 

Figure 5 shows the Morningstar Large Core market-factoradjusted 

rolling annual performance versus Morningstar 

Large Blend for the entire test period. Note that while the 

aggregate average is positive, there are significant variations 

during the test period. 

Figure 6 contains information regarding the statistical 

continued on page 49 

Nov 

‘03 

Aug 

‘06 

May 

‘09 


Benchmarking 

Microfinance Equity Investments 

Indexes for a developing asset class 

By Peter Wall 

40 


Microfinance, generally understood to mean the provision 

of financial services (particularly microloans) 

to poor people in developing economies, is increasingly 

attracting international institutional investor attention 

as an alternative asset investment opportunity, particularly 

as a socially responsible and financially attractive way to 

promote human well-being. The California State Teachers’ 

Retirement System’s (CalSTRS) proposal to include microfinance 

as an eligible asset in its alternative assets allocation 

is just one recent example of this trend. 1 

Yet there has been no consistent measure of performance 

of equity invested in the providers of microfinance. This article 

outlines the growing need for such measures and the approach 

used to create the first indexes for this new asset category—the 

WSAS Microfinance Institutions Shareholder Value Indexes. 

The Evolution Of Microfinance Institutions 2 

Specialized financial institutions that provide microfinance 

services, generically called ”microfinance institutions” 

(MFIs), arose from humanitarian efforts to meet the financial 

needs—principally the credit needs—of very poor people in 

developing economies. Grameen Bank in Bangladesh is one 

widely recognized name in this regard. 

MFIs take many possible legal forms, including nonprofit, 

nongovernmental organizations (NGOs); member-owned 

cooperatives/credit unions; nonbank financial institutions; 

and licensed, deposit-taking banks, with the latter two forms 

most commonly being shareholder-owned. 

Experience has shown that MFIs, to be truly effective in 

the long term in meeting the needs of their communities, 

need to be financially self-sustaining; that is, they need to 

produce a financial surplus to their operating and funding 

costs. And to truly scale up operations, they need to tap commercial 

sources of funding; in particular, equity finance. 

Equity Investment In MFIs 3 

While there were several far-sighted institutional equity 

investors in MFIs before 1995, the period since 2004 has 

seen the start of a boom of equity investment in MFIs that 

shows signs of only accelerating. 

Investments in specialized funds and investment vehicles 

are the principal channels for this. A survey of the microfinance 

funds industry by the MFI rating and information 

services company MicroRate, using 2008 data for 68 microfinance 

investment vehicles (MIVs) 4 —which invest the pooled 

assets of multiple investors with MFIs—found that such MIVs 

had about $490 million in equity investments, compared with 

about $78 million in equity from specialized MIVs in 2005. 

The Consultative Group to Assist the Poor (CGAP), using 

different definitions of MIVs/investors (including development 

finance institutions not covered by MicroRate), found 

about $1.5 billion invested by the end of 2008. This compares 

with CGAP’s findings of $370 million at the end of 2006. 

There are a number of reasons for this trend, including: 

VËË0jË ajÄÍÁ?ÍjaË ?aË jaÖÁ~Ë ~aË ÁjÍÖÁË Ë jÖÍßË 

(ROE) performance of high-profile MFIs—top-quartile performers 

turning in annual ROE of around 20 percent; 5 

VËË0jË ÜjÁßË ¬ÄÍÜjË ¬ÖMWÍßË ÄÖÁÁÖa~Ë WÁw?WjË 

and “Bottom of the Pyramid” business opportunities 

in developing economies, such as the UN’s Year of 

Microcredit (2005), the awarding of the Nobel Peace 

Prize to Dr. Muhammad Yunus and Grameen Bank 

(2006), and the favorable feature articles on MFI results 

in Forbes magazine (2007); 6 

VËË0jËÄÖWWjÄÄËwËÍ?Ë¬ÖMWËÄÍWËwwjÁ~ÄËMßË ÄËÄÖWË 

as Compartamos Banco and Financiera Independencia 

(Mexico, 2007); and 

VËË0jË~ÁÝ~ËjÖÍßËW?¬Í?ËjjaÄËwË ÄË®?ÄËjÄÍ?MÄjaË 

ones grow, new ones are launched, and nonprofits are 

“transformed” into shareholder types), resulting in 

larger investment opportunities. 

Further, studies have indicated that the recent financial 

results of MFIs’ operations tend to be largely insulated from 

domestic economic downturns. Moreover, their financial 

results have been largely uncorrelated to traditional asset 

classes, suggesting MFI equity investments can help diversify 

the sources of risk and return for broad-based portfolios. 7 

The microfinance-specific trends have been supported by 

broader trends in the capital markets. Microfinance opportunities 

exist as a bridge of two traditional asset classes— 

emerging markets and private equity—and both of those 

market segments have been performing very well recently. 

In short, it seems equity investors could “do well while 

doing good” by investing in carefully selected MFIs—or 

simply do well financially, if doing good wasn’t an investment 

requirement. Their experiences in emerging markets 

public and private equity make them willing to dip their 

toes in the MFI equity pool. 

Yet the performance of funds—particularly when active 

investment decisions are involved—presents some problems if 

you are looking to use the data as a way to measure an asset category’s 

performance or to make asset allocation decisions. Fund 

performance reflects the impacts of effects like fund manager 

selection risk, “cash drag” and funds closing to new investment. 

Also, with microfinance equity investing in particular, there is 

a very small set of funds with very short operating histories. 

Microfinance equity investment needs a benchmark of the performance 

of the ultimate source of fund performance; in other 

words, indexes of MFI equity performance. 

Benchmarking MFIs’ 

‘Shareholder Value’ Performance 

The Wall’s Street Advisor Services MFI Shareholder Value 

Indexes (WSAS MFI SVIX) are the first sample-consistent 

measures of changes over time in the value of shareholders’ 

investments in the equity of a broad sample of microfinance 

institutions. No other such measures exist. 

The WSAS MFI SVIX consist of a series of annual “Vintage 

Indexes” that measure changes to shareholders’ value by 

year of inception of their investment in a set of MFIs), and a 

Composite Index that blends investment results across the 

annual Vintage Indexes. 

The MFIs selected for inclusion in the indexes are 

screened for investor equity investment activity, financial 

transparency and geographic spread, all characteristics of 

interest to investors. 


41

Figure 1 

Illustration: Determining Shareholder Value (SV) - Results For One MFI’s Investor Vintages 2004 & 2005 

Step 1: Assemble basic MFI financial data 

Items End 2004 

Transactions 

during 2005 

End 2005 

Notes on Items 

Capital increase from New Investor in 

Total Shareholder Capital $1,000 $2,000 $3,000 2005 @ 2x 2004 book value = creation 

of Investor Vintage 2005 

Shares in issue 100 100 200 

+ Net Income in 2005 $200 $200 

= Total Book Value Equity 2005 $3,200 

+ Cash dividend pd. from 2004 $100 $100 

= Total Shareholder Value $1,000 $3,300 

Step 2: Apportion Shareholder Value among Investor Vintages (IVs) 

IV 2004’s Book Value $1,000 $1,600 

IV 2004 diluted to 50% ownership 

via capital increase 

Shares owned 100 100 

+ Cash dividend received $50.00 Assumes half of annual cash dividend 

IV 2004’s SV at end-2005 $1,000.00 $1,650.00 

Increase due to new capital, earnings, 

& cash dividend 

Change in IV 2004’s SV in 2005 $650.00 

IV 2005’s Shareholder Value 

Shares owned Expansion 0 100 100 

IV 2005 buys 50% of MFI via capital 

increase 

Capital paid-in $2,000.00 

Total Cash dividend received $50 Assumes half of annual cash dividend 

IV 2005’s SV end-2005 $1,650 

= 50% share of MFI 2005 Book Value + 

cash dividend received 

Step 3: Calculate Changes in Investor Vintages’ SV & Composite Results - 2005 

IV 2004 65.0% 

$1,000 in capital produced $1,650 

Shareholder Value by end-2005 

IV 2005 -17.5% 




Composite Result 10.0% 


Source: Wall’s Street Advisor Services 

The MFI Shareholder Value Indexes 

The basic steps involved in the construction of the 

WSAS MFI Shareholder Value Indexes are measuring “MFI 

Shareholder Value,” selecting MFIs for the indexes’ coverage, 

grouping MFIs’ shareholders’ investments into “Investor 

Vintages” by the period in which their investments are first 

made, and measuring changes in Shareholder Value among 

the Investor Vintages to generate index levels. 

‘MFI Shareholder Value’ Terms Defined 

WSAS uses “Shareholder Value” in the classic sense, as a 

measure of financial value. It considers the total accumulation 

of value (or loss) an investment generates for investors. 

For index purposes, “Shareholder Value” can be summarized 

as the cumulative result of the cash flows that equity 

investors in an MFI experience in a period, with book value 

owned at the end of any particular period as the “terminal 

value” for the period. 

In the context of the principally private equity investing 

approach used for MFI investment, equity investors’ outflows 

consist of initial negotiated buy-in prices, and subscriptions 

to capital increases—typically rights issues for current shareholders 

or capital expansions, but also negotiated purchases 

from other current shareholders representing shifts in ownership 

between Investor Vintages. 

Equity investors’ inflows are determined by: 

42 


Figure 2 

Eligible MFI Investor Vintages, By Year (Where 0 = The Base Investor Vintage For The Year*) 

MFI Selection Year MFI SVIX Index 2004 2005 2006 2007 2008 2009 

2005 2005 

0 1 2 3 4 5 

2006 2006 

0 1 2 3 4 

2007 2007 

0 1 2 3 

2008 2008 

0 1 2 

2009 2009 

0 1 

2010 2010 

0 

Composite 

All Investor Vintages, in order of inclusion 

How to read this table: The Investor Vintages formed during a year belong to the MFI SVIX annual index in the period they are created, 

no matter the MFI’s Selection Year. For instance, the MFI SVIX 2009 (light blue) contains the 2009 results of all Investor Vintages formed in 

2008, from all MFIs selected for 2009 and all prior periods (indicated in the 2008 light blue column). 


*Not all MFIs will add a new Investor Vintage every year, so Investor Vintage numbers used here are for illustrative purposes only. 

VË?ÄËÁjWjÜjaË?ÄËW?ÄËaÜajaÄÇ 

VËËWÁj?ÄjÄË®ÄÄjÄ¯ËËÍjÁËÄ?ÁjËwËÍjËW¬?ß¾ÄËMËÜ?ÖjË 

?ÁÄ~ËwÁËjÍËWjË®jÍËÄÄjÄ¯ËwÁË Ë¬jÁ?ÍÄÇ 

VËËWÁj?ÄjÄË®ÄÄjÄ¯ËËÍjËÜ?ÖjËwËÍjÁËÄ?ÁjËwËÍjËW- 

¬?ß¾ÄËMËÜ?ÖjË?ÁÄ~ËwÁËaÖÍËwËÍjÁËÝjÁ- 

Ä¬Ë wÁË jÝË ÄÄÖjÄË wË Ä?ÁjÄË ®wÁ?ÍË Íß¬W?ßË 

jMjaajaËË?ÖaÍjaËw?W?ËÄÍ?ÍjjÍÄ¯ÇË?a 

VËË+ÁWjjaÄË ?ÁÄ~Ë wÁË Ä?jÄË wË ÍjÁË jÖÍßË ¬ÄÍÄË 

®ÖÄÖ?ßË j~Í?ÍjaË ?aË wÁjÖjÍßË ÍË ¬ÖMWßË aÄ- 

WÄja¯Ë ÝjË Ë jÝË Ä?ÁjÄË ?ÁjË ÄÄÖja±Ë .ÖWË ÍÁ?Ä?W- 

ÍÄË?ÁjËwÍjËW?jaË»ÄjWa?ÁßËÄ?jÄ±¼ 

0Ë ÍjÄjË jjjÍÄË wË Ä?ÁjajÁË Ü?Öj^Ë jË ÖÄÍË ?ÄË 

?aaËÍjË¬?WÍËwËjÞW?~jËÁ?ÍjËÜjjÍÄËËÜ?Öj^ËjÄ¬j- 

W?ßËÝjËWM~ËwÁ?ÍË?WÁÄÄËMÁajÁÄ±Ë 

ÁËajÞË¬ÖÁ¬ÄjÄ^Ë8..ËajÄËÍËWÖaj^ËË?ßËÝ?ß^Ë 

ÍjËw?W?ËÜ?ÖjÄËÍ?ÍË?ßËÜjÄÍÁÄËËWÁw?WjË 

ÄjjË ÍË ?Þãj^Ë ÄÖWË ?ÄË ÍjË Ö?Í?Á?Ë WÍÁMÖÍÄË 

?Ë Ë?ßË?jËË¬ÁË¬j¬j¾ÄËÜjÄË?ÄË?ËÁjÄÖÍËwËÍjÁË 

ÄjÁÜWjÄ±Ë0jË?ÍÍjÁËÄË?Ë¬ÁÍ?ÍËWÄajÁ?ÍËwÁË?ßË 

ÜjÄÍÁÄË Ë WÁw?Wj^Ë ?aË ?ßË j?a~Ë ¬Á?WÍÍjÁÄË 

?Á~ÖjË Í?ÍË Ä¾Ë w?W?Ë ÁjÄÖÍÄË ÄÖaË MjË WÄÍÁ?jaË ?ÄË 

ÖWË?ÄË¬ÄÄMjÊ¬Á?WÍW?Mj^ËËw?ÜÁËwËÍjË¬ÁËWjÍÄ± 8 

ÖÍË?ÍË¬ÁjÄjÍ^Ë?ÍËj?ÄÍ^ËÍjÁjË?ÁjËwjÝËWßË?WWj¬ÍjaË 

j?ÄÖÁjÄËwË Ä¾ËÄW?Ë¬jÁwÁ?WjË?aËÁj?ÍjaËÍjÄjÁjÄË 

a?Í?ËÄÖwwWjÍËÍËWÖajËÍjÄjËËajÞËW?WÖ?ÍÄ± Ë #WjË 

ÄÖWË wÁ?ÍË ÄË Ë ¬?Wj^Ë 8..Ë ÝÖaË WÄajÁË ÝË ÍË 

~ÍËWÁ¬Á?ÍjËÍjÄjËÍËÍjËajÞjÄ¾ËÁjÄÖÍÄ± 

Grouping MFIs’ Shareholders 

Into ’Investor Vintages’ 

ÁË¬ÖÁ¬ÄjÄËwËj?ÄÖÁ~ËW?~jÄËË».?ÁjajÁË7?Öj^¼Ë 

ÍjË ajÞË jÍa~ßË ÁjW~ãjÄË Í?ÍË Ä?ÁjajÁÄË Ë ?Ë 

ËÝËjÞ¬jÁjWjËawwjÁjÍËÁjÄÖÍÄËaj¬ja~ËËÍjËÍjÁÄË 

?aËÍ~ËwËÍjÁËjÍÁßË?aËjÞÍÄËÍËÍjËW?¬Í?ËwËÍjË ^Ë 

MÖÍËÍ?ÍËÄÖWËwÁ?ÍËÄ¾ÍËWßË?Ü??MjËjÞWj¬ÍË?ÄË 

?~~Áj~?ÍjaËË Ëw?W?ËÄÍ?ÍjjÍÄ±Ë0jÁjwÁj^ËÄ?ÁjajÁÄ¾ËÜjÄÍjÍÄË?ÁjË~ÁÖ¬jaËÍË?Ë»ÜjÄÍÁË7Í?~j¼ËMßËÍjË 

ßj?ÁËÝjËÍjßËwÁÄÍËÜjÄÍË?ÄË?Ë ¾ÄËÄ?ÁjajÁ±Ë 

.Ë wÁË ÄÍ?Wj^Ë ?Ë Ä?ÁjajÁÄË ¬ÁjÄjÍË Ë ÍjË M?ÄjË ßj?ÁË 

ËÝWË?Ë ËjÍjÁÄËÍjËajÞË?ÁjËajjjaËjMjÁÄËwËÍjË 

¾ÄË.?ÁjajÁËÜjÄÍÁË7Í?~jËwÁËÍ?ÍËßj?ÁË®ÄßMËsË. 0¯± 

ÜjÄÍÁÄËÍ?ÍËjÍjÁËajÞË ÄËÄÖMÄjÖjÍßË?ÁjË~ÁÖ¬jaË 

Í~jÍjÁËËÍjË¬jÁaËÍjßËjÍjÁË?aË?ÁjËajÄ~?ÍjaË. 1^Ë. 2^Ë 

. 3^ËjÍW±^ËÍË. Ë®. ËÄ~wß~ËÍjËÍËÜjÄÍÁË7Í?~jËË?Ë 

¾ÄËajÞËwj¯± 

ÍËÄË¬ÁÍ?ÍËÍËÁjjMjÁËÍ?ÍËÍËjÜjÁßË Ë?ÄËjÝË 

ÜjÄÍÁÄË jÜjÁßË ßj?Á^Ë ?aË ?ÄË ?Ë ÁjÄÖÍ^Ë ÄjË ÄË ?ßË Ä¬Ë 

ßj?ÁÄË MjÍÝjjË . 0 Ë ?aË . 1 Ë wÁË ÄÍ?Wj±Ë Ä^Ë ?Ë ÜjÄÍÁË 

7Í?~jËW?Ë~ËÖÍËwËjÞÄÍjWjqj±~±^Ë. 0 Ë?ßËÄjËÖÍËW- 

¬jÍjßËÍË?ËjÝËÄjÍËwËÄ?ÁjajÁÄËËÄÖMÄjÖjÍËßj?ÁÄ±ËÁË 

jÞ?¬j^Ë?ËÄ~jË¬ÁÜ?ÍjËjÖÍßËwÖaË?ßËMÖßËÍËÍjË Ë 

ËÔååÉ^ËwÁ~ËÍ?ÍË ¾ÄËÜjÄÍÁË7Í?~jËÔååÉ±ËwËÍ?ÍË+Ë 

wÖaË ÝjÁjË ÍË W¬jÍjßË ÄjË ÖÍË ÍÄË ¬ÄÍË Ë Ôååo^Ë ÍjË 

¾ÄË ÜjÄÍÁË 7Í?~jË ÔååÉË ÝÖaË Wj?ÄjË ÍË jÞÄÍË ?aË Í?ÍË 

ËÝÖaË?ÜjËËwÖÁÍjÁËÁj¬ÁjÄjÍ?ÍËËÍjË8..Ë Ë 

.79ËwÁË7ÔååÉËÄjÁjÄËwÁËÔååËÝ?Áa± 

0jË8..Ë Ë.79ËjÍa~ßË?ÄÄÖjÄËÜjÄÍÁË7Í?~jË 

jMjÁÄË¬?ÁÍW¬?ÍjËËWÁ¬Á?ÍjË?WÍÄË¬Á¬ÁÍ?ÍjßËÍË 

ÍjÁËÝjÁÄ¬ËjÜjË?aËj~MÍjÄ±ËwË8..Ë?ÄËwÁ?ÍË 

ÍjÁÝÄj^ËÍËÝË?ÍÍj¬ÍËÍË?ÜjËÍjËa?Í?ËÁjwjWÍËÍjËw?WÍÄ±Ë 

ÖÁÁjÍß^Ë ÍjË 8..Ë Ë .79Ë ÄjÁjÄË ?ÁjË ajÜj¬jaË 

wÁË ?Ö?Ë ?ÖaÍjaË ÄÍ?ÍjjÍÄË ß^Ë ÄË ÜjÄÍÁË 7Í?~jÄË 

?ÁjË WÄÍÁÖWÍjaË »MßË ßj?Á¼Ë ßÇË ÍjÄjË ajÞjÄË W?Ë ßË MjË 

Ö¬a?ÍjaË WjË ¬jÁË ßj?Á±Ë ÖÍÖÁjË ¬ÁÜjjÍÄË Ë Ë aÄ- 

WÄÖÁj^Ë jÞÍja~Ë ÍË ajÍ?jaË Äj?Ö?Ë ?aÊÁË Ö?ÁÍjÁßË 

¬ÖMWËÄÍ?ÍjjÍÄ^ËÝË?ÝËÍjËÄjÁjÄËÍËMjË¬ÁaÖWjaËÁjË 

wÁjÖjÍßË?aËËwjÁËajÍ?±Ë 

Selecting MFIs For Index Inclusion: 

The MFI Selection Year Concept 

ÄË jÍjaË j?ÁjÁ^Ë ÄË Í?jË ?ßË j~?Ë wÁÄ^Ë ?aË 

?Ë Í?MjË wj?ÍÖÁjË wË ÍjË aÖÄÍÁßË ÄË Í?ÍË jÝË Ä?ÁjajÁ 

M?ÄjaËÄÍÍÖÍÄË?ÁjËMj~ËWÁj?ÍjaË?ËÍjËÍj^ËÁË?ÁjËMj~Ë 

ÍÁ?ÄwÁjaËwÁË!#ÄËÍËÄ?Ája~ËjÍÍjÄ±ËË?aaÍ^Ë 

?ßË ÄË ?ÜjË ~Ë ~ÁÝÍË Á?ÍjÄË ?aË ÁjÖÁjË ?aaÍ?Ë 

jÖÍßËW?¬Í?ËÍË¬ÁÖajÍ?ßËÄÖÄÍ?ËÄÖWË~ÁÝÍ±Ë 


43

Figure 3 

Establishing MFI SVIX Annual Vintage Results 


Note: For illustrative purposes only - not actual data or results. 

2004 2005 2006 2007 2008 

Vintage 2005 MFI Shareholder Values - Base Period 2004 

MFI 1 S 0 V $ $100.0 $100.0 $100.0 $100.0 $100.0 

MFI 2 S 0 V $ $200.0 $240.0 $288.0 $345.6 $414.7 

MFI 3 S 0 V $ $300.0 $300.0 $300.0 $300.0 $300.0 

V2005 SV $ $600.0 $640.0 $688.0 $745.6 $814.7 

MFI SVIX 2005 - Index levels 100.00 106.67 114.67 124.27 135.79 

MFI SVIX 2005 - Index’s annual results 0.0% 6.7% 7.5% 8.4% 9.3% 

Vintage 2006 MFI Shareholder Values - Base Period 2005 

MFI 1 S 1 V $ $100.0 $100.0 $100.0 $100.0 

MFI 2 S 1 V $ $100.0 $120.0 $144.0 $172.8 

MFI 3 S 1 V $ $200.0 $300.0 $300.0 $300.0 

MFI 4 S 0 V $ $100.0 $100.0 $100.0 $100.0 

MFI 5 S 0 V $ $200.0 $240.0 $288.0 $345.6 

MFI 6 S 0 V $ $300.0 $300.0 $300.0 $300.0 

V2006 SV $ $1,000.0 $1,160.0 $1,232.0 $1,318.4 

MFI SVIX 2006 - Index levels 100.00 116.00 123.20 131.84 

MFI SVIX 2006 - Index’s annual results 0.0% 16.0% 6.2% 7.0% 

So, for the WSAS MFI SVIX to be as broad and representative 

as possible, MFIs are considered for inclusion in the indexes on 

an annual review basis. That is, every year a list of MFIs (those 

reporting to MIX Market, a firm that keeps track of MFI-related 

data) is reviewed for eligibility, and MFIs are added to coverage. 

For purposes of tracking when MFIs first become members of 

the index, they are grouped into an “MFI Selection Year.” 

For consideration for inclusion in the WSAS MFI Indexes, 

MFIs must have the following qualities: 

VËË0jßË ÖÄÍË MjË Ä?Ája~Íß¬jË W¬?jÄqM?Ä^Ë 

! ÄËÁËÁÖÁ?ËM?ÄqÄÍjaËËÍjË 9Ë ?ÁjÍÇË 

VËË0jßË ÖÄÍË ?ÜjË ?Ë 9Ë ?ÁjÍË Ö?ÍßwaÄWÄÖÁjË 

ajÄ~?ÍËwË»|Ë?aÄ¼ËÁËMjÍÍjÁqÍ?ÍËÄ^ËÍjË Ë 

must have issued audited financial statements for the 

¬jÁaËÖajÁËÁjÜjÝÇ 10 

VËË0jßË ÄÖaË ?ÜjË ÍÍ?Ë jÖÍßË W?¬Í?Ë Ë 2±.±Ë a?ÁË 

terms such that 20 percent of that amount is equal to or 

greater than the median amount of equity investments 

made in the review year, as recorded in WSAS’ database 

of MFI equity investments 11 (for the MFI SVIX 2005 and 

ÔååÉËajÞjÄ^ËÍÄË?ÖÍËÄËg|yå^ååå¯Ç 

VËËÍË ÍjË ÍjË wË ÁjÜjÝËwÁËWÖÄ^ËÍjË ËÄÖaË 

not be 95 percent or more owned by one party that 

has expressed or demonstrated its intention to retain 

Í?ÍË jÜjË wË WÍÁÇË ?aË ?ÄÄÖ~Ë ÍjË ¬ÁjWja~Ë 

qualifications are met, 

VËË+ÁjwjÁjWjËwÁË ËWÖÄÄËÝËMjË?ajËwÁË¬ÖÁ¬ÄjÄË 

of increasing the number of countries covered, maximizing 

the number of microcredit clients served, and 

representing the share-denominated MFI universe. 

Starting with a July 2009 qualifying universe of 378 MFIs 

from MIX Market’s database (with total 2005 equity capital 

of $1.92 billion), WSAS “screened in” 64 MFIs. 

2¬ËwÖÁÍjÁËajÍ?jaËÁjÜjÝËwËÍjÄjË Ä^ËÏåË ÄËÖ?fied 

for the MFI SVIX 2005 and 21 for the MFI SVIX 2006. 

Their combined end-2005 equity capital represented about 

40 percent of the 378 MFI universe, making the MFI SVIX 

series quite representative. 

MFI Investor Vintages And Their Inclusions 

In The MFI SVIX Composite Index 

MFIs that are constituents in one MFI Selection Year may 

have shareholder value changes included in subsequent 

years’ MFI SVIX, if they have new Investor Vintages formed 

in subsequent periods. 

For instance, the MFI SVIX 2005 has a base period of 

2004. The only Investor Vintage contained in the Selection 

Year 2005 Index, throughout its history, will be that present 

at the end of 2004. An Investor Vintage formed in 2005 in 

MFIs from Selection Year 2005 will have its results added to 

the Investor Vintage 2006, and carry through in that index 

throughout its history. In other words, portions of an MFI’s 

results may be represented in several annual indexes, as 

Investor Vintages are formed. 

The MFI SVIX Composite includes all Investor Vintages’ 

results, so all MFIs and their related Investor Vintages are 

carried in the SVIX Composite from when they become 

represented in any Vintage Index. The MFI SVIX Composite 

Index therefore represents the dollar-weighted results of all 

investors in all the selected MFIs. 

44 


Figure 4 

WSAS MFI SVIX Composite Index: Calculation Elements Illustrated 


Note: For illustrative purposes only - not actual data or results. 

2004 2005 2006 2007 2008 

A) V2005 SV $ $600.0 $640.0 $688.0 $745.6 $814.7 

% SVIX Composite 100.0% 39.0% 37.2% 37.7% 38.2% 

B) V2006 SV $ $1,000.0 $1,160.0 $1,232.0 $1,318.4 

% SVIX Composite 61.0% 62.8% 62.3% 61.8% 

C) MFI SVIX Composite SV$ (gross) $600.0 $1,640.0 $1,848.0 $1,977.6 $2,133.1 

less: period IV SV added $600.0 $1,000.0 

= MFI SVIX Composite SV for period – $640.0 $1,848.0 $1,977.6 $2,133.1 

Annual Result % 0.0% 6.7% 12.7% 7.0% 7.9% 

MFI SVIX Composite Index 100.00 106.67 120.20 128.62 138.74 

Deletions From Index Coverage 

MFIs may be deleted from index coverage for any of the 

following reasons: 

VËË0jË Ë?ÄË~jËÖÍËwËjÞÄÍjWjËÍÁÖ~ËjÁ~jÁËÁË 

M?ÁÖ¬ÍWßÇ 

VËË0jË ËÄËajjjaËÍË?ÜjËWj?ÄjaËMj~Ë?ËWÁw?WjË 

ÄÍÍÖÍËË¬Á?WÍWjÇ 12 or 

VËË0jË Ë?ÄËÄjÝËW?~jaËÍÄËWÁ¬Á?ÍjËwÁËwÁË 

a shareholder-based entity. 

Company And Country Exposures 

0jË 8..Ë Ë .79Ë ajÞjÄË ÁjW~ãjË Í?ÍË ÜjÄÍÁÄË w?WjË 

WÄÍÁ?ÍÄËÍ?ÍËÄÖaËMjËÁjwjWÍjaËË¬jÁ?Í?ËMjW?ÁÄ^Ë 

MÖÍËÍ?ÍËÍjßË?ÄËÝ?ÍËÄÍÁ?Íj~WËMjW?ÁÄËÍ?ÍËWjË?ÄËWÄjË 

?ÄË¬ÄÄMjËÍËÁj¬ÁjÄjÍ~ËÍjË»?ÁjÍË¬¬ÁÍÖÍß±¼Ë 

ÁË¬jÁ?Í?ËMjW?ÁÄ^ËÜjÄÍÁÄËÍß¬W?ßËÖÄÍËW- 

ÄajÁË ÍjÁË ÁjÄ¬jWÍÜjË ¬ÁÖajÍ?Ë ®ÁË Áj~Ö?ÍÁß¯Ë aÜjÁÄwW?- 

ÍËjjaÄÊÁjÖÁjjÍÄ^ËÄÖWË?ÄËaÜaÖ?ËW¬?ßËjÞ¬- 

ÄÖÁjË ÍÄË ?aË WÖÍÁßË jÞ¬ÄÖÁjË ÍÄ±Ë #ÍjÁË ¬jÁ?Í?Ë 

ÁjÖÁjjÍÄË?ßËWÖajË?Þã~ËÄ¬Áj?aËwËÜjÄÍjÍÄË 

?~ËÍ?Á~jÍËWÖÍÁjÄËÁËÁj~Ä±Ë 

ËÄÍÁ?Íj~WËMjW?Á^ËÝjÜjÁ^ËÁj¬ÁjÄjÍÄËÍjË?W?ÍË 

?aË¬jÁwÁ?WjËwËÍjËW¬jÍjË»¬¬ÁÍÖÍßËÄjÍ±¼ËwËjËÁË 

ÍÝË ÄËÝjÁjËÜjÁßË?Á~j^ËÁËÄÍËwËÍjËj~MjË ËjÖÍßË 

ÄËËa?ËÁË+jÁÖ^ËwÁËjÞ?¬j^ËÄÖWË?ËMjW?ÁËÝÖaËÍÍË 

ÍÝ?ÁaËÄÖWËWWjÍÁ?ÍjaËjÞ¬ÄÖÁjÄ±Ë 

0jË8..Ë Ë.79¾ÄË Ë7Í?~jËÄjjWÍË~ÖajjÄË 

?ÁjË ajÄ~jaË ÍË M??WjË ÍjË ¬jÁ?Í?Ë ?aË ÄÍÁ?Íj~WË 

MjW?ÁËjjaÄ^ËMÖÍËÝÍË?ËW?ÍËÍËÍjËÄÍÁ?Íj~WË 

benchmark category. 

ÄËÍjËajÞËÄjÁjÄË~?ÄË¬ÖMWËjÞ¬ÄÖÁj^ËÖÄjÁËwjjaM?WË 

ÝËMjËw?WÍÁjaËÍËÍjË ËÄjjWÍË~ÖajjÄ±ËÍËÄË?ÄË 

¬ÄÄMjËÍ?ÍËÍjËÄjÁjÄËW?ËMjËjÞ¬?ajaËÍË¬ÁÜajË?ÍjÁ?- 

ÍÜjËj?ÄÖÁjÄ^ËWÖa~ËÄÖWËÍ~ÄË?ÄËÁj~?ËÁËWÖÍÁßË 

MjW?ÁÄ^Ë?aÊÁË»M??WjaËÄWÁjW?Áa¼ÄWÁjjjaË Ä±Ë 

Methodology For Compiling The Index Series 

8jË wÁÄÍË WÄajÁ~Ë ÍjË WÁj?ÍË wË ÍjË ajÞË ÄjÁjÄ^Ë 

8..ËÄÍ?ÁÍjaËwÁËÍÝËM?ÄWË?ÄÄÖ¬ÍÄ] 

VË0?ÍËajÞËÁjÄÖÍÄËÄÖaËMjËa?ÁÝj~ÍjaÇË?a 

VËË0?ÍË ÍjÁjË Ý?Ä¾ÍË ?Ë ÄÖwwWjÍßË ~j~Á?¬W?ßË aÜjÁÄwjaË?aËMÁ?aË»WÁÍW?Ë?ÄÄ¼ËwË 

ËÄ?ÁjajÁËjÖÍßË 

ÖÍË?ÁÖaËÔååy±Ë 

The WSAS MFI SVIX Annual Vintage Series 

0jË8..Ë Ë.79Ë?Ö?Ë7Í?~jËÁjÄÖÍÄËÁj¬ÁjÄjÍË 

ÍjËÜ?ÖjÝj~ÍjaËW?~jÄËËÄ?ÁjajÁËÜ?ÖjËwÁËÍjË 

ÜjÄÍÁË7Í?~jËËj?WË Ë¬ÁjÄjÍËwÁËÍjËM?ÄjËÜ?ÖjË 

wËÍjË Ë.jjWÍË:j?ÁË®wÁËjÝßËÄjjWÍjaË Ä¯^Ë?aË 

wÁËÍjËÜjÄÍÁË7Í?~jÄËwÁjaËj?WËßj?ÁËË ÄËwÁË 

¬ÁjÜÖÄË.jjWÍË:j?ÁÄ± 

0jË8..Ë Ë.79ËÄjÁjÄËÄËMÖÍËwÁËÍjË.jjWÍË:j?ÁË 

?aËÜjÄÍÁË7Í?~jÁjÄÖÍËjÜj^ËÖ¬±Ë0?ÍËÄ]Ë 

VËËÁÄÍ^Ë8..ËÄjjWÍÄË ÄËj?WËßj?ÁËËÍjËM?ÄÄËwËÍjË 

~ÖajjÄË?Áj?aßËÖÍja^Ë?a 

VËË0jÁj?wÍjÁ^Ë8..ËajÜj¬ÄËwÁ?ÍËËj?WË ¾ÄËÜjÄÍÁË 

7Í?~jË?aËÍÄË¬jÁwÁ?WjËÁjÄÖÍÄ^Ë¬jÁaËMßË¬jÁa± 

.^Ë wÁË ÄÍ?Wj^Ë ÍjË 8..Ë Ë .79Ë ÔååyË ®.79Ôååy¯^Ë 

ÝÍË?ËM?ÄjË¬jÁaËwËÔåå|^ËÄËÍjËwÁÄÍË?Ö?ËajÞËÄjÁjÄ^Ë 

?aËßË7ÔååyË®Ôååy¾ÄË. å Ë¯ËÁjÄÖÍÄËÝË?wwjWÍËÍjË.79Ôååy± 

0jË ÁjÄÖÍÄË wË ÄÖMÄjÖjÍË ÜjÄÍÁË 7Í?~jÄË Ë ÄË 

ÄjjWÍjaË wÁË .79Ë WÜjÁ?~jË ?ÁjË WÖajaË Ë ÍjË ¬jÁaË ÍjË 

ÜjÄÍÁË7Í?~jÄË?ÁjËjÄÍ?MÄja±Ë 

ÁËÄÍ?Wj^ËÍjËÁjÄÖÍÄËwË?ËÜjÄÍÁË7Í?~jËjÄÍ?M- 

ÄjaË aÖÁ~Ë ÔååyË Ë ?Ë .jjWÍË :j?ÁË ÔååyË Ë ÝÖaË 

wÁËÍjËM?ÄjË¬jÁaËÜ?ÖjËwÁËÍ?ÍË ¾ÄËÁjÄÖÍÄËWÍÁ- 

MÖÍË Ë ÍjË .79ÔååÉ^Ë ?aË ÍjË ¾ÄË ÜjÄÍÁË 7Í?~jË 

jÄÍ?MÄjaË Ë ÔååÈË ÝÖaË wÁË ÍjË M?ÄjË ¬jÁaË Ü?ÖjË 

wÁËÍ?ÍË ¾ÄËWÍÁMÖÍËÍËÍjË.79Ôååo¾ÄËÁjÄÖÍÄË®ÄjjË 

jÞ?¬jËË~ÖÁjËÔ¯±Ë 

~ÖÁjË ÏË ÖÄÍÁ?ÍjÄË ÝË ÍjË ÁjÄÖÍÄË wË ÜjÄÍÁË 7Í?~jÄË 

at the MFI level get carried into the MFI year in which the 

ÜjÄÍÁË7Í?~jÄË?ÁjËwÁja±Ë!ÍjËÍ?ÍË ÄË!Ä±Ë¤^ËÔË?aË 

Ï^ËwÁË Ë.jjWÍËÔååy^Ë?ÜjËÜ?ÖjËÜÜjaËË.79ÔååÉË 

wÁËjÝËÜjÄÍÁË7Í?~jÄË®. 1 7g¯ËwÁjaËËÔååy±Ë 

The WSAS MFI SVIX Composite Series 

0jË8..Ë Ë.79Ë¬ÄÍjËWMjÄËÍjËÁjÄÖÍÄËwË?Ë 


45

Figure 5 

WSAS MFI SVIX Series - Basic Data & Results 

MFI SVIX 2005 


*All values for the years after 2006 will change as new MFIs & annual Investor Vintages are added 

2004 2005 2006 2007 2008 

Index levels, US$ terms, end of period 100.00 145.20 203.47 431.01 408.29 

Number of MFIs included 30 30 30 30 30 

IV ‘05 Shareholder Value, USD mn. $395.08 $573.66 $803.86 $1,702.83 $1,613.08 

IV ‘05 SV, USD Change % 45.2% 40.1% 111.8% -5.3% 

MFI SVIX 2006 

Index levels, US$ terms, end of period 100.00 99.42 148.46 248.28 

Number of MFIs included 29 29 29 29 

IV ‘06 Shareholder Value, USD mn. end-period $204.25 $203.07 $303.23 $507.11 

IV ‘06 SV, USD Change % 0.0% -0.6% 49.3% 67.2% 

MFI SVIX Composite * 

Index levels, US$ terms, end of period 100.00 145.20 187.95 374.44 395.74 

Number of MFIs included 30 30/51 51 51 51 

Combined IVs’ SV, USD mn. end-period $395.08 $777.91 $1,006.93 $2,006.07 $2,120.20 

Combined IVs’ SV USD, Change % 0.0% 45.2% 29.4% 99.2% 5.7% 

Investor Vintages from all MFIs. The simplified illustration in 

Figure 4 uses the data from Figure 3 to show how this works. 

It should be pointed out that the SVIX Composite result for 

2006 in Figure 4 is based on the year 2005 Composite’s SV, 

“adding back” the $1,000 in 2005 SV arising from the addition 

of 2006’s base period SV. 

Note that V2005’s SV $’s share of the Composite’s 

SV at the close of 2005 was 39 percent, meaning the 

2006 results of V2005’s SV provides 39 percent of SVIX 

Composite’s 2006 result. 

Results From The WSAS MFI SVIX Series 

So far, WSAS has compiled complete Investor Vintage data 

through the end of 2008 for 30 MFIs from the MFI Selection 

Year 2005 (whose results comprise the MFI SVIX 2005), and for 

21 MFIs from MFI Selection Year 2006, whose results—together 

with those of the eight 2006 Investor Vintages formed in 

Selection Year 2005 MFIs—form the MFI SVIX 2006. 

The MFI SVIX Composite blends these results, according 

to the changes in their U.S.-dollar-denominated shareholder 

values. Please note that while the MFI SVIX Composite 

results are shown here through the end of 2008, the years 

subsequent to 2006 will be revised when the MFI SVIX 2007 

and 2008 results are available, which should be in the fall of 

2010, and the MFI SVIX 2009 series before the end of 2010. 

Changes To Shareholder Value 

Figure 5 presents the MFI SVIX 2005, 2006 and Composite 

results and related data, through the end of 2008. 

Looking at the annual changes in shareholder value, some 

things quickly become apparent. In general, the results are 

quite positive: MFI SVIX 2005 shows a cumulative four-year 

growth of 308 percent (a 42 percent compound annual 

growth rate), while MFI SVIX 2006 shows a three-year cumulative 

growth of 148 percent (35 percent CAGR), and the MFI 

SVIX Composite has a four-year cumulative growth of 295 

percent (41 percent CAGR). At the same time, the timing of 

entry investments seems to have a big impact on returns, 

and year-to-year changes in shareholder value can be quite 

variable. Regardless, the dollar value of “shareholder value” 

of the indexes is micro by almost any institutional investor’s 

standard, even after the period’s high growth. 

It should also be noted that while most of the MFIs’ 

Investor Vintages show positive returns in the period, a few 

“home run” results bring up the averages, as is typical for 

private equity investment. 

MFI SVIX 2005 Highlights: 

VËË0jË ajÞË WÖajÄË ÏåË ÄË wÁË Ô|Ë WÖÍÁjÄa?Ë 

had four; Bolivia, Mexico and Peru had two each; and 

the other countries one each; 

VËËÔÈË ÄË¬ÁaÖWjaË¬ÄÍÜjË.?ÁjajÁË7?ÖjËË2±.±Ëa?ÁË 

terms over the period, while three produced declines; 

VËËÍË ÍjË Ë jÜj^Ë ÍjË ?ÜjÁ?~jË WÖÖ?ÍÜjË WÁj?ÄjË 

was about 369 percent; leaving out three “home run” 

results, the average cumulative increase was about 151 

percent (26 percent CAGR); 

VËË.¬?a??Ë ®a?¯Ë Ý?ÄË ÍjË MjÄÍË aÜaÖ?Ë ¬jÁwÁjÁ^Ë 

with Investor Vintage 2005 Shareholder Value growing 

at a 155 percent CAGR over the four years; 

VËË+?¬Ö?Ë!jÝËÖj?Ë WÁw?WjË¬ÁaÖWjaËÍjË~Áj?ÍjÄÍË 

decline in IV2005 Shareholder Value, a cumulative loss 

of -70 percent over the four years; and 

VËËË ÍjÁÄË wË Áj~?Ë WWjÍÁ?ÍË wË ÜjÄÍÁË 7Í?~jË 

46 


Figure 6 

Figure 7 

WSAS MFI SVIX - Levels, 2004-2008 

WSAS MFI SVIX - Value Changes % By Year 

450 

400 

350 

300 

250 

200 

150 

100 

50 

2004 2005 2006 2007 2008 

120% 

110% 

100% 

90% 

80% 

70% 

60% 

50% 

40% 

30% 

20% 

10% 

0% 

-10% 

2005 2006 2007 2008 

N SVIX 2005 N SVIX 2006 N SVIX Composite 

N SVIX 2005 N SVIX 2006 N SVIX Composite 


Source: Wall's Street Advisor Services 

2005’s book values at the end of 2008, Latin America/ 

Caribbean held 44 percent; East Europe/Central Asia held 

25 percent; Africa had 19 percent; and Asia 15 percent. 

MFI SVIX 2006 Highlights 

VËËÔË ÄË wÁË ¤Ë WÖÍÁjÄË ÝjÁjË ÜÜjaË ®Ô¤Ë ÄË 

selected for coverage based on 2006 data, and eight 

wÁË ÍjË ÔååyË Ë .jjWÍË MjW?ÖÄjË ÍjßË ?aË jÝË 

ÜjÄÍÁË7Í?~jÄËwÁjaËaÖÁ~ËÔååy¯Ç 

VËËÔÉËwËÍjËÜjÄÍÁË7Í?~jÄË¬ÁaÖWjaË¬ÄÍÜjËÁjÍÖÁÄ^Ë 

ÍÖ~ËÄÞË¬ÁaÖWjaËÍÁjjßj?ÁËWÖÖ?ÍÜjËWÁj?ÄjÄËË 

shareholder values of under 10 percent; 

VËË0jË Ä~j?Á~jÄÍË WÁj?ÄjË Ë 7åÉË Ü?ÖjË Ý?ÄË wÁË 

..Ë WÁw?WjË Ía±Ë ®a?¯q?Ë ÄÍ?~~jÁ~Ë ÍÁjjßj?ÁË 

WÖÖ?ÍÜjË WÁj?ÄjË wË Ô^yÉÉË ¬jÁWjÍË ®¤Ë ¬jÁWjÍË 

-¯±ËWajÍ?ß^Ë..Ë WÁw?WjËÄË¬?~Ë?Ë+#Ë 

Ëa?ËÄjÍjËËÖßËÔå¤åÇ 

VËË.?ÁjajÁË7?ÖjÄËajWÁj?ÄjaËÜjÁËÍjË¬jÁaËwÁË-j¬Ë 

?Ë®jß?¯ËMßËÔÉË¬jÁWjÍ^ËwÁË0?jjÁË WÁw?WjË 

?Ë®+?ÄÍ?¯ËMßËÏÔË¬jÁWjÍ^Ë?aËwÁËÍjËjÝË7åÉËwË 

+!Ë WÁw?WjË®+?¬Ö?Ë!jÝËÖj?¯ËMßËÉÉË¬jÁWjÍÇË 

VËË0jË Ä~j?Á~jÄÍË 7ÔååÉË Ë ÍjË Ë .79Ë ÔååÉË Ý?ÄË 

wÁË?WjÁ?Ëaj¬jajW?Ë® jÞW¯^Ë?ÍËÏåË¬jÁWjÍË 

of the index’s shareholder value at the start of the series 

in 2005, and it produced a cumulative 214 percent 

WÁj?ÄjËË7ÔååÉËÄ?ÁjajÁËÜ?Öj^ËaÖjË?Á~jßËÍËÍjË 

ÄjÖÍË ¬ÁWjjaÄË Áj?ãjaË aÖÁ~Ë Ôååo±Ë 0ÄË ÄjÖÍË 

ÁjÜjÄË jÞW?Ë Áj¬ÁjÄjÍ?ÍË wÁË ÍjË Ë .79Ë 

ÔååÉËwÁËÔååËwÁÝ?ÁaÇË?a 

VËË0ÝËÍjÁË Ë.79ËÔååÉËWÄÍÍÖjÍÄËÄ?ÝËÍjÁË7åÉË 

ÜjÄÍÁÄËÄjËÖÍËaÖÁ~ËÔååoq+jÁÖ¾ÄË ?WËajË0Á?M?Ë 

®Ü?Ë?ËÍÁ?ajËÄjËÍË.WÍ?M?^Ë¬ÁaÖW~Ë?ËÍÁjjßj?ÁË 

WÁj?ÄjË wË Ô¤åË ¬jÁWjÍ¯^Ë ?aË 2~?a?Ë WÁw?WjË 

Ía±Ë®Ü?Ë?ËÍÁ?ajËÄ?jËÍËÖÍßË ?Ëjß?^Ë¬ÁaÖW~Ë?Ë 

ÍÁjjßj?ÁËWÁj?ÄjËwËÏÏyË¬jÁWjÍ¯± 

MFI SVIX Composite Highlights 

0jÄjË ~~ÍÄË ?ÁjË MÁjw^Ë ÄWjË ÍjÄjË Áj¬ÁjÄjÍË ?Ë a?Á 

Ýj~ÍjaËMjaËwËÍjË?Ö?Ë Ë.79ËÄjÁjÄ¾ËÁjÄÖÍÄ±Ë0ÁÖ~ÖÍË 

ÍjË¬jÁa^Ë Ë.79ËÔååyËÁj¬ÁjÄjÍÄË?ÁÖaËÈyË¬jÁWjÍËwËÍjË 

¬ÄÍj¾ÄË .?ÁjajÁË 7?ÖjÇË a?Ë ?aË ÍjË ?Á~jÄÍË ÖMjÁË 

wË Ë ÜjÄÍÁË 7Í?~jÄË Áj¬ÁjÄjÍjaË ®j~Í¯^Ë wÝjaË MßË +jÁÖË 

®ÄjÜj¯^Ë?Ma?Ë®wÖÁ¯Ë?aË jÞWË®ÍÁjj¯^ËÝjËÄjÜjËWÖ- 

ÍÁjÄË?aËÍÝË7ÄËÁj¬ÁjÄjÍja^Ë?~ËyËÜjÄÍÁË7Í?~jÄËwÁË 

y¤Ë Ä±Ë ßËÍjËjaËwËÔååo^ËÍjËWMjaËMËÜ?ÖjÄËwËÍjË 

¬ÄÍj¾ÄË yË 7ÄË Áj¬ÁjÄjÍjaË ?MÖÍË g¤±Ï|Ë M^Ë Ö¬Ë wÁË 

Ôååy¾ÄËWMjaËMËÜ?ÖjÄËwËgå±É¤ËM± 

.WjË¬jÁajaËMËÜ?ÖjËÁj¬ÁjÄjÍÄËjßËÄÍËÜjÄÍjaËËÍjË 

Ä^ËÍËÄË?Ëj?ÄÖÁjËwË»jßË?ÍËÁÄ^¼Ë?aËÍËÄËÍjË 

MjÄÍËj?ÄÖÁjËwËajÞËaÜjÁÄwW?Í±Ë0jËÍ¬ËwÜjËWÖÍÁjÄË 

ËÍÄËÁj~?ÁaË?ÍËÍjËjaËwËÔååoËÝjÁjË jÞWË®¤oË¬jÁWjÍ¯^Ë 

jß?Ë®¤|Ë¬jÁWjÍ¯^Ë+jÁÖË®¤åË¬jÁWjÍ¯^Ë?Ma?Ë®ÈË¬jÁWjÍ¯Ë 

?aË Ö~?Á?Ë®yË¬jÁWjÍ¯^ËwÁË?ËWMjaËy|Ë¬jÁWjÍËÄ?ÁjËwË 

MËÜ?Öj±Ë0ÄËÝ?ÄËÜjÁßËÄ?ÁËÍËÔååy¾ÄËyÉË¬jÁWjÍËÄ?ÁjËMßË 

ÍjËÄ?jËwÜjËWÖÍÁjÄ^ËÍÖ~ËÝj~ÍÄËÄwÍjaËÄ~wW?ÍßËË 

ÄjËW?ÄjÄË®jß?ËÖ¬ËwÁË|±yË¬jÁWjÍËÍË¤|Ë¬jÁWjÍ^Ë jÞWË 

aÝËwÁËÔÉË¬jÁWjÍËÍË¤oË¬jÁWjÍ^ËwÁËÄÍ?Wj¯± 

?ÄÍß^Ëa?Ë ÄË¬ÁaÖWjaËÄjËÄ¬jWÍ?WÖ?ÁË~?ÄËwÁË 

ÜjÄÍÁÄËwËÔååyË?aËÔååÉË®?Á~jßËwÁËW?¬Í?ËjÞ¬?ÄÄË 

?ÍË ~Ë ÖÍ¬jÄË ÍË MË Ü?ÖjÄ¯^Ë MÖÍË Íß¬W?ßË wÁË ÝË 

2±.±Ëa?ÁËÄÍ?ÁÍ~Ë¬ÍÄ±Ë 

Closing Comments 

WÁw?WjË ÄË WÁj?Ä~ßË ?ÍÍÁ?WÍ~Ë ÍjË ÍjÁjÄÍË ?aË 

ÜjÄÍjÍËwËÄÍÍÖÍ?ËÍjÁ?Í?ËjÖÍßËÜjÄÍÁÄ^ËwÁË 

ÍÄË¬ÍjÍ?Ë?ÄË?ËÄW?ßËÁjÄ¬ÄMj^Ë?ÄÄjÍaÜjÁÄwß~Ë?aË 

¬ÁwÍ?MjË?ÄÄjÍËW?Íj~Áß±Ë0jË8..Ë Ë.?ÁjajÁË7?ÖjË 

ajÞjÄ^ËÝWËÄËw?ÁËMj?ÁËÖÍËÍjË¬ÁwÍ?MjË?Ä¬jWÍËwËÍjË 

?ÄÄjÍË W?Íj~Áß^Ë Áj¬ÁjÄjÍË ÍjË wÁÄÍË WÄÄÍjÍË j?ÄÖÁjÄË wË 

the financial value shareholders received over time across a 

MÁ?aßËaÜjÁÄwjaËÄjÍËwË Ä± 

ÍË ÄË ¬ÁÍ?ÍË ÍË ÁjW~ãjË Í?ÍË ÍjË j?ÄÖÁjjÍÄË wË 

W?~jÄËË» Ë.?ÁjajÁË7?Öj¼Ë?ÁjËjWjÄÄ?ÁßË?¬¬ÁÞ- 

?Íj±Ë0jÁjË?ÁjË?ßË~aËÁj?ÄÄËwÁËÍÄqÍjË?MÄjWjËwË 

complete information about shareholder actions, the neces- 

ÄÍßËwËÄjË?ÄÄÖ¬ÍÄË?MÖÍËÍ~ËwËÍÁ?Ä?WÍÄ^ËÍjË 


47

aggregation and resultant “smoothing” of individual transactions, 

and the use of exchange rates to translate inputs and 

results into U.S. dollars that may not have occurred in reality 

are principal among these. That’s the nature of any private 

equity investment, and microfinance investments at this 

point are largely private transactions. 

But the series does provide benchmarks of MFI shareholder 

value performance whose results will approximate what 

investors achieved in the past across a fairly representative 

and diversified set of MFIs , and provide the basis for accumulating 

historical performance characteristics of the important 

and fast-growing niche asset category, MFI equity. 

As microfinance becomes a more widely practiced and 

commercially funded business activity, and as more annual 

indexes are added, the indexes will become increasingly 

important to investors looking to benchmark their portfolios’ 

performance, and to potential investors who want to know 

MFI equity’s performance characteristics. 

Endnotes 

1 

See Global Pensions, “CalSTRs explores microfinance,” April 6, 2010. http://www.globalpensions.com/global-pensions/news/1599783/calstrs-explores-microfinance 

2 

There is a huge and rapidly growing body of literature about the sources and evolution of microfinance and of MFIs. Interested readers can start at www.microfinancegateway.com. 

3 

Good starting points for this subject are “The Growth of Commercial Microfinance: 2004-2006” (September 2006) and “Characteristics of Equity Investments in Microfinance” 

(April 2004), by Elisabeth Rhyne & Brian Busch (2006), and by Rhyne and James Kaddaras (2004), respectively, for the Council of Microfinance Equity Funds. See http://www.cmef. 

com/Page.aspx?pid=1687 for access to these reports. 

4 

See MicroRate, “The State of Microfinance Investment” at http://microrate.com/wp-content/uploads/2009/08/2009_MIV_Survey_Presentation_Final.pdf . See also the series of 

survey reports released by CGAP, which show similar trends and volumes. Most recent was The CGAP 2009 MIV Survey 

http://www.cgap.org/gm/document-1.9.38774/CGAP%20MIV%20Survey%20-%20Results%20Presentation.pdf. 

5 

The Microfinance Information Exchange, Inc. (MIX) is the world’s leading source of data and research on MFIs. The MIX Market (www.mixmarket.org) and its research sister site 

www.themix.org are MIX’s dissemination points for its work. 

MIX Provider provides a great range of data and research products on MFI performance. See particularly MIX’s Trend Lines reports for time series’ data on the medians for top 

(75th) quartile, bottom (25th) quartile, and overall results. 

As for actual results, the latest available median RoE results in U.S. dollar terms for the top quartile performers of MFI Banks was 16.2 percent (2005), 20.0 percent (2006), 18.2 percent 

(2007 – latest available 6-30-2010). NBFIs achieved similar results. See www.themix.org/sites/default/files/MBB%2018%20-%20Before%20the%20Crisis.pdf for the most recent report. 

6 

See Forbes, “The World’s Top 50 Microfinance Institutions,” Dec. 20, 2007. http://www.forbes.com/2007/12/20/top-philanthropy-microfinance-biz-cz_1220land.html 

7 

See A. Gonzalez/MIX, “Resilience of Microfinance Institutions to National Macroeconomic Events: An Econometric Analysis of MFI Asset Quality,” http://www.themix.org/ 

publications/resilience-microfinance-institutions-macroeconomic-events; Krauss & Walter, “Can Microfinance Reduce Portfolio Volatility?” http://papers.ssrn.com/sol3/papers. 

cfm?abstract_id=943786 and also Galema et al. “International Diversification and Microfinance,” http://www.microfinancegateway.org/gm/document-1.9.34326/56904.pdf. 

8 

Much has been written recently about “The battle for the soul of microfinance,” and the topic promises to be a continuing debate. Interested readers can start with The Financial 

Times article, “The battle for the soul of microfinance” (Dec. 6, 2008). http://us.ft.com/ftgateway/superpage.ft?news_id=fto120520081936136455 . 

9 

The microfinance industry is working furiously through a stakeholder task force to develop “MFI social performance” standards, and to have the standards widely adopted and 

reported upon by MFIs. Check the Social Performance Task Force website (www.sptf.info) for more on this initiative, and the related MIX website that is accumulating MFI social 

performance reports – www.themix.org/standards/sp-reports. MIX also has applied its own measures of the “balanced scorecard” on MFI operations, in its series of MIX MFI 

Global 100 Composite rankings. These can be seen at www.themix.org/publications/2009-mix-global-100-composite. 

10 

For explanations and details about MIX’s “Diamonds for Disclosure” system, see www.mixmarket.org/faq/diamond-rankings. WSAS may seek financial statements from another 

public source, such as the MFI’s own website, if for some reason such information is not found in MIX Market. 

11 

WSAS compiles records of terms and amounts of publicly announced equity investments (and divestments) in MFIs, as well as records of FY year-end shareholdings in MFIs to 

“triangulate” information for use in determining MFI cohort results. 

12 

WSAS uses MIX’s acceptance of an institution as an MFI as sufficient to include it as an MFI in the WSAS MFI SVIX Indexes, assuming it meets the other index inclusion requirements. 

Roundtable continued from page 35 


performance? 

Woods: Performance, in the broadest sense: There will be 

periods when indices that use alternative weighting schemes 

do underperform, but equally there will be periods when 

they outperform their cap-weighted equivalents. Generally 

they have very respectable performance over extended time 

periods. They are less susceptible to bubbles, and most are 

designed to have a broader representation of stocks than 

their cap-weighted equivalents. 



statistics? 

Woods: I would say diversifying your weighting methodology 

is a good thing. Certainly not converting completely to 

an alternative weighting scheme, but an allocation to it will 

likely improve portfolio performance. 

But I’m not sure that the rising correlations are the connection 

there. Just for the sake of illustration, one of the reasons 

these indices can outperform even in sideways markets is that 

they rebalance more frequently and, in effect, because they’ll be 

rebalancing to fixed weights, they’ll sell stocks that have gone 

up and buy stocks that have gone down, so they’re capturing 

the volatility in markets. People call that volatility pumping. 

You get an increase in performance from capturing that 

volatility, and you partly detract from that with the extra 

transaction cost you’re incurring. But that’s not due to rising 

correlations; it’s just another reason why these indices 

in certain market regimes will outperform cap-weighted 

equivalents. We certainly know they don’t outperform in all 

market regimes. 

48 


Blanchett continued from page 39 

Figure 6 

Monthly Returns Test: Morningstar Core Vs. Russell, July 1997- May 2010 

Raw Return Difference 

Replicating Portfolio Alpha Difference 

Large Mid Small Large Mid Small 

Geomean -0.01% -0.07% 0.22% 0.02% -0.07% 0.27% 

Std Dev 1.67% 2.04% 2.18% 1.37% 1.43% 1.52% 

t-stat -0.07 -0.42 1.25 0.15 -0.57 2.21 


significance of the differences in the monthly returns for the 

Morningstar Core indexes versus the Russell indexes on both 

a raw return basis and on a market-factor-adjusted basis. 

While the Morningstar Large Core Index had a higher average 

monthly geometric return than the Russell 1000 (0.293 percent 

vs. 0.274 percent), it had a negative return when the differences 

were compared on a monthly basis. The results of the 

market- factor-adjusted test were generally the same as the 

raw return test, except the Morningstar Large Core Index had 

a higher return than the Russell 1000 when the returns were 

factor-adjusted. The outperformance of the Morningstar Small 

Core Index over the Russell 2000 becomes more pronounced 

versus the raw return at +0.27 percent per month (which 

works out to 3.27 percent per year). Over the entire test 

period, the cumulative Morningstar Small Core outperformed 

the Russell 2000 by over 50 percent. The outperformance is 

also statistically significant at the 5 percent level (i.e., has a 

t-statistic greater than 2 in absolute terms). 

Conclusion 

The results from this analysis suggest that Core appears 

to be a better “middle” investing strategy than Blend. The 

Morningstar Core indexes tended to have both higher relative 

returns and higher factor-adjusted returns than their 

respective Morningstar Blend and Russell capitalization peers, 

although most of the results were not statistically significant. 

Therefore, it may be worthwhile for investors seeking non- 

Value and non-Growth style exposures to consider investing in 

the true “middle,” which would be a Core approach, versus a 

Blend approach, which is currently the most popular. 

Works Cited 

Banz, Rolf. 1981. “The relationship between return and market value of common stocks.” Journal of Financial Economics, vol. 9: 3-18. 

Basu, S. 1977. “Investment Performance of Common Stocks in Relation to Their Price-Earnings Ratios: A Test of the Efficient Markets Hypothesis.” Journal of Finance, vol. 32, 

No. 3: 663-682. 

Blanchett, David. 2010. “Can Indexes Generate Alpha?” Journal of Indexes, vol. 13, No 2 (March/April): 32-37. 

Carhart, Mark M. 1997. “On Persistence in Mutual Fund Performance.” Journal of Finance, vol. 52, No 1: 57-82. 

Cremers, Martijn, Antti Petajisto, and Eric Zitzewitz. 2008. “Should Benchmark Indices Have Alpha? Revisiting Performance Evaluation.” Working paper version July 31, 2008: http:// 

www.mccombs.utexas.edu/Dept/Finance/fea/papers/f2-should-benchmark-indices.pdf. 

Fama, Eugene F., and Kenneth R. French. 1992. “The Cross-Section of Expected Stock Returns.” Journal of Finance, vol. 47, No 2: 427-466. 

Fama, Eugene F., and Kenneth R. French. 1993. “Common Risk Factors in the Returns on Bonds and Stocks.” Journal of Financial Economics, vol. 33: 3-53. 

Israelsen, Craig. 2007. “Variance Among Indexes.” Journal of Indexes, vol. 10, No 3 (May/June): 26-29. 

Appendix I 

Four Factor Results 

Morningstar Core Morningstar Blend Russell 

Large Mid Small Large Mid Small Large Mid Small 

Intercept 0.03% 0.03% 0.05% -0.02% 0.03% -0.07% 0.01% 0.09% -0.24% 

Mkt-RF 0.89 0.98 1.00 0.96 1.02 1.01 0.98 1.03 1.00 

SMB -0.26 0.08 0.53 -0.23 0.18 0.61 -0.14 0.21 0.75 

HML 0.12 0.48 0.48 -0.06 0.26 0.30 0.02 0.23 0.27 

Mom. -0.02 -0.07 -0.08 -0.01 -0.04 -0.09 -0.02 0.01 0.01 

R² 89.91% 87.79% 91.89% 98.66% 94.71% 96.38% 99.42% 95.62% 97.36% 

Sources: Morningstar Direct, Kenneth French 


Talking Indexes 

Sorting And Digging 

Breaking down the broad market 

By David Blitzer 

Most of traditional securities analysis falls into one 

of two areas—sorting or digging. Sorting consists 

of assigning stocks to classifications, groups or categories; 

digging involves delving into the financial, legal or 

operational details one company at a time. 

Sorting begins when securities are divided into asset 

classes. Most investors believe that stocks are different 

from bonds, have different risks and returns, and usually 

behave differently. Any two stocks are likely to be more 

similar to one another than a stock is to a bond. This illustrates 

a key aspect of sorting: Two securities in the same 

category have less variation than two securities in different 

categories. While asset classes may be obvious, things 

become more interesting and useful when we divide stocks 

into style (growth and value); size (large, mid and small); 

or sectors. Most broad-based indexes sort stocks by style, 

size and sector. Further divisions are often included, such 

as more narrowly defined industries, style versus pure style 

or financial measures such as dividend yield. 

Since the ultimate objective is to understand why some 

stocks perform better than others, a successful categorization 

should tell us something about past returns and risks 

at different periods of time. Unfortunately, a categorization 

of stocks—like all other securities analysis—cannot provide 

truly reliable predictions of future returns. Of course, that 

doesn’t stop analysts from trying. The three categorizations 

mentioned—style, size and sector—are the most oft-used 

sorts. These weren’t just the creation of index providers 

searching for more ways to slice and dice their indexes; 

rather, they are the result of an established and still-growing 

body of literature on comovement; that is, how some groups 

of stock tend to comove or move together. 

The basic concept behind comovement is that fundamental 

factors such as earnings, capital structure or a company’s 

business model determine a stock’s underlying value. If one 

brings together a group of stocks with similar fundamentals, 

the stocks should have similar risks and returns. Dividing 

stocks into growth and value, usually done with various 

financial measures such as price-to-book value, dividend 

yield or others, is one example. Some fundamental factors 

such as earnings per share or dividends are easy to measure, 

others such as industry supply conditions or shifts in demand 

for a group of products are harder to identify and quantify. 

One way to deal with hard-to-measure factors is to find an 

easy-to-measure proxy such as what industry a company is in. 

Grouping stocks by sector or industry works in the sense that 

stocks within a sector are more alike than stocks in different 

sectors. Another example of a proxy measure is size. Smallcap 

stocks often behave differently than large-cap stocks; 

one group may rise when the other falls. While there are lots 

of ideas about why small-caps don’t mimic large-caps, the 

key idea is that size can be a useful way to classify stocks. 

All these sorts relate, more or less, to some fundamental 

factor affecting a stock’s returns. However, comovement can 

be a useful tool to examine situations where other, nonfinancial 

or noneconomic, factors are involved. One that should be 

familiar is home-country bias—despite globalization, many 

investors prefer stocks in their home market and therefore 

grouping stocks by nationality is common. Another might 

be whether the stock is listed on the NYSE or Nasdaq, even 

though the listing requirements can be quite similar. A more 

reasonable, and more studied, factor in stock performance 

is the extent of analyst coverage and the speed with which 

news about a company spreads through the markets. Widely 

followed stocks are heavily analyzed and discussed, while 

stocks with little analytical coverage are sometimes seen as 

50 


either undiscovered gems or ignored for good reason. A lack 

of notoriety could be seen as a plus or a minus. 

Among the possible factors that might affect a stock’s 

performance and be a useful categorization tool is index 

membership. Recently some studies have argued that 

stocks that are added to the S&P 500 tend to see a shift in 

their performance and become more like other stocks in 

the index after they are added. 1 Specifically, they compare 

the beta calculated for stocks within the 500, based on 

the 500, with the beta of similar stocks outside the index, 

based on an index excluding stocks in the 500. That means 

that the two beta calculations distinguish between stocks 

included in or excluded from the 500, with the result 

that the beta for stocks newly added to the 500 generally 

increases after those stocks join the index. 

The guidelines that S&P uses to select stocks for the 

500 specify adequate liquidity, at least 50 percent public 

float, four quarters of positive earnings and a minimum 

market cap of $3.5 billion. As a result, there are similarities 

among the stocks in the index, so their performance 

would be expected to have some similarities. 

However, the econometric analyses in the research above 

find that by being added to the index, the stock’s performance 

shifts to become similar to other stocks in the index. One 

possible explanation for this is that stocks in the S&P 500 are 

more likely to be covered by analysts because they are part 

of the index. Further, the increased coverage may mean that 

news is spread faster so that S&P 500 member stocks react 

together, and more quickly, to market developments. 

A reason cited in the research is based on the trading of 

ETFs, mutual funds and other funds that track the 500. Since 

this trading tracks the index, it would involve the buying or 

selling of all the stocks in the index at the same time and could 

make the stocks move together. This explanation assumes 

that index investors all agree on whether to buy or sell; if 

the choice of buying or selling is random, then one investor’s 

buying would simply offset another’s selling. While directional 

trading could make index-member stocks comove, there probably 

isn’t enough trading to cause such an effect. First, much 

of the in-and-out trading of the 500 in order to establish or 

hedge equity positions is done through futures and would not 

necessarily push the stocks to move together. 

Second, the creation/redemption of ETF shares doesn’t 

seem large enough to cause this effect. Looking at the 

SPDRs, the largest S&P 500 ETF (NYSE Arca: SPY), the average 

daily change in shares outstanding over the three years 

ended Oct. 28, 2010 was about 0.1 percent per day. While 

there were a few days with very large changes, about 87 

percent of the time the change in shares was 3 percent or 

less. Suppose for a moment that the experience in SPY is 

the same as in all S&P 500 funds. Since about 10 percent of 

the outstanding float shares of any stock in the 500 are held 

by S&P 500 funds of one kind or another, this would mean 

that the average turnover from trading S&P 500 funds was 

30 basis points. This is well below the typical turnover of 

stocks in the index and not likely to drive stocks to move 

together. Moreover, while there may be substantial in-andout 

trading in a very liquid ETF like SPY, there is a lot of S&P 

500 money in institutional funds that rarely trade. 

While index membership may be one factor behind 

comovement, it is most likely the attention and analytical 

coverage the index brings—not the trading—that matters. 

Endnote 

1 Nicholas Barberis, Andrei Shleifer and Jeffrey Wurgler, “Comovement,” Journal of Financial Economics vol. 75 (2005), pp 283-317. 

Why advertise in the Journal of Indexes? 

JOURNAL OF INDEXES PRINT SUBSCRIPTIONS ONLINE AT WWW.JOURNALOFINDEXES.COM/SUBSCRIPTIONS 

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News 

Indexes Regain Edge Over Active 

Most actively managed U.S. equity 

mutual funds underperformed their 

benchmarks in the first half of this year, 

according to the latest Standard & Poor’s 

Indices Versus Active Funds Scorecard. 

The SPIVA report, covering the 12 

months ended June 30, marks a departure 

from the last report, when many 

active investors fared better than their 

benchmarks at a time of upheaval following 

the market crash of 2008-2009. 

The new report shows 56 percent 

of managers failed to beat their benchmarks, 

re-establishing the historical 

trend. Active management has consistently 

underperformed in most stock 

and bond asset classes for the better 

part of the last decade. Apart from 2009, 

the last year active management fared 

better was in 2000, when 40.5 percent 

of managers failed to beat their indexes, 

versus 2009’s 41.67 percent. 

The data show that the percentage 

of equity managers besting their benchmarks 

over the last year is largely in 

line with historical averages over one-, 

three- and five-year figures. 

Active bond managers fared a bit better 

than their counterparts in equities in 

the past year. The most shining examples 

were managers of short- and intermediate-term 

government securities, about 

60 percent of whom outperformed their 

respective benchmarks in the 12 months 

ended June 30. However, those bright 

spots of outperformance completely disappeared 

in the three- and five-year time 

frames, according to the S&P report. 

TD Ameritrade Offers 

Commission-Free ETFs 

TD Ameritrade launched a massive 

new zero-commission ETF trading 

platform in early October, becoming 

the fourth major brokerage center to 

eliminate commissions on certain ETF 

trades. The new platform allows investors 

to purchase more than 100 ETFs 

commission free, including funds from 

Vanguard, State Street Global Advisors, 

PowerShares and iShares. 

TD’s program is far more comprehensive 

than current offerings from other 

providers. Last year, Charles Schwab 

became the first major brokerage to 

offer zero-commission trading, waiving 

commissions for its family of Schwab 

ETFs; currently, Schwab offers 11 ETFs. 

Fidelity came next, striking a deal with 

iShares to offer 25 ETFs commission free. 

Vanguard then waived commissions for 

brokerage customers who bought any of 

the 68 Vanguard-listed ETFs. 

TD’s program breaks the mold by 

not aligning itself with any provider and 

by offering access to so many funds. 

The program includes a fairly comprehensive 

list of U.S. equity, international 

equity and fixed-income ETFs, with a 

smattering of commodities. Notably 

missing from the offering are gold bullion 

ETFs and sector funds. 

Commission-free trading programs 

are designed in part to make ETFs more 

palatable to individual investors, who 

can be dissuaded from investing in ETFs 

by commission costs. 

The ETFs in the TD Ameritrade program 

were selected by Morningstar 

Associates LLC, a registered investment 

adviser and unit of Morningstar. 

The TD Ameritrade program has an 

important catch: An investor who buys a 

fund and then sells it within 30 days will 

be charged a short-term trading fee. The 

program is designed to help long-term 

investors access the ETF space. 

MIT Launches Inflation Indexes 

The Massachusetts Institute of 

Technology has launched an Internetbased 

resource to measure price changes 

from around the world and to give 

real-time inflation estimates. 

In what is called the Billion Prices 

Project, MIT’s researchers monitor 

more than 5 million prices of items 

sold by online retailers from around the 

world, as well as computing inflation 

statistics on a daily basis. 

The price data collected are from 

categories such as food and beverages, 

household products, electronics, apparel 

and real estate. MIT tracks prices in more 

than 50 countries and currently publishes 

data for a smaller subset of countries, 

including the U.S., the U.K., Argentina, 

Australia, Brazil, Chile, China, Colombia, 

France, Italy, Turkey and Venezuela. 

According to Professor Roberto 

Rigobon of the MIT Sloan School of 

Management, the project provides 

pricing data at much greater speed 

than conventional inflation indexes, 

but also in greater detail across countries. 

MIT’s inflation indexes also offer 

a real-time indicator of economic conditions, 

says Rigobon. 

Wave Of ETF Closures 

Announced In Sept., Oct. 

September saw the announcement 

of several fund closures, with two of 

the announcing firms closing their 

ETF operations entirely. 

Geary Advisors said it would be closing 

its two ETFs, the Texas Large Companies 

ETF (NYSE Arca: TXF) and the Oklahoma 

ETF (NYSE Arca: OOK), which had combined 

net outflows in August of $7 million—about 

50 percent of total assets 

under management. NYSE Arca halted 

trading in TXF and OOK on Sept. 27. 

Later in the month, Old Mutual 

announced it would be closing all five 

of its funds: the GlobalShares FTSE 

All-World Fund (NYSE Arca: GSW), 

GlobalShares FTSE Emerging Markets 

Fund (NYSE Arca: GSR), GlobalShares 

FTSE All-Cap Asia Pacific ex Japan Fund 

(NYSE Arca: GSZ), GlobalShares FTSE All- 

World ex US Fund (NYSE Arca: GSO) and 

GlobalShares FTSE Developed Countries 

ex US Fund (NYSE Arca: GSD). Trading 

in the ETFs was suspended prior to the 

market opening on Oct. 6, and share- 

52 


holders were to receive the net asset 

value of their shares as of Oct. 8. 

Meanwhile, Javelin Investment 

Management said during September 

that it would be closing one of its 

two funds. The JETS Dow Jones Islamic 

Market International Index Fund (NYSE 

Arca: JVS) shut down on Oct. 19; it was 

launched in July 2009. The firm’s JETS 

Contrarian Opportunities Index Fund 

(NYSE Arca: JCO) will continue to trade. 

Finally, PowerShares announced it 

was closing 10 ETFs in early October, 

including funds tied to its Dynamic and 

FTSE RAFI families of ETFs. The funds’ 

final day of trading was Dec. 14. 

INDEXING DEVELOPMENTS 

New Russell Indexes Target 

Institutional Investors 

Russell Investments has launched a 

new global benchmark specifically aimed 

at institutional investors that meets a 

growing demand for broader exposure to 

investable companies around the world. 

The Russell World Cap Index tracks 

U.S. equities from all capitalization tiers 

and global large-cap stocks by combining 

the U.S. broad-market Russell 3000 

Index and the Russell Global ex-U.S. 

Large Cap Index into one. 

The new benchmark currently comprises 

more than 5,100 securities and 

about 93 percent of the Russell Global 

Index. Russell said in a press release 

that the launch underscores its focus 

on consultants and pension plan sponsors 

in its index business. According to 

Russell, the index mimics the strategies 

of institutional investors who include 

small-cap stocks in their domestic portfolios 

but focus on large-cap stocks in 

their international portfolios. 

S&P Launches 

CDS Sovereign Indexes 

Standard & Poor’s launched two 

credit default swap indexes for sovereign 

entities designed to track a 

big part of the over-the-counter credit 

derivative contracts market. 

The S&P International Developed 

Nation Sovereign CDS Index has similar 

country constituents and weightings 

as the S&P/Citigroup International 

Treasury Bond (ex U.S.) Index, according 

to S&P. Meanwhile, the S&P Eurozone 

Developed Nation Sovereign CDS Index 

provides about the same country constituents 

and weightings as the S&P 

Eurozone Government Bond Index. 

The country constituents and weights 

for each index are set at the inception of 

each index series, S&P said. On each rollover 

date, a new series will be launched 

with the current weights and constituents 

of the respective bond indexes. The 

new indexes have a 5 ¼-year maturity, 

as measured from the effective date. 

Barclays Debuts EM Bond Index 

Barclays Capital expanded its platform 

of government inflation-linked 

bond indexes in October with the 

launch of a more liquid version of its 

flagship inflation-linked emerging markets 

sovereign debt index. 

The Emerging Markets Tradable 

Government Inflation-Linked Bond 

Index (EMTIL) is a subset of the company’s 

Emerging Markets Government 

Inflation-Linked Bond Index (EMGILB), 

the first comprehensive local-currency-issued 

government inflation-linked 

debt index from developing nations, 

which itself debuted in 2007. 

The new benchmark strives to be 

more “tradable” than its parent by investing 

only in the most liquid bonds included 

in the larger index, with measures 

in place to ensure regional diversification, 

according to the company. EMTIL 

is a rule-based benchmark comprising 

the most liquid local-currency bonds 

from various emerging markets inflationlinked 

issuers included in the company’s 

flagship index. Among them, Brazil, 

Chile, Mexico, Poland, South Africa, 

South Korea and Turkey make the list. 

The index will be rebalanced annually. 

Mexico Joins 

Citigroup Bond Index 

Citigroup announced at the end of 

September that Mexico would officially 

join its World Government Bond Index, 

following a June announcement outlining 

Mexico’s eligibility. 

Mexico’s fixed-income market met 

the requirements regarding size, credit 

and barriers to entry, with 19 Mexican 

government bonds—with a market 

value of $116.8 billion—initially eligible 

for inclusion in the index. Together 

those 19 bonds represented 0.65 percent 

of the WGBI’s total market value. 

The newly added Mexican bonds 

are also included in the World Broad 

Investment-Grade (WorldBIG) Bond 

Index, of which the WGBI is a component, 

Citigroup said. 

Russell Rolls Out 

Frontier Index Family 

Russell Investments expanded into 

the far reaches of the developing world 

by launching a new series of indexes 

that focus on frontier markets. The family 

covers 41 countries that aren’t part 

of either its emerging markets or its 

developed country benchmarks. 

The broad Russell Frontier Index is 

a float-adjusted market-capitalizationweighted 

benchmark that limits any 

one country weight in the mix to 15 

percent in order to minimize singlecountry 

risk and distribute exposure in 

a more regional manner. It comprises 

683 securities; the top countries in 

terms of weight are Argentina, Bahrain, 

Jordan, Kuwait and Qatar. 

Other indexes in the series include 

the Russell Frontier Large Cap Index 

and Russell Frontier Small Cap Index. 

In addition, the company is launching 

a Russell Frontier ex-GCC Index, 

as well as a large-cap and a small-cap 


53

News 

version of it, which excludes Bahrain, 

Kuwait, Oman and Qatar. 

FTSE Debuts 

Diversification Indexes 

Late September saw the launch 

of the alternatively weighted FTSE 

Diversification Based Investing Index 

Series, which FTSE Group developed via 

a partnership with investment firm QS 

Investors LLC. The three indexes in the 

initial launch are designed to maximize 

diversification across countries and sectors 

with the aim of increasing returns 

and decreasing downside risk relative 

to standard market-cap-based indexes. 

The FTSE DBI Developed, FTSE 

DBI Developed ex US and FTSE DBI 

Developed ex Japan indexes are each 

based on indexes from FTSE’s “All 

World” index family, but their country 

and sector weightings have been 

adjusted based on correlations data to 

reduce risks associated with concentration 

and momentum. 

Morningstar Announces 

Revised Classifications 

Morningstar said in October that it 

had revised its equities classification 

structure to target the markets served 

by each company rather than the type 

of business it engages in. 

Morningstar will retain the usage 

of “super sectors” but they have been 

restructured based on sensitivity to macroeconomic 

cycles as cyclical, defensive 

and sensitive rather than the manufacturing, 

services and information categories 

that were used in the old classification 

system. A Morningstar representative 

called the new system more “intuitive.” 

Revenue and income are initially 

used to classify a company into an 

industry, of which there are 148. Each 

industry falls under the umbrella of 

one of 69 industry groups depending 

on its market characteristics. The 

industry groups in turn roll up into 11 

sectors that each fall into one of the 

three super sectors. 

Full implementation throughout 

Morningstar’s product groups is expected 

by the close of the first quarter of 2011. 

SSgA Switches US Funds 

To S&P Indexes 

State Street Global Advisors planned 

to change indexes on seven of its equity 

funds in December from Dow Jones 

to Standard & Poor’s, aligning its U.S. 

large-, mid- and small-cap funds so that 

they all use benchmarks from the same 

family as those of the SPDR S&P 500 ETF 

(NYSE Arca: SPY) and SPDR S&P MidCap 

400 ETF (NYSE Arca: MDY), according to 

a document circulated to financial advisers 

and by IndexUniverse.com. 

In addition to getting new names and 

indexes, the seven funds will also adopt 

new tickers that build on the “SPY” and 

“MDY” trading symbols. The seven ETFs 

and their proposed new tickers are: 

VË.+-ËÝËjÄË?Á~jË?¬Ë7?ÖjË0Ë 

®!:.ËÁW?]Ë7¯^Ë».+:7¼ 

VË.+-ËÝËjÄË?Á~jË?¬ËÁÝÍË 

ETF (NYSE Arca: ELG), “SPYG” 

VË.+-ËÝËjÄË aË?¬Ë7?ÖjË0Ë 

®!:.ËÁW?]Ë 7¯^Ë» :7¼ 

VË.+-ËÝËjÄË aË?¬ËÁÝÍË 

ETF (NYSE EMG), “MDYG” 

VË.+-ËÝËjÄË.?Ë?¬Ë0Ë®!:.Ë 

Arca: DSC), “SLY” 

VË.+-ËÝËjÄË.?Ë?¬Ë7?ÖjË0Ë 

®!:.ËÁW?]Ë.7¯^Ë».:7¼ 

VË.+-ËÝËjÄË.?Ë?¬ËÁÝÍË 

ETF (NYSE Arca: DSG), “SLYG” 

Dow Jones Indexes Launches 

Venture Capital Index 

In late October, Dow Jones Indexes 

ajMÖÍjaË ÍÄË ÝË jÄË 2±.±Ë 7jÍÖÁjË 

Capital Index, which the firm developed 

in collaboration with Sand Hill 

Econometrics. 

The new index tracks U.S.- 

headquartered companies financed by 

venture capital that are not yet publicly 

traded, with components selected from 

ÍjË ÝË jÄË 7jÍÖÁj.ÖÁWjË a?Í?- 

base of private investment firms and 

venture-backed companies. Because 

the components are not publicly traded, 

the index’s values are determined 

ÖÄ~Ë a?Í?Ë wÁË ÍjË 7jÍÖÁj.ÖÁWjË 

database and Sand Hill’s proprietary 

valuation methodology. The index is 

published quarterly, as the underlying 

data required to value the companies is 

only available on a quarterly basis. 

Components can be included only 

after receiving an initial venture capital 

investment, and are removed in the 

case of a merger, acquisition or initial 

public offering—or if they go out of 

business. Firms funded by a private 

investor or leveraged buyout fund are 

not eligible for inclusion. 

Stoxx Expands 

International Lineup 

Stoxx Ltd. said in November that 

it had expanded its offering of bluechip 

indexes beyond Europe. The firm 

launched three new indexes targeting 

the largest and most liquid stocks 

in their respective markets: the Stoxx 

North America 50, the Stoxx Asia/Pacific 

50 and the Stoxx Global 150. 

The two regional blue-chip indexes are 

each subsets of two broader indexes: the 

Stoxx Americas 600 and the Stoxx Asia/ 

Pacific 600. Meanwhile, the global index 

represents the combined components of 

the Stoxx North America 50, the Stoxx 

Asia/Pacific 50 and the Stoxx Europe 50, 

another of the firm’s blue-chip indexes. 

Nasdaq Rolls Out Alpha Indexes 

At the start of November, Nasdaq 

OMX Group launched five indexes that 

track the relative performance of a single 

stock or ETF against another single stock 

54 


News 

or leading ETF. In doing so, the indexes 

reflect the correlation between individual 

stocks and ETFs in a way that can help 

investors capture returns even when the 

market is down, according to Nasdaq. 

The initial indexes in the series 

include: 

VËË!?Äa?Ë# 9Ë¬?Ë+ËÜÄ±Ë.+:Ë 

ajÞË®¬¬jËÄÍWËÜÄ±ËÍjË.+-Ë.F+Ë 

500 ETF) 

VËË!?Äa?Ë# 9Ë¬?ËËÜÄ±Ë.+:Ë 

ajÞË®ÍjË.+-ËaË0ÁÖÄÍËÜÄ±ËÍjË 

.+-Ë.F+ËyååË0¯ 

VËË!?Äa?Ë# 9Ë¬?Ë00ËÜÄ±Ë.+:Ë 

ajÞË®0Áj?ÄÖÁßËÍjÄËÜÄ±ËÍjË.+-Ë 

.F+ËyååË0¯ 

VËË!?Äa?Ë# 9Ë¬?ËËÜÄ±Ë9ËajÞË 

®Í~ÁÖ¬ËÄÍWËÜÄ±ËÍjË?W?Ë 

.jjWÍË.jWÍÁË.+-ËÖa¯Ë 

VËË!?Äa?Ë# 9Ë¬?Ë ËÜÄ±Ë.+:ËajÞË 

®ÍjË.?ÁjÄË .ËjÁ~~Ë ?ÁjÍÄË 

0ËÜÄ±ËÍjË.+-Ë.F+ËyååË0¯ 

The indexes began real-time calcula- 

ÍËË#WÍ±Ë¤¤± 

AROUND THE WORLD OF ETFs 

Vanguard Debuts 

Russell-Based ETFs 

Vanguard launched seven new funds 

M?ÄjaË Ë -ÖÄÄjË ajÞjÄË Ë .j¬ÍjMjÁË 

as part of an ambitious expansion plan 

to have a broad lineup of Vanguard 

products that give different advisers who 

favor different indexes the tools they 

need. In keeping with Vanguard’s aggressive 

stance on expenses, the new funds 

carry annual expense ratios ranging from 

å±¤ÔË¬jÁWjÍËÍËå±ÔåË¬jÁWjÍq¬ÁWjaËÍË 

ÖajÁWÖÍËÍÄË?ËW¬jÍÍÁ^Ë.?ÁjÄ± 

0jËjÝË-ÖÄÄjM?ÄjaËwÖaÄ^ËÍjÁËÍWers 

and annual expense ratios are: 

VËË7?~Ö?ÁaË-ÖÄÄjË¤åååË0Ë®!?Äa?Ë 

]Ë7#!¯^Ëå±¤ÔË¬jÁWjÍ 

VËË7?~Ö?ÁaË-ÖÄÄjË¤åååË7?ÖjË0Ë 

®!?Äa?Ë ]Ë7#!7¯^Ëå±¤yË¬jÁWjÍ 

VËË7?~Ö?ÁaË-ÖÄÄjË¤åååËÁÝÍË0Ë 

®!?Äa?Ë ]Ë7#!¯^Ëå±¤yË¬jÁWjÍ 

VËË7?~Ö?ÁaË-ÖÄÄjËÔåååËajÞËÖaË 

®!?Äa?Ë ]Ë708#¯^Ëå±¤yË¬jÁWjÍ 

VËË7?~Ö?ÁaË-ÖÄÄjËÔåååË7?ÖjËajÞË 

ÖaË®!?Äa?Ë ]Ë7087¯^Ëå±ÔåË¬jÁWjÍ 

VËË7?~Ö?ÁaË-ÖÄÄjËÔåååËÁÝÍËajÞË 

ÖaË®!?Äa?Ë ]Ë708¯^Ëå±ÔåË¬jÁWjÍ 

VËË7?~Ö?ÁaË-ÖÄÄjËÏåååËajÞËÖaË 

®!?Äa?Ë ]Ë70-¯^Ëå±¤yË¬jÁWjÍ 

Van Eck Debuts 

First-Ever Rare Earths ETF 

Ë?ÍjË#WÍMjÁ^Ë7?ËWËM?ËÁjaË 

ÖÍË ÍjË ?ÁjÍË 7jWÍÁÄË -?ÁjË ?ÁÍÊ 

.ÍÁ?Íj~WË jÍ?ÄË 0Ë ®!:.Ë ÁW?]Ë - 9¯^Ë 

which invests in companies engaged in the 

production, refining and recycling of rareearth 

and strategic metals and minerals. 

The new fund is the latest foray by 

?Ë 2±.±Ë 0Ë Ä¬ÄÁË ÍË ÁË jÍ?Ä^Ë 

amid surging global demand for materials 

like gallium, which is used to make a 

host of specialized high-tech equipment. 

.ÍÁ?Íj~WË jÍ?ÄË ?ÁjË jÄÄjÍ?Ë ÍË ?ßË 

aÖÄÍÁjÄ±ËÝjÜjÁ^ËÍjÄjËjÍ?Ä^ËwÍjË 

byproducts of other mining operations, 

are not only difficult to extract but are 

also likely to be in increasingly short 

supply in the next few years. 

- 9ËÍÁ?WÄË?Ë?ËajÞËwËÔ|ËW¬?- 

nies engaged in the production, refin- 

~Ë ?aË ÁjWßW~Ë wË Ö¬Ë ÍË |Ë Á?ÁjË 

earth and strategic metals and miner- 

?Ä±Ë - 9Ë ?ÄË ?Ë jÍË jÞ¬jÄjË Á?ÍË wË 

0.57 percent. 

US ETF Assets Top $900 Billion 

ÄÄjÍÄËË2±.±Ë0ÄËWÁÄÄjaËÍjËgååË 

billion threshold for the first time on 

.j¬Í±ËÔo^Ë¬Á¬jjaËMßËwÝÄËÍË~aË 

and other commodities and a strong 

move in the equity markets. 

To be sure, the jitters coursing 

through the global economy have made 

the asset gathering anything but linear. 

Europe’s sovereign debt crisis caused a 

dip in assets in early spring. But since 

June, assets have been rising again. The 

ÁjËÍ?Ë¤^åååË2±.Ë0ÄË?aË?ËÍÍ?ËwË 

gåÏ±ÏoËMË?ÄËwË.j¬Í±ËÔo±Ë 

M?ß^Ë ÍjË 0Ë aÖÄÍÁßË ÝË ?ÄË 

?MÖÍËg¤±ÏyËÍÁËË?ÄÄjÍÄ^ËWÖa~Ë 

wÖaÄËÄÍjaËËÖÁ¬jË?aËÄ?±Ë 

WisdomTree Launches 

Commodity Currency ETF 

8Äa0ÁjjË ?ÖWjaË ?Ë jÝË 

“commodity currency” fund in late 

.j¬ÍjMjÁ± 

0jË8Äa0ÁjjËÁjßwÖÄËaÍßË 

ÖÁÁjWßËÖaË®!:.ËÁW?]Ë9¯ËÍÁ?WÄË?Ë 

basket of eight currencies of emerging 

as well as developed commodity-proaÖW~ËjWjÄ]ËÍjËÖÄÍÁ??Ëa?Á^Ë 

??a?Ëa?Á^Ë!ÁÝj~?ËÁj^Ë!jÝË 

=j??aË a?Á^Ë Á?ã?Ë Áj?^Ë j?Ë 

¬jÄ^Ë -ÖÄÄ?Ë ÁÖMjË ?aË .ÖÍË wÁW?Ë 

rand. The countries’ exports include 

everything from milk and mutton to 

gold, diamonds, silver and oil. 

9Ë?ÄËÍËÍÁ?WËÍjËjßË?ÁjÍË 

rates in each of the eight currencies as 

well as the changes in those rates rela- 

ÍÜjË ÍË ÍjË 2±.±Ë a?ÁË MßË WM~Ë ?Ë 

2±.±Ë W?ÄË M?ÄjË ÝÍË wÁÝ?ÁaË WÖÁÁjWßË 

contracts. The new fund carries an 

expense ratio of 0.55 percent. 

Pimco Launches BABs, 

Corporates ETFs 

+WË ?ÖWjaË ÍÝË jÝË 0ÄË Ë 

.j¬ÍjMjÁË wWÖÄjaË Ë ÜjÄÍjÍ 

grade corporate bonds and Build 

jÁW?Ë aÄËÖW¬?ËajMÍ± 

0jË +WË ÜjÄÍjÍË Á?ajË 

Á¬Á?ÍjË aËajÞËÖaË®!:.ËÁW?]Ë 

#-+¯Ë ÄË M?ÄjaË Ë ÍjË wË jÁÁË 

ßWË 2.Ë Á¬Á?ÍjË ajÞ^Ë ?Ë Ö?- 

?~jaË ajÞË W¬ÁÄ~Ë 2±.±Ë a?Á 

denominated, fixed-rate corporate debt 

ÝÍË?ÍËj?ÄÍËgÔyåËËÖÍÄÍ?a~±Ë 

+W¾ÄËwÖaËÝËÍÁ?WËÍjËajÞËÖÄ~Ë 

a proprietary representative sampling 

¬ÁWjÄÄ±Ë #-+Ë W?ÁÁjÄË ÍÍ?Ë ¬jÁ?Í~Ë 

jÞ¬jÄjÄËwËå±ÏÔË¬jÁWjÍ± 

0jË +WË ÖaË jÁW?Ë aÄË 

.ÍÁ?Íj~ßË ÖaË ®!:.Ë ÁW?]Ë =¯Ë ÄË 

an actively managed ETF that invests 

Ë ÜjÄÍjÍ~Á?ajË ÖaË jÁW?Ë 

aÄË ÄÍjaË Ë ÍjË ?ÁW?ßÄË ?¬Í?Ë 

ÖaËjÁW?Ë aÄËajÞËÝÍË?Ë¬?ÁË 

Ü?ÖjË wË ?ÍË j?ÄÍË gÔyåË ±Ë =Ë 

W?ÁÁjÄË?ËjÞ¬jÄjËÁ?ÍËwËå±|yË¬jÁ- 

WjÍ±Ë ÖaËjÁW?Ë aÄË?ÁjËÍ?Þ?MjË 

municipal bonds. 

Claymore Changes 

Name To Guggenheim 

?ßÁjË .jWÖÁÍjÄË W±Ë W?~jaË ÍÄË 

?jËÍËÖ~~jjËÖaÄËÄÍÁMÖÍÁÄË 

Inc. in the latest developments following 

?ßÁj¾ÄË ?WÖÄÍË MßË Ö~~jjË 

?ÄÍË#WÍMjÁ±Ë0jËW¬?ßË?ÄËW?~jaË 

the names of most of its ETFs to include 

ÍjËÖ~~jjË?j± 

0jËÁj?jaË?ßÁjËMÖÄjÄÄËÝË 

continue to support the current product 

lineup of ETFs, unit investment 

trusts and closed-end funds, and their 

respective strategies and investment 

56 


policies won’t change, Guggenheim 

said in a press release. 

The renamed ETFs will retain their 

existing ticker symbols. 

iShares Launches 3 

Emerging Market ETFs 

iShares, the world’s biggest ETF 

company, launched three targeted 

emerging markets funds to its lineup 

in September, adding competition to 

what has become the hottest asset class 

in the investment universe. 

The funds include the following: 

VËË.?ÁjÄË .Ë Á?ãË.?Ë?¬ËajÞË 

Fund (NYSE Arca: EWZS) 

VËË.?ÁjÄË .Ë?Ë.?Ë?¬ËajÞË 

ÖaË®!:.ËÁW?]Ë!.¯Ë 

VËË.?ÁjÄË .Ë+¬¬jÄËÜjÄÍ?MjË 

?ÁjÍËajÞËÖaË®!:.ËÁW?]Ë+¯ 

Emerging markets have become 

increasingly popular among investors 

since the 2008-2009 global stock market 

crash. While the developed world 

is mired in sluggish growth, developing 

countries are growing. 

iShares is charging annual expense 

ratios of 0.65 percent on all three funds. 

Credit Suisse Debuts 

Merger Arb ETN 

ÁjaÍË .ÖÄÄj^Ë ÍjË .ÝÍãjÁ?a 

based investment bank and asset 

manager, rolled out an ETN in early 

October that attempts to profit from 

merger activity. 

0jË ÁjaÍË .ÖÄÄjË jÁ~jÁË ÁMÍÁ?~jË 

ÖaË ajÞË 0!Ë ®!:.Ë ÁW?]Ë . ¯Ë 

aims to capture the spread between 

the price at which the stock of a target 

company trades after a proposed 

acquisition is announced, and the price 

which the acquiring company has proposed 

to pay for the target. 

The ETN is based on an index that 

uses a quantitative methodology to 

track a dynamic basket of securities 

held as long or short positions and 

W?Ä±Ë ÍÄË a~ÄË ÝË MjË Ýj~ÍjaË Ë 

accordance with certain rules to include 

publicly announced merger and acquisition 

transactions that meet certain 

qualifying conditions. 

The ETN has an annual expense ratio 

of 0.55 percent. 

iShares Chip ETF Gets 

New Name And Index 

iShares changed the index and name 

wËÍÄË.F+Ë!ÁÍËjÁW?Ë0jW~ßË 

Ë .jWaÖWÍÁÄË ajÞË ÖaË ®!:.Ë 

ÁW?]Ë8¯Ë?aË?ÄËÄwÍjaËÍÄË¬Á?ÁßË 

listing to Nasdaq, effective Oct. 15. 

The ETF’s new underlying benchmark 

ÄËÍjË+9Ë.jWaÖWÍÁË.jWÍÁËajÞ^Ë 

ÝË?ÄË».#9¼ÇËÍ¾ÄËMjjËÁj?jaË?ÄËÍjË 

.?ÁjÄË+9Ë.#9Ë.jWaÖWÍÁË.jWÍÁË 

ajÞËÖa±ËÍÄËjÝËÍWjÁËÄË».#99^¼Ë?aË 

it has shifted to the Nasdaq from the New 

York Stock Exchange’s Arca platform. 

iShares said in a press release that 

the changes are aimed at attracting 

more investors to the microchip fund, 

which had an industry-leading $243.6 

million under management. 

Although leveraged and inverse-levjÁ?~jaË 

0ÄË Í?ÍË ÍÁ?WË ÍjË .#9Ë ajÞË 

?ÁjË ?Ü??Mj^Ë .?ÁjÄ¾Ë jÝË .#99Ë wÖaË 

will become the first single-exposure, 

~ßË.#9M?ÄjaË0± 

iShares said that investors who want 

to remain in its semiconductor sector 

fund needn’t take any action in connection 

with the changes. 

iShares, JP Morgan In Race 

To Launch Copper ETF 

Ë ?ÍjË #WÍMjÁ^Ë ÍÝË ÝjÝË 

firms filed for physical copper ETFs. 

The first registered product was the 

±+±Ë Á~?Ë +ßÄW?Ë ¬¬jÁË .?ÁjÄË 

ETF. According to the filing, its investment 

objective “is for the shares to 

reflect the performance of the price of 

+ßÄW?Ë¬¬jÁËÁ?ajË^¼ËjÄÄËjÞ¬jÄjÄ±Ë 

0jË 0Ë ÄË ÍjË ßË jË Í?ÍË ±+±Ë 

Á~?Ë ?ÄË Ë Áj~ÄÍÁ?Í^Ë ?aË ÍjË 

firm has no listed ETFs. 

A few days later, iShares filed for a 

similar product. The price of iShares 

¬¬jÁË 0ÁÖÄÍË Ä?ÁjÄË ÝË MjË M?ÄjaË Ë 

ÄjÍÍjjÍË¬ÁWjÄËwËÍjËaË jÍ?ÄË 

Exchange, the filing said. The copper will 

be stored in warehouses at locations in 

the United States or in other places if it 

has approval from the trustee and the 

sponsor, the paperwork said. 

The only existing exchange-traded 

product similar to the proposed funds 

ÄË ÍjË +?ÍË ÝË jÄ2 .Ë ¬¬jÁË 

Subindex Total Return ETN (NYSE Arca: 

¯^Ë ?Ë ajMÍË ÄÍÁÖjÍË Í?ÍË ÍÁ?WÄË 

jÞËW¬¬jÁËwÖÍÖÁjÄ±Ë 

¬¬jÁË ?ÄË MjjË Äjj~Ë WÁj?Ä~Ë 

demand, and its usage is looked at as a 

bellwether for the global economy. 

Neither filing specified a ticker symbol 

or an expense ratio. 

New ETFS Product 

Has All That Glitters 

ETF Securities launched during 

October the first-ever physical precious 

metals basket to list in the U.S.. The ETFS 

+ßÄW?Ë +ÁjWÖÄË jÍ?Ë ?ÄjÍË .?ÁjÄË 

(NYSE Arca: GLTR) is already available 

to investors in Europe, Australia and 

Japan, and is designed to streamline the 

process of achieving the portfolio diversification 

that precious metals afford. 

Each share of GLTR holds 0.03 ounces 

wË~aÇË¤±¤ËÖWjÄËwËÄÜjÁÇËå±åå|ËÖWjÄË 

wË ¬?ÍÖÇË ?aË å±ååÉË ÖWjÄË wË ¬?ladium. 

According to ETFS, the formula 

comes from using a screen that takes into 

account variables on each of the metals, 

including liquidity, new mining supply, 

total demand and industrial supply. 

The fund charges an annual expense 

ratio of 0.60 percent. 

Van Eck Debuts 

China A-Shares Fund 

Ëa#WÍMjÁ^Ë7?ËWË?ÖWjaË?Ë 

0Ë Í?ÍË Í?¬ÄË ÍË ÍjË ?Ë Ä?ÁjÄË 

?ÁjÍ±Ë 0jË ?ÁjÍË 7jWÍÁÄË ?Ë 0Ë 

®!:.Ë ÁW?]Ë +¯Ë ~ÜjÄË ÜjÄÍÁÄË wÁÍ 

row access to the performance of the 

?ËÄ?ÁjÄË?ÁjÍËÍÁÖ~ËÍjËÖÄjËwË 

swaps and derivative instruments while 

ÍÁ?W~Ë ÍjË .Ë ÏååË ajÞ^Ë ÝWË W- 

¬ÁÄjÄËÄjßË?ËÄ?ÁjÄËÄjWÖÁÍjÄ± 

?ËÄ?ÁjÄË?ÁjËÄjWÖÁÍjÄËwÁË 

??aË ?M?ÄjaË W¬?jÄ^Ë 

and have been mostly off limits to 

foreign investment and tightly regu- 

?ÍjaËMßËÍjË~ÜjÁjÍ±Ë ÖÍË~~Ë 

financial reforms that aim to improve 

foreign investment in mainland companies 

are slowly making A-shares 

more accessible. 

The fund, which in its initial stages 

will comprise swaps and derivatives, 

will eventually hold actual A-Shares 

ÄjWÖÁÍjÄË?wÍjÁË7?ËWËÁjWjÜjÄËÖ?wjaËwÁj~ËÄÍÍÖÍ?ËÜjÄÍÁË®,¯Ë 


57

News 

status, according to Van Eck. 

PEK comes with an expense ratio of 

0.72 percent. 

BACK TO THE FUTURES 

CME Launches Weekly 

Nasdaq-100 Options 

The CME Group listed weekly 

options on futures on the Nasdaq-100 

index as of Oct. 18. The options are 

available for both the standard ($100 

multiplier) and the e-mini ($20 multiplier) 

futures on the index. 

With weekly options available, investors 

will be able to buy options on 

futures expiring on any Friday of the 

month, rather than just on the third 

Friday. The contracts are Europeanstyle 

exercise, with the expiration price 

based on a weighted average of e-mini 

Nasdaq-100 futures in the last 30 seconds 

of trading Friday. 

CME Group Volume 

Rises In October 

October saw the CME Group experience 

a 6 percent jump in volume from the 

prior year, reaching an average daily contract 

volume of 11.4 million contracts. 

However, its suite of equity index 

products was the worst-performing 

group. Index-related derivatives represented 

about 24 percent of the 

exchange’s total ADV for October 2010; 

in October 2009, they were 26 percent 

of the total ADV. That decline in the 

share of ADV was accompanied by an 

actual decline in ADV of 6 percent from 

the prior year to 2.7 million contracts. 

KNOW YOUR OPTIONS 

CFTC Mulls Options 

Trade On PPLT, PALL 

The Commodity Futures Trading 

Commission is considering a request 

to allow trading in options of ETF 

Securities’ physical platinum and palladium 

exchange-traded funds. 

If the CFTC approves the request, 

it would mark the first time options 

trading is allowed for platinum and palladium 

ETFs. 

To begin trading options on its ETFS 

Physical Platinum Shares (NYSE Arca: 

PPLT) and the ETFS Physical Palladium 

Shares (NYSE Arca: PALL), ETF Securities 

and its clearing agent must first obtain 

“exemptive relief” under Rule 4(c) of 

the Commodity Exchange Act. 

The request for exemptive relief was 

made by the Options Clearing Corporation, 

which acts as both the issuer and guarantor 

of options and futures contracts. 

CBOE’s October Volume Falls 

The Chicago Board Options Exchange 

saw its average daily volume fall 6 percent 

year-over-year in October, working 

out to 4.3 million contracts. 

Interestingly, the October volumes 

for ETF and equity index options 

were higher than those of the equity 

options. While the ADV for equity 

options was down 12 percent, the 

ADV for equity index options was up 4 

percent and the ADV for ETF options 

was down just 2 percent. 

Among the index and ETF options 

contracts, the contracts on the S&P 500 

Index, SPDR S&P 500 (NYSE Arca: SPY), 

CBOE Volatility Index (VIX), PowerShares 

QQQ Trust (Nasdaq GM: QQQQ) and 

SPDR Gold Trust (NYSE Arca: GLD) were 

the most actively traded. 

ON THE MOVE 

Bowen Takes Control 

Of First Trust 

First Trust LP’s President James 

Bowen has acquired control of the 

Wheaton, Ill.-based money management 

firm, and while the transaction 

won’t affect the day-to-day operations 

of any First Trust funds, the change 

of ownership means shareholders will 

have to approve the new management. 

Terms of the transaction under 

which Bowen acquired 100 percent 

of the voting stock of the fund company’s 

general partner, The Charger 

Corp., weren’t disclosed in a press 

release First Trust disseminated on 

Oct. 12. Bowen has been with First 

Trust since it was founded in 1991 

and was part of a predecessor entity 

before then as well. 

Shareholders of record as of Sept. 30 

were to vote on the new management 

in December, the press release said. 

The transaction closed on Oct. 12. 

Ebner To Head SPDR 

Product Development 

State Street Global Advisors, the 

world’s No. 2 ETF company, named 

Scott Ebner to the newly created post 

of managing director and global head 

of ETF product development. 

Ebner joins State Street from NYSE 

Euronext, where he oversaw the listing 

and trading of ETFs, warrants, certificates, 

structured notes and other 

exchange-traded products. 

In his new role, Ebner will head 

product development and focus on 

expanding State Street’s share of the 

growing global ETF business. 

Bourcier Leaves Lyxor 

According to reports in Portfolio 

International and the Financial Times, 

Isabelle Bourcier, global head of ETFs 

at Lyxor, the subsidiary of French bank 

Société Générale, is leaving the firm to 

pursue other opportunities. 

Portfolio International reported additionally 

that Thomas Meyer zu Drewer, 

head of Lyxor’s German ETF operation, 

is shortly to move to Commerzbank as 

head of European business. 

Lyxor is adding other senior staff, 

however, including Simon Klein, formerly 

global head of sales for Deutsche Bank’s 

db x-trackers arm, says the publication. 

Nizam Hamid, previously head of 

sales strategy at iShares, is shortly to 

join Lyxor; originally his planned role at 

Lyxor, according to sources, was to be 

global co-head of ETFs with Bourcier. 

Topix Head Moves To Singapore 

As of November, Masayuki Kato has 

moved to Singapore, where he is now the 

general manager and chief representative 

of the Tokyo Stock Exchange’s office 

there. He will be responsible for managing 

the exchange’s relationships with other 

exchanges and financial institutions. 

Kato was previously the head of 

the TSE’s index operations, overseeing 

index development and licensing. 

The exchange’s best-known index is 

the widely used Topix benchmark, 

which covers Japan’s stock market 

and underlies a broad range of products 

around the world. 

58 


Global Index Data 

Selected Major Indexes Sorted By YTD Returns 

January/February 2011 

Total Return % Annualized Return % 

Index Name YTD 2009 2008 2007 2006 2005 2004 2003 3-Yr 5-Yr 10-Yr 15-Yr Sharpe Std Dev 

MSCI Sri Lanka* 72.97 184.15 -62.09 -15.15 42.78 30.70 7.81 42.09 22.78 12.17 20.73 6.80 0.61 50.98 

MSCI Colombia* 57.93 76.50 -27.68 12.64 10.92 102.31 125.66 59.01 25.83 27.93 39.73 17.80 0.83 33.87 

Alerian MLP 31.03 76.41 -36.91 12.72 26.07 6.32 16.67 44.54 12.11 14.35 18.94 - 0.56 23.90 

AMEX Gold Miners* 24.00 37.30 -26.79 16.86 21.86 29.08 -9.56 47.07 4.82 16.98 - - 0.32 47.88 

S&P MidCap 400/Citi Growth 18.43 41.08 -37.61 13.50 5.81 14.39 15.79 37.32 -0.05 5.89 5.37 12.93 0.10 26.07 

DJ Transportation Average 17.51 18.58 -21.41 1.43 9.81 11.65 27.73 31.84 0.76 6.10 7.08 7.62 0.14 28.28 

Wilshire 4500 Completion 16.24 36.99 -39.03 5.39 15.28 10.03 18.10 43.84 -3.02 4.41 3.78 7.99 -0.02 26.23 

Barclays EM 16.15 34.23 -14.75 5.15 9.96 12.27 11.89 26.93 9.94 9.83 11.16 12.09 0.65 15.13 

S&P MidCap 400 15.42 37.38 -36.23 7.98 10.32 12.56 16.48 35.62 -1.41 4.93 6.12 11.02 0.05 26.07 

Russell 2000 Growth 14.97 34.47 -38.54 7.05 13.35 4.15 14.31 48.54 -3.81 3.99 1.15 4.53 -0.03 27.97 

S&P SmallCap 600/Citi Growth 14.91 28.35 -32.94 5.60 10.54 7.02 24.27 38.43 -3.20 3.59 5.91 8.40 -0.01 27.06 

NASDAQ 100 14.83 54.61 -41.57 19.24 - - - - -1.10 - - - 0.06 26.38 

MSCI World Small Cap 14.72 44.12 -41.88 0.79 17.20 15.71 24.31 57.79 -4.46 4.22 7.98 - -0.05 27.63 

Barclays US Corp High Yield 14.41 58.21 -26.16 1.87 11.85 2.74 11.13 28.97 9.46 9.08 8.58 7.46 0.56 17.32 

Russell Micro Cap 14.02 27.48 -39.78 -8.00 16.54 2.57 14.14 66.36 -7.26 -0.40 5.32 - -0.14 29.31 

MSCI EM 13.97 78.51 -53.33 39.39 32.17 34.00 25.55 55.82 -3.98 14.94 - - 0.02 33.03 

Wilshire 5000 Equal Weight* 13.80 83.16 -44.96 -6.69 18.91 5.53 28.89 92.83 1.44 5.19 12.21 14.52 0.17 31.51 

Russell 2000 13.58 27.17 -33.79 -1.57 18.37 4.55 18.33 47.25 -3.91 3.07 4.89 7.33 -0.03 28.00 

MSCI EAFE Small Cap 13.38 46.78 -47.01 1.45 19.31 26.19 30.78 61.35 -7.56 3.53 8.67 - -0.15 28.76 

S&P SmallCap 600 13.28 25.57 -31.07 -0.30 15.12 7.68 22.65 38.79 -3.43 3.12 6.56 9.20 -0.02 27.67 

Dow Jones US Select Dividend 13.02 11.13 -30.97 -5.16 19.56 3.77 18.14 30.16 -6.64 -0.17 6.83 9.59 -0.20 23.94 

S&P 500 Equal Weighted 12.72 46.31 -39.72 1.53 15.80 8.06 16.95 40.97 -2.28 4.22 5.62 9.37 0.02 26.93 

S&P MidCap 400/Citi Value 12.52 33.73 -34.87 2.65 14.62 10.80 17.19 33.81 -2.78 3.88 6.77 9.15 0.00 26.43 

Russell 2000 Value 12.10 20.58 -28.92 -9.78 23.48 4.71 22.25 46.03 -4.13 2.02 8.16 9.54 -0.03 28.72 

S&P SmallCap 600/Citi Value 11.75 22.85 -29.51 -5.54 19.57 8.33 21.06 39.09 -3.82 2.58 7.02 9.83 -0.02 28.57 

Barclays US Credit 10.67 16.04 -3.08 5.11 4.26 1.96 5.24 7.70 7.93 6.74 7.10 6.69 0.88 8.09 

Barclays US Treasury TIPS 9.84 11.41 -2.35 11.64 0.41 2.84 8.46 8.40 7.44 6.32 7.64 - 0.76 8.92 

Russell 3000 Growth 9.76 37.01 -38.44 11.40 9.46 5.17 6.93 30.97 -3.93 3.28 -2.25 5.44 -0.10 22.63 

Russell 1000 Growth 9.35 37.21 -38.44 11.81 9.07 5.26 6.30 29.75 -3.94 3.21 -2.52 5.58 -0.10 22.29 

DJ Industrial Average 8.96 22.68 -31.93 8.88 19.05 1.72 5.31 28.28 -4.50 4.01 2.53 8.15 -0.17 19.99 

Russell 3000 8.88 28.34 -37.31 5.14 15.72 6.12 11.95 31.06 -5.96 2.08 0.62 6.92 -0.19 22.73 

S&P 500/Citi Growth 8.82 31.57 -34.92 9.13 11.01 1.14 6.97 27.08 -3.43 3.12 -2.22 6.42 -0.10 21.03 

Barclays Treasury 8.57 -3.57 13.74 9.01 3.08 2.79 3.54 2.24 7.09 6.32 6.10 6.16 1.10 5.72 

Citigroup WGBI 8.54 2.55 10.89 10.95 6.12 -6.88 10.35 14.91 8.07 7.74 7.93 6.06 0.83 8.93 

Russell 1000 8.48 28.43 -37.60 5.77 15.46 6.27 11.40 29.89 -6.14 1.99 0.29 6.94 -0.20 22.38 

Barclays US Aggregate Bond 8.33 5.93 5.24 6.97 4.33 2.43 4.34 4.10 7.23 6.45 6.38 6.36 1.51 4.14 

Barclays Global Aggregate 8.31 6.93 4.79 9.48 6.64 -4.49 9.27 12.51 7.23 7.28 7.50 6.15 0.84 7.70 

Russell 3000 Value 8.00 19.76 -36.25 -1.01 22.34 6.85 16.94 31.14 -8.14 0.73 3.04 7.75 -0.27 23.51 

Barclays US Government 7.98 -2.20 12.39 8.66 3.48 2.65 3.48 2.36 6.92 6.23 6.08 6.15 1.20 5.07 

S&P 500 7.84 26.46 -37.00 5.49 15.79 4.91 10.88 28.68 -6.49 1.73 -0.02 6.74 -0.23 21.92 

Russell 1000 Value 7.63 19.69 -36.85 -0.17 22.25 7.05 16.49 30.03 -8.49 0.62 2.64 7.65 -0.30 23.17 

06&,3DFLûF 7.23 24.18 -36.42 5.30 12.20 22.64 18.98 38.48 -7.58 2.17 2.40 1.38 -0.26 22.89 

S&P 500/Citi Value 6.85 21.18 -39.22 1.99 20.80 5.82 15.71 31.79 -9.68 0.17 0.87 6.50 -0.34 23.86 

MSCI Kokusai (World Ex Japan) 6.56 33.14 -41.96 10.66 21.95 7.67 14.62 32.83 -7.95 3.07 1.61 6.77 -0.25 24.40 

Barclays Municipal 6.53 12.91 -2.47 3.36 4.84 3.51 4.48 5.31 5.79 5.20 5.59 5.63 0.82 6.03 

MSCI BRIC* 6.49 88.79 -60.27 56.12 52.87 39.81 13.63 84.18 -8.83 16.32 14.17 10.55 -0.09 36.12 

MSCI World 6.41 29.99 -40.71 9.04 20.07 9.49 14.72 33.11 -8.09 2.54 1.33 5.57 -0.27 23.72 

DJ UBS Commodity 5.92 18.91 -35.65 16.23 2.07 21.36 9.15 23.93 -6.35 -0.10 5.96 6.11 -0.18 24.24 

Russell Top 200 5.86 24.21 -36.07 5.89 15.53 3.77 8.31 26.68 -7.16 1.22 -1.45 6.12 -0.28 20.99 

Dow Jones Utilities Average 5.42 12.47 -27.84 20.11 16.63 25.14 30.24 29.39 -5.00 4.06 4.16 8.57 -0.27 16.74 

MSCI EAFE 4.72 31.78 -43.38 11.17 26.34 13.54 20.25 38.59 -9.60 3.31 3.17 4.97 -0.28 26.08 

STOXX Europe Total Market 4.50 37.50 -46.75 13.07 35.39 10.11 21.42 41.39 -10.16 4.32 4.00 7.60 -0.26 28.50 

STOXX Europe 600 3.98 36.65 -46.54 13.49 35.04 9.93 20.95 40.58 -10.28 4.18 3.82 7.72 -0.26 28.35 

MSCI EAFE GDP Weighted 2.37 30.38 -44.82 12.88 27.39 13.68 22.57 42.95 -11.06 2.72 3.10 5.85 -0.31 27.86 

MSCI EAFE Value 1.91 34.23 -44.09 5.96 30.38 13.80 24.33 45.30 -10.69 2.51 4.26 6.30 -0.29 27.82 

EURO STOXX Total Market -0.48 32.77 -47.59 18.26 37.82 9.35 21.58 46.01 -12.47 3.74 3.23 7.43 -0.27 32.23 

S&P GSCI -1.41 13.48 -46.49 32.67 -15.09 25.55 17.28 20.72 -15.20 -7.78 1.43 4.38 -0.38 31.59 

Citigroup Greek GBI -18.36 6.98 -3.84 13.25 11.88 -8.95 15.73 25.58 -5.22 1.08 7.90 - -0.18 21.44 

MSCI Ireland -21.49 12.28 -71.92 -20.09 46.81 -2.29 43.07 43.83 -40.12 -20.41 -7.98 -2.54 -1.26 35.26 

MSCI Greece -33.63 25.05 -66.01 32.91 35.05 16.10 46.06 69.52 -34.39 -11.23 -2.93 - -0.64 47.80 

Source: Morningstar. Data as of October 31, 2010. All returns are in US dollars, unless noted. YTD is year-to-date. 3-, 5-, 10- and 15-year returns are annualized. Sharpe is 12-month Sharpe ratio. 

Std Dev is 3-year standard deviation. *Indicates price returns. All other indexes are total return. 



59

Index Funds 

Largest U.S. Index Mutual Funds Sorted By Total Net Assets In $US Millions 


FPO 

January/February 2011 

Fund Name Ticker Assets Exp Ratio 3-Mo YTD 2009 2008 3-Yr 5-Yr 10-Yr 15-Yr P/E Std Dev Yield 

Vanguard Total Stock Market - Inv VTSMX 65,976.0 0.18 8.38 9.00 28.70 -37.04 -5.70 2.30 0.85 6.88 14.4 22.68 1.81 

Vanguard Institutional - Instl VINIX 50,875.2 0.05 7.95 7.82 26.63 -36.95 -6.44 1.76 0.00 6.80 14.3 21.91 2.07 

Vanguard 500 - Inv VFINX 46,539.0 0.18 7.92 7.73 26.49 -37.02 -6.54 1.65 -0.11 6.67 14.3 21.92 1.95 

Vanguard Total Intl Stock - Inv VGTSX 39,442.6 0.32 10.65 7.43 36.73 -44.10 -8.52 5.33 4.69 - 12.1 28.06 2.22 

Vanguard Total Stock Market - Adm VTSAX 35,237.0 0.07 8.41 9.13 28.83 -36.99 -5.60 2.40 0.93 6.94 14.4 22.70 1.92 

Vanguard 500 Admiral Class VFIAX 33,155.2 0.07 7.96 7.83 26.62 -36.97 -6.44 1.75 -0.03 6.73 14.3 21.92 2.06 

Vanguard Institutional - Instl+ VIIIX 29,529.6 0.02 7.96 7.85 26.66 -36.94 -6.41 1.79 0.03 6.82 14.3 21.91 2.10 

Vanguard Total Bond Market II - Inv VTBIX 25,214.5 0.11 1.81 8.21 - - - - - - - - 3.29 

Fidelity Spartan 500 - Inv Cl FUSEX 25,067.0 0.10 7.93 7.77 26.51 -37.03 -6.53 1.69 -0.10 6.61 14.7 21.93 1.76 

Vanguard Total Bond Market - Adm VBTLX 23,853.1 0.12 1.86 8.37 6.04 5.15 7.27 6.49 6.19 6.21 - 4.19 3.51 

Vanguard Total Stock Market - Instl VITSX 22,934.9 0.06 8.45 9.13 28.83 -36.94 -5.58 2.43 0.97 6.99 14.4 22.67 1.92 

Vanguard Total Bond Market - Instl VBTIX 21,195.5 0.07 1.87 8.40 6.09 5.19 7.31 6.53 6.24 6.28 - 4.19 3.55 

Vanguard Total Bond Market - Inv VBMFX 20,400.9 0.22 1.84 8.27 5.93 5.05 7.16 6.39 6.11 6.16 - 4.19 3.40 

Vanguard 500 - Signal VIFSX 17,076.0 0.07 7.97 7.83 26.61 -36.97 -6.44 1.73 -0.07 6.70 14.3 21.92 2.06 

Fidelity Spartan 500 - Adv Cl FUSVX 13,365.1 0.07 7.97 7.82 26.55 -37.01 -6.49 1.72 -0.09 6.63 14.7 21.91 1.79 

Vanguard Instl Total Stock Mkt - Instl+ VITPX 12,530.1 0.02 8.41 9.14 28.92 -36.89 -5.53 2.49 - - 14.4 22.71 1.94 

T. Rowe Price Equity 500 PREIX 12,407.4 0.30 7.89 7.59 26.33 -37.06 -6.65 1.51 -0.26 6.47 14.7 21.89 1.58 

Fidelity U.S. Bond FBIDX 11,259.9 0.32 1.67 8.14 6.45 3.76 6.70 5.89 6.13 6.16 - 3.83 3.06 

Schwab S&P 500 SWPPX 10,020.5 0.09 7.97 7.84 26.25 -36.72 -6.41 1.75 -0.06 - 15.2 21.84 1.31 

Vanguard Total Bond Market II - Instl VTBNX 9,976.7 0.07 1.82 8.25 - - - - - - - - 3.32 

Vanguard Total Bond Market - Signal VBTSX 8,510.1 0.12 1.86 8.37 6.04 5.15 7.27 6.47 6.15 6.18 - 4.19 3.51 

Vanguard Mid Cap - Instl VMCIX 7,580.1 0.08 9.97 15.08 40.51 -41.76 -3.65 3.91 5.97 - 15.5 26.38 1.05 

Vanguard European Stock - Inv VEURX 7,126.8 0.27 11.54 4.66 31.91 -44.73 -10.35 4.04 3.60 7.58 11.1 29.20 3.62 

Vanguard Mid Cap - Inv VIMSX 7,093.9 0.27 9.88 14.93 40.22 -41.82 -3.82 3.75 5.81 - 15.5 26.36 0.93 

Vanguard Developed Markets - Instl VIDMX 6,921.5 0.08 10.26 5.73 28.17 -41.62 -9.51 3.45 3.19 - 11.5 27.05 1.08 

Vanguard Short-Term Bond - Signal VBSSX 6,674.7 0.12 1.29 5.13 4.38 5.51 5.73 5.43 5.01 5.30 - 2.59 2.30 

Fidelity Spartan Intl - Inv Cl FSIIX 6,462.4 0.10 10.18 4.91 28.48 -41.43 -9.53 3.40 3.05 - 13.4 27.15 1.99 

Vanguard Small Capitalization - Inv NAESX 6,444.5 0.28 8.85 15.04 36.12 -36.07 -2.52 4.13 5.84 8.33 15.4 28.80 0.86 

Vanguard Total Bond Mkt - Instl+ VBMPX 6,180.5 0.05 1.88 8.39 5.93 5.05 7.20 6.42 6.12 6.16 - 4.19 - 

Fidelity Spartan Total Market - Inv Cl FSTMX 5,814.5 0.10 8.32 9.25 28.39 -37.18 -5.77 2.31 0.86 - 14.9 22.61 1.64 

Fidelity Series 100 FOHIX 5,784.2 0.20 7.04 5.42 22.14 -35.44 -7.53 - - - 14.1 20.86 2.15 

Vanguard Growth - Inv VIGRX 5,664.4 0.28 10.94 9.76 36.29 -38.32 -3.69 3.30 -0.93 6.81 16.6 22.35 1.04 

Vanguard Emerging Markets VEIEX 5,597.1 0.40 11.92 13.86 75.98 -52.81 -4.66 14.27 14.38 9.44 14.2 33.54 1.06 

Vanguard Short-Term Bond - Inv VBISX 5,538.9 0.22 1.27 5.04 4.28 5.43 5.63 5.36 4.97 5.28 - 2.59 2.19 

Vanguard Extended Market - Instl VIEIX 5,418.5 0.08 9.45 15.23 37.69 -38.58 -3.03 4.17 3.80 8.13 15.8 27.21 1.01 

Vanguard Small Capitalization - Instl VSCIX 5,240.2 0.08 8.90 15.22 36.40 -35.98 -2.34 4.30 6.02 8.48 15.4 28.80 1.00 

Vanguard Total Stock Market - Signal VTSSX 5,011.7 0.07 8.43 9.10 28.85 -36.99 -5.61 2.38 0.88 6.91 14.4 22.67 1.91 

Vanguard Emerging Markets Stock - Adm VEMAX 4,761.1 0.27 11.97 13.98 76.18 -52.76 -4.55 14.39 14.44 9.48 14.2 33.56 1.15 

Schwab 1000 SNXFX 4,578.5 0.29 8.22 8.52 27.68 -37.28 -6.14 1.92 0.25 6.73 15.3 22.07 1.62 

Vanguard Extended Market - Inv VEXMX 4,409.4 0.30 9.40 15.06 37.43 -38.73 -3.22 3.97 3.61 7.97 15.8 27.20 0.85 

)LGHOLW\,QüDWLRQ3URWHFWHG%RQG FSIPX 4,343.2 0.20 3.03 6.64 - - - - - - - - 0.43 

Vanguard FTSE All-World ex-US - Instl VFWSX 4,145.9 0.15 10.59 7.92 39.01 -43.96 -7.79 - - - 12.5 28.46 1.90 

Vanguard REIT - Inv VGSIX 4,116.1 0.26 8.02 25.18 29.58 -37.05 -4.22 3.71 11.23 - 36.3 40.47 3.07 

Fidelity Spartan Total Market - Adv Cl FSTVX 4,062.7 0.07 8.32 9.26 28.43 -37.16 -5.75 2.34 0.87 - 14.9 22.59 1.67 

Vanguard Small-Cap Value - Inv VISVX 3,928.3 0.28 6.70 13.45 30.34 -32.05 -2.61 2.84 7.64 - 13.4 29.38 1.65 

Vanguard Intermediate-Term Bond - Inv VBIIX 3,837.3 0.22 3.59 13.24 6.79 4.93 9.18 7.56 7.35 6.88 - 6.93 3.87 

ING US Stock Index Port I INGIX 3,794.1 0.26 7.90 7.62 26.22 -37.12 -6.68 1.51 - - 14.3 21.97 0.64 

Vanguard Intermediate-Term Bond - Adm VBILX 3,766.1 0.12 3.61 13.34 6.89 5.01 9.28 7.65 7.42 6.93 - 6.93 3.98 

9DQJXDUG3DFLûF6WRFN,QY VPACX 3,707.0 0.27 7.56 7.33 21.18 -34.36 -7.81 2.09 2.25 1.31 12.3 23.53 2.46 

Vanguard Growth - Instl VIGIX 3,698.7 0.08 10.98 9.94 36.50 -38.19 -3.51 3.47 -0.78 6.94 16.6 22.36 1.21 

Vanguard Small-Cap Growth - Inv VISGX 3,561.5 0.28 11.03 16.64 41.85 -40.00 -2.59 5.20 5.52 - 18.3 29.01 0.24 

Vanguard Balanced - Inv VBINX 3,506.9 0.25 5.73 8.98 20.05 -22.21 -0.17 4.33 3.36 7.01 14.4 14.05 2.42 

Vanguard Mid Cap - Adm VIMAX 3,475.2 0.14 9.95 15.05 40.48 -41.78 -3.69 3.87 5.91 - 15.5 26.37 1.02 

Vanguard Emerging Markets Stock - Instl VEMIX 3,473.4 0.23 11.97 14.00 76.35 -52.74 -4.50 14.46 14.56 9.57 14.2 33.54 1.18 

Fidelity Spartan Extended Mkt - Inv Cl FSEMX 3,381.6 0.10 9.86 16.25 36.65 -38.45 -2.76 4.56 3.87 - 15.9 26.30 0.99 

ING U.S. Bond - Portfolio Class I ILBAX 3,323.6 0.46 1.53 7.81 5.88 - - - - - - - 2.65 

Vanguard Balanced - Instl VBAIX 3,267.8 0.08 5.83 9.19 20.18 -22.10 0.00 4.48 3.49 7.10 14.4 14.03 2.59 

Vanguard Extended Market - Adm VEXAX 3,253.3 0.13 9.42 15.21 37.65 -38.63 -3.08 4.13 3.73 8.05 15.8 27.19 0.98 

Vanguard Value - Instl VIVIX 3,235.5 0.08 5.79 6.72 19.79 -35.88 -8.42 0.94 1.40 6.84 12.6 22.58 2.67 

Vanguard Value - Inv VIVAX 3,216.6 0.26 5.74 6.52 19.58 -35.97 -8.58 0.77 1.26 6.73 12.6 22.56 2.49 

Source: Morningstar. Data as of October 31, 2010. Exp Ratio is expense ratio. YTD is year-to-date. 3-, 5-, 10- and 15-yr returns are annualized. P/E is price-to-earnings ratio. 

Std Dev is 3-year standard deviation. Yield is 12-month. 

60 


Morningstar U.S. Style Overview Jan. 1 - Oct. 31, 2010 

Trailing Returns % 

3-Month YTD 1-Yr 3-Yr 5-Yr 10-Yr 

Morningstar Indexes 

US Market 8.47 8.90 18.28 –5.77 2.36 0.66 

Large Cap 8.15 6.60 14.70 –6.84 1.65 –1.07 

Mid Cap 9.50 14.79 27.56 –3.54 3.97 4.98 

Small Cap 8.79 15.32 28.99 –1.91 4.36 6.38 

US Value 6.92 8.92 16.46 –8.10 0.76 3.75 

US Core 6.78 8.29 17.48 –4.38 3.32 2.25 

US Growth 12.26 9.67 21.28 –5.10 2.67 –4.58 

Morningstar Market Barometer YTD Return % 

Large Cap 

US Market 

8.90 

6.60 

Value 

8.92 

Core 

8.29 

Growth 

9.67 

7.43 5.54 7.16 

Large Value 6.87 7.43 13.02 –10.33 –0.28 1.92 

Large Core 5.82 5.54 13.43 –5.23 2.78 0.35 

Large Growth 12.49 7.16 18.21 –5.24 2.00 –6.28 

Mid Cap 

14.79 

12.48 15.76 16.04 

Mid Value 7.25 12.48 24.56 –2.87 2.89 8.08 

Mid Core 9.85 15.76 28.86 –2.87 4.35 7.48 

Mid Growth 11.50 16.04 29.18 –5.07 4.39 –0.54 

Small Cap 

15.32 

14.16 14.25 17.61 

Small Value 6.49 14.16 29.74 0.78 4.66 11.09 

Small Core 7.85 14.25 26.42 –2.50 4.18 8.39 

Small Growth 12.19 17.61 30.87 –3.99 3.90 –0.13 

–8.00 –4.00 0.00 +4.00 +8.00 

Sector Index YTD Return % 

Media 20.06 

Consumer Goods 16.11 

Industry Leaders & Laggards YTD Return % 

Resorts & Casinos 91.62 

Drug Related Products 60.61 

Biggest Influence on Style Index Performance 

Best Performing Index 

YTD 

Return % 

Small Growth 17.61 

Constituent 

Weight % 

Consumer Services 16.05 

Industrial 13.95 

Hardware 9.36 

9.08 

Business Services 8.46 

Software 6.62 

Utilities 6.50 

Energy 4.57 

Financial Services 3.86 

Healthcare 2.91 

Recreational Goods, Other 54.42 

Auto Parts Stores 54.41 

Major Airlines 50.32 

General Entertainment 45.93 

–16.58 Publishing - Newspaper 

–16.87 Aluminum 

–17.08 Long-Term Care Facilities 

–21.69 Regional - Mid -Atlantic Banks 

–32.06 Education & Training Services 

–42.35 Dairy Products 

Riverbed Technology Inc. 150.72 0.58 

TIBCO Software Inc. 99.58 0.74 

Sotheby's 96.00 0.69 

Informatica Corp. 57.23 1.05 

WABCO Holdings Inc. 79.99 0.75 

Worst Performing Index 

Large Core 5.54 

Philip Morris International Inc. 25.78 2.87 

International Business Machines Corp. 11.33 5.41 

McDonald's Corp. 27.60 2.11 

Caterpillar Inc. 41.49 1.11 

Procter & Gamble Co. 8.07 5.56 

1-Year 

3-Year 

5-Year 

Value 

Core 

Growth 

Value 

Core 

Growth 

Value 

Core 

Growth 

Large Cap 

13.02 

13.43 

18.21 

Large Cap 

–10.33 

–5.23 

–5.24 

Large Cap 

–0.28 

2.78 

2.00 

Mid Cap 

24.56 

28.86 29.18 

Mid Cap 

–2.87 

–2.87 –5.07 

Mid Cap 

2.89 

4.35 4.39 

Small Cap 

29.74 

26.42 30.87 

Small Cap 

0.78 

–2.50 –3.99 

Small Cap 

4.66 

4.18 3.90 

–20 –10 0 +10 +20 

–20 –10 0 +10 +20 

–20 –10 0 +10 +20 

Source: Morningstar. Data as of Oct. 31, 2010. 

Notes and Disclaimer: ©2010 Morningstar, Inc. All Rights Reserved. Unless otherwise noted, all data is as of most recent month end. Multi-year returns are annualized. NA: Not Available. Biggest Influence on Index Performance lists 

are calculated by multiplying stock returns for the period by their respective weights in the index as of the start of the period. Sector and Industry Indexes are based on Morningstar's proprietary sector classifications. The information ? 

contained herein is not warranted to be accurate, complete or timely. Neither Morningstar nor its content providers are responsible for any damages or losses arising from any use of this information. 



61

Dow Jones U.S. Industry Review 

Dow Jones U.S. Industry Review 

Performance 

Index Name Weight 1-Month 3-Month YTD 1-Year 3-Year 5-Year 10-Year 

Dow Jones U.S. Index 100.00% 3.94% 8.47% 8.79% 18.16% -5.77% 2.35% 0.58% 

Dow Jones U.S. Basic Materials Index 3.60% 6.34% 15.14% 16.25% 32.45% -2.32% 9.90% 9.60% 

Dow Jones U.S. Consumer Goods Index 10.39% 4.66% 8.58% 13.81% 21.62% 1.04% 6.13% 6.35% 

Dow Jones U.S. Consumer Services Index 12.22% 5.09% 12.35% 16.88% 27.67% -0.30% 3.50% 1.81% 

Dow Jones U.S. Financials Index 15.85% 1.84% 0.57% 3.45% 8.00% -19.10% -9.12% -2.12% 

Dow Jones U.S. Health Care Index 11.09% 1.69% 9.74% 2.29% 14.21% -1.82% 3.06% 0.90% 

Dow Jones U.S. Industrials Index 12.73% 3.35% 7.34% 14.54% 26.85% -5.81% 3.76% 1.38% 

Dow Jones U.S. Oil & Gas Index 10.51% 5.24% 10.53% 4.01% 6.59% -6.94% 5.90% 8.78% 

Dow Jones U.S. Technology Index 16.79% 6.50% 11.15% 7.66% 20.37% -1.96% 6.08% -4.52% 

Dow Jones U.S. Telecommunications Index 3.02% 1.15% 11.27% 10.98% 24.00% -8.04% 4.54% -3.85% 

Dow Jones U.S. Utilities Index 3.80% 1.55% 6.16% 7.03% 18.23% -5.57% 3.84% 3.06% 

Risk-Return 

5% 

3-Year Annualized Return 

0% 

-5% 

-10% 

-15% 

-20% 

Consumer Goods 

Health Care 

Utilities Composite 

Telecommunications 

Consumer Services 

Oil & Gas 

Technology 

Industrials 

Basic Materials 

Financials 

-25% 

14% 16% 18% 20% 22% 24% 26% 28% 30% 32% 34% 36% 

3-Year Annualized Risk 

Industry Weights Relative to Global ex-U.S. 

Asset Class Performance 

Basic Materials 

-8.54% 

U.S. [83.67] Global ex-U.S. [79.48] Commodities [82.15] 

REITs [83.25] Infrastructure [94.54] 

Consumer Goods 

-2.24% 

160 

Consumer Services 

4.72% 

140 

Financials 

-8.97% 

Health Care 

5.51% 

120 

Industrials 

-0.37% 

100 

Oil & Gas 

0.81% 

80 

Technology 

11.91% 

Telecommunications 

-2.21% 

60 

Utilities 

-0.62% 

40 

-15% -10% -5% 0% 5% 10% 15% 

Underweight Overweight 

20 

10/07 1/08 4/08 7/08 10/08 1/09 4/09 7/09 10/09 1/10 4/10 7/10 10/10 

Chart compares industry weights within the Dow Jones U.S. Index to industry weights within the Dow Jones U.S. = Dow Jones U.S. Index | Global ex-U.S. = Dow Jones Global ex-U.S. Index 

Global ex-U.S. Index 

Commodities = Dow Jones-UBS Commodity Index | REITs = Dow Jones U.S. Select REIT Index 

Infrastructure = Dow Jones Brookfield Global Infrastructure Index 

© Dow Jones & Company, Inc. 2010. All rights reserved."Dow Jones", "Dow Jones Indexes", "Dow Jones U.S. Index", "Dow Jones Global ex-U.S. Index" and "Dow Jones U.S. Industry Indexes" are service marks of Dow Jones & Company, Inc. "UBS" is a registered trademark of UBS AG. "Dow Jones-UBS Commodity Index" is a service 

mark of Dow Jones & Company, Inc. and UBS. "Brookfield" is a service mark of Brookfield Asset Management Inc. or its affiliates. The "Dow Jones Brookfield Infrastructure Indexes" are published pursuant to an agreement between Dow Jones & Company, Inc. and Brookfield Asset Management. Investment products that may be based 

on the indexes referencedare not sponsored,endorsed, sold or promoted by Dow Jones, and Dow Jones makes no representationregarding the advisability of investing in them. Inclusion of a company in these indexes does not in any way reflect an opinion of Dow Jones on the investment merits of such company. Index performance is for 

illustrative purposes only and does not represent the performance of an investment product that may be based on the index. Index performance does not reflect management fees, transaction costs or expenses. Indexes are unmanaged and one cannot invest directly in an index. 

The Dow Jones U.S. Index, the Dow Jones Global ex-U.S. Index and the Dow Jones U.S. Industry Indexes were first published in February 2000. The Dow Jones Brookfield Infrastructure Index was first published in July 2008. To the extent this document includes information for the index for the period prior to its initial publication date, 

such information is back-tested (i.e., calculations of how the index might have performed during that time period if the index had existed). Any comparisons, assertionsand conclusions regarding the performance of the Index during the time period prior to launch will be based on back-testing. Back-tested information is purely hypothetical 

and is provided solely for informational purposes. Back-tested performance does not represent actual performance and should not be interpreted as an indication of actual performance. Past performance is also not indicative of future results. 

Data as of October 29, 2010 

Source: Dow Jones Indexes Analytics & Research 

For more information, please visit the Dow Jones Indexes Web site at www.djindexes.com. 

62 


Exchange-Traded Funds Corner 

Largest New ETFs Sorted By Total Net Assets In $US Millions 

Covers ETFs and ETNs launched during the 12-month period ended October 31, 2010. 

Fund Name Ticker ER 1-Mo 3-Mo YTD Launch Date Assets 

Market Vectors Junior Gold Miners GDXJ 0.59 8.32 35.66 40.56 11/10/2009 1,573.9 

Vanguard Short-Term Corp Bond VCSH 0.15 0.26 1.57 6.09 11/19/2009 893.2 

PowerShares Build America Bond BAB 0.28 -1.92 1.84 13.04 11/17/2009 569.5 

ETFS Physical Platinum PPLT 0.60 2.71 8.05 - 1/8/2010 566.8 

ETFS Physical Palladium PALL 0.60 14.37 29.79 - 1/8/2010 538.2 

PIMCO Enh Short Maturity Strategy MINT 0.35 0.10 0.64 1.47 11/16/2009 441.5 

Vanguard Intermediate Corp Bond VCIT 0.15 0.43 3.49 13.03 11/19/2009 391.6 

WisdomTree Emrg Mrkts Local Debt ELD 0.55 0.99 - - 8/9/2010 374.0 

Schwab International Equity SCHF 0.13 3.39 9.49 5.20 11/3/2009 362.2 

Schwab U.S. Broad Market SCHB 0.06 3.88 8.38 9.06 11/3/2009 354.9 

Schwab U.S. Large-Cap SCHX 0.08 3.92 8.21 8.44 11/3/2009 333.4 

Alerian MLP AMLP 0.85 3.17 - - 8/25/2010 309.5 

Schwab U.S. Small-Cap SCHA 0.13 4.08 8.55 15.03 11/3/2009 246.3 

iShares MSCI Indonesia EIDO 0.65 3.37 15.48 - 5/5/2010 224.4 

Schwab Emerging Markets Equity SCHE 0.25 3.17 11.57 - 1/14/2010 216.4 

Global X China Consumer CHIQ 0.65 0.79 18.45 24.48 12/1/2009 174.9 

First Trust BICK BICK 0.64 3.01 11.81 - 4/12/2010 170.7 

Global X Silver Miners SIL 0.65 8.85 39.79 - 4/20/2010 167.6 

PowerShares CEF Income PCEF 1.81 0.74 5.96 - 2/19/2010 166.1 

E-TRACS Alerian MLP Infrastr ETN MLPI 0.85 4.93 7.39 - 4/1/2010 159.1 

Source: Morningstar. Data as of October 31, 2010. ER is expense ratio. 1-Mo is 1-month. 3-Mo is 3-month. YTD is year-to-date. 

Selected ETFs In Registration 

DBX MSCI Japan Currency-Hedgd Eqty 

Emerald Rock Dividend Growth 

EGS INDXX India Mid Cap 

ETFS Leveraged Wheat 

Global X Russell Emrg Mkts Growth 

Grail Western Asset Enh Liquidity 

iShares Barclays 0-5 Year TIPS Bond 

J.P. Morgan Physical Copper 

Jefferies S&P 500 VIX Shrt-Tm Futures 

Market Vectors All China Healthcare 

Pimco Prime Ltd Maturity Strategy 

PowerShares S&P 500 Low Beta 

PowerShares S&P Bank Loan 

ProShares Hedge Replication 

Riverfront Strategic Income 

Russell 1000 High Volatility 

Rydex Russell BRIC Equal Weight 

SPDR Emerging Markets Dividend 

Teucrium Soybean 

WisdomTree EMEA Bond 

Source: IndexUniverse.com’s ETF Watch 

Largest U.S.-listed ETFs Sorted By Total Net Assets In $US Millions 


Fund Name Ticker Assets Exp Ratio 3-Mo YTD 2009 2008 3-Yr 5-Yr Mkt Cap P/E Std Dev Yield 

SPDR S&P 500 SPY 80,633.8 0.09 8.00 7.82 26.31 -36.70 -6.43 1.80 45,787 15.1 1.86 21.77 

SPDR Gold GLD 55,972.2 0.40 14.83 23.59 24.03 4.92 19.04 23.37 - - - 21.59 

iShares MSCI Emerging Markets EEM 48,035.3 0.72 11.40 11.91 68.82 -48.87 -4.18 13.65 18,699 14.9 1.30 33.11 

Vanguard Emerging Markets VWO 40,817.0 0.27 11.94 14.32 76.26 -52.54 -4.67 14.09 15,648 14.2 1.17 32.89 

iShares MSCI EAFE EFA 36,185.1 0.35 9.82 5.02 26.88 -41.00 -9.75 3.19 30,636 13.7 2.38 27.43 

PowerShares QQQ QQQQ 23,150.2 0.20 14.15 14.64 54.67 -41.72 -1.26 6.55 43,514 19.4 0.63 26.50 

iShares S&P 500 IVV 23,053.1 0.09 8.00 7.88 26.61 -37.00 -6.47 1.78 44,129 14.7 1.87 21.84 

iShares Barclays TIPS Bond TIP 20,751.7 0.20 5.28 9.48 8.95 -0.53 7.32 6.15 - - 2.50 9.03 

Vanguard Total Stock Market VTI 15,397.6 0.07 8.49 9.23 28.89 -36.95 -5.59 2.51 21,970 14.4 1.91 22.63 

iShares iBoxx $ Inv Gr Corp Bond LQD 14,481.9 0.15 3.06 11.97 8.58 2.44 7.66 6.54 - - 4.80 12.27 

iShares Russell 2000 IWM 12,767.5 0.28 8.38 13.51 28.53 -34.15 -3.84 3.23 858 15.8 1.12 27.47 

iShares Barclays Aggregate Bond AGG 12,474.5 0.24 1.45 7.99 3.01 7.90 6.91 6.26 - - 3.46 5.48 

iShares MSCI Brazil EWZ 11,658.0 0.65 9.46 3.71 121.50 -54.37 0.10 23.06 27,301 13.9 3.40 40.73 

iShares Russell 1000 Growth IWF 11,437.8 0.20 10.55 9.23 36.73 -38.21 -3.98 3.13 34,871 17.3 1.34 22.31 

SPDR S&P MidCap 400 MDY 10,875.8 0.25 9.32 15.09 37.52 -36.40 -1.73 4.76 2,983 18.1 1.02 25.72 

Vanguard Total Bond Market BND 9,162.4 0.12 1.88 8.03 3.67 6.88 7.17 - - - 3.48 4.92 

iShares Russell 1000 Value IWD 8,888.8 0.20 6.25 7.62 19.23 -36.45 -8.54 0.61 30,897 13.0 2.10 23.03 

iShares FTSE/Xinhua China 25 FXI 8,553.6 0.73 9.05 7.61 47.28 -47.73 -13.20 20.69 70,251 15.3 1.52 35.56 

iShares Barclays 1-3 Yr Treas Bond SHY 8,482.1 0.15 0.58 2.66 0.36 3.00 3.89 4.36 - - 1.09 1.96 

iShares S&P 400 MidCap IJH 8,112.8 0.22 9.43 15.32 37.81 -36.18 -1.48 4.95 2,980 18.1 1.18 25.65 

SPDR DJ Industrial Average DIA 8,058.8 0.17 6.89 8.80 22.72 -32.10 -4.56 3.93 100,162 13.9 2.37 19.80 

iShares Silver SLV 7,839.7 0.50 37.49 46.14 47.67 -23.79 18.95 - - - - 36.15 

Market Vectors Gold Miners GDX 7,366.1 0.53 18.82 23.99 36.72 -26.07 4.88 - 14,617 27.8 0.19 47.95 

Barclays 1-3 Year Credit Bond CSJ 7,358.9 0.20 0.82 3.23 7.17 3.84 5.15 - - - 2.69 4.28 

iBoxx $ High Yield Corporate Bond HYG 7,149.7 0.50 4.52 10.34 28.86 -17.40 5.09 - - - 8.40 19.36 

Source: Morningstar. Data as of October 31, 2010. Exp Ratio is expense ratio. 3-Mo is 3-month. YTD is year-to-date. 3-Yr and 5-Yr are 3-year and 5-year annualized returns, respectively. 

Mkt Cap is geometric average market capitalization. P/E is price-to-earnings ratio. Std Dev is 3-year standard deviation. Yield is 12-month. 


63

Weighty Matters 

HUMOR 

Better Beta By Bananas 

By Lara Crigger 

Devising a perfect 

weighting scheme is 

harder than it looks 

Not too long ago, our editor-in-chief Jim 

Wiandt called the entire analytics R&D team 

into his office and offered a proposition. 

“I grow weary of today’s weighting 

schemes,” he said. “Let us devise a better one.” 

Market-cap indexes, he dismissed, are 

too hierarchal, while equal weighting is 

too communist. Fundamental indexes, he 

said, “encourage polygamy.” 

“Make me an index that’s flash-crash proof,” 

he said. “One that fairly and accurately represents 

all companies in a sector, regardless of 

size. And smells like fresh-baked muffins.” 

For hours, my fellow analysts and I sequestered 

ourselves away in our top secret Index 

Laboratory. We scribbled down differential 

equations and Fourier transforms until our 

fingers bled. We converted a pickle barrel 

into a coffee pot. We programmed 10,000 

lines of BASIC using only our left pinky toes. 

And then we got to work. 

Suzanne King, our fundamentals expert, 

advocated a Darwinian approach. Each 

year, she argued, one male and one female 

from the executive board of each company 

should be sent into an elaborate maze, 

where they’d fight other companies’ teams 

to the death—the results of which would 

be filmed before a live studio audience. 

“Isn’t that the plot of ‘The Hunger 

Games’?” I asked. 

“Investing is just like children’s literature,” 

she said. “Only the strongest survive.” 

Our head technician, Percival Polynomian, 

argued that trend-following would 

better serve investors. He derived an algorithm 

that weighs companies based on 

their daily volume, stochastic oscillators 

and current ranking on the Billboard charts. 

It worked pretty well, too—at least until 

we discovered the algorithm ranked Justin 

Bieber higher than Apple. 

Frustrated, I turned at last to Bobo, our 

senior vice president of Analytics. Bobo, 

for our newer readers, is an overweight 

chimp in a lab coat, who we pay in ripe 

fruit. He also has a corner office. 

Vice President Bobo offered up a detailed 

plan that would weight stocks by their 

intrinsic Banana Quotient, a quantitative 

factor that predicts the presence or absence 

of vegetative quality. I probed for details, 

but Bobo soon became too engrossed in 

grooming Suzanne for nits. 

In any case, we presented our various 

proposals to Jim the next day. He listened 

to the presentation with his trademark 

placid smile and congratulated us on our 

fine work. But as the research team filed 

out of the room, he pulled me aside. 

“Crigger, I’m concerned,” he said. “None 

of these proposals is quite right.” 

“It’s a thorny problem, sir,” I replied. 

“People have been searching for the perfect 

index for decades. You gave us four 

hours and a hungry monkey.” 

“It’s good enough for the SEC,” Jim said 

sternly. “Do better.” 

Thus, the team and I went back to the 

laboratory. After an all-nighter and two 

cases of Red Bull, we finally hit upon the 

perfect index: a Platonic ideal of technicals 

and fundamentals, immune to even the 

tiniest market flicker; whereby, if it were to 

be put into practice, the absolute minimum 

number of employees would be sent to jail. 

It also smelled faintly of snickerdoodles. 

I pitched our plan to Jim the next day. 

“Not bad, Crigger,” he said. 

I flushed with pride and an absence of nits. 

“But,” he said, after a thoughtful pause, 

“I still think it needs something. Something 

new, something exciting.” 

He clapped his hands. “I know,” Jim 

said. “It needs more cowbell.” 

And thus I unveil Index Publications’ newest 

enterprise: The Justin Bieber Bobotronic 

Mockingjay And Now With More Cowbell 

Total Return Index. Keep checking back for 

our methodology, which we’ll publish as soon 

as Legal approves the use of Comic Sans. 

It’ll be scratch ‘n’ sniff. 

64 


INVESTING IN 

THE FUTURE OF THE U.S. DOLLAR 

POWERSHARES DB CURRENCY FUNDS PROVIDE CONVENIENT AND IMMEDIATE ACCESS TO THE PERFORMANCE 

OF THE U.S. DOLLAR. THE POWERSHARES DB US DOLLAR BULLISH FUND (UUP) AND THE POWERSHARES DB US 

DOLLAR BEARISH FUND (UDN) TRADE ON THE NYSE ARCA AND CAN BE PURCHASED JUST LIKE STOCKS THROUGH 

A BROKERAGE ACCOUNT. 

THE U.S. DOLLAR HISTORICALLY HAS EXPERIENCED LONG PERIODS OF APPRECIATION AND DEPRECATION 

AGAINST OTHER CURRENCIES. YOU CAN ACT ON YOUR VIEW OF WHETHER THE U.S. DOLLAR WILL APPRECIATE OR 

DEPRECIATE BY INVESTING IN EITHER UUP OR UDN. 

UUP 

The PowerShares DB US Dollar Bullish Fund 

UDN 

The PowerShares DB US Dollar Bearish Fund 

The PowerShares DB US Dollar Bullish Fund allows investors to take 

a long position on the U.S. Dollar against the Euro, Japanese Yen, 

British Pound, Canadian Dollar, Swedish Krona and Swiss Franc. 

The PowerShares DB US Dollar Bearish Fund allows investors to take 

a short position on the U.S. Dollar against the Euro, Japanese Yen, 

British Pound, Canadian Dollar, Swedish Krona and Swiss Franc. 

Euro 

Euro 

Japanese yen 

Japanese yen 

U.S. dollar 

vs. 

British pound 

Canadian dollar 

British pound 

Canadian dollar 

vs. 

U.S. dollar 

Swedish krona 

Swedish krona 

Swiss franc 

Swiss franc 

PowerShares.com/DB

VTI 

Vanguard Total Stock Market ETF 

Are you Vanguarding TM your clients’ portfolios? 

VTI has a 63% lower expense ratio than the industry average. * 

Take a closer look at VTI. With a lower expense ratio than the industry average, your 

clients can keep more of their returns. To see how VTI can have an impact on your 

clients’ portfolios and help them reach their goals, visit advisors.vanguard.com/VTI. 

All investments are subject to risk. Vanguard funds are not insured or guaranteed. 

To buy or sell Vanguard ETFs, contact your financial advisor. Usual commissions apply. Not redeemable. Market price may be more or less 

than NAV. 

For more information about Vanguard ETF Shares, visit advisors.vanguard.com/VTI, call 800-523-8845, or contact your broker 

to obtain a prospectus. Investment objectives, risks, charges, expenses, and other important information are contained in the 

prospectus; read and consider it carefully before investing. 

* Source: Morningstar as of 6/14/2010. Based on industry average expense ratio for total stock market ETFs of 0.19 o /o and Vanguard VTI 

expense ratio of 0.07 o /o. 

© 2011 The Vanguard Group, Inc. All rights reserved. U.S. Pat. No. 6,879,964 B2; 7,337,138. Vanguard Marketing Corporation, Distributor.

RYDEX EQUAL WEIGHT ETFs 

Find out why Rydex Equal Weight ETFs should be considered as part of a well-allocated portfolio. Call 

800.258.4332. Or go to www.rydex-sgi.com/equalweight and watch The Rydex Equal Weight Video—a brief 

look at the potential risk/performance advantages of increased diversification 1 and systematic rebalancing. 

RSP • RCD • RHS • RYE • RYF • RYH • RGI • RTM • RYT • RYU • EWRI • EWRS • EWRM • EWEF • EWEM • EWAC 

1 Diversification does not ensure a profit nor eliminate the risk of experiencing investment losses. 

For more complete information regarding RydexShares ȘM call 800.258.4332 or visit www.rydex-sgi.com for a prospectus and a summary prospectus 

(if available). Investors should carefully consider the investment objectives, risks, charges and expenses of a fund before investing. The fund’s 

prospectus and its summary prospectus (if available) contain this and other information about the fund. Please read the prospectus and summary 

prospectus (if available) carefully before you invest or send money. 

Rydex Equal Weight ETFs may not be suitable for all investors. • Investment returns and principal value will fluctuate so that when shares are redeemed, 

they may be worth more or less than original cost. Most investors will also incur customary brokerage commissions when buying or selling shares of an ETF. 

• Investments in securities are subject to market risks that may cause their prices to fluctuate over time. • ETF Shares may trade below their net asset value 

(“NAV”). The NAV of shares will fluctuate with changes in the market value of an ETF’s holdings. In addition, there can be no assurance that an active trading 

market for shares will develop or be maintained. • Please review a prospectus carefully for more information of the risks associated with each ETF. 

The funds are distributed by Rydex Distributors, LLC (RDL). Security Global Investors SM is the investment advisory arm of Security Benefi t Corporation (Security Benefi t). Security Global 

Investors consists of Security Global Investors, LLC, Security Investors, LLC and Rydex Investments. Rydex Investments is the primary business name for Rydex Advisors, LLC, and Rydex 

Advisors II, LLC. Security Global Investors and RDL are affi liates and subsidiaries of Security Benefi t, which is wholly owned by Guggenheim SBC Holdings, LLC, a special purpose entity 

managed by Guggenheim Partners, LLC.

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