dfa institutional review 2007q4

FIXED INCOMEE

IINTERNATIONALNTERNATIONAL E EQQUITYQUITY

US EQUITY

MMARKET PPERFORMANCE

RESEARCHRR UPDATE

“I“IITT ISIS HARDHARD TOTO CAPTURECAPTURE AA RETURNRETURN PREMIUMPREMIUM IFIF ONEONE ISIS NOTNOTEXPOSED TO THE FACTOR THAT GENERATES THAT PREMIUM.””

“THETT BOTTOM LINE IS THAT...DIMENSIONAL FOCUSESON THE OVERALL CHARACTERISTICS OF A STRATEGY.”

LARGEL ORDERS OF NEEDED NAMES AND ASKTTRADERST TO ACT ON THEM WITH PATIENCE.”

QUARTERLY INSTITUTIONAL REVIEW

FOURTH QUARTER 2007

Research Update p. 2 Despite the magnitude and reliability of the value premium, there is evidence that the average value mutual fund fails to capture and deliver that premium to investors. Dimensional Fund Advisors, on the other hand, delivered the value premium in large and small cap value strategies to its investors from April 1993, the first full month in which Dimensional had large cap value and small cap value strategies in the US, to December 2005. The present article describes in detail the investment process that allowed Dimensional to succeed where so many others have failed.

What’s New at Dimensional p. 11

THE COST OF IMMEDIACY

The inclusion of the ask-bid spread in transaction costs can be understood best by considering the neglected problem of “immediacy” in supply and demand analysis. Predictable immediacy is a rarity in human actions, and to approximate it requires that costs be borne by persons who specialize in standing ready and waiting to trade with the incoming orders of those who demand immediate servicing of their orders. The ask-bid spread is the markup that is paid for predictable immediacy of exchange in organized markets.

Under competitive conditions the ask-bid spread, or markup, will measure the cost of making transactions without delay. A person who has just purchased a security and who desires immediately to resell it will, on the average, be forced to suffer a markdown equal to the spread found in the market place. This markdown (plus brokerage commissions) measures the cost of an immediate round-trip exchange. Under less competitive conditions, this spread may somewhat exaggerate the underlying cost to those who stand ready and waiting of quick round-trip transactions, but, for any given degree of competition (since brokerage commissions do not vary with the time taken to complete a transaction), differences in spread will indicate differences in the cost of quick exchange.

—Harold Demsetz

“The Cost of Transacting.” Quarterly Quarterly QJournal of Economics 82, no. 1 (February 1968): 33-53.

The material in this publication is provided solely as background information for registered investment advisors and institutional investors and is not intended for public use. It should not be distributed to investors of products managed by Dimensional Fund Advisors or potential investors. Unauthorized copying, reproducing, duplicating, or transmitting of this material is prohibited. Dimensional Fund Advisors is an investment advisor registered with the Securities and Exchange Commission.

This article contains the opinions of the author but not necessarily the opinions of Dimensional. All materials presented are compiled from sources believed to be reliable and current, but accuracy cannot be guaranteed. This article is distributed for educational purposes, and it is not to be construed as a recommendation of any particular security, strategy, or investment product.


2

RESEARCH UPDATE

Part II

The first part of this article, published in the third quarter 2007 issue of Dimensional’s Quarterly Institutional Review,1

showed that the average value mutual fund has failed to capture a meaningful share of the value premium in stocks, and thus has failed to reliably deliver excess returns relative to the average growth mutual fund. On the other hand, Dimensional’s US small and large cap value strategies captured and delivered the value premium in stocks to investors from April 1993, the first full month in which Dimensional had large cap value and small cap value strategies in the US, to December 2005. Additionally, the Dimensional US Large Cap Value strategy outperformed the universe of large cap value mutual funds by an average of 17 basis points per month, while the Dimensional US Small Cap Value strategy outperformed the universe of small cap value mutual funds by an average of 30 basis points per month during that same period.2

Dimensional’s ability to capture and deliver that premium is not a random event,3 but rather the result of a skillful investment process that combines scientific research into the sources of financial risks and returns with rigorous portfolio design and expert implementation of the strategies through clever portfolio management and unique trading techniques.

The second part of this article, published here, describes that process. From the set of clearly defined philosophical investment principles to the dynamic integration of portfolio engineering, portfolio management, trading, and the adoption of rules at every stage of the investment process, every step of the investment process facilitates that integration and, more importantly, contributes to the delivery of value-added returns to investors.

Core Beliefs

At the core of Dimensional’s investment approach is the proposition that public capital markets are informationally efficient. Competition among different firms for investors’ capital and between buyers and sellers to find the most attractive returns on their investments ensures that market prices reflect most available information about fundamental values. In these efficient and competitive public markets,

traded securities are fairly priced. For investors in search of large expected returns, the key, then, is to identify the dimensions of expected stock returns and to target those dimensions that are compensated with higher expected returns.

Dimensional’s approach does not require one to determine which securities are mispriced today or to forecast which of those securities will be fairly valued tomorrow, or to have an ability to identify and profit from any market inefficiencies that may or may not exist. It does require, however, that once the dimensions of expected stock returns have been identified, strategies be designed and implemented to target those dimensions accurately, continuously, and in a cost-effective manner. In addition, Dimensional believes that there are opportunities to provide value-added returns by managing around market frictions such as momentum and by providing services such as liquidity to other investors who want immediacy in their trades and are willing to pay for that immediacy.

A Dynamically Integrated Investment Process

Portfolio engineering, portfolio management, and portfolio trading are the three main steps of the investment process. At Dimensional, those three steps are dynamically integrated so that decisions made at one level of the investment process facilitate decisions made and tasks performed at the other two levels. For instance, how an asset class is defined—a decision made at the portfolio engineering level—will impact the flexibility of portfolio managers to manage around market frictions and to minimize turnover—a portfolio management function—as well as how efficiently trading can be implemented—issues related to the portfolio trading function. Other money managers, on the other hand, don’t necessarily take into account the impact that decisions made at one step of the investment process have on the other two steps, and they segregate their activities.

Portfolio Engineering

Portfolio engineering can be broadly divided into three steps: (1) finding the dimensions that determine expected returns; (2) designing strategies that target those dimensions; and (3) defining asset classes along those dimensions in such a way so as to (a) accurately capture the returns of those asset classes and (b) set rules that minimize unnecessary turnover.

As shown in the first part of this article, US large cap value stocks outperformed US large cap growth stocks by an average of 4.4% per year from January 1928 to December 2006, while US small cap value stocks outperformed US small cap growth stocks by an average of 6.1% per year. In

DIMENSIONAL INVESTORS DO CAPTURE THE VALUE PREMIUM


“AFTER ALL, IT IS HARD TO CAPTURE A RETURN PREMIUM IF ONE IS NOT EXPOSED

TO THE FACTOR THAT GENERATES THAT

PREMIUM.”

3

throughout the whole period. Having hold ranges in time may make an index easier to track and reduce portfolio turnover, but it does so at the expense of what matters: continuous exposure to the asset class and the investment of cash flows in the desired asset class.

Table 1 shows Russell indices’ average allocation in June, just before their reconstitution, to securities that leave the indices during the reconstitution process. Between 1990 and 2006, an average of

35% of the Russell 2000 Growth Index was allocated in June to securities that left that Index at reconstitution. For the Russell 2000 Value Index, that percentage was 29%. So, if one wanted to gain exposure to the small cap value asset class by investing $100 in a fund indexed to the Russell 2000 Value Index in June, on average, $29 of those $100 would be invested in securities that do not satisfy an updated definition of the asset class.

TABLE 1AVERAGE ALLOCATION IN JUNE TO SECURITIES

LEAVING THE INDEX AT RELEAVING THE INDEX AT RELEA CONSTITUTION

1990-2006

RUSSELL 3000 1.59%

RUSSELL 3000 VALUE 11.86%

RUSSELL 3000 GROWTH 12.69%

RUSSELL 1000 2.18%



RUSSELL 2000 20.06%



Source: Compiled by Dimensional from Russell securities data. Indices are not available for direct investment. Their performance does not reflect the expenses associated with the management of an actual portfolio.

A look at the difference between the weighted average market capitalization of the Russell 2000 Index and that of the Dimensional US Small Cap Portfolio over time leads to a similar conclusion. On Chart 1, as we move further away in time from reconstitution, the difference between the two weighted average market capitalizations gradually increases. At reconstitution, the weighted average market capitalization of the Index sharply drops and moves closer to that of the Dimensional US Small Cap Portfolio. That is an indication that, unlike the Dimensional strategy, the Index does not necessarily maintain a focused exposure to the small cap asset class throughout the year—only during the reconstitution period and soon thereafter.

international developed markets and in emerging markets, value stocks have also historically outperformed growth stocks.

Similarly, from January 1928 to December 2006, US small cap stocks outperformed US large cap stocks by an average of 4.6% per year. In international developed markets and in emerging markets, small company stocks have also historically outperformed large company stocks.

Those patterns in the behavior of stock returns are consistent with the Fama/French three-factor model, which posits that, in equity markets, expected returns are a function of an asset’s exposure to three common factors: a market factor, a size factor, and a relative price factor. Thus, strategies that have higher expected returns are those that have more focused exposure to those factors. Investors that seek high expected returns—and are willing to accept the corresponding multifactor risks—should tilt strategies toward small cap and value stocks.

To capture the return premiums associated with the factors that determine expected returns, strategies need to be designed in such a way so as to be continually exposed to those factors. After all, it is hard to capture a return premium if one is not exposed to the factor that generates that premium. This may seem to be an obvious point—and, perhaps, it is—but it is one that is often overlooked by many market participants, which may partially explain why the average value mutual fund fails to capture and deliver the value premium. Achieving a continuous exposure to those factors has a cross-sectional component as well as a time-series component.

First, value strategies need to be broadly diversified across the universe of value stocks to maximize the reliability of the outcomes. As financial economists Eugene F. Fama and Kenneth R. French have shown,4 the value premium largely comes from an unpredictable set of value companies that dramatically outperform their peers and move from value toward growth. A value strategy that is not well diversified may exclude from its holdings the very same companies that generate most of the value premium, which may prevent value investors from capturing that premium. The size premium is the result of a similar process. These processes reinforce the need to have diversified portfolios to reliably capture the factor premiums.

Second, strategies need to maintain that broadly diversified exposure over time. To minimize turnover, many indices and money managers have hold ranges in time, whereby they hold a set of securities for a given period of time regardless of whether those securities fit the definition of the asset class


“HAVING HOLD RANGES IN TIME MAY

MAKE AN INDEX EASIER TO TRACK AND REDUCE PORTFOLIO TURNOVER,

BUT IT DOES SO AT THE EXPENSE OF WHAT MATTERS:

CONTINUOUS EXPOSURE TO THE

ASSET CLASS AND THE INVESTMENT OF CASH

FLOWS IN THE DESIRED ASSET CLASS.”

4

CHART 1WEIGHTED AVERAGE MARKET CAPITALIZATION OF THE

RUSSELL 2000 INDEX – US SMALL CAP PORTFOLIO

Russell data copyright © Russell Investment Group 1995-2007, all rights reserved.

The failure of many in the investment industry to rebalance strategies on a continuous basis to ensure that exposure to an asset class is maintained throughout the year introduces unnecessary uncertainty into an investor’s asset allocation. In the case of managers whose strategies are indexed to the Russell indices—or, to any internally developed or commercially available index—their rigid tracking of the indices ensures that they do not have continuous exposure to the asset class they are targeting. The reason is that those indices allow their exposure to the asset classes they intend to represent to drift from one reconstitution period to the next, in some cases twelve months later. As they drift away from higher expected return dimensions toward lower expected return dimensions, the long-term performance of those indices and the strategies indexed to them suffers. The effects of hold ranges in time are even more pronounced when we analyze investors’ cash flows. Because those flows are unexpected, they could come in on reconstitution day, in which case the manager would be buying the desired asset class on the perfect day (i.e., on the day on which the index most closely approximates the targeted asset class); or those flows could come in on any other day, in which case the manager may be forced to buy what used to be the desired asset class.

Having a continuous exposure to an asset class is not costless, however. Frequent rebalancing can continually expose a strategy to the asset class whose returns it is targeting. However, frequent rebalancing also means that portfolio

turnover will be higher and that portfolio trades will be more exposed to downward and upward momentum—that is, the tendency of losers (winners) to continue to perform worse (better) than indicated by their exposures to the market, size, and relative price factors.

Table 2 shows the annualized returns from 1977 to 2006 for various momentum factors around the world. These factors are derived by ranking stocks on their cumulative performance over the previous twelve months.5 For US small cap stocks, the difference between the returns on a portfolio of winning stocks and the returns on a portfolio of losing stocks rebalanced on a monthly basis is 15% per year. For large cap stocks in the United States and in international developed markets, that difference is around 5.5%. Given those magnitudes, the failure to take into account the effects of momentum when evaluating the costs and benefits of rebalancing a portfolio and the frequency of rebalancing can have a large impact on returns.

TABLE 2MOMENTUM RETURNS

1977-2006

UNITED STATES ANNUALIZED RETURNS

US Large Caps 5.6%

US Small Caps 15.0%

WORLD DEVELOPED EX US

Large Caps 5.4%

WORLD DEVELOPED EX US, EX JAPANWORLD DEVELOPED EX US, EX JAPANWORLD DEVELOPED EX US, EX

Large Caps 7.3%

Source: Kenneth R. French and Dimensional Fund Advisors.

Fortunately, those costs can be mitigated during the portfolio management process if portfolios are designed to give portfolio managers and traders flexibility. In the absence of flexibility, a security may have to be purchased or sold at specific points in time, regardless of whether that security is exposed to downward or upward momentum. With flexibility, the negative impact of new value or small cap companies with downward momentum characteristics can be mitigated by delaying their purchase, while the positive impact of securities leaving the asset class with upward momentum characteristics can be captured by delaying their sale. The idea is not to become a momentum trader and try to capture the returns of the momentum factor—which trading costs will prevent;

300

250

200

150

100

501/2005 4/20072006 2007


“DIMENSIONAL DESIGNS ITS

STRATEGIES IN SUCH A WAY THAT

PORTFOLIO MANAGERS AND TRADERS

HAVE ENOUGH FLEXIBILITY TO DELAY

THE PURCHASE OF STOCKS AFFECTED

BY DOWNWARD MOMENTUM AND TO

DELAY THE SALE OF STOCKS AFFECTED BY

UPWARD MOMENTUM.”

5

rather, the idea is to deny liquidity for momentum traders, which would ease this market friction, and to avoid being hurt by downward momentum while enjoying the benefits of upward momentum in the strategies.

Table 3 shows the effects of having a continuous exposure to an asset class—in this case, large cap value stocks in the US and in international developed markets—while at the same time controlling for the impact of upward and downward momentum. Moving from a large cap strategy to a large cap value strategy increases the annual return by 3% in the United States and by 4% in international developed markets. Adding a momentum filter to avoid buying companies that are exposed to downward momentum mitigates the negative impact of downward momentum and adds an additional 44 basis points per year to the international large cap value strategy and 89 basis points per year to the US large cap value strategy. Because there is more rebalancing and trading in value and small cap strategies, momentum effects become more important for those types of strategies. For that reason, the monthly rebalanced strategy controls for both upward and downward momentum. These monthly rebalanced strategies add 38 basis points to the international large cap value strategy and 28 basis points to the US large cap value strategy.

The value added by rebalancing monthly and applying both momentum filters—117 basis points in total for the US large cap value strategy and 82 basis points in total for the international large cap value strategy—can be delivered for at least two reasons. First, Dimensional designs its strategies in such a way that portfolio managers and traders have enough flexibility to delay the purchase of stocks affected by downward momentum and to delay the sale of stocks affected by upward momentum, even if doing so temporarily increases tracking error relative to some target portfolio. That flexibility limits the negative impact that momentum can have on the strategies. It also denies momentum traders the liquidity that they need to lower their trading costs and perhaps make a momentum-based strategy profitable. Second, the

momentum screens that Dimensional has implemented have enabled portfolio managers to incorporate momentum-related information when deciding potential trades.

The final step in the portfolio engineering process is to define an asset class in a way that accurately captures the returns of that asset class and makes implementation of asset class strategies less costly.

Value stocks are often defined as stocks with a low market price relative to fundamentals such as book value, cash flows, earnings, dividends, or sales. That definition, while appropriate in most cases, is not appropriate in all cases. Recognizing those cases in which it is not appropriate can help us capture more accurately the returns of the asset class. Let’s instead define value stocks as stocks with high expected returns. This definition allows us to exclude from a value portfolio stocks such as real estate investment trusts and regulated utilities that are often considered to be value stocks but are fundamentally different from value stocks.6

Table 4 shows monthly average returns and three-factor regression coefficients for REITs, utilities, and several indices. From 1979 to 2006, the monthly average return for the REIT portfolio was 1.28%, while the monthly return for the Dimensional US Small Cap Value Index was 1.58%. Looking at the factor exposures to the market, size, and relative price factors—as measured by the b, s, and h coefficients, h coefficients, hrespectively—for REITs and small cap value stocks, we can see that 27 basis points, or 90%, of the performance differential between REITs and the Dimensional US Small Cap Value Index can be attributed to their different exposure to the market factor.7 REITs and small cap value stocks had

TABLE 3PROVIDING A CONTINUOUS EXPOSURE AND CONTROLLING FOR

MOMENTUM INDEX RETURNS (NO COSTS)1976-2006

WORLD DEVELOPED EX US LARGE CAP LARGE CAP VALUE

REBALANCING FREQUENCY ANNUAL ANNUAL MONTHLY

DOWNWARD MOMENTUM FILTER YES YES

UPWARD MOMENTUM FILTER YES

AVERAGE ANNUAL AVERAGE ANNUAL A TOTAL RETURN (%) 13.08 17.28 17.72 18.10

UNITED STATES MARKETWIDE MARKETWIDE VALUEMARKETWIDE VALUEM

REBALANCING FREQUENCY ANNUAL ANNUAL MONTHLY

DOWNWARD MOMENTUM FILTER

UPWARD MOMENTUM FILTER

AVERAGE ANNUAL AVERAGE ANNUAL A TOTAL RETURN (%) 13.43 16.54 17.43 17.71

Source: Data simulated by Dimensional Fund Advisors. Simulated returns do not represent trading in actual accounts, and the performance results do not represent the impact that material economic and market factors might have on the decision-making process of a prospective investor if the assets had been actually invested during that period. Simulated performance may differ from actual performance because simulations are built through the retroactive application of a strategy designed with the benefit of hindsight.


6

a similar exposure to the value factor (0.64 for REITs vs. 0.69 for small cap value stocks), so they both capture a comparable fraction of the value premium.

TABLE 4ASSET CLASS RETURNS

1979-2006

MONTHLY AVERAGE RETURNAVERAGE RETURNAVERAGE RE b s h

Utilities 1.12% 0.66 -0.16 0.61

Dow Jones Wilshire REIT Index 1.28% 0.66 0.45 0.64

Russell 3000 Index 1.15% 1.01 -0.08 0.02

S&P 500 Index 1.15% 1.00 -0.22 0.01

US Large Cap Value IndexUS Large Cap Value IndexUS Large Cap Value Inde 1.38% 1.11 -0.06 0.60

US Small Cap Value Index 1.58% 1.07 0.81 0.69

MKT – T-BILLS SMB HML

AVERAGE MONTHLY PREMIUM 0.65% 0.17% 0.42%Sources: Utilities data provided by Fama/French. Dow Jones Wilshire data provided by Dow Jones Indexes. Russell data copyright © Russell Investment Group 1995-2008, all rights reserved. The S&P data are provided by Standard and Poor’s Index Services Group. US value indices are compiled by Dimensional from CRSP and Compustat securities data. Indices are not available for direct investment. Their performance does not reflect the expenses associated with the management of an actual portfolio.

A similar story emerges when we look at regulated utilities and compare them to large cap value stocks. From 1979 to 2006, the monthly average return for the utilities portfolio was 1.12%, while the monthly average return for the US Large Cap Value Index was 1.38%. Again, most of the performance differential between utilities and large cap value stocks can be attributed to their different exposures to the market factor. The differences in exposure between utilities and large cap value stocks to the size and value factors are trivial, as is the impact on the performance of utilities relative to large cap value stocks that arises from those differences.

In contrast, Russell and other fund managers do not exclude these lower expected return securities from their value indices and strategies, which drives down their expected returns by not capturing a fraction of the largest of the three factor premiums, the market premium (see Table 5).

TABLE 5REITS AND UTILITIES ALLOCATION WITHIN RUSSELL INDICES

AS OF DECEMBER 31, 2006

RUSSELL 1000

RUSSELL 1000

VALUE

RUSSELL 1000

GROWTH

RUSSELL 2000

RUSSELL 2000

VALUE

RUSSELL 2000

GROWTH

UTILITIES 3.9% 6.5% 1.4% 3.0% 0.1% 5.6%

REITS 2.2% 3.5% 0.8% 7.5% 11.8% 2.9%

Source: Russell data copyright © Russell Investment Group 1995-2008, all rights reserved.

Finally, we look at how an asset class is defined, as that

can have a considerable impact on portfolio turnover and, by extension, trading costs. For instance, let’s focus our attention on an example that is easy to explain, the international small cap asset class. There are at least three ways of establishing the breakpoints that separate small cap stocks from large cap stocks. First, size breakpoints can be determined using a market percentile approach, in which a fixed percentage of the market (e.g., the bottom 12.5% of the total market capitalization of a country or group of countries) is targeted. Under this approach, each country or group of countries has a weight in the strategy that is proportional to the size of its market and, additionally, each country’s weight is identical across all size segments (e.g., all countries have the bottom 12.5% of the total market capitalization in the small cap universe and the top 87.5% of the total market capitalization in the large cap universe). However, the market percentile approach has two well-known problems. First, it does not have size integrity across markets. For instance, the company at market capitalization percentile 87.5 in New Zealand has a market capitalization of approximately $370 million. In Switzerland, the company at that same percentile has a market capitalization of approximately $8.5 billion. Second, the value of the market percentile breakpoint that divides large caps and small caps in a market is driven by the performance of the mega cap companies in that market. That performance induces large turnover in small caps due to breakpoint movements and not due to companies outgrowing their size range. Mega cap companies tend to represent a large fraction of the market capitalization in each country; so, given that the breakpoint is a function of the market capitalization (e.g., 12.5% of a country’s total market capitalization), differences in performance—and volatility—between those large companies and the rest of the market affect the breakpoint between large and small companies and generate unnecessary turnover.

A second way of defining size breakpoints is to use a fixed market capitalization level measured in USD or some other international currency (e.g., small cap companies are defined as those companies with a market capitalization of $3.2 billion or less). The main advantage of this approach is that it achieves perfect size integrity across markets. The main disadvantage of this approach is that the percentage of the market that falls under a size segment can vary widely across countries. For instance, using a $3-$3.5 billion breakpoint, the small cap universe would cover as much as 47% of the total market capitalization in New Zealand and as little as 4% of the total market capitalization in Switzerland. In addition, a hard-dollar ceiling does not reflect market movements, which can lead to an inaccurate definition of an asset class and unnecessary turnover. This breakpoint methodology may also trigger higher turnover due to currency volatility, which is an


“THE MOMENT A PORTFOLIO MANAGER

WANTS TO HAVE ZERO TRACKING

ERROR WITH RESPECT TO SOME TARGET

PORTFOLIO, HE CHANGES THE NATURE

OF HIS TRADES IN A WAY THAT IS HARMFUL

FOR THE PORTFOLIO. HE NOW REQUIRES IMMEDIACY IN HIS

TRADES.”

7

external factor. In the particular case of a USD breakpoint in a world ex US portfolio, it is not natural to have a breakpoint denominated in a currency that is not the currency of any of the countries in the portfolio. A large appreciation or depreciation of the US dollar relative to the currencies of the countries in the portfolio could trigger large—as well as costly and unnecessary—rebalancing events.

The third approach to defining size breakpoints, the approach that Dimensional developed more than five years ago, is to use a fixed number of names to define size segments within each market. This approach avoids the turnover generated by the performance of mega cap companies in a country or group of countries and the turnover due to currency volatility. It also provides good asset class stability because changes in the value of the breakpoint are correlated with the performance of small caps, and turnover is generally a function of companies outgrowing the asset class, not the result of breakpoint volatility. To maintain a consistent market capitalization representation and size integrity across markets, the number of names can be chosen so that (a) it represents a similar fraction of the total market capitalization of each country or group of countries and (b) the market capitalization value of the breakpoint in any country is within a given range of the market capitalization breakpoints of all the other countries. These two goals can be enforced with bounds to minimize turnover and guarantee the appropriate asset class exposure.

We have attempted to describe some of the steps of the portfolio engineering process. In the next two sections, we explain how those steps and rules in portfolio engineering facilitate the task of portfolio managers and traders.

Portfolio Management

Because trading does not occur in a vacuum, portfolio managers must balance the benefits of implementing portfolios that achieve the desired asset class exposure versus the costs of achieving that exposure. How portfolio rules are implemented can have a significant impact on (a) whether the actual portfolio achieves what it set out to do (i.e., deliver the returns of the asset class); and (b) at what cost. The rules also give portfolio managers enough flexibility to deal with events that cannot easily and efficiently be codified into rules, such as cash flows from clients, for instance.

The interaction between portfolio engineering and

portfolio management can be expressed by the Fama/French three-factor model as shown in the following equation:

R – Rtarget

= a + (b – btarget

= a + (b – btarget target

) (Mkt – Tbills) + (s – s

target) SmB + (h – h

target

) SmB + (h – htarget

target) HmL + e (1)

Equation 1 gives us an indication of how close an actual portfolio is to the target portfolio. It is the responsibility of portfolio managers to move the actual portfolio in the direction of the target portfolio. Pure indexers and other rigid portfolio managers intend to have zero tracking error with respect to some target portfolio, which means that those portfolio managers need to closely match the target weights

of every security in their portfolio at every point in time. In the case of pure indexers, those weights are determined by the behavior and composition of the index; in the case of active managers, those weights are a function of their real or perceived ability to identify mispriced securities that will be fairly valued at some time in the future. Zero tracking error also means that the difference between the actual and the target exposures to the market, size, and value factors is always equal to zero (i.e., b = b = b b

target, s = s = s s

target, and h = h = h h

target), as is the

target), as is the

target

error term, e, in Equation 1. But there are no clear benefits to that

type of rigid management style. The expected return on the error term is equal to zero, so making the error term equal zero produces no net benefits (i.e., it does not increase the expected return of a portfolio)

beyond the reduction of tracking error relative to an arbitrary benchmark. What is worse, zero tracking error with respect to some target portfolio is likely to have a negative impact on performance because it leads to higher trading costs as a result of trading specific securities at specific points in time. The moment a portfolio manager wants to have zero tracking error with respect to some target portfolio, he changes the nature of his trades in a way that is harmful for the portfolio. He now requires immediacy in his trades, which forces him to conduct those trades as a liquidity seeker, not as a liquidity provider, and pay a cost for that immediacy.

At Dimensional, on the other hand, actual portfolios are close but not exact approximations of the target portfolios. How close of an approximation will depend on transactions costs and, in some cases, taxes. But, clearly, in a world where trading is not frictionless, continuous rebalancing to achieve the exact target portfolio weights at all times is not a sensible strategy.


“IF THERE IS NO NEED FOR IMMEDIACY IN ANY GIVEN NAME,

PORTFOLIO MANAGERS CAN CREATE LARGE ORDERS OF NEEDED

NAMES AND ASK TRADERS TO ACT ON

THEM WITH PATIENCE, BUYING WHEN

LIQUIDITY SEEKERS ARE WILLING TO PAY

FOR THEIR IMMEDIACY NEEDS.”

“THE BOTTOM LINE IS THAT, UNLIKE

INDEXING AND ACTIVE MANAGEMENT, WHICH

FOCUS ON EXACT NUMBERS OF SHARES

FOR EACH INDIVIDUAL STOCK, DIMENSIONAL

FOCUSES ON THE OVERALL

CHARACTERISTICS OF A STRATEGY.”

8

Therefore, accepting some tracking error with respect to a target portfolio gives portfolio managers and traders a lot of flexibility. For instance, a portfolio manager expecting cash inflows or outflows in the near future can use those flows to rebalance portfolios closer to their target weights by buying securities that belong to the asset class or by selling securities in the portfolio that no longer meet the definition of the asset class. Although the portfolio engineer obviously does not have information about cash flows when designing the portfolios, the portfolios are designed in such a way that portfolio managers can use that information in a valuable way.

This flexibility allows portfolio managers to substitute, for instance, securities that are not currently affected by momentum for otherwise similar securities that are affected by momentum. It also allows portfolio managers to create flexibility when trading. If there is no need for immediacy in any given name, portfolio managers can create large orders of needed names and ask traders to act on them with patience, buying when liquidity seekers are willing to pay for their immediacy needs. The ability to substitute names and the interaction between traders and portfolio managers create unique opportunities to capture value. For example, using fictitious numbers, a portfolio manager can give traders $100 million in orders to trade the best $50 million, the $50 million worth of trades in which he can trade as a liquidity provider and earn a premium for doing so. In contrast, a non-flexible portfolio manager must give $50 million in orders to trade $50 million, and these $50 million must be allocated to a specific number of shares for specific names, regardless of liquidity constraints or other market conditions.

The bottom line is that, unlike indexing and active management, which focus on exact numbers of shares for each individual stock, Dimensional focuses on the overall characteristics of a strategy. This allows portfolio managers to treat stocks that have similar characteristics and belong to the same asset class as close substitutes for one another, which facilitates trading. In other words, a trader will have more chances to trade opportunistically and take advantage of favorable trading conditions if he is, within certain diversification

limits, indifferent as to stock A or stock B, than if he has to hold X shares of stock X shares of stock XA and Y shares of stock B at all times, or Y shares of stock B at all times, or Yif he has to buy X shares of stock A and X shares of stock A and XY shares of stock B at a specific point in Y shares of stock B at a specific point in Ytime. And this is what allows portfolio managers to manage portfolios around market frictions such as momentum using substitution and a patient trading approach. To be sure, flexible trading can generate biases in the portfolio, but it is the responsibility of portfolio managers to control those biases over time without the need to pay for immediacy. Like herding cows, you do not waste resources imposing the right position to each cow; you just get all of them moving in the right direction.

Portfolio Trading

The final element of the investment process is trading. Since its inception in 1981, Dimensional Fund Advisors has sought to enhance net returns by keeping trading costs low, avoiding trades that greatly impact market prices, trading patiently and with flexibility, and trying to provide liquidity to the market. Dimensional supplies liquidity to index and active managers in search of immediacy. The bid/ask spread is a very simple measure of the cost of immediacy for a nominal amount of shares.8 Liquidity providers earn the spread, while liquidity seekers pay for it. Table 6 shows bid/ask spreads for stocks in international developed markets in different market capitalization segments. The spread increases from 20 basis

points for the largest stocks to 189 basis points for the smallest stocks.

To get an idea of the benefits of being able to trade as a liquidity provider instead of a liquidity seeker, we conduct a simple experiment. We take the Russell 1000 Value Index, the Russell 2000 Index, and the Russell 2000 Value Index and reconstitute them at the end of September, approximately three months after the official reconstitution date, for every year between 1990 and 2006. We then look at the monthly average returns for the actual indices and for the lagged indices from July to June, from October to June, and from July to September. The lagged indices outperform the actual indices


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by an average of 3, 15, and 18 basis points per month, respectively, from July to June (see Table 7). In the case of the Russell 2000 Index and the Russell 2000 Value Index, those performance differentials are statistically reliable.

From October to June, when the actual and the lagged indices are the same, there are no differences in performance between the two sets of indices. But from July to September, when the actual indices and the lagged indices have different components, the differences in performance between the two sets of indices are huge: 13 basis points per month in the case of the Russell 1000 Value Index, 59 basis points per month in the case of the Russell 2000 Index, and 70 basis points per month in the case of the Russell 2000 Value Index.

TABLE 6TRADING COSTS

MARKET CAP ($ MILLIONS) NAMES

PERCENT OF MARKET CAP

BID/ASK SPREAD (%)

TRADING VOLUME PER ISSUE ($)

> 5,000 677 79.3 0.20 111,044,603

1,500-5,000 842 0.31 0.31 14,558,882

500-1,500 1,128 5.3 0.56 3,542,329

200-500 1,150 2.0 0.86 965,087

25-200 1,848 1.0 1.89 312,924

Source: Bloomberg (accessed Nov. 10, 2006). The bid/ask spread is generally regarded as an indication of the cost of liquidity. The twenty-two developed markets are those in Dimensional’s eligible universe (Canada, Europe, Japan, Asia Pacific, UK).

Having flexibility allows portfolio managers to avoid the costs of immediacy in the execution of trades, and traders may participate in the market anonymously with the intent to capture liquidity in different ways and in different trading venues. The cumulative performance drag created by trading as a liquidity seeker instead of a liquidity provider, as quantified in our simple experiment, ranges from 40 basis points for the Russell 1000 Value Index to 211 basis points for the Russell 2000 Value Index over the three-month period following reconstitution. The behavior of pure indexers around reconstitution time, when they give priority to immediacy of execution of trades to minimize tracking error over cost of execution, tends to depress index returns. The prices of stocks that enter the indices are pushed up around reconstitution and then gradually come down, while the prices of stocks leaving the indices are pushed down at reconstitution and then gradually increase.

Although we cannot predict the future costs of immediacy and careless trading, for as long as those costs remain positive there will be market participants, Dimensional included, who will try to take advantage of situations in which providing liquidity earns some premium.

TABLE 7THE COSTS OF IMMEDIACYTHE COSTS OF IMMEDIACYTHE COSTS OF IMMEDIAC

LIQUIDITY SEEKERS VS. LIQUIDITY PROVIDERS

JANUARY 1990-DECEMBER 2006

JULY-JUNE

INDEX (%)

LAGGED INDEX (%) DIFFERENCE (%)

S.E.(%) T-STAT

RUSSELL 1000 VALUE 1.05 1.08 -0.03 0.02 -1.79

RUSSELL 2000 1.03 1.19 -0.15 0.04 -4.32

RUSSELL 2000 VALUE 1.20 1.38 -0.18 0.04 -4.31

OCTOBER-JUNE

RUSSELL 1000 VALUE 1.45 1.45 0.01 0.01 0.95

RUSSELL 2000 1.59 1.60 -0.01 0.01 -1.87

RUSSELL 2000 VALUE 1.73 1.74 -0.01 0.00 -0.01

JULY-SEPTEMBER

INDEX (%)

LAGGED INDEX (%) DIFFERENCE (%)

S.E.(%) T-STAT

CUMULATIVE RETURN DIFF.

OVER THREE MONTHS

RUSSELL 1000 VALUE -0.15 -0.02 -0.13 0.06 -2.34 -0.40

RUSSELL 2000 -0.63 -0.05 -0.59 0.12 -4.69 -1.76

RUSSELL 2000 VALUE -0.40 0.31 -0.70 0.15 -4.82 -2.11

Source: Russell data copyright © Russell Investment Group 1995-2008, all rights reserved. Note: The lagged indices are reconstituted at the end of September, three months after the official reconstitution date for the Russell indices. Indices are not available for direct investment; their performance does not reflect the expenses associated with the management of an actual portfolio. Past performance is not a guarantee of future results.

Conclusion

At the end of the first part of this article, we stated that Dimensional’s delivery of the value premium is not a random event, but rather the result of a skillfully designed and implemented investment process. This process, schematically described here, has been successfully tested in real markets for more than twenty-five years. As markets evolve and our understanding of the sources of financial risks and returns continues to improve, Dimensional’s dynamically integrated investment process will remain focused on the issues that matter: providing a diversified and continuous exposure to the dimensions of expected stock returns, balancing the different premiums and costs related to the factors that determine expected returns, and giving portfolio managers and traders the flexibility to trade patiently as a liquidity provider and using substitution to efficiently capture the desired premiums. Together, all these ingredients form the basis of Dimensional’s value-added proposition.

— Eduardo A. Repetto and L. Jacobo Rodríguez


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REFERENCESREFERENCESR

1. See Eduardo A. Repetto and L. Jacobo Rodríguez, “Dimensional Investors Do Capture the Value Premium: Part I,” Quarterly Institutional Review (Dimensional Fund Advisors), third quarter 2007: 2-7.Review (Dimensional Fund Advisors), third quarter 2007: 2-7.Review

2. The data presented do not reflect recent market conditions, values, or returns. Past performance is no guarantee of future results and current performance may be higher or lower than the performance shown. To obtain performance data current to the most recent month end, access our website at www.dimensional.com/strategies.

3. From July 1927 to June 2007, the value premium, the difference between the average returns on a portfolio of value stocks and the average returns on a portfolio of growth stocks, was about 5.6% per year on average. Despite the magnitude of the value premium in the United States in the eighty years for which data are available, there is a risk that the premium will not be positive in any given period of time. If the premium is compensation for common, non-diversifiable risks associated with the ratio of book-to-market equity or with any other measure of relative prices, investors must be exposed to the risks that produce the value premium if they seek to capture it.

4. See Eugene F. Fama and Kenneth R. French, “Migration,” Financial Analysts Journal 63, no. 3 (May/June 2007): 48-58. Fama is a director Financial Analysts Journal 63, no. 3 (May/June 2007): 48-58. Fama is a director Financial Analysts Journaland consultant and French is a director, consultant and head of investment policy at Dimensional Fund Advisors.

5. For more information on how these momentum measures are constructed, please see http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_mom_factor.html.

6. For more information on REITs, see L. Jacobo Rodríguez, “Real Estate Investment Trusts,” Quarterly Institutional Review (Dimensional Quarterly Institutional Review (Dimensional Quarterly Institutional ReviewFund Advisors), fourth quarter 2006: 2-8.

7. The monthly market premium (0.65%) times the b coefficient for REITs (0.66) gives us the contribution to the return of REITs from b coefficient for REITs (0.66) gives us the contribution to the return of REITs from bexposure to the market factor (0.43%). Similarly, the market premium times the b coefficient for the US Small Cap Value Index gives us the b coefficient for the US Small Cap Value Index gives us the bcontribution to the return of small cap value stocks from their exposure to the market factor (0.65% × 1.07 = 0.70%).

8. See Harold Demsetz, “The Cost of Transacting,” Quarterly Journal of Economics 82, no. 1 (February 1968): 33-53.


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New Products LaunchedDimensional Fund Advisors launched a US Social Core Equity 2 strategy in October. This strategy targets the same universe of companies as the US Core Equity 2 strategy—that is, marketwide coverage of US stocks with increased exposure to small cap and value stocks—but excludes certain companies and industries that do not pass the social screens set up by an independent third-party provider of social issue exclusions. These screens do not necessarily reflect the beliefs held by or causes supported by Dimensional.

Dimensional also launched a TA US Core Equity 2 strategy in October. This strategy targets the same universe of companies as the US Core Equity 2 strategy and employs a tax-advantaged investment approach to minimize the federal income tax implications of investment decisions.

For more information about these new strategies or any other Dimensional products, please contact your regional director.

Quarterly Institutional Review Wins Spotlight AwardDimensional Fund Advisors’ Quarterly Institutional Review was honored with a Spotlight Award by the League Quarterly Institutional Review was honored with a Spotlight Award by the League Quarterly Institutional Reviewof American Communications Professionals. The Spotlight Awards recognize excellence in print, video, and web communications. The Quarterly Institutional Review won a platinum award for being ranked first in its class—Quarterly Institutional Review won a platinum award for being ranked first in its class—Quarterly Institutional Reviewmagazine/newsletter for companies with $100 million to $1 billion in annual revenue—and number 72 overall out of more than 900 entries from industries and organizations in seven countries. In its class, the Quarterly Institutional Review edged out publications from ESPN and Wells Fargo Institutional Services, among others. All of us involved Review edged out publications from ESPN and Wells Fargo Institutional Services, among others. All of us involved Reviewin the production of this newsletter would like to thank the League of American Communications Professionals for their recognition of our work.

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