Fixed income tradeoffs global diversification vs increased expected returns dfa 4q2011

DIMENSIONAL FUND ADVISORS

4th Quarter 2011

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inside:2 Research Update

What are the necessary

conditions for global

diversification in fixed income

to be a sensible approach?

What are the tradeoffs investors

face when seeking global

diversification in their fixed

income allocation? What

opportunities do they have

to add value in a reliable and

robust way by diversifying

globally? Joseph Kolerich

and Jacobo Rodríguez

address these questions and

consider the benefits of global

diversification in fixed income.

9 What’s New at Dimensional

10 Appendix

Investors around the world have his-

torically had a greater home bias in

their fixed income investments than in

their equity investments. But investing

in global bonds has important bene-

fits. A dynamic approach that consid-

ers the tradeoffs between increased

diversification and increased expected

returns is a reliable and robust way to

add value in a global bond strategy.

page 2 >>

Fixed Income Tradeo"s:

GlobalDiversi#cationvs. IncreasedExpected Returns

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. 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.

Expressed opinions are subject to change without notice in reaction to shifting market conditions. 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.

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The casino view sees the stock market as largely a place where investors place bets on the

near future prices of stocks rather than on the numbers on a roulette wheel or the spots

on a pack of cards. The casino interpretation seems even more apt for futures and options

exchanges where the very structure of contracts traded emphasizes the “zero-sum” nature

of the market. Casinos, of course, as suppliers of artificial risks to those with a taste for them,

may well have their place in society, though presumably only a small place in a world already

amply supplied with naturally occurring hazards. The danger some economists see is that as

socially acceptable casinos, stock markets may actually be too attractive. They may mislead

the unsophisticated into believing that stock market speculation offers a better, and certainly

a quicker, way to wealth than working or saving.

That short-term trading of stocks (or futures or options) is a risky activity can hardly be denied.

Indeed, much of the research thrust of the academic discipline of finance has been precisely

to specify the probability distributions of returns from investments in different assets and over

various horizons. But those distributions arise in a way fundamentally different from those of

a casino. The distributions of returns on risky stock market investments are driven not by the

random fall of dice or the spin of a wheel (although it is sometimes convenient in exposition

to pretend that such is the case), but by the revelation or disclosure of new information about

the underlying value of a security.

The information needed to value securities is not, however, just a mass of computer printout

stored in a vault somewhere. Rather than a single objective entity, information, as Hayek

(1945) has stressed, consists of millions of subjective bits and pieces scattered over the whole

set of economic actors. One key function of secondary trading in the stock market is to

aggregate these separate fragments of information. The prospect of speculative profits is

the “bribe,” so to speak, society offers investors to speed the incorporation of the dispersed

bits of information into prices. Once the information is incorporated, of course, everyone,

including the uninformed, and not just the successful speculators, benefits from having more

accurate prices on which to base decisions.

—Merton H. Miller and Charles W. Upton

“Strategies for Capital Market Structure and Regulation.” In Grundy, Bruce D., ed.,

Selected Works of Merton H. Miller, Vol. II: Economics. (Chicago: University of Chicago

Press, 2002): 578-79.

Two contrasting views of stock markets

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2

RESEARCH

UPDATE

Financial economists have identi"ed two factors that determine the expected returns of "xed income securities: credit and term.1 #e credit (default) factor is a proxy for changes in economic conditions that change the likelihood of default, while the term (maturity) factor is a proxy for unexpected changes in interest rates. Together, those factors explain much of the common variation in the cross-section of bond returns.

Research has shown that: (a) changes in interest rates are largely unpredictable and (b) the market prices of "xed income securities contain reliable information about future return di$erences between those securities.2 More speci"cally, di$erences in credit spreads reliably indicate when credit risk is likely to be compensated with higher expected premiums. When credit spreads widen, credit risk is expected to outperform on a relative basis; when credit spreads narrow, taking credit risk is expected to deliver less attractive credit risk premiums. #is allows investors to use market-based information contained in current credit spreads to design "xed income strategies that dynamically vary the exposure to credit risk.

Similarly, current term spreads, or forward rates, contain reliable information about future term risk premiums. In particular, wider (narrower) term spreads predict larger (smaller) term premiums. Again, investors can use this market-based information contained in the current shape of yield curves to design "xed income strategies that dynamically vary the exposure to term risk.

Because those relationships are linear, investors can combine the information in credit and term spreads to design strategies that dynamically change the exposure to credit and term risk within both corporate and government bonds. Using data from 1973 to 2010, Plecha and Rodríguez (2011) show a simulated strategy with variable credit and variable maturity had a similar standard deviation, a similar allocation to government bonds, and a small tracking error relative to a comparable benchmark. However, the variable credit/variable maturity strategy outperformed the benchmark by 0.4% per year by incorporating market-based information

in the allocation decisions. #at performance di$erential is reliably di$erent from zero.

If it is worth it for investors to globally diversify the "xed income component of their portfolios, can this market-driven approach also add value through global diversi"cation? If so, what are the necessary conditions for that approach to work globally? Finally, what tradeo$s do investors need to make to add value through global diversi"cation? #is article considers the bene"ts of global diversi"cation in "xed income and the tradeo$s investors need to make to achieve those bene"ts.

GLOBAL INVESTMENT GRADE BOND MARKETTable 1 shows the global investment grade bond market, as measured by the Barclays Capital Aggregate Bond Index (ex securitized issues), by issuer, duration, and type of security as of July 2011. Some details to note: First, about 80% of investment grade "xed income is government-issued debt. Despite the debt problems many governments have experienced and are likely to continue to experience, this is a market dominated by government issuers. Second, in ag-gregate, the US represents the biggest fraction of the global bond market, about 27% of the total market. #at suggests investors who concentrate their "xed income allocation in just one country, even if that country is the US, may not be very well diversi"ed.3

Fixed Income Tradeoffs: Global Diversification vs. Increased Expected Returns

Joseph Kolerich

Portfolio Manager and Vice President

Dimensional Fund Advisors

L. Jacobo Rodríguez

Vice President, Institutional Services

Dimensional Fund Advisors

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would make global diversi"cation in "xed income a sensible strategy? First, markets must be su'ciently competitive, transparent, and liquid to make prices fair for all investors. Second, "xed income markets across the world must not be perfectly correlated. If bond markets across the world are not perfectly correlated, global "xed income may provide enhanced diversi"cation due to asynchronous changes in the term structure of interest rates in di$erent countries. On the other hand, if markets are perfectly (or even highly) correlated, the bene"ts from international diversi"cation in "xed income may not be su'cient to outweigh the costs of achieving that diversi"cation.

Table 2 shows that monthly changes in the term structure of interest rates are not perfectly correlated across countries. Using the Citigroup World Government Bond Indices 1-3 years (hedged), these correlations are computed by looking at monthly changes in short-term yields across countries (or, more appropriately, currencies). Because we use the hedged indices, the correlations re*ect only changes in yields, not currency *uctuations. #e bottom half of the matrix shows the correlations for the entire sample period, 1985 to 2010, while the upper half shows the correlations from 1999, when the euro was introduced, to 2010.

It is not just that yield curves across countries move very di$erently; the yield curves themselves have di$erent shapes and nominal levels. #is provides important diversi"cation bene"ts, expands the variable maturity opportunity set, and, as will be shown below, creates opportunities to add value by using market-based information across countries. Using the Citigroup World Government Bond Indices 1-3 years and 3-5 years (hedged), every month we calculate the slope between the short (1-3 years) and the intermediate (3-5 years) indices for each country in the sample.4 We then

Some investors may have sensible reasons for limiting their "xed income exposure to just one country (e.g., for tax considerations), but, because no single issuer represents more than 30% of the investment universe, it makes sense for most investors to look at "xed income investments outside their home country. What are the necessary conditions that

Table 1Global Investment Grade Bond Market

Type of Security

IssuerEffective Duration(in years)

Government Corporate

United States 0-5 11.50% 3.81%

5-10 4.35% 2.93%

10+ 2.27% 1.96%

Japan 0-5 11.11% 0.85%

5-10 6.56% 0.17%

10+ 5.44% 0.01%

EMU 0-5 11.35% 2.86%

5-10 7.00% 1.40%

10+ 3.47% 0.27%

United Kingdom 0-5 1.44% 0.96%

5-10 1.12% 0.72%

10+ 1.72% 0.31%

Canada 0-5 1.48% 0.52%

5-10 0.77% 0.23%

10+ 0.77% 0.19%

Australia 0-5 0.72% 0.40%

5-10 0.40% 0.19%

10+ 0.01% 0.01%

Other Developed 0-5 0.99% 0.71%

5-10 0.66% 0.38%

10+ 0.21% 0.04%

Supranational 0-5 1.34% 0.00%

5-10 0.49% 0.00%

10+ 0.24% 0.00%

Other Countries 0-5 2.73% 0.25%

5-10 1.84% 0.21%

10+ 0.58% 0.06%

TOTAL 80.54% 19.46%

Source: Barclays Capital Global Aggregate Bond Index(ex securitized issues), as of July 2011.

Table 2Correlations among Changes in Short-Term Yields of Different Countries 1985–2010

Australia CanadaSwitzer-

land

Ger-many/EMU

UK Japan US

Australia 0.57 0.53 0.65 0.65 0.09 0.59

Canada 0.30 0.53 0.63 0.63 0.10 0.75

Switzerland 0.15 0.17 0.72 0.57 0.20 0.59

Germany/

EMU

0.31 0.34 0.54 0.70 0.17 0.68

UK 0.26 0.34 0.35 0.53 0.11 0.66

Japan 0.13 0.21 0.21 0.36 0.31 0.13

US 0.29 0.63 0.31 0.46 0.36 0.25

1985–2010

1999–2010

Source: Citigroup World Government Bond Indices, 1-3 years (hedged).

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compute the average slope for all the countries, the standard deviation, and the maximum and minimum slope for all the months in the sample (see Figure 1).

To get a more concise view of the variability of yield curves, Table 3 shows time series statistics for the whole sample pe-riod of 1985 to 2010 and for the post-euro sample period of 1999 to 2010.

As the preceding analysis shows, yield curves don’t move in lockstep. #at is good news for investors who want to diversify their "xed income allocation with global bonds. Table 4 shows that investing in global bonds substantially reduces the volatility of a "xed income portfolio relative to a US-only portfolio. For instance, using the monthly re-turns of the Citigroup World Government Bond Index 1-3 years (hedged) from 1985 to 2010, global bonds had simi-lar returns to US bonds (49 basis points for the global index versus 50 basis points for the US index), but the monthly volatility of the global index was 24% lower than that of the US index (42 basis points for the global index versus 55 basis points for the US index). In the intermediate segment of the yield curve, 3–5 years, we obtain a similar reduction in volatility (27%) when we move from a US index to a global one with about the same monthly average returns

(52 basis points per month for the global index versus 54 basis points per month for the US index).5

Asynchronous changes in yield curves and very di$erent yield curve shapes across countries create the necessary con-ditions to add value by investing in global bonds. In the next section, we look at how a market-driven approach can be implemented in global portfolios

VARIABLE MATURITY SELECTING COUNTRIESResearch has shown that changes in interest rates are largely unpredictable. #at research has also shown that di$erenc-es in expected returns among "xed income securities can be inferred from current market prices. #at is the basis for a market-based, variable maturity approach. #is approach uses information in the yield curve to shift the maturity of a portfolio so that term risk is taken when it is expected to pay o$ (that is, when expected term premiums are posi-tive). #at happens when yield curves are steep, when for-ward rates are high. #e key then is to identify the steepest segments of the curve, the highest forward rates, because this is where expected returns are highest.

Table 3Time Series Averages of Cross-Sectional Statistics

1985–2010 1999–2010

Cross-Sectional Mean Slope 0.17 0.20

Cross-Sectional Std. Dev. 0.19 0.15

Maximum Monthly Slope 0.48 0.53

Minimum Monthly Slope -0.19 -0.09

Source: Citigroup World Government Bond Indices, 1-3 years and 3-5 years (hedged).

Table 4Diversification Benefits of Global Bonds 1985–2010

1-3 Years Index

USWGBI

(hedged)

Monthly Average Return 0.50 0.49

Monthly Std. Dev. 0.55 0.42

Reduction in Volatility 24.0%

1-5 Years Index

USWGBI

(hedged)

Monthly Average Return 0.54 0.52

Std. Dev. 0.74 0.54

Reduction in Volatility 26.9%

Source: Citigroup World Government Bond Indices,1-3 years and 1-5 years (hedged).

-1.13

-0.75

-0.38

0

0.38

0.75

1.13

1.50

1985 1990 1995 2000 2005 2010

Avg. Std. Dev. Max. Min.

Figure 1Short–Intermediate Term Slopes across Countries 1985–2010

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We can apply this variable maturity approach in a glob-al setting by selecting the countries with the steepest yield curves, with the highest forward rates, within a given term window rather than moving up and down the maturity spectrum within one country.

Let’s use a simple example to see the bene"ts of this ap-proach. Assume that the term window is 3 to 5 years. To ensure su'cient diversi"cation, our variable maturity strat-egy will cover 50% of the total global market capitalization by selecting those countries with the steepest yield curves; otherwise, if diversi"cation was not a concern, we could just allocate 100% of the portfolio to the country with the steepest yield curve to have the highest expected return. #e individual country cap is equal to twice the market capitalization of that country (e.g., if the UK represents 1% of the global bond market in the 3–5-year segment of the market, the maximum allocation to the UK cannot exceed 2%). Finally, the currency exposure is hedged.

Table 5 shows the bene"ts of this dynamic approach rela-tive to a static approach that weights each country by its market capitalization and includes all countries in the uni-verse. #e volatility of the portfolio, as measured by the standard deviation, is a proxy for the diversi"cation ben-e"ts of investing globally. Moving from a US strategy to a static global strategy with full market coverage reduces the monthly standard deviation from 114 basis points to 75 basis points, and the average monthly return from 61 basis points to 57 basis points due to the shape of the US curve during this time period. #e variable country strategy gives up some diversi"cation (its global coverage is only 50% ver-sus 100% for the plain strategy) and some of the bene"ts that come with it (the standard deviation increases from 75 basis points to 93 basis points). But it does so with the explicit intention of increasing expected returns—in this case, by 7 basis points a month during this period. #is return di$erential between the variable country approach and the static maturity approach is reliably di$erent from zero, as indicated by a t-statistic that is over three standard errors away from zero. Compared with the initial US-only strategy, the global strategy reduces volatility and enhances expected returns by expanding the opportunity set of bonds and yield curves.

To check the robustness of these results, we repeat the ex-periment in two other segments of the yield curve, 5–7 years and 7–10 years. #e results are very similar. A move from a US strategy to a global strategy leads to signi"cant reductions in volatility without much variation in expected returns. Moving from a global strategy with all countries included to our variable maturity strategy that invests in only those countries with the steepest yield curves increases expected returns but decreases the diversi"cation bene"ts. Once again, the di$erences in expected return between the plain strategy and the variable maturity strategy are reliably di$erent from zero in both cases, as indicated by the large t-statistics (see Tables 6 and 7).

Table 5Variable Maturity Selecting Countries, 3–5 Years

Global Strategy

Plain 3-5 (hedged)

US

Avg. Monthly Return 0.64 0.57 0.61

Standard Deviation 0.93 0.75 1.14

Diff. Monthly Return 0.07

t-statistic 3.03

Compound Return (Annualized)

7.86 7.01 7.46

Avg. Hedged Yield 5.87 5.40 5.50

Avg. UnHedged Yield 4.53 4.63

Avg. Duration 3.48 3.49 3.44

Avg. Maturity 4.00 4.00 4.00

Source: Citigroup World Government Bond Indices, 1-3 and 3-5 years (hedged). The countries in the sample are Australia, Canada, Switzerland,

Belgium, Germany, Spain, France, the Netherlands, the UK, Japan, Norway, New Zealand, Sweden, and the US, as data become available. The sample

period is 1985 to 2010.

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In all these cases, we "rst expand the universe of countries to add diversi"cation and reduce the volatility of the portfo-lio, and then give up some of that diversi"cation to increase expected returns. #us, the tradeo$ investors need to make is one between diversi"cation and expected returns. In these examples, we give up 50% of the global market capitalization to increase expected returns by investing only in those coun-tries with the highest expected returns, as represented by the steepest yield curves.

VARIABLE MATURITY SELECTING COUNTRIES AND MATURITIESIn the previous examples, we selected the countries with the steepest yield curves at a given maturity segment in each pe-riod. But there is nothing that prevents us from varying the maturity within each of the chosen countries as the term premiums in those countries vary. #e goal is to constantly choose the countries with the steepest yield curves and to constantly choose the steepest segments of those curves within each country to maximize expected term premiums across and within countries.

Table 8 shows summary statistics for a simulated strategy that dynamically varies the country and term exposure. For this example, the term window is 3–7 years, the mar-ket capitalization coverage is also 50%, and the individual country cap is equal to twice the market capitalization of that country. As in the previous examples, moving from a US strategy to a global strategy leads to signi"cant re-ductions in volatility (the monthly standard deviation goes from 1.3% for the US strategy to 0.9% for the static global strategy) without much variation in expected returns (63 basis points per month for the US vs. 60 basis points per month for the plain global strategy). Moving from a static global strategy with all countries included to our variable maturity strategy that invests in only those countries with the steepest yield curves increases expected returns by 6 basis points per month but decreases the diversi"cation bene"ts (the monthly standard deviation goes from 0.9% to 1.0%). Once again, the di$erence in expected return be-tween the plain strategy and the variable maturity strategy is reliably di$erent from zero, as indicated by a t-statistic of 2.6.6


Global Strategy

Plain 5-7 (hedged)

US




t-statistic 3.13

Compound Return (annualized)

9.00 7.89 8.15


Avg. Unhedged Yield 4.67 4.75

Avg. Duration 4.95 4.96 4.81

Avg. Maturity 6.00 6.00 6.02

Source: Citigroup World Government Bond Indices, 3-5 and 5-7 years (hedged). The countries in the sample are Australia, Canada, Switzerland, Belgium,

Germany, Spain, France, the Netherlands, the UK, Japan, Norway, New Zealand, Sweden, and the US, as data become available. The sample period

is 1985 to 2010.


Global Strategy

Plain 7-10 (hedged)

US




t-statistic 2.17

Compound Return (annualized)

9.16 8.23 8.52



Avg. Duration 6.74 6.69 6.35

Avg. Maturity 8.53 8.53 8.55

Source: Citigroup World Government Bond Indices, 5-7 and 7-10 years (hedged). The countries in the sample are Australia, Canada, Switzerland,



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#e previous examples showed the diversi"cation bene"ts of moving from a US strategy to a global strategy and the tradeo$s between diversi"cation and higher expected returns in the case in which the market coverage of the variable ma-turity strategy is only 50% of the global market. How do the diversi"cation bene"ts and the tradeo$s between diversi"ca-tion and higher expected returns vary as we vary the level of market coverage? Using the same term window and assump-tions as in Table 8, we repeat the exercise with di$erent levels of market coverage, from 20% coverage, in which case the country cap for every country in the strategy would be "ve times their market cap, to a strategy with 100% market cov-erage, which is in e$ect the plain, market-cap-weighted strat-egy with all the countries shown in Table 8. Table 9 shows summary statistics for these strategies with di$erent levels of market coverage. #e monthly average return increases al-most monotonically from 60 basis points per month for the market-cap-weighted index to 69 basis points to the strategy with only 20% market coverage. #e return di$erences be-tween the global index and the strategies with partial market coverage are all reliably di$erent from zero, as indicated by t-statistics that are well above two in all cases, including the case in which market coverage is 90%. #e monthly stan-dard deviation increases monotonically as we move from the market-cap-weighted index to the strategy with 20% market

Table 8Variable Maturity within and across Countries, 3–7 years

Global Strategy

Plain 3-7 (hedged)

US


Std. Deviation 1.03 0.85 1.26


t-statistic 2.55

Comp. Return (annualized)

8.15 7.34 7.69

Avg. Hedged Yield 5.96 5.51


Avg. Duration 3.92 4.05

Avg. Maturity 4.62 4.77

Source: Citigroup World Government Bond Indices, 1-3, 3-5, and 5-7 years (hedged). The countries in the sample are Australia, Canada, Switzerland,



coverage. In other words, as we decrease the level of market coverage, the diversi"cation bene"ts of investing in global bonds also decrease.

CONCLUSIONResearch demonstrates there is a reliable relationship between current observable credit/term spreads and future return dif-ferences. Wider (narrower) credit/term spreads predict larger (smaller) credit/term premiums. Market data provide the in-formation needed to structure diversi"ed "xed income strate-gies that dynamically vary the exposure to the credit and term factors, depending on whether credit and term premiums are expected to be high or low, while considering investors’ needs. If there were no relationship between spreads and returns, hav-ing a constant factor exposure would make sense. But because the relationship is strong and reliable, portfolios can use the information in current yield curves to satisfy investors’ needs without any need to forecast changes in yield curves.

Investors around the world have historically had a greater home bias in their "xed income investments than in their equity investments. Investing in global bonds has important bene"ts that result from changes in yield curves not being per-fectly correlated. First, global bonds enhance diversi"cation in two ways: (a) #ey add issuers to a domestic global bond port-folio, and (b) they increase diversi"cation by expanding the set of available yield curves. Second, a global allocation in "xed income increases the opportunities to add value by provid-ing new term structures to expand opportunities for varying maturities.

Table 9Trading Off Diversification with Expected Returns

CoverageMonthly Average

Return

Monthly Standard Deviation

t-statisticComp. Return

(annualized)

1.0 0.60 0.85 7.34

0.9 0.62 0.88 2.85 7.60

0.8 0.63 0.89 3.23 7.75

0.7 0.64 0.92 2.79 7.87

0.6 0.64 0.97 2.38 7.95

0.5 0.66 1.03 2.55 8.15

0.4 0.67 1.07 2.56 8.28

0.3 0.68 1.13 2.67 8.45

0.2 0.69 1.21 2.51 8.56

US 3-7 0.63 1.26 7.69

Source: Citigroup World Government Bond Indices 1-3, 3-5, and 5-7 years (hedged). The countries in the sample are Australia, Canada, Switzerland,



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A dynamic approach that focuses on the steepest yield curves and the steepest segments of those curves and considers the tradeo$s between increased diversi"cation and increased ex-pected returns is a reliable and robust way to add value in a global bond strategy.

REFERENCES

The helpful comments of Steve Garth, Craig Horvath, Marlena Lee, Travis Meldau,

David Plecha, Eduardo Repetto, and Savina Rizova are gratefully acknowledged.

1. See, for instance, Eugene F. Fama and Kenneth R. French,

“Common Risk Factors in the Returns on Stocks and Bonds,” Journal of

Financial Economics 33, no. 1 (February 1993): 3–56.

2. See, for instance, Eugene F. Fama, “#e Information in the Term

Structure,” Journal of Financial Economics 13, no. 4 (December 1984):

509–28; Eugene F. Fama, “Term Premiums in Bond Returns,” Journal

of Financial Economics 13, no. 4 (December 1984): 529–46; and Eugene

F. Fama and Robert R. Bliss, “#e Information in Long-Maturity

Forward Rates,” American Economic Review 77, no. 4 (September

1987): 680–92. To see how this research can be applied in investment

strategies, see, for instance, David A. Plecha and L. Jacobo Rodríguez,

“A Market-Driven Approach to Fixed Income,” Dimensional Fund

Advisors’ Quarterly Institutional Review 7, no. 1 (2011): 2–7.

3. In comparison, US equities also make up the largest share of the global

equity market, about 45% of the total global equity market capitalization.

4. #e countries are Australia, Canada, Switzerland, Austria, Belgium,

Germany, Spain, France, Ireland, the Netherlands, Great Britain, Japan,

Norway, New Zealand, Sweden, and the US.

5. #ese di$erences in returns between the US indices, on the one hand,

and the global indices, on the other, are not reliably di$erent from zero.

6. Choosing a 5–10-year term window yields similar results. #e plain

global strategy had a monthly average return of 66 basis points and a

monthly standard deviation of 1.2% compared to a monthly average

return of 73 basis points and a monthly standard deviation of 1.2% for

the variable maturity global strategy. #at return di$erential was reliably

di$erent from zero, as indicated by a t-statistic of 2.3.

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Registration details:Please check your mail in early February, contact your Dimensional regional director, or sign up online at:http://www.dfaus.com/service/dc-professionals.html

New Paper Looks at Hedge Fund FeesRonnie Shah, PhD, a senior associate in the Research group at Dimensional, authored “#e Arithmetic of Hedge Fund Fees,” a new white paper that looks at the impact of hedge fund fees on investors’ returns, and compares that impact to the impact on returns of other investment vehicles. Any size and value-tilted strategy can be decomposed into a market portfolio that captures broad market exposure and a long/short portfolio that captures size and value tilts. Whether one investment vehicle or another is better depends on the fees charged. Shah estimates that investing in a combination of market-indexed and long/short funds relative to a size and value-tilted strategy reduces net returns by 0.50% to 0.75% per year. His "ndings suggest the traditional 2% management fee and 20% incentive fee imposed by hedge funds cause a signi"cant reduction in net after-fee performance. #e paper is available at https://my.dimensional.com/insight/papers_library/79248/.

New Dimensional Strategies LaunchedDimensional has broadened its "xed income o$ering for all investors with the launch of the World ex US Government Fixed Income Portfolio, which seeks to maximize total returns by investing in the investment grade debt securities of non-US government issuers and supranational organizations and their agencies. #e fund, a complement to Dimensional’s Intermediate US Government Fixed Income Portfolio, expects to target a duration range around the Citigroup WGBI ex US Index (1–30+ years) and hedge its currency exposure to the US dollar.

Dimensional also broadened its "xed income o$ering for investors in California with the launch of the California Intermediate-Term Municipal Bond Portfolio, which seeks to provide current income expected to be exempt from both federal personal income taxes and California state personal income taxes. #e fund is designed to be a stand-alone solution or a complement to the California Short-Term Municipal Bond Portfolio by providing investors an option for extending their maturity exposure in California munis as well as the ability to match longer-term liabilities. Under normal circumstances, the fund will maintain a dollar-weighted average portfolio maturity range of 3–10 years.

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

WHAT’S NEW AT DIMENSIONAL

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FOURTH QUARTER 2011

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However, the global fixed income portfolios may utilize forward currency contracts to minimize

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DISCLOSURE

BRO-QIR

Economy & Finance

Fixed income tradeoffs global diversification vs increased expected returns dfa 4q2011