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DOMESTIC & FOREIGN CASH

Methodology Overview

REVISION DATE: 7/1/2015

TABLE OF CONTENTSOVERVIEW .................................................................... 1

What is This Document? ..................................................1Time Horizon ......................................................................2

DOMESTIC & FOREIGN CASH ................................ 3Asset Class Overview .......................................................3Expected Return Methodology ......................................4

Yield ...................................................................................5Growth ..............................................................................7Valuation Change ..........................................................7

Results ..................................................................................9REFERENCES .............................................................................................................. 11

DOMESTIC & FOREIGN CASH | 1

OVERVIEWWhat is This Document?

This is one in a series of plain-language white papers setting forth Research Affiliates’ building block approach to developing long-term capital market expectations by asset class. (For information about the objectives and

guiding principles of our asset allocation initiative, please refer to “Capital Market Expectations: Methodology Overview,” the first of these white papers.) In working out our risk and return forecasts and making them publicly available, we keep three criteria in mind: transparency, robustness, and timeliness. By describing the conceptual framework and calculations behind the projected asset class risks, returns, and correlations in these papers, we hope to achieve a meaningful level of transparency without excessive details. By constructing simple, economically sound models for major asset classes, we strive to achieve a fitting standard of robustness for forecasting to a 10-year horizon. By initially refreshing our expectations on a quarterly basis, we seek to provide information that is updated with useful frequency. We will continue to refine our methods, extend the scope of our capital market expectations, and improve this documentation over time.The remainder of this document addresses how we think about domestic and foreign cash asset class returns from a building block perspective, and provides transparency into the methods employed to develop these return expectations.

“We understand that some of our insights will never find their way into products, but we provide them in support of investors and the finance community.”

— ROB ARNOTTCHAIRMAN & CEO

2 | DOMESTIC & FOREIGN CASH

Time Horizon

One of the major considerations when embarking on the journey to generate asset class return expectations is the issue of time horizon. Because the focus here is on generating capital market expectations for strategic

asset allocation, and not tactical overlays, a significantly long time horizon of 10 years was selected.

The 10-year time horizon is not meant to imply a 10-year buy-and-hold strategy, but instead incorporates a strategy consisting of asset classes with constant duration targets. Said another way, asset classes with shorter durations (e.g., fixed income) need to be periodically rebalanced to maintain the constant duration. The rebalance period chosen here is one year which means that a two-year bond, for example, will be held for one year, at which time the bond with one year remaining to maturity would be sold and the proceeds used to purchase a new two-year bond. Asset classes with significantly long duration (e.g., equities) can be considered buy-and-hold because the change in duration from the passage of 1, 2, or even 10 years on these types of assets is minimal.


Domestic & Foreign CashAsset Class Overview

The discussion of expected returns begins with a very short duration asset, cash. Investing in domestic cash is simply purchasing short-term (30–90 day) bills denominated in the investor’s home currency whereas

investing in foreign cash means selling one’s domestic currency to purchase the currency of another country. That foreign currency is then invested in the foreign economy’s short-term (30-90 day) bills to earn, for argument’s sake, the foreign cash rate. Upon repatriation, the return on foreign cash consists of both the cash rate earned on the short-term investment and the return due to any change in the foreign exchange rate over the holding period.

In the context of our work in the domain of asset allocation, we treat the U.S. dollar as the domestic (base) currency and include 22 foreign currencies (see Table 1). The choice of the U.S. dollar as the domestic currency is arbitrary; we could have selected any currency to illustrate the concepts in this white paper.

Developed Markets

1 United States USD Dollar

2 Australia AUD Dollar

3 Canada CAD Dollar

4 Eurozone EUR Euro

5 Hong Kong HKD Dollar

6 Japan JPY Yen

7 Sweden SEK Krona

8 Switzerland CHF Franc

9 United Kingdom GBP Pound

Source: Research Affiliates, LLC

TABLE 1Developed and Emerging Market Currencies


Emerging Markets

1 Brazil BRL Real

2 China CNY Yuan

3 India INR Rupee

4 Indonesia IDR Rupiah

5 Malaysia MYR Ringgit

6 Mexico MXN Peso

7 Poland PLN Zloty

8 Russia RUB Ruble

9 South Africa ZAR Rand

10 South Korea KRW Won

11 Taiwan TWD Dollar

12 Thailand THB Baht

13 Turkey TRY Lira

Source: Research Affiliates, LLC

For currency baskets, cash returns are first modeled by country, and then combined as weighted averages to create aggregate currency return forecasts.1

Expected Return Methodology

The goal of the modeling framework is to provide a set of cumulatively exhaustive components with which to capture drivers of return. This is particularly important when it comes to forecasting cash returns because they

become either a direct component of or an adjustment to nearly every other asset class forecast. Cash forecasts are created utilizing a derivation of the component framework outlined in the methodology background whitepaper.

The first component, the local real cash rate, represents the real yield received from an investment in short-term local currency bills. Given the exceedingly short duration of such an investment, it would be unwise to assume a prevailing cash rate represents the average cash rate over the next 10 years. Instead, a 10-year average real cash rate is forecasted by first recognizing that equilibrium cash rates can vary almost one-for-one with real GDP (Laubach and Williams, 2001). This insight leads to creating a country-by-country GDP forecast by modeling the various components of GDP growth: productivity growth, labor force growth, and capital accumulation, all of which are affected by demographic trends. (The components are explained in further detail in the section on yield). The GDP growth forecasts are then mapped into year-by-year cash rate forecasts after accounting for savings preferences and the speed at which central banks move toward their respective equilibrium cash rates. The second phase is creating long-term real foreign currency exchange rate forecasts, which can change due to movements associated with international productivity differentials and reversion toward relative purchasing power parity. The base currency is assumed to be the U.S. dollar throughout this document.

1 Depending on the context, equity market capitalization, country GDP or a variety of other metrics could be used as the basis for weights in a composite.

Real Cash Return = Local Real Cash Rate + Real Exchange Rate∆


YIELD

The yield component of foreign currency returns represents the real yield received from an investment of cash into short-duration bills (sovereign instruments denominated in the local currency). The bills pay a specified

“cash rate” (i.e., short-term interest rate) over a 30- to 90-day period. Maintaining constant exposure to these investments over a 10-year horizon inherently means rolling into another bill at the then prevailing (and likely different) short-term interest rate dozens of times. Accordingly, using the simplifying assumption that current cash rates represent what average cash rates will be over a 10-year investment horizon is unacceptable. Instead, the implicit determinants of real cash rates are used to generate year-by-year cash rate forecasts that ultimately provide average 10-year cash rate forecasts by country.

Economic theory suggests that two factors influence the dynamics of real cash rates. First and foremost, the fundamentals of an economy should drive real interest rates over long horizons. Second, monetary policy influences short-term interest rates at the frequency of business cycles.

Over long horizons, growth theory predicts that short-term interest rates are heavily influenced by the fundamentals of an economy. This starting point is grounded in the neoclassical growth models of Ramsey (1928) and Solow (1956), which provide the foundations for modern macroeconomics.2 These general equilibrium models show that cash rates are a function of productivity growth, labor-force growth, and a time-preference factor, which captures people’s appetite for saving over consumption. Because productivity and labor force growth are also the main drivers of trend GDP growth, equilibrium cash rates can be explained as follows:

The above expression denotes that the equilibrium cash rate is positively related to the rates of trend growth and time preference (Laubach and Williams, 2001). This is intuitively clear because investors enjoy higher cash rate returns when they allocate capital to faster-growing economies, which produce more goods and services for each dollar borrowed. Furthermore, investors enjoy a higher cash return when they lend to countries that have a relatively low supply of funds available for investment (reflected in the time-preference factor). Essentially, countries that tend to save less, whether due to higher levels of consumption or lower levels of discretionary income, are characterized as having a lower supply of funds for investment. All else equal, this leads to higher equilibrium cash rates.

Monetary authorities may drive actual short-term interest rates above or below the equilibrium level implied by the equation above. Conventional monetary policy influences economic activity by controlling the cost of funding and, therefore, the liquidity in the economy. For instance, Taylor (1993) estimated a policy rule that appears to approximate the behavior of the U.S. Federal Reserve:

The output and inflation gaps represent deviations from the Fed’s long-run desired output and inflation targets. This relation is known as the Taylor rule and its variants are widely used as a benchmark for the behavior of central banks around the world (e.g., Clarida, Gali, and Gertler, 2000; Nechio, 2011).

This methodology marries long and short-term drivers of real cash rates, whose relationship can be summarized by a co-integrated process and estimated as a dynamic heterogeneous panel. A co-integrated process is characterized by two stochastic variables that may appear unrelated in the short run but tend to converge over long timeframes.

2For an introduction to neoclassical growth models, see Romer (2011).

Equilibrum Cash Rate = Trend GDP Growth + Time Preference Factor

( )1Actual Cash Rate Equilibrium Cash Rate Output Gap + Inflation Gap2

= +


The two variables are the actual cash rate, which is calibrated by monetary authorities, and its implied equilibrium cash rate, which is driven by slow-moving macroeconomic fundamentals. In practice, these short- and long-run relations are estimated by using the pooled mean group technique of Pesaran, Shin, and Smith (1999) for dynamic error correction models. This approach allows heterogeneous monetary policies across a large set of developed and emerging markets but maintains common long-run relationships between fundamentals. The model translates into the following simplified specification:

Cash rates are mainly determined by an adjustment process from current levels. Over long horizons, the cash rate should be equal to trend GDP growth plus a time preference factor. However, in order to influence real growth over the business cycle, the central bank can push the actual cash rate above or below its equilibrium level. The central bank then gradually corrects this deviation, represented by the parenthesized terms, and moves the cash rate back to its equilibrium. The rapidity at which the bank does so is captured by the term “speed.”

Most of the parameters above can be estimated using historical data, but forecasting changes in real cash rates further than one period into the future requires long-run forecasts for both time-preferences and GDP growth.

To address time preferences by country, forecasted population-by-age-group data must be acquired. Here data from the United Nations3 is utilized. Fortunately, demography is one of the rare social sciences in which forecasts have surprisingly little uncertainty. Looking 10 years into the future, there is some uncertainty in the number of people under 10 and in the number of people over 70, but surprisingly little uncertainty in the number of people aged 10-70, barring war, pestilence, or other catastrophes (Arnott and Chaves, 2012).

Using this data, a proxy for a country’s representative time preference is constructed as the ratio of two demographic cohorts: the number of people aged 40-49 and the number of people aged 20-29. These age cadres are chosen for two reasons. First, middle-aged workers represent positive contributions to the supply of funds available for investment as they tend to save more for their retirement and families. Young adults, however, detract from the supply of funds available for investment as they tend to borrow against their future income for housing, education, or other consumption needs. Taking the ratio of the two groups provides a means to appropriately capture a country’s changing preference for savings over consumption. Second, this ratio sidesteps productivity growth, which is modeled separately as a component of the GDP forecasts detailed below. Because these two demographic cohorts have approximately the same contribution to productivity growth, they are good candidates to capture the savings channel exclusively.

To obtain long-term GDP forecasts, we adopted a well-known approach set forth in the growth-accounting literature, going back to the seminal works of Solow (1957) and Jorgenson and Griliches (1967). This approach is broadly consistent with other institutions’ models, such as the OECD (Johansson et al., 2013). Using historical data from the Penn World Table,4 forecasts are generated for each of the main components of real GDP: productivity growth, capital accumulation, capital share of national income, labor force growth, and human capital accumulation. Each of these components is aggregated to derive year-by-year changes in GDP forecasts by country. The model can be expressed in the form of an equation, where α stands for the capital share of national income:

3 http://esa.un.org/unpd/wpp/Excel-Data/population.htm. 4Feenstra, Inklaar, and Timmer (2013) describe the Penn World Table (PWT), which presents real GDP comparisons across countries and over time. They also describe certain technical enhancements implemented in Version 8 of the PWT.

( )1 1 1Cash Rate Speed Cash Rate GDP Growth Time Preference ShortRun Effects− − −∆ = − × − − +t t t

( ) ( )GDP = Productivity Capital 1 × Human Capital + Labor Forceα α∆ + ∆ + − ∆ ∆


The first component of the GDP forecast is productivity growth. To create forecasts, historical data was used to estimate the relationship between productivity, per-capita income, and demographics. The relationship between productivity and per-capita income is part of a well-known conditional convergence hypothesis, which stipulates that, depending on global technological progress and country-specific factors, each country should converge to its own long-run equilibrium growth path. The relationship between productivity and demographics was explored by Arnott and Chaves (2012), who estimate how various age cohorts impact productivity growth. Arnott and Chaves obtain results consistent with intuition. Children, for example, are not immediately helpful to GDP. They do not contribute to economic growth, and their parents are likely dissaving to pay for their support. Young adults, however, are the driving force behind GDP growth. Not only are they the primary sources of innovation and entrepreneurship, but they also become progressively more productive as they acquire experience. Older adults make the highest per capita contribution to total output, but their contribution to growth in output starts to decline once they acquire the experience needed to reach peak productivity. Finally, senior citizens seem to erode GDP growth as their declining contribution to output goes to zero upon retirement. Forecasts for productivity growth are obtained by inputting the U.N.’s predictions of demographic shares and per-capita income data to our model and iterating the model forward.

The second component of the GDP forecast is capital accumulation. Similar to the process for forecasting productivity growth, panel data techniques are employed to model capital accumulation across countries. Here again, demographic variables, such as the ratio of middle-aged to young people (MY Ratio), have meaningful explanatory power because middle-aged people tend to save more than young people. Intuitively, an increasing MY Ratio is consistent with depressed real returns. Furthermore, both productivity growth and population growth are significantly related to capital accumulation. This is not surprising because fast growing economies tend to attract more capital than slow growing economies. These historical relationships, together with the productivity forecasts and U.N. population forecasts, are used to generate capital growth forecasts.

The remaining components of the GDP forecast are capital share of national income, labor force growth, and human capital accumulation. Capital share of national income is gathered from the Penn World Table under the assumption that recently observed proportions will remain constant. Labor force growth is estimated utilizing the U.N. forecasts for total population and working-age population by country. We assume that growth of human capital accumulation gradually decreases over time; this dynamic is modeled through a simple time-dependent process.

GROWTHThe yield on cash does not “grow” because each short-term bill is a separate investment. During the length of the investment (30-90 days), the yield does not change; therefore, the growth component of returns to cash is always zero.

VALUATION CHANGE Valuation changes from a foreign cash investment are the result of movements in the real exchange rate between countries.5 For modelling purposes, changes in the exchange rate are decomposed into: (a) reversion toward relative purchasing power parity, and (b) adjustments associated with productivity differentials between countries.

5 Because the real exchange rate of a currency with itself is always 1, there is no real currency valuation change of the base currency (USD in this document). Therefore this section only applies to foreign currency investments and their change in real exchange rate to USD.

Real Exchange Rate = Relative PPP Reversion Productivity Differential ∆ +


Productivity Differential Productivity Foreign Country Productivity Domestic Country= −

The first component, Purchasing Power Parity (PPP), is derived from a simple theory holding that the nominal exchange rate between two currencies should be equal to the ratio of aggregate price levels between the two countries, so that a unit of currency from one country will have the same purchasing power in another. Most economists instinctively believe in some variant of PPP as a long-run anchor for real exchange rates (Taylor and Taylor, 2004).

The strongest form of this theory, Absolute PPP, stipulates that the purchasing power of a unit of currency is exactly equal in both economies and implies that no deviations from this relationship should exist. Empirically, however, Absolute PPP does not hold true. Instead, a less stringent form, Relative PPP, is followed, which holds that the foreign exchange rate reverts to its equilibrium level over time because the percentage change in the exchange rate in a given period offsets the difference in the countries’ inflation rates. Relative PPP reversion is a theoretically sound and empirically sufficient methodology to aid in the determination of long-run real exchange rate targets.

In forecasting PPP reversion, one must determine both an equilibrium level of real exchange rates and the speed of reversion to equilibrium. Our model employs a rolling 10-year average real exchange rate as the equilibrium level in recognition that, over time, a nation’s exchange rate is subject to different exchange rate regimes. Using a 10-year average allows the model to capture the current exchange rate regime over the last two business cycles without going too deeply into the distant past. The assumed speed of reversion toward equilibrium is based on evidence of a higher correlation between relative inflation and exchange rate depreciation over periods of 10 years or more. Accordingly, a 10-year half-life in reversion toward real exchange-rate equilibrium is assumed (Taylor and Taylor, 2004).

The second component, the productivity differential, is based on the Harrod-Balassa-Samuelson effect, which suggests a relationship between relative productivity and real exchange rates. Essentially, countries with higher rates of productivity growth will have higher wage growth leading to higher price inflation in non-tradeable goods and a corresponding increase in their consumption basket relative to the less-productive country. These dynamics tend to result in a rising real exchange rate (Shepherd, 2014). In fact, the long-run expected increase in the real exchange rate between two countries can be approximated by the difference in productivity growth rates, modeled as follows:

Where:

In determining productivity differentials, we utilize the same GDP forecasts and population growth estimates that we used in creating the average cash rate forecasts (see the yield section above). The average yearly productivity advantage (or disadvantage) in the foreign country versus the domestic country is a percentage adjustment to the capital returns associated with changes in the real exchange rate, and denotes movement in the equilibrium real exchange rate over time.

A country's ability to deliver long-term productivity gains is related to the maturity of its institutions and standards of governance. For example, a country with very high productivity, but poor rule of law, has a lower probability of delivering the benefits of surplus productivity growth to an investor, whereas a country with high productivity and strong rule of law has a higher probability.

( )10 10Productivity = GDP Growth Population Growth−Avg Yr Fcst Avg Yr Fcst

( )( )

10Real CPI adjusted Exchange Rate1Relative PPP Reversion ln20 Real CPI adjusted Exchange Rate

YrAvg

Current

= ×


This idea is incorporated into the model as a probability-weighted result of two independent states that affect both the expected return and the expected volatility of the currency. The first state captures a country that allows the benefit of excess productivity growth to be shared with investors. The second state captures outcomes whose benefits, if realized, are channeled away from investors to those well connected to decision makers in the country. As the outcomes are probabilistic, countries that find themselves in this second, less investor-friendly state are expected to miss out on currency appreciation related to excess productivity growth.

We do not claim to be experts at grading the quality of institutions in countries across the globe; instead, a number of organizations provide this data. Given the subjectivity in grading countries, we do not rely on a single organization’s rankings, but average together the rankings of, at present, two different organizations, the World Bank and The Heritage Foundation. These organizations rank countries on a variety of metrics. Based on these scores, we calculate a percentage rank for each country, which acts as the probability weight for each state.6 The weighted average, based on the country’s percentile rank, of the productivity value in each of the two states is the overall weighted productivity change for each country.

Results

Consistent with the expectation of slower global growth over the next decade, as of this writing (Summer 2015), future real interest rates are expected to be lower than in the past, with real interest rate differentials between

the United States and other developed economies expected to be in the -1% to 1% range, and between the United States and emerging economies remaining in the 2% to 4% range. Figure 1 and Figure 2 reflect these interest rate differentials along with the expected appreciation or depreciation of real exchange rates going forward.

6The rank of the country, 100% being the highest, represents the weight given to state 1. One minus the rank is the weight for state 2.


1.9% 2.0%

0.7% 0.5%

1.6% 1.4%

-0.1% -0.2%

-4%

-2%

0%

2%

4%

6%

8%

10%

AUSTRALIANDOLLAR

CANADIANDOLLAR

EURO HONG KONGDOLLAR

JAPANESE YEN SWEDISH KRONA SWISS FRANC BRITISH POUND

Source: Research Affiliates, LLC, based on data from Bloomberg and the United Nations

5.8%4.9% 5.2%

6.0% 5.9%

3.6% 3.7%

4.9%4.0% 4.1%

2.0%2.5%

5.7%

-4%

-2%

0%

2%

4%

6%

8%

10%

BRAZILIANREAL

CHINESEYUAN

INDIANRUPEE

INDONESIANRUPIAH

MALAYSIANRINGGIT

MEXICANPESO

POLISHZLOTY

RUSSIANROUBLE

SOUTHAFRICAN

RAND

KOREANWON

TAIWANESEDOLLAR

THAILANDBAHT

TURKISHLIRA

Source: Research Affiliates, LLC, based on data from Bloomberg and the United Nations

FIGURE 1Forecasted Developed Market Excess Cash Returns to USD

FIGURE 2Forecasted Emerging Market Excess Cash Returns to USD


REFERENCES

Arnott, Robert D., and Denis B. Chaves. "Demographic Changes, Financial Markets, and the Economy." Financial Analysts Journal, vol. 68, no. 1 (January/February):23-46.

Clarida, Richard, Jordi Galí, and Mark Gertler. 2000. "Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory." Quarterly Journal of Economics, vol. 115, no. 1 (February):147-180.

Feenstra, Robert C., Robert Inklaar, and Marcel Timmer. 2013. The Next Generation of the Penn World Table. NBER Working Paper No. 19255.

Johansson, Åsa, Yvan Guillemette, Fabrice Murtin, David Turner, Giuseppe Nicoletti, Christine de la Maisonneuve, Phillip Bagnoli, Guillaume Bousquet, and Francesca Spinelli. 2013. "Long Term Growth Scenarios." OECD Economics Department Working Papers, No. 1000 (January 28). Paris, France: OECD Publishing.

Jorgenson, D.W., and Z. Griliches. 1967. "The Explanation of Productivity Change." Review of Economic Studies, vol. 34, no. 3 (July):249-283.

Laubach, Thomas, and John C. Williams. 2001. Measuring the Natural Rate of Interest. Washington D.C.: Board of Governors of the Federal Reserve System. Available at http://www.federalreserve.gov/Pubs/FEDS/2001/200156/200156pap.pdf.

Nechio, Fernanda. 2011. "Monetary Policy When One Size Does Not Fit All." FRBSF Economic Letter. Federal Reserve Bank of San Francisco. (June 13.) Available at http://www.frbsf.org/economic-research/publications/economic-letter/2011/june/monetary-policy-europe.

Pesaran, M. Hashem, Yongcheol Shin, and Ron P. Smith.1999. "Pooled Mean Group Estimation of Dynamic Heterogeneous Panels." Journal of the American Statistical Association, vol. 94, no. 446 (June):621-634.

Ramsey, F.P. "The Mathematical Theory of Saving." 1928. Economic Journal, vol. 38, no. 152 (December):543-559.

Romer, David. 2011. Advanced Macroeconomics, 4th edition. New York: McGraw-Hill .

Shepherd, Shane. 2014. "The Outlook for Emerging Market Bonds." Research Affiliates. Available at http://www.researchaffiliates.com/Our%20Ideas/Insights/Fundamentals/Pages/227_The_Outlook_for_Emerging_Market_Bonds.aspx.

Solow, Robert M. 1956. "A Contribution to the Theory of Economic Growth." Quarterly Journal of Economics, vol. 70, no. 1 (February):65-94.

Solow, Robert M. 1957. "Technical Change and the Aggregate Production Function." Review of Economics and Statistics, vol. 39, no. 3 (August):312-320.

Taylor, Alan M., and Mark P. Taylor. 2004. "The Purchasing Power Parity Debate." Journal of Economic Perspectives, vol. 18, no. 4 (Fall):135-158.

Taylor, John B. 1993. "Discretion versus Policy Rules in Practice." Carnegie-Rochester Conference Series on Public Policy, vol. 39 (December):195-214.


DISCLAIMER

The information contained herein regarding Asset Allocation and Expected Returns may represent real return forecasts for several asset classes and not for any Research Affiliates (“RA”) fund or strategy. These forecasts are forward-looking statements based upon the reasonable beliefs of RA and are not a guarantee of future performance. Forward-looking statements speak only as of the date they are made, and RA assumes no duty to and does not undertake to update forward-looking statements. Forward-looking statements are subject to numerous assumptions, risks, and uncertainties, which change over time. Actual results may differ materially from those anticipated in forward-looking statements.

All projections provided are estimates and are in U.S. dollar terms, unless otherwise specified. Given the complex risk-reward trade-offs involved, one should always rely on judgment as well as quantitative optimization approaches in setting strategic allocations to any or all of the above asset classes. Please note that all information shown is based on qualitative analysis. Exclusive reliance on the above is not advised. This information is not intended as a recommendation to invest in any particular asset class or strategy or as a promise of future performance. Note that these asset class and strategy assumptions are passive only–they do not consider the impact of active management. References to future returns are not promises or even estimates of actual returns a client portfolio may achieve. Assumptions, opinions and estimates are provided for illustrative purposes only. They should not be relied upon as recommendations to buy or sell any securities, commodities, derivatives or financial instruments of any kind. Forecasts of financial market trends that are based on current market conditions or historical data constitute a judgment and are subject to change without notice. We do not warrant its accuracy or completeness. This material has been prepared for information purposes only and is not intended to provide, and should not be relied on for, accounting, legal, tax, investment or tax advice. There is no assurance that any of the target prices mentioned will be attained. Any market prices are only indications of market values and are subject to change.

Hypothetical or simulated performance results have certain inherent limitations. Unlike an actual performance record, simulated results do not represent actual trading, but are based on the historical returns of the selected investments, indices or investment classes and various assumptions of past and future events. Simulated trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. Also, since the trades have not actually been executed, the results may have under or over compensated for the impact of certain market factors. In addition, hypothetical trading does not involve financial risk. No hypothetical trading record can completely account for the impact of financial risk in actual trading. For example, the ability to withstand losses or to adhere to a particular trading program in spite of the trading losses are material factors which can adversely affect the actual trading results. There are numerous other factors related to the economy or markets in general or to the implementation of any specific trading program which cannot be fully accounted for in the preparation of hypothetical performance results, all of which can adversely affect trading results.

The asset classes are represented by broad-based indices which have been selected because they are well known and are easily recognizable by investors. Indices have limitations because indices have volatility and other material characteristics that may differ from an actual portfolio. For example, investments made for a portfolio may differ significantly in terms of security holdings, industry weightings and asset allocation from those of the index. Accordingly, investment results and volatility of a portfolio may differ from those of the index. Also, the indices noted in this presentation are unmanaged, are not available for direct investment, and are not subject to management fees, transaction costs or other types of expenses that a portfolio may incur. In addition, the performance of the indices reflects reinvestment of dividends and, where applicable, capital gain distributions. Therefore, investors should carefully consider these limitations and differences when evaluating the index performance.

No investment process is risk free and there is no guarantee of profitability; investors may lose all of their investments. No investment strategy or risk management technique can guarantee returns or eliminate risk in any market environment. Diversification does not guarantee a profit or protect against loss. Investing in foreign securities presents certain risks not associated with domestic investments, such as currency fluctuation, political and economic instability, and different accounting standards. This may result in greater share price volatility. The prices of small- and mid-cap company stocks are generally more volatile than large-company stocks. They often involve higher risks because smaller companies may lack the management expertise, financial resources, product diversification and competitive strengths to endure adverse economic conditions.

Bond prices fluctuate inversely to changes in interest rates. Therefore, a general rise in interest rates can result in the decline of the value of your investment. High-yield bonds, also known as junk bonds, are subject to greater risk of loss of principal and interest, including default risk, than higher-rated bonds. Investing in fixed-income securities involves certain risks such as market risk if sold prior to maturity and credit risk especially if investing in high-yield bonds which have lower ratings and are subject to greater volatility. All fixed-income investments may be worth less than original cost upon redemption or maturity. Income from municipal securities is generally free from federal taxes and state taxes for residents of the issuing state. While


the interest income is tax-free, capital gains, if any, will be subject to taxes. Income for some investors may be subject to the federal alternative minimum tax (AMT).

There are special risks associated with an investment in real estate, including credit risk, interest-rate fluctuations and the impact of varied economic conditions. Distributions from REIT investments are taxed at the owner’s tax bracket.

Hedge funds or alternative investments are complex, speculative investment vehicles and are not suitable for all investors. They are generally open to qualified investors only and carry high costs and substantial risks and may be highly volatile. There is often limited (or even nonexistent) liquidity and a lack of transparency regarding the underlying assets. They do not represent a complete investment program. The investment returns may fluctuate and are subject to market volatility so that an investor’s shares, when redeemed or sold, may be worth more or less than their original cost. Hedge funds are not required to provide investors with periodic pricing or valuation and are not subject to the same regulatory requirements as mutual funds. Investing in hedge funds may also involve tax consequences. Speak to your tax advisor before investing. Investors in funds of hedge funds will incur asset-based fees and expenses at the fund level and indirect fees, expenses and asset-based compensation of investment funds in which these funds invest. An investment in a hedge fund involves the risks inherent in an investment in securities as well as specific risks associated with limited liquidity, the use of leverage, short sales, options, futures, derivative instruments, investments in non-U.S. securities, junk bonds and illiquid investments. There can be no assurances that a manager’s strategy (hedging or otherwise) will be successful or that a manager will use these strategies with respect to all or any portion of a portfolio. Please carefully review the Private Placement Memorandum or other offering documents for complete information regarding terms, including all applicable fees, as well as other factors you should consider before investing.

Buying commodities allows for a source of diversification for those sophisticated persons who wish to add commodities to their portfolios and who are prepared to assume the risks inherent in the commodities market. Any purchase represents a transaction in a non-income producing commodity and is highly speculative. Therefore, commodities should not represent a significant portion of an individual’s portfolio. Buying gold, silver, platinum and palladium allows for a source of diversification for those sophisticated persons who wish to add precious metals to their portfolios and who are prepared to assume the risks inherent in the bullion market. Any bullion or coin purchase represents a transaction in a non-income-producing commodity and is highly speculative. Therefore, precious metals should not represent a significant portion of an individual’s portfolio.

Trading foreign exchange involves a high degree of risk. Exchange rates between foreign currencies change rapidly do to a wide range of economic, political and other conditions, exposing one to risk of exchange rate losses in addition to the inherent risk of loss from trading the underlying financial product. If one deposits funds in a currency to trade products denominated in a different currency, one’s gains or losses on the underlying investment therefore may be affected by changes in the exchange rate between the currencies. If one is trading on margin, the impact of currency fluctuation on that person’s gains or losses may be even greater.

Investments that are concentrated in a specific sector or industry increase their vulnerability to any single economic, political or regulatory development. This may result in greater price volatility.

This information has been prepared by RA based on data and information provided by internal and external sources. While we believe the information provided by external sources to be reliable, we do not warrant its accuracy or completeness.

Research Affiliates is the owner of the trademarks, service marks, patents and copyrights related to the Fundamental Index methodology. The trade names Fundamental IndexTM, RAFITM, Research Affiliates EquityTM, RAETM, the RAFI logo, and the Research Affiliates corporate name and logo among others are the exclusive intellectual property of Research Affiliates, LLC. Any use of these trade names and logos without the prior written permission of Research Affiliates, LLC is expressly prohibited. Research Affiliates, LLC reserves the right to take any and all necessary action to preserve all of its rights, title and interest in and to these terms and logos.

Various features of the Fundamental IndexTM methodology, including an accounting data-based non-capitalization data processing system and method for creating and weighting an index of securities, are protected by various patents, and patent-pending intellectual property of Research Affiliates, LLC. (See all applicable US Patents, Patent Publications, and Patent Pending intellectual property located at http://www.researchaffiliates.com/Pages/legal.aspx#d, which are fully incorporated herein.)

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