SYSTEMATIC DIVERSIFICATION USING BETA

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1 August 2015 Paul Bouchey Parametric CIO Vassilii Nemtchinov Director of Research - Equity Strategies Tianchuan Li Quantitative Analyst SYSTEMATIC DIVERSIFICATION USING BETA Beta is a measure of risk representing an asset s sensitivity to the movements of the broad market. It is defined as the covariance with the market divided by the variance of the market. A beta of less than one indicates that either the asset has a lower volatility than the stock market, or the asset is just as volatile, but is uncorrelated with the broad market. Parametric 1918 Eighth Avenue Suite 3100 Seattle, WA T F Parametric Portfolio Associates LLC. For Informational Purposes Only; Not an Offer to Buy or Sell Securities.

2 Higher risk investments deserve higher expected returns to compensate for the extra risk, or so theory tells us. Historically, this has not always been the case for U.S. and other developedmarket stocks. This beta anomaly, which is now well established by academics, has started to gain traction with investors. This is demonstrated by the large flows into the numerous low-beta and low-volatility strategies which were established in the wake of the Global Financial Crisis. In the first part of this paper, we explore the beta anomaly in the academic literature and provide an empirical analysis for stocks in the U.S., developed, and emerging markets. Our primary finding is that beta is not a strong predictor of expected returns, but it is beneficial when used to help reduce risk in a portfolio. In the second part of the paper, we present results for an investment strategy that filters out the highest-beta stocks, while controlling concentration risks by country and sector. 1 See Sharpe [1964]. 2 See Clarke, De Silva and Thorley [2006], Blitz and van Vliet [2007], and Bouchey and Nemtchinov [2013]. 3 Garcia-Feijoo et. al. [2015], Scherer [2011] and Bali, Cakici, Yan, and Zhang [2002]. 4 Clarke, De Silva and Thorley [2014] or Liu, Orr and Wang [2011]. 5 Frazzini and Pedersen [2014] and Baker, Bradley, and Taliaferro [2014]. 6 de Boer, Campagna and Norman [2013] and Baker, Bradley and Wurgler [2014]. THE LOW-RISK ANOMALY Beta plays a central role in the Capital Asset Pricing Model (CAPM), which postulates that the expected asset return depends only on the beta times the expected market return 1. Almost immediately after the CAPM theory was published, empirical researchers found no significant positive relationship between beta and realized historical returns. In fact, in some studies an inverse relationship was found. This observation, now known as the low-risk anomaly, is often discussed in the context of the many other market-efficiency anomalies, including the size, value and momentum effects. The early works of Black, Jensen and Scholes [1972], Fama and French [1992], Haugen and Baker [1991] first documented the flat relationship between returns and beta. Using volatility as a measure of risk, more recent studies documented that portfolios of low-volatility stocks outperformed portfolios of high-volatility stocks within equity markets and size segments 2. Low-beta and low-volatility terms are often used interchangeably, since intuitively they are very similar. There are differences that should be considered. Beta is important because it measures risk that cannot be diversified away. Beta is the risk that an investment adds to an already diversified portfolio. Volatility includes beta and security-specific volatility effects. Ang, Hodrick, Xing, and Zhang [2006] reported an additional anomaly using short-term specific volatility the volatility of the security that remains after removing the effect of beta. Portfolios formed using low stock-specific volatility outperformed portfolios formed using high stock-specific volatility. All of these low-risk portfolios produce similar outperformance historically, regardless of whether researchers were looking at volatility, beta, or stock-specific volatility. Some studies have pointed out that the low-risk anomaly could be attributed to other known anomalies such as value, momentum, or size 3. However, numerous studies that followed have established that the low-risk anomaly is robust and cannot be explained by other known risk factors, with the beta factor being among the strongest in a multi-factor framework 4. Why does this contradiction of the efficiency of markets exist? Recent research has pointed to behavioral explanations for the low-risk anomaly, arguing that leverage constraints imposed on some investors drive up the prices and reduce expected returns on the high-risk assets; or that the constraints on the deviations from the benchmarks force some managers to increase their exposures to the high risk stocks thereby lowering their expected returns 5. Several works have focused on comparing the macro and micro components of low-risk investing and have attributed safe low-risk non-cyclical sectors and the low-risk countries as the main drivers of the long-term excess return 6. Asness, Frazzini, and Pedersen [2014] rejected these findings and demonstrated that industry neutral low-beta portfolios earned substantial positive excess returns in the U.S. market, refuting 2015 Parametric Portfolio Associates LLC. For Informational Purposes Only; Not an Offer to Buy or Sell Securities. 02

3 industries as the main drivers of the low-risk investing. These diverse findings highlight the complex nature of the low-risk anomaly. We focus on beta as the primary factor of interest in the low-risk anomaly for the following reasons: The specific risk anomaly has only been established for short-term measures of volatility, implying that it would require a high-turnover strategy to exploit the anomaly. Total volatility includes both beta and specific risk. Beta captures the asset s systematic risk, which cannot be diversified away (except by reducing the weight of the stock); whereas specific risk should be diversifiable by increasing the number of assets. Beta has been studied more thoroughly, over many decades and in many contexts. We focus on the practical construction of low-beta investment strategies, broadly exposed to the U.S. and international markets, which derive excess returns from the diversification and rebalancing. Our goal is not necessarily to exploit the beta anomaly, but instead to use beta to reduce portfolio risk and enhance long-term gains from rebalancing. Assets with lower beta are less correlated with the market movement and enhance portfolio cross-sectional volatility. This increases the diversification return, but they also result in lower realized portfolio volatility 7. We first examine traditional decile portfolios formed using beta in the U.S., developed, and emerging markets. Next we focus on a practical portfolio construction approach, which avoids problems with concentrations that are typical for lower-risk portfolios. DATA AND METHODOLOGY In our analysis we use S&P Global Broad Market Index (BMI) data from January 1997 to June Our sample includes large-, mid- and small-capitalization stocks. We sort countries into developed or emerging markets based on MSCI classifications, and use the Global Industry Classification Standard (GICS) for assigning stocks to sectors. Historical betas are computed by MSCI using a time-series regression of the trailing 60-month stock returns against market returns and reported in the Global Risk Model. For the few stocks that do not have beta estimates, we assign a beta of one to avoid biases for stock selections. Beta Deciles in U.S., Developed, and Emerging Markets As an initial test, we form decile portfolios using historical beta for three equity asset classes: U.S. stocks, developed market ex-u.s. stocks, and emerging market stocks. At the beginning of each month, we sort stocks on their historical beta estimates into ten portfolios, rebalancing them each month based on the latest estimates. The top decile portfolio contains the stocks with the largest beta and the bottom decile portfolio contains the stocks with the lowest beta. Stocks are equally weighted in each decile. We report annualized mean returns, annualized continuouslycompounded returns, and annualized volatility for the decile portfolios in the U.S., developed, and emerging markets in Figure 1. 7 Bouchey, Nemtchinov, Paulsen and Stein [2014] Parametric Portfolio Associates LLC. For Informational Purposes Only; Not an Offer to Buy or Sell Securities. 03

4 Figure 1: Return and Risk Characteristics for Beta Decile Portfolios, Deciles D1 (Low) D2 D3 D4 D5 D6 D7 D8 D9 D10 (High) U.S. Stocks Volatility % Growth Rate % Annual Average Return % Developed Market (Ex-U.S.) Stocks Volatility % Growth Rate % Annual Average Return % Emerging Market Stocks Volatility % Growth Rate % Annual Average Return % Source: Parametric, S&P Global BMI, MSCI, Barra and Factset, For illustration purposes only. It is not possible for invest directly in an index; they are unmanaged and do not reflect the deduction of fees and expenses. Unsurprisingly, there is a strong relationship between beta and volatility. The high-beta decile portfolios are significantly more volatile than the low-beta portfolios. There are two reasons for this: (1) low-beta stocks tend to be lower volatility, and (2) low-beta stocks tend to have lower correlation with one another, thus portfolios of low-beta stocks garner more diversification benefit. There also appears to be a strong relationship between continuously-compounded growth rates and beta. The growth rates tend to decrease as beta increases, as shown in Figure Parametric Portfolio Associates LLC. For Informational Purposes Only; Not an Offer to Buy or Sell Securities. 04

5 Figure 2: Growth Rates for Beta Decile Portfolios, % 12% 10% 8% 6% 10.32% 11.29% 11.01% U.S. Stocks 13.03% 11.95% 9.55% 7.99% 9.33% 5.89% 4% 2.65% 2% 0% D1 (low) D2 D3 D4 D5 D6 D7 D8 D9 D10 (high) Developed Market Ex-U.S. Stocks 10% 8% 6.72% 8.57% 8.96% 8.15% 8.32% 7.75% 7.15% 6% 4% 5.18% 3.86% 2% 0% -2% -0.74% D1 (low) D2 D3 D4 D5 D6 D7 D8 D9 D10 (high) 10% 9% 8% 7% 6% 5% 4% 3% 2% 1% 0% Emerging Market Stocks 8.84% 8.24% 8.13% 8.11% 7.85% 7.51% 7.48% 6.64% 6.29% 5.29% D1 (low) D2 D3 D4 D5 D6 D7 D8 D9 D10 (high) Source: Parametric, S&P Global BMI, MSCI, Barra and Factset, For illustration purposes only. It is not possible for invest directly in an index; they are unmanaged and do not reflect the deduction of fees and expenses Parametric Portfolio Associates LLC. For Informational Purposes Only; Not an Offer to Buy or Sell Securities. 05

6 While these results suggest beta may be useful for enhancing long-term portfolio growth rates, care should be taken before concluding that beta is a good predictor of future returns. Part of the decrease in growth rates is due to volatility drag. The annualized average returns, which exclude the effects of volatility on compounded returns, tell a different story. For U.S. stocks, the average returns are similar, regardless of beta decile. In developed markets outside the U.S., there is a negative relationship between beta and return evidence of the beta anomaly. On the other hand, in the emerging markets there is a positive relationship between beta and returns 8. Figure 3 tests the statistical significance of the observed differences in annualized average returns between decile 1 and decile 10. None of the p-values were significant, thus we cannot rule out the possibility that the observed differences come from random noise. Figure 3: Statistical Significance of Decile 10 (High Beta) Versus Decile 1 (Low Beta), Annual Average Return U.S. Stocks Developed Market Ex-U.S. Stocks Emerging Market Stocks Decile Decile Difference P-value Note: A p-value of less than 0.05 indicates a significant difference between the deciles exists; and implies there is only a 5% chance that noise in the data is hiding the fact that there is actually no difference between deciles. Source: Parametric, S&P Global BMI, MSCI, Barra and Factset, For illustration purposes only. It is not possible for invest directly in an index; they are unmanaged and do not reflect the deduction of fees and expenses. Regardless of whether beta can predict returns or not, beta is a strong predictor of risk. In the U.S. and developed markets, it may be a return enhancer, while in emerging markets it appears to be a return drag, with neither result being statistically significant. In the next part of the paper, we look at various methods for incorporating beta into the portfolio construction process, with an eye towards reducing portfolio volatility, controlling concentration risk, and enhancing long-term portfolio growth. 8 The results obtained for emerging markets are in agreement with positive trend for historical beta factor returns in the MSCI Barra Emerging Markets Model (EEM1) reported by Morozov (2014) and with the findings by Blitz (2013) which observed a flat relationship between beta and return for the quintile portfolios sorted on historical beta. FILTERING HIGH-BETA STOCKS BY SECTOR IN THE U.S. AND DEVELOPED MARKETS A common issue in constructing lower-risk portfolios is concentration. For example, tilting or optimizing portfolios towards low-risk stocks creates unintended bets by sector particularly in utilities and consumer staples stocks. Some critics have argued that most of the benefits come from the sector allocation. To explore this issue, we look within each sector and remove stocks with the highest betas. At the beginning of each month, we remove stocks in the highest beta quartile in each sector and form a portfolio using remaining stocks at capitalization weights. We rescale the stock weights in the portfolio to match the sector and country capitalization weights of the market portfolio. Performance and risk characteristics for various developed-country portfolios, which have been beta filtered by sector, are reported in Figure 4. By removing the highest-beta stocks from each sector, the resulting portfolios outperform the corresponding capitalization-weighted portfolios in international and U.S. markets, as well as, in most developed countries considered individually. All of the country portfolios show an improvement in the risk-adjusted performance in comparison to their capitalization-weighted counterparts, as shown by their return-to-volatility ratios. Removing the highest beta securities in each sector reduces portfolio risk without sacrificing long-term returns Parametric Portfolio Associates LLC. For Informational Purposes Only; Not an Offer to Buy or Sell Securities. 06

7 Figure 4: Return and Risk Characteristics for Portfolios Filtered on Historical Beta by Sector, Cap Weighted Beta-Filtered Average Weight Total Return Volatility Ratio Total Return Volatility Beta Ratio Excess Return Tracking Information Error Ratio Australia 5.60% 10.98% 22.12% % 21.33% % 3.40% 0.07 France 9.14% 8.17% 21.31% % 19.63% % 4.46% 0.69 Germany 7.67% 8.13% 24.21% % 21.44% % 5.89% 0.19 Hong Kong 2.39% 7.77% 25.17% % 22.86% % 5.14% 0.46 Italy 3.48% 6.94% 25.25% % 23.57% % 5.62% Japan 21.69% 1.78% 18.47% % 17.63% % 2.90% 0.26 Spain 3.42% 9.50% 24.93% % 23.62% % 6.35% 0.30 Sweden 2.76% 10.12% 27.02% % 25.07% % 7.01% Switzerland 7.50% 9.80% 17.34% % 17.73% % 5.20% 0.42 United Kingdom 23.98% 7.06% 16.55% % 16.54% % 2.86% 0.00 Other 12.37% 7.79% 21.17% % 19.70% % 3.56% 0.17 All International 6.52% 17.25% 7.15% 16.07% % 2.34% 0.27 United States 8.23% 16.11% % 14.18% % 3.23% 0.03 Source: Parametric, S&P Global BMI, MSCI, Barra and Factset, For illustration purposes only. It is not possible for invest directly in an index; they are unmanaged and do not reflect the deduction of fees and expenses. We also report excess sector returns by country in Figure 5. Excess returns are computed as the difference between the annualized mean sector returns for portfolios filtered by historical beta and the annualized mean cap-weighted sector returns. Roughly two-thirds of the sectors have a positive excess return. Financials tend to have negative excess returns, perhaps due to less leverage used by those firms with lower beta. Much of the benefit of low-beta investing can be captured by removing or underweighting the highest beta stocks within each sector while preserving the broad market exposure and diversifying by sectors. Figure 5: Excess Returns Reported by GICS Sectors Within the Ten Largest Countries for Portfolios Filtered on Historical Beta in Developed International Markets Energy Materials Industrials Consumer Discretionary Consumer Staples Health Care Financials Tech Telecom Utilities Australia 2.74% 1.11% 0.67% -0.60% -0.33% -3.60% -0.29% 1.11% -7.23% -3.45% France 5.59% -1.05% -0.10% 0.57% 1.18% 6.13% 0.51% 2.04% 4.73% 2.67% Germany 3.41% -0.91% -3.69% 3.34% 0.08% -1.38% -1.29% 1.33% 4.77% -0.19% Hong Kong 6.02% 3.10% -0.45% 0.34% -3.50% 0.05% 1.31% -0.49% 10.76% -0.34% Italy -6.55% -1.13% 3.96% 0.24% 3.45% 3.89% -1.25% % 3.68% 5.18% Japan 2.52% 0.50% 0.37% 0.80% 1.57% -0.72% -1.23% 1.39% 0.73% -0.47% Spain 0.57% 2.52% 0.45% -2.83% 1.25% 8.93% -1.18% 8.85% 7.66% 1.18% Sweden -3.91% 2.16% 2.45% 0.31% 3.78% 3.44% -0.96% -9.03% -3.17% 1.44% Switzerland -4.91% 0.70% 3.78% 0.19% 3.30% 3.41% 0.57% 2.83% 0.00% 1.34% UK 0.22% -0.35% -1.25% 0.68% 0.79% 0.30% -1.21% 0.11% 1.62% 0.26% Source: Parametric, S&P Global BMI, MSCI, Barra and Factset, For illustration purposes only. It is not possible for invest directly in an index; they are unmanaged and do not reflect the deduction of fees and expenses Parametric Portfolio Associates LLC. For Informational Purposes Only; Not an Offer to Buy or Sell Securities. 07

8 CONTROLLING COUNTRY AND SECTOR CONCENTRATIONS When developing a global portfolio, concentration risk is also an issue at the country level. For example, in a developed-markets ex-u.s. portfolio, Japan and the UK constitute more than 40% of the total market capitalization. At the sector level, the market portfolio also exhibits high concentration. Information Technology and Telecommunication Services were the most concentrated sectors in the international markets during the period leading to the dot-com crash period and they exhibited substantial contraction of market share afterwards. We use a modified equal-weight portfolio construction approach to create a diversified portfolio which simultaneously reduces country and sector concentrations, filters out high-beta stocks and achieves market representation. Figure 6 shows performance and risk characteristics for three portfolio strategies: Portfolio A: Diversification Targets Only. We first create a diversified country target by underweighting large countries and overweighting small countries. The largest country is underweighted by no more than 40% and the smallest country is overweighed by no more than 300% from their respective market capitalization values. Next, we reduce sector concentrations in a similar fashion in each country by underweighting the largest sectors and overweighting the smallest sectors. This results in a modified equal-country and equal-sector strategy. Stocks are cap-weighted within sector. Portfolio B: Beta Filter Only. A strategy which filters out the top quartile highest beta stocks by sector within each country. Remaining stocks are cap-weighted. Portfolio A+B: Diversification Targets and Beta Filter. Applies the modified equal-weight strategy, as well as, the beta filter within each sector. Remaining stocks are cap-weighted within each country-sector grouping. For both the U.S. and international markets we compare the cap-weighted index to the three portfolio diversification strategies 9. 9 This approach is not fruitful in the emerging markets due to the strong positive relationship between beta and return, as well as, the increased costs due to strategy turnover Parametric Portfolio Associates LLC. For Informational Purposes Only; Not an Offer to Buy or Sell Securities. 08

9 Figure 6: Return and Risk Characteristics for Portfolio Diversification Strategies, Developed Market Ex-U.S. Cap-Weight Index Total Return Volatility Beta Return/ Volatility 6.52% 17.25% 0.38 Excess Return Tracking Error Information Ratio (A) Diversification Targets Only 8.35% 17.33% % 2.03% 0.90 (B) Beta Filter Only 7.15% 16.07% % 2.34% 0.27 (A+B) Diversification Targets and Beta Filter 8.68% 16.20% % 3.02% 0.71 Total Return Volatility Beta Return/ Volatility U.S. Cap-Weight Index 8.23% 16.11% 0.51 Excess Return Tracking Error Information Ratio (A) Diversification Targets Only 9.26% 15.00% % 3.20% 0.32 (B) Beta Filter Only 8.33% 14.18% % 3.23% 0.03 (A+B) Diversification Targets and Beta Filter 9.18% 13.38% % 5.55% 0.17 Source: Parametric, S&P Global BMI, MSCI, Barra and Factset, For illustration purposes only. It is not possible for invest directly in an index; they are unmanaged and do not reflect the deduction of fees and expenses. Controlling concentration risk by adopting a modified equal-weight strategy for countries and sectors enhances returns over this historical period. For U.S. stocks, it resulted in an annualized excess return of 1.03% and for developed international stocks, 1.83%. Using a beta filter, with no country and sector controls, reduces the portfolio volatility for U.S. stocks from 16.11% to 14.18% and for developed international stocks from 17.25% to 16.07%. Applying these techniques in combination creates both return enhancement and risk reduction. The return over volatility ratio increased from 0.51 to 0.69 for U.S. stocks and from 0.38 to 0.54 for international stocks. CONCLUSION Our study finds mixed results for beta as an anomaly. Low beta outperforms for developed international markets, underperforms in emerging markets, and is flat in the US. None of these differences appear to be statistically significant. Beta is very useful, however, as a tool for controlling risk. This is especially true in the context of strategies that diversify across countries and sectors Parametric Portfolio Associates LLC. For Informational Purposes Only; Not an Offer to Buy or Sell Securities. 09

10 REFERENCES Ang, A., R. Hodrick, Y. Xing, and X. Zhang. The Cross-Section of Volatility and Expected Returns. Journal of Finance, Vol. 61, No. 1 ( 2006), pp Asness C., A. Frazzini, and L. Pedersen. Low-Risk Investing Without Industry Bets. Financial Analysts Journal, Volume 70, No 4 (2014), pp Baker M., B. Bradley, and R. Taliaferro. The Low-Risk Anomaly: A Decomposition Into Micro and Macro Effects. Financial Analysts Journal, Vol. 70, No 2 (2014), pp Baker M., B. Bradley, and J. Wurgler. Benchmarks as Limits to Arbitrage: Understanding the Low Volatility Anomaly. Financial Analysts Journal, Vol. 67, No 2 (2011), pp Bali, T., N. Cakici, X. Yan, and Z. Zhang. Does Idiosyncratic Risk Really Matter? The Journal of Finance, Vol. LX, No. 2 (2005), pp Black F., M. Jensen, and M. Scholes. The Capital Asset Pricing Model: Some Empirical Tests. Studies in the Theory of Capital Markets, Praeger Publishers Inc. (1972), pp Blitz D., J. Pang, and P. van Vliet. The Volatility Effect in Emerging Markets. Emerging Markets Review, Vol 16 (2013), pp Blitz D., and P. van Vliet. The Volatility Effect. Journal of Portfolio Management, 34(1) (2007), pp Bouchey P., and V. Nemtchinov. Bending CAPM: Why Do High Volatility Stocks Underperform? Parametric White Paper (2013). Bouchey, P., Nemtchinov, V., Paulsen, A., and Stein, D. (2012) Volatility Harvesting: Why Does Diversifying and Rebalancing Create Portfolio Growth? The Journal of Wealth Management 15(2), pp Clarke R.G., H. De Silva and S. Thorley. Minimum-Variance Portfolios in the U.S. Equity Market. Journal of Portfolio Management, 33 (1) (2006), pp Clarke R.G., H. De Silva and S. Thorley. The Not-So-Well-Known Three-and-One-Half-Factor Model. Financial Analysts Journal, 70 (5) (2014), pp De Boer S., J. Campagna, and J. Norman, Country and Sector Drive Low-Volatility Investing in Global Equity Markets. QS Investors Paper (2013). De Boer S., and J. Norman, Country and Sector Drive Minimum-Volatility Investing in Emerging Markets Too. QS Investors Paper (2013). Fama E., and K. French. The Cross-Section of Expected Stock Returns. Journal of Finance, 47 (2) (1992), pp Frazzini A., and L. Pedersen. Betting Against Beta. Journal of Financial Economics, (2014), pp Parametric Portfolio Associates LLC. For Informational Purposes Only; Not an Offer to Buy or Sell Securities. 10

11 Garcia-Feijoo L., L. Kochard, R. Sullivan, and P. Wang. Low-Volatility Cycles: The Influence of Valuation and Momentum on Low-Volatility Portfolios. Financial Analysts Journal, Vol. 71, No. 3 (2015). Haugen R., and N. Baker. The Efficient Market Inefficiency of Capitalization-Weighted Stock Portfolios. Journal of Portfolio Management, Vol. 17, No. 3 (1991), pp Morozov A., I. Balint, L. Borda., P. Ward., and M. Bayraktar. Barra Emerging Markets Equity Model. Research Notes, MSCI (2014). Sharpe W. Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk. Journal of Finance, 19(3) (1964), pp Scherer B. A Note on the Returns from Minimum Variance Investing. Journal of Empirical Finance, Vol 18 (4) (2011), pp Parametric Portfolio Associates LLC. For Informational Purposes Only; Not an Offer to Buy or Sell Securities. 11

12 About Parametric Parametric, headquartered in Seattle, WA, is a leading global asset management firm, providing investment strategies and customized exposure management to institutions and individual investors around the world. Parametric offers a variety of rules-based, risk-controlled investment strategies, including alpha-seeking equity, alternative and options strategies, as well as implementation services, including customized equity, traditional overlay and centralized portfolio management. Parametric is a majority-owned subsidiary of Eaton Vance Corp. and offers these capabilities through investment centers in Seattle, WA, Minneapolis, MN and Westport, CT (home to Parametric subsidiary Parametric Risk Advisors LLC, an SEC-registered investment adviser). Disclosures This information is intended solely to report on investment strategies and opportunities identified by Parametric. Opinions and estimates offered constitute our judgment and are subject to change without notice, as are statements of financial market trends, which are based on current market conditions. We believe the information provided here is reliable, but do not warrant its accuracy or completeness. This material is not intended as an offer or solicitation for the purchase or sale of any financial instrument. Past performance is not indicative of future results. The views and strategies described may not be suitable for all investors. Investing entails risks and there can be no assurance that Parametric will achieve profits or avoid incurring losses. Parametric does not provide legal, tax and/or accounting advice or services. Clients should consult with their own tax or legal advisor prior to entering into any transaction or strategy described herein. Charts, graphs and other visual presentations and text information were derived from internal, proprietary, and/or service vendor technology sources and/or may have been extracted from other firm data bases. As a result, the tabulation of certain reports may not precisely match other published data. Data may have originated from various sources including, but not limited to, Bloomberg, MSCI/Barra, FactSet, and/or other systems and programs. Parametric makes no representation or endorsement concerning the accuracy or propriety of information received from any other third party. Benchmark/index information provided is for illustrative purposes only. It is not possible for invest directly in an index; they are unmanaged and do not reflect the deduction of fees and expenses. The S&P Global Broad Market Index is a comprehensive, rules-based index measuring global stock market performance. Standard & Poor s and S&P are registered trademarks of S&P Dow Jones Indices LLC ( S&P ), a subsidiary of The McGraw-Hill Companies, Inc. S&P makes no representation regarding the content of this material. Please refer to the specific service provider s website for complete details on all indices. All contents copyright 2015 Parametric Portfolio Associates LLC. All rights reserved. Parametric Portfolio Associates, PIOS, and Parametric with the iris flower logo are all trademarks registered in the U.S. Patent and Trademark Office. Parametric is located at th Avenue, Suite 3100, Seattle, WA For more information regarding Parametric and its investment strategies, or to request a copy of Parametric s Form ADV, please contact us at or visit our website, Parametric Portfolio Associates LLC. For Informational Purposes Only; Not an Offer to Buy or Sell Securities. 12