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The Surprising “Alpha” from Malkiel’s Monkey & Upside-Down Strategies Jason Hsu, Ph.D. Chief Investment Officer Research Affiliates, LLC.

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Research Affiliates 2

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Research Affiliates, LLC Mission » Concentrate on Research and product development » Partner with world-class Affiliates to bring product to market Global leader in » Global tactical asset allocation (GTAA) » Innovative indexation Profile » Approximately $142 billion in assets managed using RA investment strategies as of March » Founded in 2002 by Rob Arnott » Majority employee-owned 3 1 As of 3/31/2013 based on estimates. Includes assets managed or sub-advised by Research Affiliates or licensees using RAFI®, Enhanced RAFI®, or GTAA strategies. Note: please refer to the important information slide at the end for all relevant disclaimers, disclosures, and information on our intellectual property.

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4 *March 2013 data based on estimates. Includes GTAA, RAFI and Enhanced RAFI assets managed or sub-advised by Research Affiliates and RAFI licensees Managed Assets Growth Since Inception

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The Surprising “Alpha” from Malkiel’s Monkey & Upside-Down Strategies 5

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Smart Beta Strategies Fundamental Index ® Strategy Equal Weighting Diversity Weighting Minimum Variance Maximum Diversification Risk Efficient Index Equal Weighting of Risk Clusters 6

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1. High Risk—High Reward Claim » Investors are compensated for taking risk. The higher the risk of the strategy, the higher the return Implications 1.Risk-weighted strategies should have higher return. 2.The following strategies should outperform cap-weighted benchmark: » Volatility Weighted » Market Beta Weighted » Downside Semi-Deviation Weighted 7

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StrategyReturn Standard DeviationSharpe Ratio Information Ratio Volatility Wt %19.1% Market Beta Wt %19.8% Downside Semi-Deviation Wt %18.9% U.S. Cap Wt 4 9.7%15.3% See slide 37 for disclosures regarding individual strategies. Source: Research Affiliates, LLC. 1. High Risk—High Reward Performance vs. Cap (U.S. 1964–2012)

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StrategyReturn Standard Deviation Sharpe Ratio Information Ratio Inverse of Volatility Wt %15.6% Inverse of Market Beta Wt %15.0% Inverse of Downside Semi-Deviation Wt %15.6% Inverse of U.S. Cap Wt 4 9.7%15.3% Volatility Wt %19.1% Market Beta Wt %19.8% Downside Semi-Deviation Wt %18.9% U.S. Cap Wt 4 9.7%15.3% See slide 37 for disclosures regarding individual strategies. Source: Research Affiliates, LLC. 1. High Risk—High Reward What about the inverse strategies? (U.S. 1964–2012) Upside-down strategies also outperform … by a bigger margin! 9

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2. Strong Fundamentals—High Reward Claim » Strong fundamentals deliver high return Implications » Strategies weighted on accounting variables should outperform cap-weighted benchmark: » Strategies weighted on growth of fundamentals should outperform cap-weighted benchmark: 10

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11 See slide 37 for disclosures regarding individual strategies. Source: Research Affiliates, LLC. 2. Strong Fundamentals—High Reward Fundamentals-based strategies outperform the market U.S – 2012 StrategyReturnStandard DeviationSharpe RatioInformation Ratio Book Value Wt %15.7% Yr Avg Earnings Wt %15.1% Fundamentals Wt %15.4% Earnings Growth Wt %19.0%0.38 U.S. Cap Wt 4 9.7%15.3%

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U.S – 2012 StrategyReturn Standard Deviation Sharpe RatioInformation Ratio Inverse of Book Value Wt %18.5% Inverse of 5-Yr Avg Earnings Wt %18.3% Inverse of Fundamentals Wt %18.8% Inverse of Earnings Growth Wt %18.0% U.S. Cap Wt 4 9.7%15.3% Book Value Wt %15.7% Yr Avg Earnings Wt %15.1% Fundamentals Wt %15.4% Earnings Growth Wt %19.0%0.38 U.S. Cap Wt 4 9.7%15.3% See slide 37 for disclosures regarding individual strategies. Source: Research Affiliates, LLC. 2. Strong Fundamentals—High Reward Upside-down strategies also outperform! 12

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3. Popular Smart Beta Strategies Minimum Variance—low risk generates high return Maximum Diversification—return is proportional to volatility Risk-Efficient (λ=2) —return is proportional to downside semi- deviation Risk Cluster Equal Weight—equally weight country/industry clusters Diversity Weighting—a blend between cap weighting and equal weighting Fundamentals Weighting—strong fundamentals deliver high return 13

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14 See slide 37 for disclosures regarding individual strategies. Source: Research Affiliates, LLC. 3. Popular Smart Beta Strategies Popular smart beta strategies outperform the market U.S – 2012 StrategyReturn Standard DeviationSharpe RatioInformation Ratio Minimum Variance %11.7% Maximum Diversification %14.0% Risk-Efficient (λ=2) %16.8% Risk Cluster Equal Weight %14.6% Diversity Weighting %15.5% Fundamentals Wt %15.4% U.S. Cap Wt 4 9.7%15.3%

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U.S. 1964–2012 StrategyReturn Standard DeviationSharpe Ratio Information Ratio Inverse of Minimum Variance %18.1% Inverse of Maximum Diversification %17.6% Inverse of Risk-Efficient (λ=2) %17.3% Inverse of Risk Cluster Equal Weight %19.0% Inverse of Diversity Weighting %18.3% Inverse of Fundamentals Wt %18.8% U.S. Cap Wt 4 9.7%15.3% Minimum Variance %11.7% Maximum Diversification %14.0% Risk-Efficient (λ=2) %16.8% Risk Cluster Equal Weight %14.6% Diversity Weighting %15.5% Fundamentals Wt %15.4% U.S. Cap Wt 4 9.7%15.3% See slide 37 for disclosures regarding individual strategies. Source: Research Affiliates, LLC. 3. Popular Smart Beta Strategies Upside-down strategies also outperform! 15

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Malkiel’s Monkey Throwing Darts 16

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4. Malkiel’s Monkey Throwing Darts 17 In his bestselling book A Random Walk Down Wall Street, Burton Malkiel claimed: “a blindfolded monkey throwing darts at a newspaper's financial pages could select a portfolio that would do just as well as one carefully selected by experts”

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StrategyReturn Standard Deviation Sharpe Ratio Information Ratio Average of 100 Malkiel's Monkey Portfolios %18.3% U.S. Cap Wt 4 9.7%15.3% See slide 37 for disclosures regarding individual strategies. Source: Research Affiliates, LLC. 4. Malkiel’s Monkey Throwing Darts We randomly chose 30 of the 1,000 largest-cap stocks every year, from 1964 to 2012, and tracked the results… We repeated the exercise 100 times, replicating 100 monkeys Only 2 out of 100 portfolios underperformed the cap-weighted benchmark… It takes an unlucky monkey to underperform the cap-weighted index!

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Value and Size Factors 19

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Four-Factor Model Decomposition (U.S. 1964–2012) Strategy Annual FFC Alpha Alpha t-stat Market Exposure Size Exposure Value Exposure Momentum Exposure Volatility Wt % Market Beta Wt % Downside Semi-Deviation Wt % Inverse of Volatility Wt % Inverse of Market Beta Wt % Inverse of Downside Semi-Deviation Wt % U.S. Cap Wt % See slide 37 for disclosures regarding individual strategies. Source: Research Affiliates, LLC. Value and Size Factors Risk Strategies All non-cap-weighted strategies have value and small size tilt 20

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Four-Factor Model Decomposition (U.S – 2012) Strategy Annual FFC Alpha Alpha t-stat Market Exposure Size Exposure Value Exposure Momentum Exposure Book Value Wt % Yr Avg Earnings Wt % Fundamentals Wt % Earnings Growth Wt % Inverse of Book Value % Inverse of 5-Yr Avg Earnings Wt % Inverse of Fundamental Wt % Inverse of EPS Growth % U.S. Cap Wt % See slide 37 for disclosures regarding individual strategies. Source: Research Affiliates, LLC. Value and Size Factors Fundamental Strategies All non-cap-weighted strategies have value and small size tilt 21

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Four-Factor Model Decomposition (U.S – 2012) Strategy Annual FFC Alpha Alpha t-stat Market Exposure Size Exposure Value Exposure Momentum Exposure Minimum Variance % Maximum Diversification % Risk-Efficient (λ=2) % Risk Cluster Equal Weight % Diversity Weighting % Fundamentals Wt % Inverse of Minimum Variance % Inverse of Maximum Diversification % Inverse of Risk-Efficient (λ=2) % Inverse of Risk Cluster Equal Weight % Inverse of Diversity Weighting % Inverse of Fundamental Wt % U.S. Cap Wt % See slide 37 for disclosures regarding individual strategies. Source: Research Affiliates, LLC. Value and Size Factors Smart Beta Strategies All non-cap-weighted strategies have value and small size tilt 22

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Four-Factor Model Decomposition (U.S – 2012) Strategy Annual FFC Alpha Alpha t-stat Market Exposure Size Exposure Value Exposure Momentum Exposure Average of 100 Malkiel's Monkey Portfolios % U.S. Cap Wt % See slide 37 for disclosures regarding individual strategies. Source: Research Affiliates, LLC. Value and Size Factors Malkiel’s Monkey All non-cap-weighted strategies have value and small size tilt 23

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Value and Size Factors All smart beta strategies are largely similar » Any portfolio return can be decomposed: _=∙[_ _ ]=∙[_ ][_ ]+∙[_,_ ] =+∙[_,_ ] » "—"Return of equally weighted portfolio—no skill! » ∙[_,_ ]"—"skill from security selection. Jonathan Berk: Value and size factors generate returns because they sort stock based on prices! Smart Beta weights unrelated to price—no skill Smart Beta are all equal to each other! 24

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Value and Size Factors Cap-weighted—negative skill benchmark » Weighting on price is negatively related to future return » Cap-weighted is the only strategy in the study with negative skill! 25

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Choosing a Smart Beta Strategy 26

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Choosing a Smart Beta Strategy Implementation matters! » Capacity » Turnover » Trading costs 27

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Strategy Weighted Average Market Cap (USD Billions) Weighted Average Bid-Ask Spreads Weighted Average Adjusted Daily Volume (USD Millions) GlobalU.S.GlobalU.S.GlobalU.S. Market Capitalization %0.0% Equal Wt %0.1% Risk Cluster Equal Weight %0.0% Diversity Wt %0.0% Fundamentals Wt % Minimum Variance %0.1% Maximum Diversification %0.1% Risk-Efficient %0.1% See slide 37 for disclosures regarding individual strategies. Source: Research Affiliates, LLC. Choosing a Smart Beta Strategy Research Results: Capacity/Average Size (Beginning of 2012) 28

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Strategy Developed MarketsUnited States 1987– –2009 Average Annual Turnover Fundamentals Wt %13.6% Market Capitalization 4 8.4%6.7% Equal Wt %22.6% Risk Cluster Equal Weight %25.4% Diversity Wt %8.9% Minimum Variance %48.5% Maximum Diversification %56.0% Risk-Efficient %34.2% 29 See slide 37 for disclosures regarding individual strategies. Source: Research Affiliates, LLC. Choosing a Smart Beta Strategy Research Results: Turnover Characteristics

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Global Findings 30

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See slide 37 for disclosures regarding individual strategies. Source: Research Affiliates, LLC. Global Findings 1. High Risk—High Reward Upside-down strategies also outperform! Global, 1991–2012 StrategyReturn Standard Deviation Sharpe Ratio Information Ratio Volatility Wt 1 7.9%16.9% Market Beta Wt 2 6.6%18.8% Downside Semi-Deviation Wt 3 8.3%16.8% Inverse of Volatility Wt 1 9.3%13.9% Inverse of Market Beta Wt 2 9.4%12.3% Inverse of Downside Semi-Deviation Wt 3 9.1%13.9% Global Cap Wt 4 7.1%15.1%

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See slide 37 for disclosures regarding individual strategies. Source: Research Affiliates, LLC. Global Findings 2. Strong Fundamentals—High Reward Upside-down strategies also outperform! Global, 1991–2012 StrategyReturn Standard Deviation Sharpe Ratio Information Ratio Book Value Wt 5 9.5%16.1% Yr Avg Earnings Wt %15.3% Fundamentals Wt %15.3% Earnings Growth Wt 8 8.8%17.1% Inverse of Book Value %15.5% Inverse of 5-Yr Avg Earnings Wt %15.4% Inverse of Fundamental Wt %15.7% Inverse of EPS Growth 8 6.6%15.9% Global Cap Wt 4 7.1%15.1%

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See slide 37 for disclosures regarding individual strategies. Source: Research Affiliates, LLC. Global Findings 3. Popular Smart Beta Strategies Upside-down strategies also outperform! Global, 1991–2012 StrategyReturn Standard Deviation Sharpe Ratio Information Ratio Minimum Variance 9 8.4%9.9% Maximum Diversification %11.3% Risk-Efficient (λ=2) %14.8% Risk Cluster Equal Weight %15.9% Diversity Weighting %15.3% Inverse of Minimum Variance 9 8.7%16.2% Inverse of Maximum Diversification %15.9% Inverse of Risk-Efficient (λ=2) %15.5% Inverse of Risk Cluster Equal Weight %16.7% Inverse of Diversity Weighting %15.5% Global Cap Wt 4 7.1%15.1%

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StrategyReturn Standard Deviation Sharpe Ratio Information Ratio Average of 100 Malkiel's Monkey Portfolios %16.4% Global Cap Wt 4 7.1%15.1% See slide 37 for disclosures regarding individual strategies. Source: Research Affiliates, LLC. Global Findings Malkiel’s Monkey Global, 1991–

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See slide 37 for disclosures regarding individual strategies. Source: Research Affiliates, LLC. Global Findings Risk Strategies All non-cap-weighted strategies have value and small size tilt Four-Factor Model Decomposition (Global 1991–2012) Strategy Annual FFC Alpha Alpha t-stat Market Exposure Size Exposure Value Exposure Momentum Exposure Volatility Wt % Market Beta Wt % Downside Semi-Deviation Wt % Inverse of Volatility Wt % Inverse of Market Beta Wt % Inverse of Downside Semi-Deviation Wt % Global Cap Wt %

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See slide 37 for disclosures regarding individual strategies. Source: Research Affiliates, LLC. Global Findings Fundamental Strategies All non-cap-weighted strategies have value and small size tilt Four-Factor Model Decomposition (Global 1991–2012) Strategy Annual FFC Alpha Alpha t-stat Market Exposure Size Exposure Value Exposure Momentum Exposure Book Value Wt % Yr Avg Earnings Wt % Fundamentals Wt % Earnings Growth Wt % Inverse of Book Value % Inverse of 5-Yr Avg Earnings Wt % Inverse of Fundamental Wt % Inverse of EPS Growth Wt % Global Cap Wt %

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See slide 37 for disclosures regarding individual strategies. Source: Research Affiliates, LLC. Global Findings Smart Beta Strategies All non-cap-weighted strategies have value and small size tilt Four-Factor Model Decomposition (Global 1991–2012) Strategy Annual FFC Alpha Alpha t-stat Market Exposure Size Exposure Value Exposure Momentum Exposure Minimum Variance % Maximum Diversification % Risk-Efficient (λ=2) % Risk Cluster Equal Weight % Diversity Weighting % Inverse of Minimum Variance % Inverse of Maximum Diversification % Inverse of Risk-Efficient (λ=2) % Inverse of Risk Cluster Equal Weight % Inverse of Diversity Weighting % Global Cap Wt %

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Four-Factor Model Decomposition (Global 1991–2012) Strategy Annual FFC Alpha Alpha t-stat Market Exposure Size Exposure Value Exposure Momentum Exposure Average of 100 Malkiel's Monkey Portfolios % Global Cap Wt % See slide 37 for disclosures regarding individual strategies. Source: Research Affiliates, LLC. Global Findings Malkiel’s Monkey All non-cap-weighted strategies have value and small size tilt 38

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Notes: Strategy Simulation Descriptions 1 Volatility weighted: Weighted based on the standard deviation of monthly returns over the five year window prior to index construction. 2 Market Beta Weighted: Weighted based on CAPM betas using market factor kindly provided by Kenneth French on his website. The market beta loading is estimated using monthly returns data over five years window prior to index construction. 3 Downside Semi-Deviation Weighted: Weighted based on downside semi-deviation of the monthly returns over five year period prior to index construction. 4 Cap-Weighted: Weighted based on market capitalization. The market capitalization is computed using December close of the year prior to index construction. 5 Book Weighted: Weighted based on the book value of equity. We use the book value from the fiscal year two years prior to index construction. We introduce delay to avoid forward-looking bias. 6 Five-year Average Earnings Weighted: Weighted based on the average of the five-year earnings. The averaging period covers the five fiscal years ending with the fiscal year two years prior to index construction. We introduce delay to avoid forward-looking bias. 7 Fundamentals Weighted: Weighted based on the five year averages of cash flows, dividends, sales and the most recent book value of equity. We introduce two year delay to avoid forward-looking bias. Following the original method, we select top stocks with the largest fundamental weight. For details see Arnott, Hsu, and Moore (2005). 8 Earnings Growth Weighted based on five-year average dollar change in earnings divided by the average absolute dollar value of earnings over the five-year period. The last fiscal years of the measuring window is taken two years prior to index construction. We introduce delay to avoid forward-looking bias. 9 Minimum Variance: To construct the minimum variance strategy we use the method of Clarke, de Silva, and Thorley (2006). 10 Maximum Diversification Portfolio optimized to maximize expected diversification ratio, which is defined as the ratio of weighted average risk to the expected portfolio risk. For details see Choueifaty and Coignard (2008). 11 Risk-Efficient (λ=2) Mean-variance optimized portfolio assuming that expected excess returns are proportional to the stocks’ downside semi-deviation, and with stringent constraint to limit portfolio concentration. For details see Amenc et al (2010). 12 Risk Cluster Equal Weight Applying statistical methods to identify major market risk factors, assumed to be driven by industries and geographies, and then equally weight these uncorrelated risk clusters. 13 Diversity Weighting: Weighted based on the market capitalization weight raised to the power of a constant that is between zero and one to tilt the portfolio towards small cap stocks while limiting tracking error. We used the value of 0.76 in our simulation. 14 Malkiel’s Monkey: Average of 100 portfolios, where each of the individual portfolios is rebalanced annually by randomly selecting 30 stocks out of the universe of the largest 1000 stocks by market capitalization. 15 Equal Weighting: Equally weighted portfolio of 1000 largest stocks by market capitalization. 39

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Important Information » By accepting this document you agree to keep its contents confidential and not to use the information contained in this document, and in the other materials you will be provided with, for any purpose other than for considering a participation in the proposed transactions. You also agree not to disclose information regarding the transactions to anyone within your organization other than those required to know such information for the purpose of analyzing or approving such participation. No disclosure may be made to third parties (including potential co-investors) regarding any information disclosed in this presentation without the prior permission of Research Affiliates, LLC. » The material contained in this document is for information purposes only. This material is not intended as an offer or solicitation for the purchase or sale of any security or financial instrument, nor is it advice or a recommendation to enter into any transaction. The information contained herein should not be construed as financial or investment advice on any subject matter. Research Affiliates and its related entities do not warrant the accuracy of the information provided herein, either expressed or implied, for any particular purpose. Nothing contained in this material is intended to constitute legal, tax, securities or investment advice, nor an opinion regarding the appropriateness of any investment, nor a solicitation of any type. The general information contained in this material should not be acted upon without obtaining specific legal, tax and investment advice from a licensed professional. Indexes are unmanaged and cannot be invested in directly. Returns represent past performance, are not a guarantee of future performance, and are not indicative of any specific investment. » THE INDEX DATA PUBLISHED HEREIN IS SIMULATED, UNMANAGED AND CANNOT BE INVESTED IN DIRECTLY. PAST SIMULATED PERFORMANCE IS NO GUARANTEE OF FUTURE PERFORMANCE AND IS NOT INDICATIVE OF ANY SPECIFIC INVESTMENT. ACTUAL INVESTMENT RESULTS MAY DIFFER. The simulated data contained herein is based on the patented non-capitalization weighted indexing system, method and computer program product (see Robert D. Arnott, Jason Hsu and Philip Moore “Fundamental Indexation.” Financial Analysts Journal [March/April]:83-99). » Any information and data pertaining to indexes contained in this document relates only to the index itself and not to any asset management product based on the index. No allowance has been made for trading costs, management fees, or other costs associated with asset management as the information provided relates only to the index itself. With the exception of the data on Research Affiliates Fundamental Index, all other information and data are based on information and data available from public sources. » Investors should be aware of the risks associated with data sources and quantitative processes used in our investment management process. Errors may exist in data acquired from third party vendors, the construction of model portfolios, and in coding related to the index and portfolio construction process. While Research Affiliates takes steps to identify data and process errors so as to minimize the potential impact of such errors on index and portfolio performance, we cannot guarantee that such errors will not occur. » The trade names Fundamental Index®, RAFI®, the RAFI logo, and the Research Affiliates corporate name and logo are registered trademarks and 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 marks. » Fundamental Index®, the non-capitalization method for creating and weighting of an index of securities, is patented and patent-pending proprietary intellectual property of Research Affiliates, LLC (US Patent No ; 7,74702; 7,792,719; 7,778,905; and 8,005,740; Patent Pending Publ. Nos. US A1, US A1, US , US , WO 2005/076812, WO 2007/ A2, WO 2008/118372, EPN , and HK ). © Research Affiliates, LLC. All rights reserved. Duplication or dissemination prohibited without prior written permission. 40

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41 Thank You For additional information visit Use the QR code to view Fundamentals, Research Affiliates’ monthly publication.

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