Style Premia Investing: Overview and Performance Review Prepared Exclusively for Clare College

Style Premia Investing: Overview and Performance Review Prepared exclusively for Clare College

Private and Confidential

Presented by Antti Ilmanen Principal, Global Head of Portfolio Solutions, AQR

August 2019

For Institutional Investor Use Only Disclosures

The information set forth herein has been obtained or derived from sources believed by AQR Capital Management, LLC (“AQR”) to be reliable. However, AQR does not make any representation or warranty, express or implied, as to the information’s accuracy or completeness, nor does AQR recommend that the attached information serve as the basis of any investment decision. This document has been provided to you solely for information purposes and does not constitute an offer or solicitation of an offer, or any advice or recommendation, to purchase any securities or other financial instruments, and may not be construed as such. This document is intended exclusively for the use of the person to whom it has been delivered by AQR and it is not to be reproduced or redistributed to any other person. Please refer to the Appendix for more information on risks and fees. Past performance is not a guarantee of future performance. This presentation is not research and should not be treated as research. This presentation does not represent valuation judgments with respect to any financial instrument, issuer, security or sector that may be described or referenced herein and does not represent a formal or official view of AQR. The views expressed reflect the current views as of the date hereof and neither the speaker nor AQR undertakes to advise you of any changes in the views expressed herein. It should not be assumed that the speaker will make investment recommendations in the future that are consistent with the views expressed herein, or use any or all of the techniques or methods of analysis described herein in managing client accounts. AQR and its affiliates may have positions (long or short) or engage in securities transactions that are not consistent with the information and views expressed in this presentation. The information contained herein is only as current as of the date indicated, and may be superseded by subsequent market events or for other reasons. Charts and graphs provided herein are for illustrative purposes only. The information in this presentation has been developed internally and/or obtained from sources believed to be reliable; however, neither AQR nor the speaker guarantees the accuracy, adequacy or completeness of such information. Nothing contained herein constitutes investment, legal, tax or other advice nor is it to be relied on in making an investment or other decision. There can be no assurance that an investment strategy will be successful. Historic market trends are not reliable indicators of actual future market behavior or future performance of any particular investment which may differ materially, and should not be relied upon as such. Target allocations contained herein are subject to change. There is no assurance that the target allocations will be achieved, and actual allocations may be significantly different than that shown here. This presentation should not be viewed as a current or past recommendation or a solicitation of an offer to buy or sell any securities or to adopt any investment strategy. The information in this presentation may contain projections or other forward‐looking statements regarding future events, targets, forecasts or expectations regarding the strategies described herein, and is only current as of the date indicated. There is no assurance that such events or targets will be achieved, and may be significantly different from that shown here. The information in this presentation, including statements concerning financial market trends, is based on current market conditions, which will fluctuate and may be superseded by subsequent market events or for other reasons. Performance of all cited indices is calculated on a total return basis with dividends reinvested. The investment strategy and themes discussed herein may be unsuitable for investors depending on their specific investment objectives and financial situation. Please note that changes in the rate of exchange of a currency may affect the value, price or income of an investment adversely. Neither AQR nor the speaker assumes any duty to, nor undertakes to update forward looking statements. No representation or warranty, express or implied, is made or given by or on behalf of AQR, the speaker or any other person as to the accuracy and completeness or fairness of the information contained in this presentation, and no responsibility or liability is accepted for any such information. By accepting this presentation in its entirety, the recipient acknowledges its understanding and acceptance of the foregoing statement.

2 Today’s Presenters

Antti Ilmanen, Ph.D., Principal Antti manages AQR’s Portfolio Solutions Group, which advises institutional investors and sovereign wealth funds, and develops the firm’s broad investment ideas. Before AQR, Antti spent seven years as a senior portfolio manager at Brevan Howard, a macro hedge fund, and a decade in a variety of roles at Salomon Brothers/Citigroup. He began his career as a central bank portfolio manager in Finland. Antti earned M.Sc. degrees in economics and law from the University of Helsinki and a Ph.D. in finance from the University of Chicago. Over the years, he has advised many institutional investors, including Norway’s Government Pension Fund Global and the Government of Singapore Investment Corporation. Antti has published extensively in finance and investment journals and has received a Graham and Dodd award, the Harry M. Markowitz special distinction award, and multiple Bernstein Fabozzi/Jacobs Levy awards for his articles. His book Expected Returns (Wiley, 2011) is a broad synthesis of the central issues in investing. Antti received the CFA Institute's 2017 Leadership in Global Investment Award.

3 Outline

Who Is AQR?

Challenges of the Current Market Environment

Some Style Premia Reminders and Recent Performance

(Re-)Introduction to Style Premia

The Long-Term Evidence

Looking Forward

FAQs

4 Our Firm Systematic investing grounded in economic theory

Investment innovation at the nexus of economics, behavioral finance, data 1998 and technology Year founded

• Dedicated to the pursuit of investment ~1000 excellence for our clients Employees in 10 offices globally • Pioneer in quantitative investing through applied research $194 Billion in AUM • Leading provider of long-only and liquid alternative strategies

• Clients representing some of the largest and 82 most sophisticated investors across the globe Ph.D.s and 24 professors

Source: AQR. All figures approximate as of 6/30/2019; AUM includes assets managed by AQR and its advisory affiliates. Includes current and former professors 5 Our Approach Fundamental investors pursuing advantages at every step

Fundamental Investing We rely on sound economic theory and analysis to help us deliver long-term, repeatable results.

Systematically Applied A disciplined methodology underlies everything we do. Our models, built over 20 years, are based on a continuous process of design, test, refine, repeat.

Thoughtfully Designed In portfolio construction, risk management and trading we seek additional value for our clients. Using both qualitative and quantitative tools, we’re meticulous in every detail of the investment process.

Source: AQR. 6 Assets Under Management Our assets are diversified by client type and across regions

By Type By Region

Pension — Public Pension — Corporate $53 B $38 B

Financial Advisors Sovereign Wealth & Private Banks $28 B $36 B

North Australia & Latin America Europe Middle East Asia Insurance America New Zealand & Other Asset Endowment & $9 B Management Foundation $118 B $34 B $11 B $6 B $24 B $1 B $13 B $13 B Union / Multi Employer $4 B

Source: AQR. *Approximate as of 6/30/2019, includes assets managed by AQR and its advisory affiliates. 7 Our Team Experienced leadership across disciplines

Cliff Asness, Ph.D.* Managing and Founding Principal John Liew, Ph.D.* David Kabiller, CFA* Founding Principal Founding Principal

Portfolio Management, Research, Risk, and Trading Business Development Corporate Infrastructure Legal and Compliance

Ronen Israel* Lars Nielsen* Principal Principal Portfolio Management and Research Portfolio Finance Client Solutions Finance Legal Michele Aghassi, Ph.D. Ari Levine Scott Carter Gregor Andrade, Ph.D. Marco Hanig, Ph.D. John Howard* Billy Fenrich Principal Principal Principal Principal Principal Principal Principal Chief Finance Officer Chief Legal Officer Jordan Brooks, Ph.D. Tobias Moskowitz, Ph.D. Bill Cashel Michael Mendelson* Co-Chief Operating Officer Principal Principal Principal Principal Risk Management Bradley Asness Compliance Principal Andrea Frazzini, Ph.D. Yao Hua Ooi Michael Patchen, CFA Jeff Dunn Chris Palazzolo, CFA Co-Chief Operating Officer H.J. Willcox Principal Principal Principal Principal Principal Principal Chief Risk Officer Chief Compliance Officer Jacques Friedman Lasse Pedersen, Ph.D. Jeremy Getson, CFA Ted Pyne, Ph.D. Accounting, Operations, Principal Principal Trading Principal Principal and Client Administration Brian Hurst Scott Richardson, Ph.D. Isaac Chang Steve Mellas Principal Principal Managing Director Marketing Portfolio Solutions Principal John Huss Nathan Sosner, Ph.D. Suzanne Escousse Antti Ilmanen, Ph.D. Principal Principal Principal Principal Chief Marketing Officer Human Resources Roni Israelov, Ph.D. Mark Mitchell, Ph.D. Jen Frost Principal Principal (CNH) Principal Chief Human Resources Officer Michael Katz, Ph.D. Todd Pulvino, Ph.D. Principal Principal (CNH) Systems Development David Kupersmith Rocky Bryant and IT** Principal Principal (CNH) Stephen Mock Principal Oktay Kurbanov Co-Chief Technology Officer Principal Ian Roche Managing Director Co-Chief Technology Officer

Personnel as of 7/11/19 *Member of Executive Committee **Effective 7/11/2019, Stephen Mock and Ian Roche will serve as Co-Chief Technology Officers, replacing Neal Pawar 8 Industry-Leading Research Committed to advancing financial knowledge

Academic Engagement Awards and Prizes Widely-Cited Financial Research3

• Nearly half of employees hold 57 Research Awards Top Journal Article Citations advanced degrees Notable awards include1: 1. New York University (NYU) • 24 current and former professors 2. AQR Capital Management work at AQR • 9 Bernstein Fabozzi JPM Awards 3. University of Chicago • AQR Asset Management Institute • 9 Graham & Dodd Awards 4. University of Pennsylvania at London Business School • 6 Smith Breeden Awards2 established to promote excellence 5. Harvard University in asset management • 4 DFA Prizes • 4 Michael Brennan Awards2 • AQR Insight Award: annual SSRN Downloads $100,000 prize honoring • 1 Fischer Black Prize unpublished papers that provide • 1 Bernacer Prize 1. New York University (NYU) the most significant investment • 1 Markowitz JOIM Award 2. University of Chicago insights 3. AQR Capital Management • Online research library with more 4. Harvard University than 300 AQR papers, journal articles, books and periodicals, as 5. Stanford University well as our data sets

As of 6/30/2019. Source: AQR, SSRN and Google Scholar. 1Graham & Dodd Awards won in 2018, 2015, 2011, 2005, 2004, 2003, 2000, 1998, 1991; Bernstein Fabozzi Awards won in 2018, 2015, 2014, 2013, 2012, 2005, 2004, 2003; Smith Breeden Awards won in 2010, 2008, 2002, 2000, 1998; DFA Awards won in 2016, 2014, 2008, 2005; Michael Brennan Awards won in 2014, 2013, 2005 and 2004; Fischer Black Prize won in 2007; Bernacer Prize won in 2011; Markowitz Award won in 2015. 2Three Smith Breeden awards were second place mentions; two DFA awards were second place mentions; one Michael Brennan award was a second place mention. 3Social Science Research Network (SSRN) Finance Economic Network ranked by total new downloads of papers in the last 3 Years. SSRN List is as of 6/30/2019, Google Scholar list as of 10/8/2018. 9 Challenges of the Current Market Environment ...both long-term and short- term challenges The Challenge: A World of Low Expected Returns Prospective real returns are low for all long-only assets

Expected Real Return of U.S. Stocks, Bonds and the 60/40 Portfolio January 1900 – June 2019 16%

12%

4% 3.3%

0% -0.1%

-4% 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 U.S. Equity Real Yield U.S. 10Y Treasury Real Yield 12%

10%

2% 1.9%

0% 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 U.S. 60/40 Real Yield 5% Real Return Sources: AQR, Robert Shiller’s web site, Kozicki-Tinsley (2006), Federal Reserve Bank of Philadelphia, Blue Chip Economic Indicators, Consensus Economics, Morningstar. Earnings data through 3/31/2019. Prior to 1926, stocks are represented by a reconstruction of the S&P 500 available on Robert Shiller’s web site which uses dividends and earnings data from Cowles and associates, interpolated from annual data. After that, stocks are the S&P 500. Bonds are represented by long-dated Treasuries. The 60/40 Expected Real Yield is represented by Stocks/Bonds. The equity yield is a 50/50 mix of two measures: 50% Shiller E/P * 1.075 and 50% Dividend/Price + 1.5%. Scalars are used to account for long term real 11 Earnings Per Share (EPS) Growth. Bond yield is 10 year real Treasury Yield over 10 year inflation forecast as in Expected Returns (Ilmanen, 2011), with no rolldown added. Chart is for illustrative purposes only. Past performance is not a guarantee of future performance. Please read important disclosures in the Appendix. What Can Investors Do? Three broad paths to address the low expected return challenge

Possible Solution Motivation Challenges

Belief in Equity Premium: Concentration: already dominant 1. More Equities Highest conviction potential risk to many; not cheap long-term return source

2. Add Illiquid / Private ‘Endowment Model’ Beliefs: Illiquidity; contain much equity High returns historically; exposure; also historically rich; Assets perceived illiquidity premium overrated illiquidity premia

3. Add Factor Tilts Multi-Factor Beliefs: Evidence on multiple rewarded Leverage and other tools are and Alternative factors, potential diversification required to meet return targets Risk Premia benefits

Source: AQR. Diversification does not eliminate the risk of experiencing investment losses. Past performance is not a guarantee of future performance

12 What Then Are the Well-Rewarded Factors? Almost a century of evidence

Hypothetical Gross Sharpe Ratios January 1920 – June 2018

Asset Class Premia Alternative Risk Premia 1.40

1.20 1.14

1.00 0.83 0.77 0.80 0.67 0.62 0.60 0.54 0.50 0.43 0.40 0.27

0.20

0.00 Global Global Commodities Credit Value Momentum Carry Defensive Trend Equities Bonds Excess

Source: AQR. Alternative risk premia have all been scaled to 10% volatility. Value, Carry, Momentum, Carry and Defensive all begin in March 1926, credit excess begins in January 1926. Please see appendix for important disclosures regarding the construction of each return series. Performance is expressed gross of trading costs and fees. Alternative Risk Premia are applied across several asset classes as described in appendix. Hypothetical performance results have certain inherent limitations, some of which are disclosed in the Appendix. Please read important disclosures in the Appendix. Gross performance results do not reflect the deduction of investment advisory fees, which would reduce an investor’s actual return. 13 Long/Short Strategies or Factor Tilts Haven’t Helped in 2018/19 They added to the misery of down markets in 2018, and detracted in H1 19

• These long/short strategies and tilts are intended to be market-neutral (diversifiers rather than hedges), which means that sometimes they help, sometimes they hurt during equity market drawdowns o Last year they hurt. Correlations were still low, but in the big picture they did not hedge or diversify well o In the first half of this year the story is different, markets are up but these strategies have continued to struggle

• 2018/19 losses have been led by the most conventional style – value-based stock-picking, which is not just a quant strategy but famously applied by Ben Graham, and Warren Buffett o Silver lining: conviction, conventionality boosted patience

• However, it is not only Value. Other styles have not offset enough. o 2018 in particular was a bad year for diversification among long-only, between long-only and long/short, and among long/short styles o 2019 has been different, with other factors posting gains, along with long-only markets, but Value continued to drive losses

• Digging deeper, the recent period can be split into three bad parts: o Exuberance toward disruptive growth companies characterized the first nine months of 2018, hurting Value o When Value partly recovered in Q4 2018, whipsawing bear markets and some HF deleveraging brought further pain o In 2019 the exuberance toward disruptive growth companies continued, so Value has driven losses which were not fully offset by gains in other factors o Much more detail in later sections and FAQs…

Source: AQR. AQR. Past performance is not a guarantee of future performance. Diversification does not eliminate the risk of experiencing investment losses. 14 Why We Believe in These Strategies in the First Place How do we keep the faith that the strategy still works?

1) Economic intuition for why it works • Compensation for risk? Behavioral? Both? • Who is on the other side? • Aggressive use of the free lunch of diversification • Evaluate the “world has changed” concerns – based on crowding, structural changes, etc.

2) Tons of empirical evidence • Multiple geographies • Multiple asset classes • Effective using alternative definitions and implementations • Effective out-of-sample

Source: AQR. Diversification does not eliminate the risk of experiencing investment losses. 15 Some Style Premia Reminders and Recent Performance Overview of Styles and Asset Groups Seeks to harvest style premia across multiple asset groups

Value Momentum Carry Defensive

Stocks & Industries ✓ ✓ ✓

Equity Indices ✓ ✓ ✓

Fixed Income* ✓ ✓ ✓ ✓

Currencies ✓ ✓ ✓

Instruments Used Futures, Swaps and Currency Forwards

*Fixed Income includes bond and interest rate strategies. Source: AQR. Specific exposures are subject to change and not all styles are applicable in all contexts. 17 Style Premia Strategy Performance

Over a complete market cycle, the AQR Net Performance of AQR Style Premia Strategy Style Premia Strategy seeks to: September 1, 2012 – June 30, 2019 • Target a volatility of 12% 80% 60% • Achieve a net Sharpe ratio of 0.7 over a full market cycle 40% 20% • Deliver diversifying returns to traditional 0% asset classes -20%

40%

20%

AQR Style Premia Strategy Net Returns -20% 2012 2013 2014 2015 2016 2017 2018 2019 September 1, 2012 – June 30, 2019 Sharpe Beta Period Return Volatility Risk-Free Rate Ratio to MSCI World H1 2019 -6.4% 9.9% -- 0.1 1.2% 1 Year -13.3% 9.7% -1.6 0.1 2.3% 3 Year (Ann.) -3.4% 8.5% -0.6 0.1 1.4% 5 Year (Ann.) 1.0% 9.4% 0.0 0.1 0.9% Since Inception (Ann.) 4.1% 9.5% 0.4 0.1 0.7%

Source: AQR. Commentary based on net performance from September 1, 2012 through June 30, 2019 of the Style Premia Composite. Performance for the month ending June 30, 2019 is estimated and subject to change. Performance is net 0.75% mgmt. fee and 10% performance fee. Beta and volatility are calculated using estimated gross of fees daily returns. Risk-free rate is the Merrill Lynch 3 Month T-Bill. Past performance is not a reliable indicator of future performance. The information presented herein is supplemental to the GIPS® compliant presentation for the Style Premia Composite included in the Appendix. Please read important disclosures at the end of this document. All performance figures contained herein represent preliminary unaudited estimates of realized and unrealized returns prepared by AQR Capital Management, LLC (“AQR”). All performance figures contained herein are in USD. There is no guarantee, express or implied, that long-term return and/or volatility targets will be achieved. Realized returns and/or volatility may come in higher or lower than expected. Diversification does not eliminate the risk of experiencing investment losses. 18 Performance Attribution Full Year 2018

Stocks & Equity Fixed Income Currencies Total Industries Indices

Value -9.0% -1.1% -5.2% 0.5% -14.8%

Momentum -2.4% -1.7% 1.9% -1.3% -3.5%

Carry – – -0.2% 0.0% -0.2%

Defensive 7.0% -1.5% 3.3% – 8.8%

Total -4.4% -4.3% -0.2% -0.8% -9.7%

Source: AQR. Based on net performance from January 1, 2018 through December 31, 2018 of the AQR Style Premia UCITS Fund. Performance for the month ending December 31, 2018 is estimated and subject to change. Net returns for the Style Premia UCITS Fund are calculated using an investment management fee of 0.60%, administrative and operating fee of 0.14%, and a performance fee of 10% (over Merrill Lynch 3-Month T-Bill hurdle) per annum. All share classes are also subject to a 0.01% local tax per annum of the Fund’s Net Asset Value, payable quarterly. Please note, as we have varying fee arrangements, the net performance numbers above are not representative of all investors or achievable by all investors. Past performance is not a guarantee of future performance. Attribution is subject to change at any time without notice. Please read important disclosures in the Appendix. 19 Performance Attribution H1 2019

Stocks & Equity Fixed Income Currencies Total Industries Indices

Value -11.4% -3.1% 0.7% 0.6% -13.4%

Momentum -0.7% 2.7% 1.8% 0.1% 3.9%

Carry – – -0.2% 1.1% 0.9%

Defensive 1.9% 2.3% 1.3% – 5.5%

Total -10.2% 1.9% 3.6% 1.7% -3.1%

Source: AQR. Based on net performance from January 1, 2019 through June 30, 2019 of the AQR Style Premia UCITS Fund. Performance for the month ending June 30, 2019 is estimated and subject to change. Net returns for the Style Premia UCITS Fund are calculated using an investment management fee of 0.60%, administrative and operating fee of 0.14%, and a performance fee of 10% (over Merrill Lynch 3-Month T-Bill hurdle) per annum. All share classes are also subject to a 0.01% local tax per annum of the Fund’s Net Asset Value, payable quarterly. Please note, as we have varying fee arrangements, the net performance numbers above are not representative of all investors or achievable by all investors. Past performance is not a guarantee of future performance. Attribution is subject to change at any time without notice. Please read important disclosures in the Appendix. 20 Value in Context A longer term historical perspective

Hypothetical Value Factor Cumulative Contribution & Drawdown January 1990 – June 2019

$1,000

$100

$10 (Growthof$10)

$1 CumulativePerformance Jan '90 Jan '95 Jan '00 Jan '05 Jan '10 Jan '15

-10%

-20%

-30% Drawdown -40%

-50% 1990 1995 2000 2005 2010 2015 2019

Source: AQR, Stocks & Industries Value Factor data based on attribution of the Hypothetical Style Premia Standalone Factor Backtest which target 12% volatility across each factor and are gross or trading costs, undiscounted, excess of cash returns. Data is from January 1, 1990 – June 30, 2019. Please read performance disclosures in the Appendix for a description of the investment universe and the allocation methodology used to construct the Style Premia Standalone Factor Backtest. Hypothetical data has inherent limitations, some of which are disclosed in the Appendix. Past performance is not a reliable indicator of future performance. Please read important disclosures in the Appendix. All performance figures contained herein are in USD unless noted otherwise. 21 Value in Context Value comparisons

Value and Value-Growth Index Performance September 2012 – June 2019

30%

Recent value losses are • 20% not isolated to AQR • Compared to value indices, Value has 10% modestly underperformed in 2019 0% • Longer term, AQR’s Value implementation has -10% outperformed value indices -20%

-30%

-40% 2012 2013 2014 2015 2016 2017 2018 2019

S&I Val Contrib FF HML US Russell Val-Gro MSCI Val-Gro

Source: AQR, Bloomberg, Kenneth R. French Data Library. Portfolios from Kenneth R. French Data Library are formed based on book-to-market, quintiles are equal-weighted; returns are excess of cash. See Kenneth R. French Data Library for further details. For each portfolio, returns are scaled to the AQR Style Premia Strategy’s (SPF) Stocks and Industries Value target risk. S&I Value contribution is based on monthly contributions to gross performance from January 1, 2019 through June 30, 2019 of the Style Premia Composite. Performance for the month ending June 30, 2019 is estimated and subject to change. The information presented herein is supplemental to the GIPS® compliant presentation for the Style Premia Composite included in the Appendix. Please refer to the end of this document for important disclosure information. Past performance is not a reliable indicator of future performance. 22 Value in Context Multiple classic measures of value underwhelmed in 2018

We capture value in multiple ways We generally take value risk intra-industry Value investing measured in lots of ways - not just based on Balance Sheet items - has suffered lately

Hypothetical U.S. Valuation Metrics, 2000-2018 Sorted by Hypothetical Gross Annual Performance

E/P S/EV CF/EV FE/P S/EV S/EV B/P S/EV S/EV

CF/EV B/P FE/P E/P FE/P S/EV CF/EV CF/EV FE/P S/EV S/EV S/EV E/P CF/EV

S/EV CF/EV E/P S/EV E/P E/P FE/P E/P E/P E/P CF/EV FE/P CF/EV FE/P CF/EV E/P

Positive B/P FE/P S/EV B/P CF/EV FE/P B/P S/EV B/P CF/EV FE/P CF/EV FE/P E/P FE/P E/P FE/P

FE/P E/P B/P CF/EV B/P B/P E/P B/P S/EV S/EV E/P B/P E/P CF/EV B/P CF/EV B/P

CF/EV FE/P FE/P CF/EV B/P FE/P E/P B/P S/EV S/EV E/P

E/P S/EV B/P B/P FE/P B/P

CF/EV CF/EV B/P CF/EV

B/P E/P FE/P

S/EV FE/P S/EV Negative

Full Sample 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Average

B/P Book-to-price S/EV Enterprise Value-to-Sales FE/P Forecast Earnings-to-Price E/P Earnings-to-Price CF/EV Cash Flow-to-Enterprise Value

Source: AQR. For illustrative purposes only and not representative of an actual portfolio AQR currently manages. Hypothetical AQR U.S. Valuation Theme return data indicative of gross USD returns for a long-short, market neutral implementation of the theme from 1/1/2000 – 12/31/2018. Gross performance does not reflect the deduction of investment advisory fees. Hypothetical data has inherent limitations, some of which are disclosed in the Appendix. A full description of the Hypothetical AQR US Valuation Theme Backtest methodology is 23 included in the Appendix. Why Did Markets Prefer Expensive Names? What has been different recently is the market reaction to fundamentals

Cheap companies tend to be earnings winners (better than anticipated earnings) and expensive companies tend to be earnings losers – 2018 and 2019 were no different

The magnitude of earnings surprises were not abnormal relative to history*

In 2018 and 2019, markets preference for expensive names cannot be explained by better realized fundamentals

Number of Earnings Winners and Losers: Magnitude of Earnings Versus Expectations: U.S. Cheap vs. Expensive U.S. Cheap vs. Expensive April 1, 2001 - June 30, 2019 April 1, 2001 – June 30, 2019 2001-2017 2018-2019 YTD 2001-2017 2018-2019 YTD

Earnings Earnings Winners Winners

Earnings Earnings Losers Losers

45% 50% 55% 45% 50% 55% -1.0% 0.0% 1.0% -1.0% 0.0% 1.0%

Cheap Expensive

* Historical negative earnings surprises have been of somewhat similar magnitude on average but cheap companies have been skewed to be more negative due entirely to 2009 earnings surprises. Source: AQR, IBES, Xpressfeed and Compustat. Data shows the average 9-month scaled and beta-adjusted return of stocks that score positive on AQR’s Hypothetical U.S. Valuation Theme (“Cheap”) and negative on the same theme (“Expensive”) at the beginning of each quarter, sorted by relative earnings surprises (measured relative to the median earnings surprise in the universe) reported during that quarter. Date range is 4/1/2001 – 6/30/2019. The date range is limited due to availability of proprietary earnings surprise data. For illustrative purposes only and not representative of an actual portfolio AQR currently manages. Hypothetical data has inherent limitations, some of which are disclosed in the Appendix. Past performance is not a reliable indicator of future 24 performance. A full description of the Hypothetical AQR U.S. Valuation Theme Backtest methodology is included in the Appendix. Why Did Markets Prefer Expensive Names? Price change has less to do with near-term fundamentals relative to history

Given realized earnings and near-term projected earnings have not materially differed between cheap and expensive stocks, recent changes in price to earnings ratios have been almost entirely driven by price changes

This relationship tends to hold long term as well, but has become more pronounced in the recent period

P/E P/FE

12/31/2018 6/30/2019 YTD Change 12/31/2018 6/30/2019 YTD Change

Long (Cheap) 10.8 12.2 12.9% 10.2 11.7 14.7% Short (Expensive) 20.1 25.0 24.4% 17.2 19.9 15.7%

30%

25%

20%

15%

10%

-5% Long Short -10% (Cheap) (Expensive)

Impact of Increasing E Impact of Increasing P

Impact of Increasing FE Impact of Increasing P

Source: AQR. Long and Short portfolios are based on Value’s contribution to AQR Style Premia Strategy’s Stocks and Industries portfolio. Earnings growth and price-to-earnings measures use trailing twelve months earnings, and reflect a weighted average. The 'Impact of Increasing P’ is different in the P/E (Price to Earnings) and FE/P (Forward Earnings to Price) decompositions due to universe differences. Based on gross performance in USD from April 1, 2019 through June 30, 2019 of the Style Premia Composite. Performance for the month ending June 30, 2019 is estimated and subject to change. Past performance is not a guarantee of future performance. Attribution is subject to change at any time without notice. This information is supplemental to the GIPS® compliant presentation for the Style Premia Composite incepted on September 1, 2012 included in the Appendix Please read important disclosures in the Appendix. 25 Fundamentals Companies with negative earnings and cash flows outperformed

Average Annual Returns for U.S. Companies with Negative Cash Flows January 1, 1995 – December 31, 2018

30% 19% 20% 10% 6% 0% -10% -20% -30% 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 YTD Average Annual Returns for U.S. Companies with Negative Earnings January 1, 1995 – December 31, 2018

30% 27% 20% 10% 8% 0% -10% -20% -30% 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 YTD

Source: AQR. Utilizing AQR’s US Large Cap universe, which is roughly equivalent to the Russell 1000. The tables above show an equal-weighted beta-adjusted (Using the Russell 1000 Index as the beta) average annual returns for companies with Negative Cash Flows and Negative Earnings. Past performance is not a guarantee of future returns.

26 Fundamentals Companies with negative cash flows and R&D spending have outperformed

Average Annualized Excess Returns for U.S. Companies with Negative Cash Flows* January 1, 1980 – June 30, 2019 30%

25%

20%

15%

10% 5%

-5%

-10% 1980-1998 Tech Bubble 2000**-2017 Recent Period Long Term Average (1999-2000**) (2018-2019 YTD) Average Annualized Returns for U.S. Companies Based on R&D Intensity January 1, 1980 – June 30, 2019

30%

25%

20%

15%

10%

-5% 1980-1998 Tech Bubble 2000*-2017 Recent Period Long Term Average (1999-2000*) (2018-2019 YTD)

*Negative CF companies returns are shown relative to an equal-weighted average of stocks within the Russell 1000 Index. **Tech Bubble period is defined Jan 1999-Feb 2000. Source: AQR. Utilizing AQR’s US Large Cap universe, which is roughly equivalent to the Russell 1000. Returns are beta-adjusted for companies with Negative Cash Flows and Negative Earnings. Past performance is not a guarantee of future returns. 27 Fundamentals Long-term growth has performed very well recently

Average Annual Returns for U.S. Earnings Growth Measures: Trailing and Forecasted January 1, 1990 – June 30, 2019

20% 1990-2017 2018-2019 YTD 99th Percentile

15%

10%

-5%

-10%

-15% 1-Year Realized Growth Short-Term Forecasted Growth Long-Term Forecasted Growth (Beyond 1-Year)

Source: AQR, IBES. 1-Year realized growth is based on EPS growth over past 12-months. Short-Term Forecasted EPS Growth is defined as analyst forecasts 1-year ahead. Long- Term Forecasted EPS Growth is defined as forecast EPS growth beyond one year. All estimates come from IBES and measures are defined on a within-industry basis. Forecasts are for illustrative purposes only, are not a guarantee of performance and are subject to change. Please see appendix for additional disclosures. 28 Fundamentals Growth dispersion has not widened in the recent period

Growth Dispersion for U.S. Long-Term EPS Forecasted Growth January 1, 1990 – June 30, 2019

5.0

4.0

3.0

Score) - 2.0 Long-term growth dispersion is low and has not changed recently 1.0

Growth Dispersion (Z DispersionGrowth 0.0

-1.0 5th Percentile -2.0 1990 1994 1998 2002 2006 2010 2014 2018

Source: AQR, IBES. Long-Term Forecasted Growth Factor is defined as forecasted EPS growth beyond one year and is defined on a within industry basis. Dispersion between is calculated as the difference between high and low growth names. Forecasts are for illustrative purposes only, are not a guarantee of performance and are subject to change. Please see appendix for additional disclosures. 29 Fundamentals Analyst forecast accuracy has not changed in the recent period

Correlation of U.S. Short-Term Forecasted EPS Growth with Subsequent EPS Realizations January 1, 1990 – June 30, 2019

0.8 Historical Average = 0.8 0.6

0.4

0.2

0 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Correlation of U.S. Long-Term Forecasted EPS Growth with Subsequent EPS Realizations January 1, 1990 – June 30, 2019 1 Analysts’ accuracy in forecasting 0.8 long-term growth is generally low and has not changed recently 0.6

0.4 Historical Average = 0.3 0.2

0 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Source: AQR, IBES. Short-Term Forecasted EPS Growth is defined as analyst forecasts 1-year ahead. Long-Term Forecasted EPS Growth is defined as forecasted EPS growth beyond one year. Both forecasted measures are compared to subsequent realizations; for example, 1-year forecast EPS growth compared to 1-year realized EPS growth. Forecasts are for illustrative purposes only, are not a guarantee of performance and are subject to change. Data before 2005 has been shaded to reflect lower analyst coverage. Please see appendix for additional disclosures. 30 Performance of Momentum Fundamentals also impacting momentum’s performance

Hypothetical Performance of U.S. Momentum and Select Underlying Factors January 1, 2018 - June 30, 2019

Non price-based momentum measures

-2%

-4%

-6%

-8%

-10% Momentum 12 Month Price Change Margin Changes EPS Changes 2018 2019 YTD

Source: AQR. Performance of the standalone U.S. Momentum Theme and select underlying factors is based on the Hypothetical AQR U.S. Fundamental and Price Momentum Signals Backtest themes. These Hypothetical theme returns data are indicative of gross USD returns for a long-short, market neutral implementation of the theme from 1/1/2018 – 6/30/2019. Gross performance does not reflect the deduction of investment advisory fees. Hypothetical data has inherent limitations, some of which are disclosed in the Appendix. A full description of the backtest methodology is included in the Appendix. 31 Fundamentals As value has sold off, spreads have moved significantly

Spreads are meaningfully wider for a simpler non-industry neutral value portfolio than they are for an industry-neutral implementation

Industry-neutral spreads at this level have tended to revert with the notable exception of the tech bubble which eventually reverted but only after getting meaningfully wider first

Hypothetical U.S. Industry-Neutral Value Portfolio Value Spread January 1, 1984 – June 30, 2019

5.0

4.0

th 3.0 97

Percentile Score)

- 2.0

th 1.0 84 Percentile

0.0 Value Spread (Z -1.0

-2.0

-3.0 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018

Value Composite, Industry-Neutral B/P, Non Industry-Neutral

Source: AQR. Value composite includes four value measures: book-to-price, earnings-to-price, forecast earnings-to-price, and sales-to-enterprise value; spreads are measured based on ratios. To construct industry-neutrality, the value spreads are constructed by comparing the aforementioned value measures within each industry, which are then aggregated up to represent an entire portfolio. Hypothetical data has inherent limitations, some of which are disclosed in the Appendix. Please see the Appendix for the hypothetical U.S. Valuation Theme backtest methodology. Please read the Appendix for important disclosures. 32 Performance Attribution Detail for H1 2019 H1 2019 Performance Attribution Stocks and Industries

Sub-Strategy Contributions Regional Contributions To excess returns, gross of fees To excess returns, gross of fees

4% 4% 2% 2% 0% 0% -2% -2% -4% -4% -6% -6% -8% -8% -10% -10% Stock Selection Industry Selection Country-Industry North America Europe Asia Pairs Sector Contributions To excess returns, gross of fees 1.5% 1.0% 0.5% 0.0% -0.5% -1.0% -1.5% -2.0% -2.5% -3.0% Cons. Staples Financials Real Estate Materials Utilities Comm. Industrials Info Tech. Energy Cons. Disc. Health Care Services

Value Momentum Defensive Total

Source: AQR. Gross of fee performance attribution as of June 30, 2019 of the Style Premia UCITS Fund. Performance for the month ending June 30, 2019 is estimated and subject to change. Past performance is not a guarantee of future performance. Attribution is subject to change at any time without notice. Please read important disclosures in the Appendix. 34 H1 2019 Performance Attribution Developed Equity Indices

Positioning Average over the period as a % of NAV 30%

15% Value

0% Momentum

-15% Defensive

-30%

Total

Italy

U.S. U.K.

Spain

Japan

France

Canada

Sweden

Australia

Germany

Eurostoxx

Hong Kong Hong

Switzerland Netherlands Gross Contributions To excess returns, gross of fees 1.0%

0.5%

0.0%

-0.5%

-1.0%

Italy

U.S. U.K.

Spain

Japan

France

Canada

Sweden

Australia

Germany

Eurostoxx

Hong KongHong

Switzerland Netherlands

Market -2.4% 8.0% 4.3% 6.1% 6.5% 3.0% -0.1% 6.0% 3.3% 2.7% 4.1% 6.2% 1.0% 7.6% Performance* Average 3.9% 4.3%

Source: AQR. Bloomberg. Gross of fee performance attribution as of June 30, 2019 of the Style Premia UCITS Fund. Positioning represents the average views from January 1, 2019 through June 30, 2019. Performance for the month ending June 30, 2019 is estimated and subject to change. Past performance is not a guarantee of future performance. Attribution is subject to change at any time without notice. Please read important disclosures in the Appendix. *Note that market performance is indicative and might not be representative of the underlying securities traded in the portfolio. Gross performance results do not reflect the deduction of investment advisory fees, which would reduce an investor’s actual return. 35 H1 2019 Performance Attribution Bonds

Positioning Average over the period as a % of NAV 250% 200% Value 150% 100% Momentum 50% Carry 0% -50% Defensive -100% -150% Total -200% U.K. U.S. Australia Canada Germany Japan

Gross Contributions To excess returns, gross of fees

3.0%

2.0%

1.0%

0.0%

-1.0%

-2.0% U.K. U.S. Australia Canada Germany Japan

Market 1.4% 3.6% 2.8% 1.3% 2.4% 0.4% Performance* Average 2.0%

Desired Risk as a Percent of Strategic Risk Target

100% Stocks & Industries 100%

• Asset classes can become 77% Equity Indices more or less attractive as the 80% characteristics of underlying securities change 50% • Asset group attractiveness is Fixed Income based on either greater or less 67% agreement of underlying styles relative to longer term 97% averages Currencies 88%

86% Total Fund 88%

0% 20% 40% 60% 80% 100% 120%

12/31/2018 06/30/2019

Source: AQR. Risk Weights are subject to change at any time without notice. There is no guarantee that targets will be met. 37 (Re-)Introduction to Style Premia Factors, Styles and ARP The semantics wars

Factors are theoretical investment strategies that go long stocks with pre-defined, rule-based characteristics and short stocks with opposite characteristics

Many factors have been defined and researched

We believe only a handful of factors that combine pervasive empirical evidence with economic intuition deserve strategic allocations in investor portfolios ‘Smart beta’ / factor investing ‘Smart beta’ refers to factor-tilted long-only equity strategies in equities

Factors are also known as styles, and can be accessed in long-only and long/short strategies Style premia investing Style premia strategies can be applied to other asset classes too Alternative Factors/styles are part of a broader group of strategies risk premia known as alternative risk premia; this also includes classic hedge fund strategies

Source: AQR, “Buffett’s Alpha.” Frazzini, Kabiller, Pedersen. For illustrative purposes only. Past performance is not a guarantee of future performance. Please refer to the Appendix for further information. 39 Birth of a Factor: Value An investment philosophy becomes a key early factor

Old New School School

“The value of any investment is, and always Value effect first published in Rosenberg, must be, a function of the price you pay for it.” Reid, and Lanstein (1985) and Fama and Benjamin Graham, French (1992) using the book-to-market The Intelligent Investor (1949) (BE/ME) indicator

Source: “The Cross-Section of Expected Stock Returns” Fama and French (1992), Getty Images/Julian Wasser. For illustrative purposes only. Past performance is not a guarantee of future returns. Please read important disclosures in the Appendix. 40 Birth of a Factor: Momentum A centuries-old maxim describes another important factor

Old New School School

“Cut short your losses, Jegadeesh and Titman (1993) let your profits run on.” Asness (1995) David Ricardo (1772-1823) (reputed)

Source: “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency” Narasimhan Jegadeesh and Sheridan Titman (The Journal of Finance, Vol. 48, No. 1. March 1993), Portrait of David Ricardo by Thomas Phillips, circa 1821 from Wikipedia. For illustrative purposes only. Past performance is not a guarantee of future returns. Please read important disclosures in the Appendix. 41 The Origins of Alternative Risk Premia Evolution of return attribution

Time

Alpha Alpha Alternative Risk Premia

Alpha Cost

Market Risk Premia Market Risk Premia Capacity Constrained Capacity

Prior to Cap-Weighted Market Risk Premia Alternative Risk Premia Equity Indices Introduced Introduced − Returns thought of as “Alpha” Examples Examples − S&P 500 Index − Classic factors / styles (style premia) − MSCI World − Classic hedge fund strategies − Bloomberg Barclays Aggregate (hedge fund risk premia)

Source: AQR. For illustrative purposes only. Alternative risk premia are also sometimes referred to as exotic or smart betas. Bloomberg Barclays Aggregate is the Bloomberg Barclays Global Aggregate Bond Index. 42 Introduction to Style Premia Focusing on four intuitive styles

Characteristics of Style Premia: Cheaper assets tend to outperform more Value expensive ones • Persistent Long-term evidence supported by economic intuition An asset’s recent relative performance Momentum tends to continue in the near future • Pervasive Exist broadly across regions and asset groups

Higher-yielding assets tend to provide Carry • Liquid higher returns than lower-yielding assets Can be captured by trading liquid instruments

• Dynamic Lower-risk and higher-quality assets tend Defensive Limited static exposure to any to generate higher risk-adjusted returns asset or market

Source: AQR. Past performance is not a guarantee of future performance. Please read important disclosures in the Appendix. 43 Style Premia May Have Risk-Based and Behavioral Origins Some candidate explanations (much debated by academics)

Description Possible explanations

Cheaper assets tend to outperform more • Over-extrapolated growth prospects Value expensive ones • Compensation for greater default risk

• Initial underreaction An asset’s recent relative performance Momentum • Subsequent overreaction tends to continue in the near future • Disposition effect

• Capital supply/demand imbalance Higher-yielding assets tend to provide Carry • Central bank actions higher returns than lower-yielding assets • Risk compensation

• Leverage aversion Lower-risk and higher-quality assets tend Defensive • Investors overpaying for “lottery” to generate higher risk-adjusted returns characteristics

Source: AQR. Past performance is not a guarantee of future performance. Please read important disclosures in the Appendix. 44 Systematic vs. Discretionary Investing Commonalities between approaches

Fundamental Systematic Themes Factors

Cheap Companies that trade for less than what they are worth Value

With a Catalyst With a potential event that could change their earnings or price potential Momentum

Strong Companies with customers that have good prospects Indirect Momentum Customer Base

Safe Those with resilient business models that can hold up across market environments Stability

Sound Accounting Companies with conservative accounting practices Earnings Quality Practices

Not Fighting the Sentiment Favored by informed investors Investor Sentiment

Trustworthy Where management is acting in shareholders’ best interests Management Signaling Management

Source: AQR. For illustrative purposes only. 45 AQR Long History of Style Premia Research

2016 Fitzgibbons, Friedman, Pomorski and Serban argue for an integrated approach to styles in “Long-Only Style Investing: Don’t Just Mix, Integrate”

Asness, Frazzini, Israel and Moskowitz summarize what we know and dispel myths about value in “Fact, Fiction, and Value Investing” 2015 Asness, Frazzini, Israel, Moskowitz and Pedersen resurrect the size premium in “Size Matters, if You Control Your Junk” Ilmanen, Maloney and Ross explore the macro sensitivities of styles in “Exploring Macroeconomic Sensitivities” 2014 Asness, Frazzini, Israel and Moskowitz summarize what we know and dispel myths about momentum in “Fact, Fiction, and Momentum Investing” Asness, Frazzini and Pedersen examine the quality factor in “Quality Minus Junk” 2013 Frazzini, Israel and Moskowitz evaluate trading costs in “Trading Costs of Asset Pricing Anomalies” Koijen, Moskowitz, Pedersen and Vrugt document pervasiveness of carry strategies in “Carry” Frazzini and Pedersen demonstrate pervasiveness of low-risk style in “Betting Against Beta” Asness and Frazzini challenge the traditional construction of the value premium in “The Devil in HML’s Details” 2012 Israel and Moskowitz show robustness of equity styles in “How Tax Efficient Are Equity Styles” and “The Role of Shorting, Firm Size and Time on Market Anomalies” Israel, Ilmanen and Moskowitz combine four styles in multiple contexts in “Investing with Style” Asness, Frazzini and Pedersen examine applications of the low-risk style in “Leverage Aversion and Risk Parity” 2010 Ilmanen presents long-term evidence for major strategy styles in his book, Expected Returns Berger, Israel and Moskowitz describe potential role for momentum in “The Case for Momentum Investing” Asness, Moskowitz and Pedersen demonstrate the pervasiveness of value and momentum in “Value and Momentum Everywhere” 2008 Brunnermeier, Nagel and Pedersen analyze risks to carry strategies in “Carry Trades and Currency Crashes”

2006 Frazzini investigates behavioral explanations for momentum in “The Disposition Effect and Under-Reaction to News”

AQR Moskowitz and Grinblatt document the momentum effect in industries in “Do Industries Explain Momentum?” Asness, Liew and Stevens study styles across countries in “Parallels Between the Cross-Sectional Predictability of Stock and 1998 Founding Principals Country Returns” began Asness documents case for two major styles in “The Interaction of Value and Momentum Strategies” managing 1994 investments Asness shows the implications for a combined value/momentum approach in his Ph.D. dissertation

46 The Long-Term Evidence Single-Style Long-Only Premia (“Smart Beta”) Long-term evidence of hypothetical excess returns among U.S. stocks

Value (excess of market): Book-to-Market Momentum (excess of market): Price Momentum 1951-2017 1951-2017 6% 6% 4% 4% 2% 2% 0% 0% -2% -2% -4% -4% -6% -6% Expensive Cheap Losers Winners Value Quintiles Defensive (excess of market): Gross Profits-to-Assets Defensive (excess of risk-free): Low Beta 1951-2017 1951-2017

6% 45% 0.80 4% 30% 0.60 Sharpe Ratio 2% 0% 15% 0.40 -2% 0% 0.20 -4% Highest Mid Lowest -15% 0.00 -6% Unprofitable Profitable Annualized Return Annualized Risk

Source: AQR, Ken French Data Library and CRSP/Compustat data. The graphs show the average annualized returns of equal weighted quintiles in excess of the average annualized returns of an equal weighted portfolio of all stocks in the CRSP universe. For the Low Beta graph, Sharpe ratios are calculated using the ICE BofAML US 3-Month T-Bill Index as the risk free rate of return and portfolios are formed by sorting stocks on realized market beta and dividing the stocks into quintile portfolios; returns are excess of cash. These are not the returns of an actual portfolio AQR manages and are for illustrative purposes only. Past performance is not a guarantee of future performance. Please read important disclosures in the Appendix. 48 Accessing Style Premia More efficiently capturing styles

Style investing exists along a spectrum

Increasing Efficiency/Diversification

Stocks & Equity Fixed Currencies Commodities Value Momentum Carry Defensive Industries Indices Income

ValueValue Value -

(Cheap)(Cheap)(Cheap) Value

Out

High High Long

Growth (Cheap) Cheap Yielding

(Expensive) LowRisk

performers

Low

Short

Under

Yielding

High High Risk

Expensive performers

Add Go Long/Short Market Go Multi-Asset Style Tilt Go Multi-Style • Seeks to improve portfolio • More active, less constrained exposure to • Even more diversified by adding more favorable alternative premia • Higher expected risk-adjusted characteristics • Uncorrelated to traditional markets returns • Returns largely driven by • More diversified than single premia tilt • Even greater improvement from market beta implementation choices

Source: AQR. For illustrative purposes only. Diversification does not eliminate the risk of experiencing investment losses. 49 Evidence Across Many Asset Groups and Styles Single long/short style-asset portfolios and composites

Hypothetical Gross Sharpe Ratios of Long/Short Style Components Across Asset Groups January 1990 – December 2017

Single long/short strategies performed well… 3.5 Composites may be even better

3.0

2.5

2.0

1.5 Sharpe Sharpe Ratio

1.0

0.5

N/A N/A N/A N/A

EQ Indices EQ Currencies Stocks & Industries & Stocks Commodities 0.0 Fixed Income Stocks & Equity Indices Fixed Income Currencies Commodities Style Composite Asset Group Industries Composite

Value Momentum Carry Defensive

Source: AQR. Above analysis reflects a backtest of theoretical long/short style components based on AQR definitions across identified asset groups, and is for illustrative purposes only and not based on an actual portfolio AQR manages. The results shown do not include advisory fees or transaction costs; if such fees and expenses were deducted the Sharpe ratios would be lower; returns are excess of cash. Please read performance disclosures in the Appendix for a description of the investment universe and the allocation methodology used to construct the backtest and composites. Hypothetical data has inherent limitations, some of which are disclosed in the Appendix. Risk-free rate used to calculate the Sharpe ratios shown above is the Merrill Lynch 3 Month T-bill. 50 Long/Short Allows Diversification in Multiple Dimensions Low correlations to traditional assets, and between styles

Hypothetical Correlations Between Long/Short Style Premia January 1990 – December 2017

Value Momentum Carry Defensive

Value 1.00 Momentum -0.63 1.00 Carry -0.11 0.14 1.00 Defensive -0.05 0.11 -0.11 1.00

Hypothetical Correlations Between Long/Short Style-Asset Portfolios January 1990 – December 2017

Stocks & Industries Equity Indices Fixed Income Currencies Commodities

Stocks & Industries 1.00 Equity Indices 0.05 1.00 Fixed Income -0.03 0.08 1.00 Currencies 0.09 0.11 0.06 1.00 Commodities -0.03 0.04 -0.02 0.09 1.00

Source: AQR. Above analysis reflects a heavily discounted backtest of the AQR Style Premia Strategy and underlying theoretical long/short style components based on AQR definitions across identified asset groups. Charts provided for illustrative purposes only and are not based on an actual portfolio AQR manages. Please see the Appendix for further details on the investment universe and the allocation methodology used to construct the backtests. Hypothetical data has certain inherent limitations, some of which are disclosed in the Appendix. All correlations based on monthly data, excess of cash. 51 New Research Studies a Century of Style Premia Earlier data provides further supporting evidence

Sharpe Ratios Across Styles and Asset Classes January 1920 – June 2018

1.8

1.6 1.6 1.5 1.4

1.2 1.2 1.1

1.0 0.8 0.8 0.8 0.8 0.7 0.7 0.7 0.6 0.7 0.6 0.6 0.6 0.6 0.5 0.5 0.5 0.5 0.4 0.5 0.5 0.4 0.4 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.1 0.1 0.0 U.S. Stocks International Stocks Equity Indices Fixed Income Commodities Currencies Multi-Asset

Value Momentum Carry Defensive Multistyle

Source: AQR, Global Financial Data, Bloomberg, Datastream, Chicago Board of Trade, Commodity Systems Inc. The full sample period starts 1/1920 and ends 6/2018. All returns are excess of U.S. treasury bills but gross of trading costs and fees. Asset class and style definitions can be found in the Appendix. Not representative of an actual portfolio that AQR currently manages. Hypothetical data has inherent limitations some of which are discussed in the Appendix. Please read important disclosures in the Appendix. 52 Is Performance Also Consistent Across Time? Multi-asset styles performed well in every decade

Sharpe Ratios of Multi-Asset Styles by Decade 2.5

2.0

1.5

1.0

0.5

0.0

-0.5

-1.0 1920 - 1929 1930 - 1939 1940 - 1949 1950 - 1959 1960 - 1969 1970 - 1979 1980 - 1989 1990 - 1999 2000 - 2009 2010 - 2018*

Value Momentum Carry Defensive Multistyle

*Data through June 2018. Source: AQR, Global Financial Data, Bloomberg, Datastream, Chicago Board of Trade, Commodity Systems Inc. The full sample period starts 1/1920 and ends 6/2018. All returns are excess of U.S. treasury bills but gross of trading costs and fees. Asset class and style definitions can be found in the Appendix. Not representative of an actual portfolio that AQR currently manages. Hypothetical data has inherent limitations some of which are discussed in the Appendix. Please read important disclosures in the Appendix. 53 Performance Across Growth and Inflation Environments Macro diversification: mapping investments to macro risks

Long-Only Market Risk Premia 1972 – 2018 2.0 All

1.5 1.10 1.11 Growth Up + Inflation Up 0.83 1.0 0.60 0.73 0.39 0.51 Growth Up + Inflation Down 0.5 0.29 0.25 0.31 Growth Down + Inflation Up

Sharpe Ratio Sharpe 0.0 -0.03 -0.07 Growth Down + Inflation Down -0.5 -0.21 -0.09 -0.18 Global Equities Global Bonds Commodities Hypothetical Long/Short Style Premia 1972 – 2018

2.0 1.38 1.5 1.20 1.241.29 1.18 1.32 1.13 1.07 1.01 1.09 0.940.88 0.88 0.84 1.0 0.77 0.78 0.82 0.82 0.63 0.63 0.61 0.55 0.47 0.50 0.39 0.5

Sharpe Ratio Sharpe 0.0 -0.5 Value Momentum Carry Defensive Trend Hypothetical Simple Portfolios 1972 – 2018 1.98 2.0 1.77 1.801.79 1.49 1.5 1.30

1.0 0.58 0.39 0.5 0.34

SharpeRatio 0.0

-0.5 -0.23 Global 60/40 Simple Style 5

Source: Bloomberg, AQR. Data from January 1972 – December 2018. Global Equities is the MSCI World Index. Global Bonds is a GDP weighted composite of Australian, European, Canadian, Japanese, U.K. and U.S. 10-year government bonds. Commodities is an equal dollar-weighted index of 24 commodities. Long-Short Style Premia are backtests of style premia as described herein. Global 60/40 takes 60% Global Equities and 40% Global Bonds. Simple Style 5 is an equal dollar-weighted composite of the five long/short style premia. Please see Appendix for more details on the construction of the return series and macroeconomic environmental indicators. The analysis is based on hypothetical returns gross of trading costs and fees. Hypothetical performance results have certain inherent limitations, some of which are disclosed in the Appendix. Past performance is not a guarantee of future performance. 54 For Investor Professional Use Only Interest Rate Sensitivity Long/short styles exhibit limited sensitivity to U.S. hiking cycles

Defining Fed Hiking Cycles January 1972 – June 2018 20% Hiking Cycle Indicator Fed Funds Rate 15%

10% Current hiking cycle triggered in March 2017 5%

0% 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016

Hypothetical Gross Sharpe Ratios in Hiking and Non-Hiking Periods January 1972 – June 2018 1.5 Asset Classes Equity Styles Multi-Asset Styles Simple Portfolios Not Hiking 1.0 Hiking

0.5

Sharpe Ratio Sharpe 0.0

-0.5 Global Global Real Commod- Equity Equity Equity Multi-Asset Multi-Asset Multi-Asset Multi-Asset Multi-Asset Global 4 Equity 5 Multi-Asset Equities Bonds Estate ities Value Momentum Low Risk Value Momentum Carry Defensive Trend 60/40 Styles Styles

Sources: Federal Reserve, Blue Chip Economic Indicators, Consensus Economics, Federal Reserve Bank of Philadelphia, Bloomberg, AQR. Hiking Cycle Indicator is triggered when current Fed Funds and T-Bill rates over- or undershoot their 12-month averages by a given margin. Hypothetical returns gross of transaction costs and fees, excess of cash. These are not the returns of actual portfolios that AQR currently manages and are for illustrative purposes only. Hypothetical performance results have certain inherent limitations, some of which are disclosed in the Appendix. Global Equities is MSCI World, Global Bonds is the Barclays Global Aggregate, Commodities is the S&P GSCI Index, Real Estate is the average of the NCREIF Property Index converted from quarterly to monthly and the FTSE NAREIT Index. Please see Appendix for more details on the construction of the return series. 55 Hedge Funds’ Typical Long Run Factor Exposures HF industry returns reflect market and alternative risk premia

Hedge fund index returns since 1994 are regressed HF Index Composite 14-Factor Regression on various market risk premia (MRP) and January 1994 – December 2017 alternative risk premia (ARP) In-Sample Return RSQ = 77% Coefficient t-Stat The 4.3% average excess return of the HF combo Premium Contribution Intercept 0.001 1.3 0.9% 0.9% is attributed to: 1.9% MRP, 1.5% ARP, 0.9% alpha Alpha Equities 0.276 20.9 5.5% 1.5% Very significant equity market loadings and Equities Lag 0.030 2.3 5.6% 0.2% 1.9% significant loadings on many factors Bonds 0.088 1.6 2.7% 0.2% MRP Commodities 0.019 2.0 0.6% 0.0% Negative loading on defensive stock selection Val-SS -0.010 -0.7 6.8% -0.1% Mom-SS 0.059 3.8 4.6% 0.3% Def-SS -0.036 -2.8 8.6% -0.3% Val-AA 0.003 0.2 2.6% 0.0% Mom-AA 0.045 2.3 4.2% 0.2% 1.5% Car-AA 0.017 1.0 9.1% 0.2% ARP Def-AA 0.027 1.6 3.9% 0.1% VRP 0.052 2.3 7.1% 0.4% Trend 0.092 4.6 8.9% 0.8% Small Cap 0.194 5.7 -0.1% 0.0% Ronen Israel and Adrienne Ross (2017) “Measuring Factor Exposures: Uses and Abuses” Journal of Alternative Investments

Source: AQR, Bloomberg. HF Index Composite is a simple average of the CS Hedge Fund Index (asset-weighted), HFRI Fund-Weighted Composite Index (equal-weighted), and the HFRI Fund of Funds Composite Index (equal-weighted). Market risk premia (MRP): Equities is the MSCI ACWI Index, and Equities Lag is its one-month lagged return. Bonds is the Barclays U.S. Aggregate Index, Commodities is the S&P GSCI. Alternative risk premia (ARP; i.e. other factors): Val, Mom, Car, and Def are market neutral Value, Momentum, Carry, and Defensive style strategies, respectively. SS refers to stock-selection, while AA refers to styles constructed on multi-asset universes. These styles are described in more detail in Ilmanen, Israel, and Moskowitz (2012) and in the Appendix. VRP is an equity index volatility-selling strategy. Trend is a trend-following strategy applied in many asset classes. Small Cap is a global Small- Minus-Big strategy. All ARP returns sourced from AQR. All ARP except Small Cap are net of t-costs and most are partly discounted. All monthly return series are either long/short portfolio returns or total returns in excess of cash (average cash return over the period was was 2.6%). Return contribution for each factor is the product of its coefficient times its in-sample average annual return (while 12 x Intercept is the annualized alpha). Hypothetical performance results have certain inherent limitations, some of which are disclosed in the Appendix. 56 What Are Realistic Expectations for the Future? Skepticism is partly warranted

No matter how stringent our criteria, history may overstate future results

• It is important to consider the impact of trading costs and fees on historical backtest returns

• The magnitude of returns from known style premia may be smaller going forward; we assume half or less of historical rewards

• Premia are unlikely to disappear if risk-based and behavioral causes behind each style/factor premium are persistent and if there are limits to arbitrage forces

But diversification may help boost portfolio Sharpe ratio, especially in market-neutral applications

A diversified portfolio of these strategies may be less reliant on the standalone efficacy of any one style in any one asset class…

…but very reliant on efficient execution as we magnify small edges

To convert the Sharpe ratio advantage into high returns, some leverage will be needed

Real-world constraints will prevent many investors from allocating too much to these strategies

Source: AQR. Diversification does not eliminate the risk of experiencing investment losses. Please read important disclosures in the Appendix. Hypothetical data has certain inherent limitations, some of which are disclosed in the Appendix hereto. 57 Looking Forward If You Change Your Mind, Do So for the Right Reasons

We absolutely hate when any good strategy doesn’t work for a while

And we try to keep an open-enough mind • Not open enough → you stick too long with a strategy that stopped working • Too open → you drop a good strategy during an inevitable tough time

In general, we think if investors change their mind, it should be based on: • Evidence big enough to change the long-term, broad evidence that led you to believe in the first place (i.e., recent short- term performance doesn’t mean much statistically) • A theory that is supported by observation (i.e., specific cause and effect)

Source. AQR. Diversification does not eliminate the risk of experiencing investment losses. Past performance is not a guarantee of future performance. 59 Long-term Focused but Try to Understand the “Why” No real smoking gun but some recent evidence of price pressure

Has the world changed? • Are we measuring themes incorrectly? • Is the strategy too expensive today? • Has it become too costly to trade?

Unintended risks • Unintended macro exposures • Issues in risk management • Implementation problems: portfolio construction, trading, etc.

What else might explain it? • Other players: de-leveraging, de-risking, crowding, short-covering • FANG stocks • Tax cut • Trade war

Source. AQR. 60 Where We Go From Here? Sticking to our process

We continue to have high conviction in our process that seeks to capture multiple, long-term good sources of return

What are we doing at AQR: • Communicating, measuring, monitoring, and considering every theory from the reasonable to the somewhat whacky • Avoiding every bias out there that pushes you to change for the sake of change

Recovery can occur if one or several themes produce positive returns without offsetting losses (today’s underperformers may or may not be source of the recovery)

We’ve had tough periods before on the road to success — we’ve seen this movie several times and it has always ended well

61 FAQs Home → FAQs

Strategy level Theme level

Are the Strategy losses expected? Does Value really work?

How does one theme contribute meaningfully to Are We Still Measuring Value Correctly? performance? What are we observing from other market participants? Did diversification fail Style Premia? What is your outlook on Value? Did Style Premia fail to diversify? Has it become too costly to trade? Was this a failure of risk management?

Is now a good time for alternatives?

63 ← FAQs Home → Are the Strategy Losses Expected? Hypothetical & live results

AQR Style Premia Strategy Performance & Drawdown Hypothetical (Left) & Live (Right) Results* $1,000 $25

$100 scaled)

- $10 (Log

$1 $5 9/1/2012 9/1/2013 9/1/2014 9/1/2015 9/1/2016 9/1/2017 9/1/2018 Cumulative Performance Cumulative 0% 0% -5% -5% -10% -10% -15% -15% -20% -20%

-25% -25% Drawdowns -30% -30% Jan '90 Jan '93 Jan '96 Jan '99 Jan '02 Jan '05 Jan '08 Jan '11 Sep '12 Dec '13 Mar '15 Jun '16 Sep '17 Dec '18

Dates Drawdown Length of Time (Months) Rank Type Start Bottom End Trough Start-Bottom Bottom-End Start-End 1 Hypothetical Sep 98 Feb 00 Dec 00 -24.8% 17 10 27 2 Live Feb 18 Jun 19 -21.2% 16 3 Hypothetical Jun 02 Oct 02 May 03 -10.6% 4 7 11 4 Hypothetical Aug 92 Nov 92 Mar 93 -9.5% 3 4 7 5 Hypothetical Feb 94 Mar 94 Nov 94 -8.9% 1 8 9 6 Hypothetical Apr 10 Jul 10 Oct 10 -7.7% 3 3 6 7 Live Jan 15 Feb 15 Sep 15 -6.8% 1 7 8 8 Hypothetical Oct 90 Oct 90 Jan 91 -6.1% 0 3 3 9 Hypothetical Jun 08 Jul 08 Nov 08 -5.8% 1 4 5 10 Live Feb 16 Aug 16 Feb 17 -5.3% 6 6 12

Source: AQR. *Hypothetical performance of the AQR Style Premia Strategy from January 1, 1990 – August 30, 2012. Live data based on estimated gross performance of an AQR Style Premia Strategy representative account from September 1, 2012 through June 30, 2019. Performance shown is from a representative account with unique characteristics and may not be fully representative of other Style Premia portfolios AQR may manage. Portfolio construction will vary depending on individual characteristics of client mandates and constraints. The information presented herein is supplemental to the GIPS® compliant presentation for the Style Premia Composite included in the Appendix. Past performance is not a guarantee of future performance. Please read important disclosures at the end of this document. All performance figures contained herein represent preliminary unaudited estimates of net realized and unrealized returns prepared by AQR Capital Management, LLC (“AQR”) and are subject to review and revision. Please read the disclosures in the Appendix for a description of the investment universe and the allocation methodology used to construct the backtest. Hypothetical data has certain inherent limitations, some of which are disclosed in the Appendix. All performance figures contained herein are in USD. Please refer to monthly statements provided by your custodian or administrator for actual returns. 64 ← FAQs Home → Are the Strategy Losses Expected? Drawdown simulations

Periods with at Least One % of Simulations of Average Wait Drawdown Drawdown > Threshold, Drawdown Worse Than to First Drawdown Threshold Average Number of Worse Threshold (in Months)* Drawdowns

-5% 100% 10 8 -6% 100% 8 10 -7% 100% 7 13 -8% 100% 5 16 -9% 100% 5 20 -10% 100% 4 24 -11% 100% 3 29 -12% 99% 3 35 -13% 98% 2 42 -14% 96% 2 50 -15% 92% 2 59 -16% 88% 2 70 -17% 82% 1 82 -18% 76% 1 96 -19% 69% 1 112 -20% 62% 1 129

*Average Monthly Wait to First Drawdown does not use 10-year simulated windows Source: AQR. Data based on one hundred thousand ten year simulations of a strategy that has the equivalent volatility and expected return of the Style Premia strategy (assuming 12% volatility and 0.7 Sharpe Ratio). Hypothetical data has certain inherent limitations, some of which are disclosed in the Appendix. For illustrative purposes only. Please read important disclosures in the Appendix. 65 ← FAQs Home → Are the Strategy Losses Expected? “Absolute Return” doesn’t mean “works all the time”

The Sharpe Ratio (SR) of the U.S. stock market over almost the last century has been ~0.4

A 0.4 Sharpe probability of outperforming cash is: 51% in a day, 55% in a month, 66% in a year, and 90% in 10 years

We believe finding a true “three Sharpe ratio” strategy that never loses money is a bit like finding the holy grail

Probability of Underperforming Cash

60% 0.4 Sharpe Ratio 0.7 Sharpe Ratio

50%

40%

30%

20%

10%

0% Daily Weekly Monthly Quarterly Annual 3-Year 5-Year 10-Year

Investment Horizon

Source: AQR. The ~0.4 Sharpe ratio for U.S. equities is the realized Sharpe ratio of our U.S. equity proxy over the last 100 years, which is the S&P 500 Index since its inception and the Ibbotson U.S. Large Stock Index before that. The probability calculations for the bars assume a normal distribution. Cash is represented by the ICE Bank of America U.S. 3-Month T-Bill Index. For illustrative purposes only. 66 ← FAQs Home → How Does One Theme Contribute Meaningfully to Performance?

A diversified approach allows Style Premia strategy to incorporate AQR Style Premia Strategy Strategic Risk Allocations meaningful allocations to styles and asset groups while maintaining a 12% volatility target and achieving better long-term returns

Commodities This concept holds at the sub-strategy level as well 15% Stocks & Industries 30% Currencies 15% AQR Style Premia and S&I Strategic Allocation in Volatility Space

25% Equity Indices Com. Fixed Income 20% 20% 20% Curr. Diversification benefit

15% Fixed Def. Income

Diversification benefit Equity 10% Mom. Indices

5% Stocks & Ind. Value

0% AQR Style Premia Strategy AQR Style Premia Underlying Stocks & Industries Asset Groups Strategic Allocation

Source: AQR. Risk allocations are strategic targets and there are deviations from such targets over time. In January 2018, the Strategy’s method of targeting and communicating style risk was changed to better emphasize the impact of correlations. The current long-term strategic style allocation has been in place since that time and the current allocation chart better illustrates that methodology. Past performance is not a guarantee of future performance. Strategic allocations are subject to change at any time without notice. There is no guarantee, express or implied, that long-term return and/or volatility targets will be achieved. Realized volatility may come in higher or lower than expected. Diversification does not eliminate the risk of experiencing investment losses. 67 ← FAQs Home → How Does One Theme Contribute Meaningfully to Performance? “The fox knows many things, but the hedgehog knows one big thing”*

0.25 0.25 Sharpe Sharpe 0.40 Ratio Ratio Sharpe vs. Ratio 0.25 0.25 Sharpe Sharpe Ratio Ratio

The diversified fox loses many more battles… …but is more likely to win the war

Likelihood of at Least One Strategy Being Down over Likelihood of Beating Cash over Different Horizons Different Horizons 100% 100% The Hedgehog (0.4 SR) The Hedgehog (0.4 SR) 80% The Fox (0.5 SR) 90% The Fox (0.5 SR)

60% 80%

40% 70%

20% 60%

0% 50% Monthly Quarterly Annual 3-Year 5-Year 10-Year Monthly Quarterly Annual 3-Year 5-Year 10-Year

* As quoted in The Hedgehog and the Fox (1953) by Isaiah Berlin and a reference to the fragment attributed to the Ancient Greek lyric poet Archilochus. Source: AQR. For illustrative purposes only. Probabilities based on normally distributed, serially independent returns, assuming a Sharpe Ratio of 0.4 for the hedgehog and 0.5 for the fox. The fox’s four strategies are assumed to be uncorrelated. Diversification does not eliminate the risk of experiencing investment losses. Hypothetical performance has certain inherent limitations, some of which are discussed in the disclosures. Image source: https://medium.com/the-mission/accomplishments-secret-couriers-the-hedgehog-and-the-fox- c7b61a8d8002. 68 ← FAQs Home → Did Diversification Fail Style Premia? When should momentum’s gains offset value’s losses?

Over shorter holding periods, Momentum is less likely to offset Value’s worst losses

But, over longer periods, Momentum’s gains typically offset the detraction from Value

Our process is more than just these simple measures of Value and Momentum

Fama French Data: Value (HML) and Momentum (UMD) Monte Carlo Simulation: Value and Momentum Performance during Worst 20% Periods for Value Performance during Worst 20% Periods for “Value” January 1927 – June 2019 Hypothetical Simulation

25% 25%

20% 20%

15% 15%

10% 10%

5% 5%

0% 0%

Average Return Average Average Return Average

-5% -5%

-10% -10%

-15% -15% 1m 6m 12m 18m 24m 30m 36m 1m 6m 12m 18m 24m 30m 36m Time Horizon Time Horizon

VAL(HML) MOM(UMD) SUM VAL MOM SUM

Source: AQR, Fama-French Data Library. Left chart uses Fama-French HML and UMD factor data, downloaded from the Ken French data library from 1927-2019, and scaled to 10% volatility. Right chart uses a Monte Carlo simulation to produce 500 iterations of 100 hypothetical years of performance for two factors that have a -0.5 correlation, 0.3 Sharpe ratio, and are scaled to 10% volatility. Hypothetical data has inherent limitations, some of which are disclosed in the Appendix. Please read the Appendix for important disclosures. 69 ← FAQs Home → Did Style Premia Fail to Diversify? Understanding what diversification looks like

A diversifying investment seeks to produce returns that are unrelated to the market

When the market falls, returns could be positive or negative

Correlated Investment Diversifying Investment Hedge

Buying ProtectionBuying

U.S. Private Equity U.S. Private Hypothetical Style PremiaStyle Hypothetical

Market losses not S&P 500 Put 500 S&P always mitigated S&P 500 Index S&P 500 Index S&P 500 Index

Same Sign Returns Opposite Sign Returns

Source: AQR. For illustrative purposes only. Charts show quarterly excess-of-cash returns from January 1990 to December 2016. Cash is 3-month T-Bills. ‘U.S. Private Equity’ is the Cambridge U.S. Private Equity Index. ‘Hypothetical Style Premia’ is based on the AQR Style Premia Backtest from January 1990 to December 2016. ‘S&P 500 Put-Buying Protection’ is the CBOE Put Protection Index minus the S&P500 Index. Please see the Appendix for further details on the investment universe and the allocation methodology used to construct the Style Premia Strategy Hypothetical Backtest. The data presented herein is supplemental to the GIPS® compliant presentation for the Style Premia Composite, incepted on 9/1/2012, included in the Appendix. For illustrative purposes only. Diversification does not eliminate the risk of experiencing investment losses. Hypothetical performance has certain inherent limitations, some of which are discussed in the disclosures. 70 ← FAQs Home → Did Style Premia Fail to Diversify? Uncorrelated strategies can still lose in tandem with markets

Being uncorrelated means that the likelihood of gains and losses is unaffected by market direction • Two uncorrelated zero Sharpe strategies would have a ¼ of observations in each of the four quadrants • Due to positive Sharpe ratios, observations are biased to the right (equities) and up (Style Premia) • Aggregate market performance does not materially impact strategy results

Monthly (Left) and Daily (Right) Live Style Premia and MSCI World Performance September 1, 2012 – June 30, 2019

8% 8%

6% 6%

4% 4%

2% 2%

0% 0%

Style Premia Style Style Premia Style -2% -2%

-4% -4%

-6% -6% -10% -5% 0% 5% 10% -10% -5% 0% 5% 10% MSCI World MSCI World Fourth Quarter 2018

Source: AQR. ‘Theoretical’ likelihood based on realized live Sharpe ratio and an assumed zero correlation. Daily performance as displayed above is calculated using daily data on a rolling 3-day basis. Data is based on estimated gross performance of an AQR Style Premia Strategy representative account from September 1, 2012 through June 30, 2019. Performance shown is from a representative account with unique characteristics and may not be fully representative of other Style Premia portfolios AQR may manage. All returns are gross of fees, excess of cash returns. Portfolio construction will vary depending on individual characteristics of client mandates and constraints. The information presented herein is supplemental to the GIPS® compliant presentation for the Style Premia Composite included in the Appendix. Past performance is not a guarantee of future performance. 71 ← FAQs Home → Did Style Premia Fail to Diversify? Macroeconomic drivers can’t explain Value’s performance

Ilmanen, Maloney and Ross (2014) study the sensitivity of a variety of asset classes and styles to macroeconomic risks using over 40 years of data

Results show that value generally has lower macro economic sensitivities than equities

Hypothetical U.S. Long/Short Value (HML Devil) vs. U.S. Equities: Macro Environments January 1, 1972 – December 31, 2018

U.S. Long/Short Value (HML Devil) 1.5

1.0

0.5

0.0

Sharpe Ratio Sharpe -0.5

-1.0 Full Period Growth Up Growth Inflation Up Inflation Real Yield Real Yield Volatility Up Volatility Down Down Up Down Down U.S. Equities 1.5

1.0

0.5

0.0

Sharpe Ratio Sharpe -0.5

-1.0 Full Period Growth Up Growth Inflation Up Inflation Real Yield Real Yield Volatility Up Volatility Down Down Up Down Down

Source: AQR, Bloomberg. For illustrative purposes only and not representative of a portfolio AQR currently manages. The Hypothetical U.S. Long/Short Value (HML Devil) is an industry-neutral return series of the AQR U.S. HML Devil Backtest. Please see Appendix for more details on the construction of the macroeconomic environmental indicators and an explanation of the backtest. This backtest utilized for longer track record than AQR’s Hypothetical Valuation Theme backtest. Index used for U.S. Equities is the S&P 500 Index. Risk- free rate used for sharpe ratio calculations is the BAML 3-Month T-Bill Index. No representation is being made that any investment will achieve performance similar to those shown. Realized returns and/or volatility may come in higher or lower than expected. Hypothetical data has inherent limitations, some of which are disclosed in the Appendix. 72 ← FAQs Home → Did Style Premia Fail to Diversify? Failing unconventionally is harder than many appreciate

In theory, the more independent the sources of returns, the better

But that means when tough times hit, they will, by definition, seem to come out of nowhere • This only adds to the frustration • There’s often no explanation • Everyone will have reasons for why you’re wrong • And it gives us nothing simple to root for to turn things around

Diversification, maybe surprisingly, means living through — and explaining — hard times becomes even harder

Source. AQR. Diversification does not eliminate the risk of experiencing investment losses. 73 ← FAQs Home → Was This a Failure of Risk Management? Individual factor volatilities well within normal ranges

AQR Style Premia Strategy Equity Styles Relative Realized Volatilities September 1, 2012 – June 30, 2019 100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0% 2013 2014 2015 2016 2017 2018 2019

Value Momentum Defensive

Source: AQR. 6M rolling volatilities of 3 day overlapping returns of the equity styles of the AQR Style Premia Strategy, rescaled such that they sum to 1. Data is based on estimated gross performance of an AQR Style Premia Strategy representative account from September 1, 2012 through June 30, 2019. Performance shown is from a representative account with unique characteristics and may not be fully representative of other Style Premia portfolios AQR may manage. All returns are gross of fees, excess of cash returns. Portfolio construction will vary depending on individual characteristics of client mandates and constraints. The information presented herein is supplemental to the GIPS® compliant presentation for the Style Premia Composite included in the Appendix. Past performance is not a guarantee of future performance. Please read important disclosures at the end of this document. All performance figures contained herein represent preliminary unaudited estimates of net realized and unrealized returns prepared by AQR Capital Management, LLC (“AQR”) and are 74 subject to review and revision. All performance figures contained herein are in USD. ← FAQs Home → Was This a Failure of Risk Management? Strategy correlations were not elevated

AQR Style Premia Strategy Equity Style 90 Day Pairwise Correlations September 1, 2012 – June 30, 2019

2.0

1.5

1.0

0.5

0.0

-0.5

-1.0

-1.5

-2.0 2012 2013 2014 2015 2016 2017 2018

Value / Momentum Value / Defensive Momentum / Defensive

Source: AQR. 90 day pairwise correlations of the equity styles of the AQR Style Premia Strategy. Data is based on estimated gross performance of an AQR Style Premia Strategy representative account from September 1, 2012 through June 30, 2019. Performance shown is from a representative account with unique characteristics and may not be fully representative of other Style Premia portfolios AQR may manage. All returns are gross of fees, excess of cash returns. Portfolio construction will vary depending on individual characteristics of client mandates and constraints. The information presented herein is supplemental to the GIPS® compliant presentation for the Style Premia Composite included in the Appendix. Past performance is not a guarantee of future performance. Please read important disclosures at the end of this document. All performance figures contained herein 75 represent preliminary unaudited estimates of net realized and unrealized returns prepared by AQR Capital Management, LLC (“AQR”) and are subject to review and revision. All performance figures contained herein are in USD. ← FAQs Home → Was This a Failure of Risk Management? Performance is not driven by a small handful of concentrated positions

Historically, average performance contributors and detractors have been broadly distributed (contributors tend to outweigh detractors)

This pattern was no different in 2018-2019, except that contributors did not offset detractors.

Hypothetical AQR U.S. Valuation Factor Average Contributions January 1, 1990 – June 30, 2019 1.0%

0.5%

0.0%

-0.5%

-1.0% Worst 20 Next 100 Next 100 Middle Next 100 Next 100 Best 20

1990-2017 2018-2019

Source: AQR, Russell. Charts show the average contribution to gross return to Hypothetical AQR U.S. Valuation theme. Date range is 1/1/1990 – 6/30/2019. Gross performance does not reflect the deduction of investment advisory fees. Hypothetical data has inherent limitations, some of which are disclosed in the Appendix. Please read important disclosures in the Appendix. A full description of the Hypothetical AQR U.S. Valuation Theme Backtest methodologies is included in the Appendix. 76 ← FAQs Home → Was This a Failure of Risk Management? Why didn’t we apply a stop-loss-like policy?

A stop-loss policy results in a more concentrated portfolio due to a relative increase in risk allocated to other strategies, with limited aggregated risk reduction

We prefer a systematic risk control process that cuts risk at the aggregate level

Strategic Risk Contributions Stop-loss Risk Contributions Risk contributions by style Risk contributions by style

Defensive Defensive Value 19% 15% 15%

Value 33% Carry 14% Carry 15%

Momentum 49% Momentum 33%

Source: AQR. Risk allocations are strategic targets and there are deviations from such targets over time. In January 2018, the Strategy’s method of targeting and communicating style risk was changed to better emphasize the impact of correlations. The current long-term strategic style allocation has been in place since that time and the current allocation chart better illustrates that methodology. Past performance is not a guarantee of future performance. Strategic allocations are subject to change at any time without notice. There is no guarantee, express or implied, that long-term return and/or volatility targets will be achieved. Realized volatility may come in higher or lower than expected. Diversification does not eliminate the risk of experiencing investment losses. 77 ← FAQs Home → Is Now a Good Time for Alternatives? Low expected returns for stocks and bonds

Expected Real Return of U.S. 60/40 Stock/Bond Portfolio* January 1, 1900 – June 30, 2019

15% au_us_6040

10%

1.9%

-5% 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

US 60/40 Real Yield 5% Real Return U.S. 60/40 Realized Next 20Y Real Return

* Earnings data through 3/31/2019. Source: AQR, Bloomberg, Robert Shiller’s Data Library, Ibbotson Associates (Morningstar), Kozicki-Tinsley (2006), Federal Reserve Bank of Philadelphia, Blue Chip Economic Indicators, Consensus Economics. U.S. 60/40 is 60% U.S. stocks represented by the Standard&Poor’s 500 Index and 40% long-dated Treasuries represented by 10-year Treasuries. The equity yield is a 50/50 mix of two measures: 50% Shiller E/P * 1.075 and 50% Dividend/Price + 1.5%. U.S. bond yield is 10-year real Treasury Yield over 10-year inflation forecast. Scalars are used to account for long term real Earnings Per Share (EPS) Growth. Past performance is not a guarantee of future performance. Please read important disclosures in the 78 Appendix. ← FAQs Home → Does Value Really Work? Even simple versions of value have performed pretty well on average

Growth of Hypothetical $1

U.S. Equities Japan Equities Europe Equities Developed Market Equity Selection $32.0 $32.0 $4.0 $16.0

$16.0 $16.0 $8.0 $8.0 $2.0 $8.0 $4.0 $4.0 $4.0 $2.0 $2.0 $1.0

$1.0 $2.0 $1.0

$0.5 $1.0 $0.5 $0.5 1926 1946 1966 1986 2006 1988 1998 2008 2018 1988 1998 2008 2018 1925 1945 1965 1985 2005

Emerging Market Equity Developed Bond Markets Developed Market Currency Emerging Market Currency Selection Selection Selection $16 $4.0 $8.0 $16

$8 $4.0 $8 $2.0

$4 $2.0 $4

$1.0 $2 $1.0 $2

$1 $0.5 $0.5 $1 1970 1980 1990 2000 2010 1923 1943 1963 1983 2003 1974 1984 1994 2004 2014 1983 1993 2003 2013

Source: CRSP for US Equities, Compustat/XpressFeed for Japan and Europe Equities, Global Financial Data, Bloomberg and Datastream for Developed and Emerging Market Equity Selection and Bond Markets, and AQR’s production database for Currencies. All returns are gross of transactions costs and fees. U.S., Japan and Europe Equities measure value using book-to-price ratios, Developed and Emerging Market Equity Selection measure value using cyclically-adjusted earning-to-price ratios, Developed Bond Markets measures value using real bond yields, Currencies measure value using purchasing price parity. Hypothetical performance results have many inherent limitations, some of which, but not all, are described herein. No representation is being made that any investment will or is likely to achieve profits or losses similar to those shown herein. Hypothetical performance results are presented for illustrative purposes only. See Appendix for important information and data descriptions. 79 ← FAQs Home → What Are We Observing From Other Market Participants? Is there evidence of de-risking?

Compared to the Quant Crisis and the GFC, levels of intraday volatility for Value were not extreme for much of 2018

Negative returns in 2018 were spread out across many days rather than concentrated, not broad based across factors, and had intra-day patterns inconsistent with de-risking

However, volatility significantly picked up in the fourth quarter alongside broader market volatility increases

Hypothetical U.S. Long/Short Value Intraday Volatility January 1, 2006 – December 31, 2018 Q4 15%

GFC 10% Quant Crisis Jan. – Sept.

0% 2006 2008 2010 2012 2014 2016 2018

Source: AQR, Bloomberg. Intraday volatility for hypothetical AQR U.S. Long/Short Value and U.S. Long/Short Investor Sentiment factors (scaled to target 7% volatility). Date range chosen to illustrate current period vs. highlighted crisis periods. A full description of the backtest methodologies is included in the Appendix. Hypothetical data has inherent limitations, some of which are disclosed in the Appendix. 80 ← FAQs Home → What Are We Observing From Other Market Participants? Is there evidence of de-risking?

Hedge fund gross leverage decreased meaningfully in the fourth quarter of 2018 in line with aggregate market declines

Even after adjusting for market conditions, there is evidence of de-risking in line with February of 2018 and previous periods of stress in the financial markets

2018 Changes in Gross Daily Hedge Fund Exposures January 2018 – December 2018 25% Q4

20%

15%

10%

-5%

Changes Since Beginningof 2018 -10%

-15% Jan-18 Feb-18 Mar-18 Apr-18 May-18 Jun-18 Jul-18 Aug-18 Sep-18 Oct-18 Nov-18 Dec-18

Source: AQR, J.P. Morgan Prime Finance. Hedge fund exposures for the period January 2018 – December 2018. Past performance is not a guarantee of future performance. Please read important disclosures in the Appendix. 81 ← FAQs Home → What Are We Observing From Other Market Participants? Value has not seen unusual outflows

Net outflows from funds identified as value-benchmarked or value-tilted were negative in 2018, but not atypically high Flows represent a small fraction of the approximately $2T AUM base in these funds We find a weak relation between flows and value returns

Net Flows by Month ($B) January 1, 2012 – December 31, 2018

$10

-$2

-$4

-$6

-$8

-$10 2012 2013 2014 2015 2016 2017 2018

Source: AQR, Morningstar. Net flows shown for funds Morningstar funds identified as value-benchmarked or value-tilted. Classifications by Morningstar. All equity funds classified as “Value” or with a value strategic beta are included. 82 ← FAQs Home → What is Your Outlook on Value? Predictability of returns

Historically, good performance has followed both good and bad returns

Hypothetical AQR U.S. Valuation Theme Gross Returns Following 6-Month Return Period January 1, 1984 – June 30, 2019

20.0%

15.0%

10.0%

5.0%

0.0%

-5.0%

-10.0% Initial 6 Month Excess Return Next 3 Month Return Next 6 Month Return Next 12 Month Return Next 24 Month Return

Worst 2nd Quintile 3rd Quintile 4th Quintile Best

Source: AQR. For illustrative purposes only and not representative of an actual portfolio AQR currently manages. Quintiles formed by sorting the hypothetical monthly returns of the AQR U.S. Valuation theme from highest-to-lowest (best-to-worst). From there, we calculate the subsequent 3, 6, and 12 month cumulative returns for every month and average those results across each quintile. Hypothetical AQR U.S. Valuation theme return data indicative of gross USD returns for a long-short, market neutral implementation of the theme from 1/1/1984 – 06/30/19. Gross performance does not reflect the deduction of investment advisory fees. Hypothetical data has inherent limitations, some of which are disclosed in the Appendix. A full description of the backtest methodology is included in the Appendix. 83 ← FAQs Home → What is Your Outlook on Value? Is Value more or less cheap today?

Value Spreads for Hypothetical Industry-Neutral Value Portfolios January 1, 1984 – June 30, 2019

4.0

3.0 96th Percentile 2.0 93rd Percentile 1.0 th

Score) 84 - Percentile 0.0

-1.0 Value Spread (Z Spread Value

-2.0

-3.0

-4.0 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 US International Emerging

Source: AQR. Value composite includes four value measures: book-to-price, earnings-to-price, forecast earnings-to-price, and sales-to-enterprise value; spreads are measured based on ratios. To construct industry-neutrality, the value spreads are constructed by comparing the aforementioned value measures within each industry, which are then aggregated up to represent an entire portfolio. Hypothetical data has inherent limitations, some of which are disclosed in the Appendix. Please see the Hypothetical AQR Industry Neutral Value Backtest Description in the appendix. Please read the Appendix for important disclosures. 84 ← FAQs Home → Has it Become Too Costly to Trade?

We track this stuff closely (and publish it)

We’ve looked in many places, and find no evidence to support it

Value has not underperformed because it’s become more expensive to trade

Source. AQR. 85 ← FAQs Home → Has it Become Too Costly to Trade? Maybe the money chasing these strategies has caused t-costs to rise

We’ve looked in many places, and find no evidence to support it — value and our liquid alts in general have not underperformed because they have become more expensive to trade

Trading Costs Over Time: Implementation Shortfall Per Dollar Traded and the Dollar Notional Traded Global Developed and Emerging Market Equities

25 $400 B $350 B 20 $300 B 15 $250 B $200 B 10 $150 B $100 B 5 $50 B

0 $0 B Gross NotionalTraded ($)

H2 H1 H2 H1 H2 H1 H2 H1 H2 H1 H2 H1 H2 H1 H2 H1 H2 H1 H2 H1 H2

H1*

basis basis points per dollar traded Implementation Shortfall (IS) in 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Gross Notional Traded ($) IS (bps)

* 2008 H1 contains data from 04/21/2008 to 06/30/2008. Source: AQR. Data shows realized implementation shortfall (IS) and gross notional traded for all AQR GSS equity trades in global markets (frontier markets excluded) from April 21, 2008 to December 31, 2018. Implementation shortfall is measured by taking the average fill price and benchmarking against the decision price, typically defined as the closing price of the market on the day the trade decision was made (usually one day prior to the trade date). Implementation shortfall series shows weighted average with two standard errors. For illustrative purposes only. 86 Appendix Performance Disclosures

This document has been provided to you solely for information purposes and does not constitute an offer or solicitation of an offer or any advice or recommendation to purchase any securities or other financial instruments and may not be construed as such. The factual information set forth herein has been obtained or derived from sources believed to be reliable but it is not necessarily all-inclusive and is not guaranteed as to its accuracy and is not to be regarded as a representation or warranty, express or implied, as to the information’s accuracy or completeness, nor should the attached information serve as the basis of any investment decision. This document is intended exclusively for the use of the person to whom it has been delivered and it is not to be reproduced or redistributed to any other person. There is no guarantee, express or implied, that long-term return and/or volatility targets will be achieved. Realized returns and/or volatility may come in higher or lower than expected. PAST PERFORMANCE IS NOT A GUARANTEE OF FUTURE PERFORMANCE. Diversification does not eliminate the risk of experiencing investment losses. HYPOTHETICAL PERFORMANCE RESULTS HAVE MANY INHERENT LIMITATIONS, SOME OF WHICH, BUT NOT ALL, ARE DESCRIBED HEREIN. NO REPRESENTATION IS BEING MADE THAT ANY FUND OR ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THOSE SHOWN HEREIN. IN FACT, THERE ARE FREQUENTLY SHARP DIFFERENCES BETWEEN HYPOTHETICAL PERFORMANCE RESULTS AND THE ACTUAL RESULTS SUBSEQUENTLY REALIZED BY ANY PARTICULAR TRADING PROGRAM. ONE OF THE LIMITATIONS OF HYPOTHETICAL PERFORMANCE RESULTS IS THAT THEY ARE GENERALLY PREPARED WITH THE BENEFIT OF HINDSIGHT. IN ADDITION, HYPOTHETICAL TRADING DOES NOT INVOLVE FINANCIAL RISK, AND 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 TRADING LOSSES ARE MATERIAL POINTS THAT CAN ADVERSELY AFFECT ACTUAL TRADING RESULTS. THERE ARE NUMEROUS OTHER FACTORS RELATED TO THE 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 ACTUAL TRADING RESULTS. The hypothetical performance results contained herein represent the application of the quantitative models as currently in effect on the date first written above and there can be no assurance that the models will remain the same in the future or that an application of the current models in the future will produce similar results because the relevant market and economic conditions that prevailed during the hypothetical performance period will not necessarily recur. Discounting factors may be applied to reduce suspected anomalies. This backtest’s return, for this period, may vary depending on the date it is run. Hypothetical performance results are presented for illustrative purposes only. In addition, our transaction cost assumptions utilized in backtests, where noted, are based on AQR Capital Management, LLC’s, (“AQR”)’s historical realized transaction costs and market data. Certain of the assumptions have been made for modeling purposes and are unlikely to be realized. No representation or warranty is made as to the reasonableness of the assumptions made or that all assumptions used in achieving the returns have been stated or fully considered. Changes in the assumptions may have a material impact on the hypothetical returns presented. Actual advisory fees for products offering this strategy may vary. Gross performance results do not reflect the deduction of investment advisory fees, which would reduce an investor’s actual return. For example, assume that $1 million is invested in an account with the Firm, and this account achieves a 10% compounded annualized return, gross of fees, for five years. At the end of five years that account would grow to $1,610,510 before the deduction of management fees. Assuming management fees of 1.00% per year are deducted monthly from the account, the value of the account at the end of five years would be $1,532,886 and the annualized rate of return would be 8.92%. For a ten-year period, the ending dollar values before and after fees would be $2,593,742 and $2,349,739, respectively. AQR’s asset based fees may range up to 2.85% of assets under management, and are generally billed monthly or quarterly at the commencement of the calendar month or quarter during which AQR will perform the services to which the fees relate. Where applicable, performance fees are generally equal to 20% of net realized and unrealized profits each year, after restoration of any losses carried forward from prior years. In addition, AQR funds incur expenses (including start-up, legal, accounting, audit, administrative and regulatory expenses) and may have redemption or withdrawal charges up to 2% based on gross redemption or withdrawal proceeds. Please refer to AQR’s ADV Part 2A for more information on fees. Consultants supplied with gross results are to use this data in accordance with SEC, CFTC, NFA or the applicable jurisdiction’s guidelines. There is a risk of substantial loss associated with trading commodities, futures, options, derivatives and other financial instruments. Before trading, investors should carefully consider their financial position and risk tolerance to determine if the proposed trading style is appropriate. Investors should realize that when trading futures, commodities, options, derivatives and other financial instruments one could lose the full balance of their account. It is also possible to lose more than the initial deposit when trading derivatives or using leverage. All funds committed to such a trading strategy should be purely risk capital. The information set forth herein has been prepared and issued by AQR Capital Management (Europe) LLP, a U.K. limited liability partnership with its registered office at Charles House 5-11 Regent St. London, SW1Y 4LR, which is authorized by the U.K. Financial Conduct Authority (“FCA”) .This presentation is a financial promotion and has been approved by AQR Capital Management (Europe) LLP. AQR, a German limited liability company (Gesellschaft mit beschränkter Haftung; “GmbH”), is authorized by the German Federal Financial Supervisory Authority (Bundesanstalt für Finanzdienstleistungsaufsicht, „BaFin“) to provide the services of investment advice (Anlageberatung) and investment broking (Anlagevermittlung) pursuant to the German Banking Act (Kreditwesengesetz; “KWG”). The Complaint Handling Policy for German investors can be found here: https://ucits.aqr.com/. (c) Morningstar 2019. All rights reserved. Use of this content requires expert knowledge. It is to be used by specialist institutions only. The information contained herein: (1) is proprietary to Morningstar and/or its content providers; (2) may not be copied, adapted or distributed; and (3) is not warranted to be accurate, complete or timely. Neither Morningstar nor its content providers are responsible for any damages or losses arising from any use of this information, except where such damages or losses cannot be limited or excluded by law in your jurisdiction. Past financial performance is no guarantee of future results. Request ID : 273625

88 Index Definitions

Broad-based securities indices are unmanaged and are not subject to fees and expenses typically associated with managed accounts or investment funds. Investments cannot be made directly in an index. The S&P 500 Index is the Standard & Poor’s composite index of 500 stocks, a widely recognized, unmanaged index of common stock prices. The MSCI World Index is a free float-adjusted market capitalization weighted index that is designed to measure the equity market performance of developed markets. The Barclays Global Aggregate Index is a flagship measure of global investment grade debt from 23 different local currency markets. This multicurrency benchmark includes fixed-rate Treasury, government-related, corporate and securitized bonds from both developed and emerging markets issuers. The S&P GSCI is a composite index of commodity sector returns representing an unleveraged, long-only investment in commodity futures that is broadly diversified across the spectrum of commodities. The NCREIF Property Index (NPI) is a quarterly, unleveraged composite total return for private commercial real estate properties held for investment purposes only. All properties in the NPI have been acquired, at least in part, on behalf of tax-exempt institutional investors and held in a fiduciary environment. The FTSE Nareit All REITs Index is a market capitalization-weighted index that and includes all tax-qualified real estate investment trusts (REITs) that are listed on the New York Stock Exchange, the American Stock Exchange or the NASDAQ National Market List. The Russell 1000 Index is a stock market index that tracks the highest-ranking 1,000 stocks in the Russell 3000 Index (a benchmark of the U.S. stock market), which represent about 90% of the total market capitalization of that index. The Credit Suisse Hedge Fund Index is an asset-weighted hedge fund index and includes only funds, as opposed to separate accounts. The index uses the Credit Suisse Hedge Fund Database, which tracks approximately 9,000 funds and consists only of funds with a minimum of US$50 million under management, a 12-month track record, and audited financial statements. The index is calculated and rebalanced on a monthly basis, and reflects performance net of all hedge fund component performance fees and expenses. The HFRI Fund Weighted Composite Index is a global, equal-weighted index of over 1,500 single-manager funds that report to HFR Database. Constituent funds report monthly net of all fees performance in US Dollar and have a minimum of $50 Million under management or a twelve (12) month track record of active performance. The HFRI Fund Weighted Composite Index does not include Funds of Hedge Funds. HFRI Fund of Funds Composite Index: Fund of Funds invest with multiple managers through funds or managed accounts. The strategy designs a diversified portfolio of managers with the objective of significantly lowering the risk (volatility) of investing with an individual manager. The Fund of Funds manager has discretion in choosing which strategies to invest in for the portfolio. A manager may allocate funds to numerous managers within a single strategy, or with numerous managers in multiple strategies. The minimum investment in a Fund of Funds may be lower than an investment in an individual hedge fund or managed account. The investor has the advantage of diversification among managers and styles with significantly less capital than investing with separate managers. The MSCI ACWI is a market capitalization weighted index designed to provide a broad measure of equity-market performance throughout the world. The MSCI ACWI is maintained by Morgan Stanley Capital International (MSCI), and is comprised of stocks from both developed and emerging markets. The Cambridge Associates US Private Equity Index is a horizon calculation based on data compiled from 1,468 US private equity funds (buyout, growth equity, private equity energy and subordinated capital funds), including fully liquidated partnerships, formed between 1986 and 2017. The CBOE Put Protection Index is a benchmark index designed to track the performance of a hypothetical risk-management strategy that consists of a long position indexed to the S&P 500 Index (SPX Index) and a rolling long position in monthly 5% Out-of-the-Money (OTM) SPX Put options.

89 Historical Long Run Risk Premia Data Sources and Definitions

Data Sources Equities: GDP-weighted return of equity index futures of 11 developed countries Bonds: GDP-weighted return of 15 government bond indices of 8 developed countries scaled to a constant duration of 4 years. Commodities: equal weighted return of a basket of 29 commodities Credit Excess: excess return of US corporate bonds over duration-matched US treasury bonds Value: equal risk weight of a value strategy (as described in later slides) in equity indices, stocks, fixed income, commodities, and currencies Momentum: equal risk weight of a momentum strategy (as described in later slides) in equity indices, stocks, fixed income, commodities, and currencies Carry: equal risk weight of a carry strategy (as described in later slides) in equity indices, stocks, fixed income, commodities, and currencies Defensive: equal risk weight of a defensive strategy (as described in later slides) in equity indices, stocks, fixed income, commodities, and currencies Trend: equal weighted combination of 12-month time series momentum strategies for 67 markets across four major asset classes – 29 commodities, 11 equity indices, 15 bond markets, and 12 currency pairs. Descriptions of Style Premia in each asset class U.S. Stocks: Value: Book-to-Price Ratio; Momentum: Past 12 Month Return, Excluding Last Month; Defensive: Beta Equity Indices: Value: Cyclically-Adjusted Earnings-to-Price Ratio; Momentum: Past 12 Month Return, Excluding Last Month; Carry: Dividend Yield; Defensive: Beta Fixed Income: Value: Real Bond Yield; Momentum: Past 12 Month Return, Excluding Last Month; Carry: Term Premium; Defensive: Beta Commodities: Value: 5 Year Reversal; Momentum: Past 12 Month Return, Excluding Last Month; Carry: Futures Curve Rolldown; Defensive: Beta Asset class data descriptions for Style Premia U.S. Stocks: Individual stock-level data from the CRSP database from July 1926 for Value, July 1927 for Momentum, and July 1931 for Defensive strategies. Equity Indices: Returns on equity indices from 23 equity markets international which include all countries in the MSCI World Index as of 10/31/2016. Since most countries have multiple equity indices, we use the index that is investable, has the most coverage of the total stock market of that country, and has the longest history. We source monthly total returns from Global Financial Data and futures returns from Bloomberg and Datastream. Fixed Income: Nominal yield and total returns data of 10-year local currency government bonds as well as 3-month interest rates for 13 countries covering North America, Northern Europe, Japan, and Australia/New Zealand, sourced from Global Financial Data, Bloomberg, and Datastream. Commodities: Monthly futures prices of 40 commodities starting in 1877, sourced from the Annual Report of the Trade and Commerce of the Chicago Board of Trade, Commodity Systems Inc., and Bloomberg. For base metals and platinum, rolled return series from the S&P, Goldman Sachs, and Bloomberg are used.

Source: AQR, Global Financial Data, Bloomberg, Datastream, Chicago Board of Trade, Commodity Systems Inc. See Hurst-Ooi-Pedersen (2014) for global equities and government bonds (GDP-weighted composites of country indices), commodities (equal-weighted across and within sectors) and trend- following, Asvanunt-Richardson (2015) for U.S. credit (in excess of matched Treasuries), and Moskowitz, Katz, Thapar, and Wang (2017) for market- neutral style premia. The full sample period starts in 1/1920 and ends in 12/2016 (all assets become available in 1920s except for currencies in 1974). Value, Carry, Momentum, and Defensive all begin in March 1926, Credit excess begins in January 1926. All alternative risk premia reflect a backtest of 90 theoretical long/short style components based on AQR definitions applied in several asset group contexts. The results shown do not include advisory fees or transaction costs but are in excess of cash (US treasury bills). Additional Details on Macro Sensitivity Analysis Constructing macro indicators

Each of our macro indicators combines two series, which are first normalized to Z-scores: that is, we subtract a historical mean from each observation and divide by a historical volatility. We use rolling 10-year windows for means and volatilities when normalizing the last three macro indicators. However, for growth and inflation indicators we use in-sample 1972-2013 means and volatilities because we do not have long histories of economist forecasts needed to construct the surprise series below. This choice does not seem to change any major results. When we classify our quarterly 12-month periods into, say, “growth up” and “growth down” periods, we compare actual observations to the median so as to have an equal number of up and down observations (because we are not trying to create an investable strategy where data should be available for investors in real time, we use the full sample median). The underlying series for our Growth Indicator are the Chicago Fed National Activity Index (CFNAI) and the “surprise” in industrial production growth over the past year. Since there is no uniquely correct way to capture any risk factor, averaging may make the results more robust and signals humility. CFNAI takes this averaging idea to extremes as it combines 85 regular indicators of U.S. economic activity. The other series — the difference between actual annual growth in industrial production and the consensus economist forecast a year earlier — is narrower but more directly captures the surprise effect in economic developments. The Inflation Indicator is also an average of two normalized series. One series measures the de-trended level of inflation (CPIYOY minus its mean, divided by volatility), while the other measures the surprise element in realized inflation (CPIYOY minus consensus economist forecast a year earlier).

91 Interest Rate Sensitivity Analysis Data sources and methodology 1

Expected Inflation Data 1972-1978: Statistical estimate of long-term inflation expectations by Kozicki-Tinsley (2006). Their "survey-based estimates of the term structure of expected U.S. inflation" goes beyond an exponentially weighted average of past inflation rates because it also uses information in consensus forecasts of next year inflation. 1978-1989: Average of two to three available surveys: Hoey (private collection later passed to the Fed), Livingston, Survey of Professional Investors, Blue Chip Economic Indicators, and Consensus Economics (all conduct surveys at different times). Since 1990: Consensus Economics (average of 1-10y forecasts). US Equity Data Prior to 1926, the U.S. Equity series is constructed by adding price-weighted capital appreciation returns of NYSE stocks collected by Goetzmann, Ibbotson, and Peng to U.S. equity dividend returns recorded by the Cowles commission. The series consists of returns of the S&P 90 from 1926 to 1957 and returns of the S&P 500 from 1957 onwards. Fixed Income Data Nominal yield and total returns data of 10-year local currency government bonds as well as 3-month interest rates for 13 countries covering North America, Northern Europe, Japan, and Australia/New Zealand, sourced from Global Financial Data, Bloomberg, and Datastream.

Investment Returns Investment Proxy Source Global Equities MSCI World Index USD Bloomberg US Equities S&P500 Index Bloomberg Global Bonds GDP-weighted portfolio of G6 10-year government bonds (hedged to USD) Global Financial Data US Bonds 10-year U.S. Treasury Global Financial Data B/E Inflation Long 10-year U.S. TIPS, short 10-year U.S. Treasury Global Financial Data Barclays U.S. IG Credit Excess Return Index (Barclays U.S. IG Corporate Bond Index minus duration-matched US IG Credit Excess Barclays Treasuries) Commodities From 1981, Bloomberg Commodity Index. Before 1981, equal weighted portfolio of available commodity futures Bloomberg, Global Financial Data US Real Estate Average of FTSE EPRA/NAREIT US Index and NCREIF Index Bloomberg US TIPS From 1997, U.S 10-year TIPS. Before 1997, synthetic returns Bloomberg, inflation as above Global 60/40 60% Global Equities, 40% Global Bonds as defined above As above Allocates equal volatility to 3 asset classes: developed equities (GDP-weighted), government bonds (GDP-weighted) Simple Risk Parity AQR and commodities (equal-weighted). Allocations are based on rolling 12-month volatility.

92 Interest Rate Sensitivity Analysis Data sources and methodology 2

Investment Returns (continued) Investment Proxy Source Long-short factors use the methodology of Fama and French (1993) but include only a large-cap U.S. stock universe. Each factor is cap- weighted long the 1/3 best stocks and short the 1/3 worst stocks, and rebalanced annually every January. Value is based on book-to-price, using most recent price as described in Asness and Frazzini (2013). Momentum is based on 12-month price momentum excluding the most Single Equity Styles AQR recent month. Low risk is a beta-neutral factor that is cap-weighted long the 1/3 lowest-beta and short the 1/3 highest-beta stocks, with the long side levered to make the portfolio ex-ante beta-neutral as described in Frazzini and Pedersen (2014). Profitability is based on gross profits-to- assets.

4 Equity Styles 1/3 Value, 1/3 Momentum, 1/6 Low Risk and 1/6 Profitability as described above AQR

These are hypothetical long/short strategies as described in Ilmanen, Maloney and Ross (2014). Hypothetical returns are gross of transaction costs and fees, but are discounted by 50% for the period January 1972 to August 2012 (i.e., before AQR launched its Style Premia strategy), and by 25% for the period September 2012 to December 2016. The four market-neutral multi-asset style premia (Value, Momentum, Carry and Defensive) are hypothetical long/short strategies applied in stock selection, industry allocation, country allocation in equity, fixed income and currency markets, and commodities. Each style allocates 50/50 risk weights to stock and industry selection (SS) and asset allocation (AA) strategies. For AA we use the following risk weights: 33% equity country allocation, 25% fixed income, 25% currencies, 17% commodities. We combine several data sources to produce a sufficiently long dataset: • Since 1990, we use style premia strategies as described in “Investing With Style” (2012). For SS value, momentum and carry we use 50/50 risk weights between stock selection within industries and across industries. For SS carry we use the dividend yield strategy returns in Ken French’s data library.

Multi-Asset Styles • For 1972-1989, we source value and momentum style returns from “Value and Momentum Everywhere” (Journal of Finance, 2013), AQR defensive style returns from “Betting Against Beta” (Journal of Financial Economics, 2013), and SS carry from the dividend yield strategy returns in Ken French’s data library. We construct the AA carry style premia before 1990 as well as some early histories of AA value, momentum and defensive styles using AQR in-house backtests. These backtests are similar to those described above, but over a narrower universe. While the SS style premia proxies we use since 1990 are beta-neutral, the value and momentum premia before 1990, and the SS carry premium throughout, are ‘only’ dollar-neutral and may contain moderate empirical beta exposures. The defensive style premia are beta-neutral throughout. The multi-asset trend strategy applies 12-month trend-following strategies in four asset classes: equities, fixed income, currencies and commodities. From 1985, we use “Time Series Momentum” (Journal of Financial Economics, 2012). For 1972-1984, we use in-house backtests based on the same asset classes, but including 1-, 3- and 12-month momentum, and starting with a smaller asset universe that grows during the period as more assets become available. 5 Multi-Asset Styles Equal-dollar-weighted composite of the five multi-asset styles. AQR

93 Performance Disclosures

Factor Descriptions Value: Value strategies favor investments that appear cheap over those that appear expensive based on fundamental measures related to credit spreads, seeking to capture the tendency for relatively cheap assets to outperform relatively expensive assets. Momentum: Momentum strategies favor investments that either have performed well recently or have related securities that have performed well recently. It seeks to capture the tendency that an asset’s and related securities recent relative performance predict the performance of the asset in the near future. Carry: Carry strategies favor high-yielding assets, seeking to capture the tendency of high-yielding assets to outperform lower-yielding assets. Defensive: Defensive strategies favor investments with strong drivers of credit valuation. It seeks to capture the tendency for assets with strong fundamentals to generate higher risk-adjusted returns than assets with weak fundamentals. Style Premia Standalone Factor Backtest Description AQR backtests of Value, Momentum, Carry and Defensive theoretical long/short style components are based on monthly returns, undiscounted, gross of fees and transaction costs, excess of a cash rate proxied by the ICE BofAML U.S. 3 Mo. T-bill, and scaled to 12% annualized volatility. Each strategy is designed to take long positions in the assets with the strongest style attributes and short positions in the assets with the weakest style attributes, while seeking to ensure the portfolio is market-neutral. The Style Premia Strategy portfolio is based on the target asset group allocations included herein, roughly equally risk weighting styles within the asset group, resulting in a style allocation of approximately 34% to Value, 34% to Momentum, 18% to Defensive and 14% to Carry. The AQR backtest of the Style Premia Strategy is based on monthly returns, excess of a cash rate proxied by the ICE BofAML U.S. 3 Mo. T-bill and heavily discounted to reflect uncertainty in historical costs and opportunities; targeting 12% annualized volatility. The Style and Asset Group Composites, are based on an allocation to the style components and asset group components based on their liquidity and breadth. The components are then allocated with roughly equal weighting to each of the styles within an asset group (as not all four styles are present in each asset group). Please see below for a description of the Universe selection. Stock and Industry Selection: approximately 2,000 stocks across Europe, Japan, and U.S. Country Equity Indices: Developed Markets: Australia, Canada, Eurozone, Hong Kong, Japan, Sweden, Switzerland, U.K., U.S. Within Europe: Italy, France, Germany, Netherlands, Spain. Emerging Markets: Brazil, China, India, Israel, Malaysia, Mexico, Poland, Singapore, South Africa, South Korea, Taiwan, Thailand, Turkey. Bond Futures: Australia, Canada, Germany, Japan, U.K., U.S. Yield Curve: Australia Germany, United States. Interest Rate Futures: Australia, Canada, Europe (Euribor), U.K. and U.S. (Eurodollar). Currencies: Developed Markets: Australia, Canada, Euro, Japan, New Zealand, Norway, Sweden, Switzerland, U.K., U.S. Emerging Markets: Brazil, Hungary, India, Israel, Mexico, Poland, Singapore, South Africa, South Korea, Taiwan, Turkey. Commodity Selection: Silver, copper, gold, crude, Brent oil, natural gas, corn, soybeans. Hypothetical AQR U.S. Valuation Theme Backtest Description The AQR U.S. Valuation Theme Backtest utilizes the full set of underlying factors that compose the Valuation theme within AQR’s Global Stock Selection strategy to evaluate stocks and create a long-short, market- neutral and industry-neutral equity portfolio based exclusively on these signals. The Valuation Theme is designed to capture the tendency for relatively cheap assets to outperform relatively expensive ones. Backtest returns are gross of advisory fees and transaction costs from January 1, 1984 – December 31, 2018. The backtest utilizes a monthly rebalancing schedule and targets 7% annual volatility. The investment universe includes a broad subset of liquid tradeable large and mid cap stocks within the U.S. The risk model used is the Barra U.S. Equity Risk Model (USE3L).

Hypothetical AQR U.S. Fundamental and Price Momentum Signals Backtest Description The AQR U.S. Fundamental and Price Momentum Theme Backtest represent the full-set of fundamental and price momentum factors that within the Momentum theme of AQR’s Global Stock Selection strategy to evaluate stocks and create a long-short, market-neutral and industry-neutral equity portfolio based exclusively on these signals. The Momentum Theme is designed to favor stocks with improving prices and fundamentals. Backtest returns are gross of advisory fees and transaction costs from January 1, 2018 – March 31, 2019. The backtest utilizes a monthly rebalancing schedule and targets 7% annual volatility. The investment universe includes a broad subset of liquid tradeable large and mid cap stocks within the U.S. The risk model used is the Barra U.S. Equity Risk Model (USE3L).

94 Performance Disclosures

Hypothetical AQR U.S. Large Cap, Europe, Japan, Emerging Markets Valuation Theme Backtest Descriptions The AQR Valuation Theme Backtests utilize the full set of underlying factors that compose the Valuation theme within AQR’s Global Stock Selection strategy to evaluate stocks and create a long-short, market-neutral and industry-neutral equity portfolio based exclusively on these signals within each of the identified regions. The Valuation Theme is designed to capture the tendency for relatively cheap assets to outperform relatively expensive ones. Backtest returns are gross of advisory fees and transaction costs from February 1, 1984 – December 31, 2018 (when data is available by region). The backtests utilize a monthly rebalancing schedule and target 7% annual volatility. The investment universes include a broad subset of liquid tradeable large cap stocks within the various regions, except for U.S. Small Cap which exclusively includes small caps. The risk models used are the Barra U.S. Equity Risk Model (USE3L), Barra Developed Equity Risk Model (BIMDEV_noCURR_301L), Barra Japan Equity Risk Model (JPE3L), and Barra Global Equity Risk Model (GEM2L_noCurr).

Hypothetical U.S. Long/Short Value (AQR HML Devil) Backtest Description The Hypothetical AQR U.S. HML Devil is a book-to-price strategy as described in Asness and Frazzini (2013). The indicator uses lagged book value divided by contemporaneous price data. In the specification used for this analysis, we also control for industry exposure and use only large capitalization stocks. Large capitalization is defined as companies with market capitalization above the median market capitalization of stocks traded on the NYSE. The backtest utilizes a monthly rebalancing schedule from January 1, 1972 – June 30, 2018. Hypothetical AQR U.S. B/P Backtest Description The AQR U.S. Book to price (“B/”P”) industry selection backtest utilizes this single factor to rank industries relative to one another and create a long/short portfolio as a function of this ranking. This same methodology is then applied to rank stocks relative to one another within their respective industries to create a long/short stock selection portfolio. Backtest returns are gross of advisory fees and transaction costs from September 1, 2012 – March 31, 2019. The backtest utilizes a monthly rebalancing schedule and targets 7% annual volatility. The investment universe includes a broad subset of liquid tradeable large and mid cap stocks (~1100 stocks) within the U.S., similar to the Russell 1000 Index. The risk model used is the Barra U.S. Equity Risk Model (USE3L).

Hypothetical AQR U.S. Investor Sentiment Theme Backtest Description The AQR U.S. Investor Sentiment Theme Backtest utilizes the full set of underlying factors that compose the Investor Sentiment theme within AQR’s Global Stock Selection strategy to evaluate stocks and create a long-short, market-neutral and industry-neutral equity portfolio based exclusively on these signals. The Investor Sentiment Theme is designed to evaluate the actions of informed investors to infer investment opportunities or threats. Backtest returns are gross of advisory fees and transaction costs from February 1, 1995 – September 30, 2018. The backtest utilizes a monthly rebalancing schedule and targets 7% annual volatility. The investment universe includes a broad subset of liquid tradeable large and mid cap stocks within the U.S. The risk model used is the Barra U.S. Equity Risk Model (USE3L).

Hypothetical Monte Carlo Simulation of Value and Momentum Factors The Hypothetical Monte Carlo Simulation of Value and Momentum Factors is a simulation to produce 500 iterations of 100 hypothetical years of performance for two factors that have a -0.5 correlation, 0.3 Sharpe ratio, and are scaled to 10% annual volatility. The time period is intended to roughly match that of Fama-French’s Data library (dating back to 1926).

95 Performance Disclosures AQR Capital Management, LLC Firm-wide Disclosures

This presentation cannot be used in a general solicitation or general advertising to offer or sell interest in its Funds. As such, this information cannot be included in any advertisement, article, notice or other communication published in any newspaper, magazine, or similar media or broadcast over television or radio; and cannot be used in any seminar or meeting whose attendees have been invited by any general solicitation or general advertising. Firm Information: AQR Capital Management, LLC (“AQR”) is a Connecticut based investment advisor registered with the Securities and Exchange Commission under the Investment Advisors Act of 1940. AQR conducts trading and investment activities involving a broad range of instruments, including, but not limited to, individual equity and debt securities, currencies, futures, commodities, fixed income products and other derivative securities. For purposes of firm-wide compliance and firm-wide total assets, AQR defines the “Firm” as entities controlled by or under common control with AQR (including voting right). The Firm is comprised of AQR and its advisory affiliates, including CNH Partners, LLC (“CNH”). Upon request, AQR will make available a complete list and description of all Firm composites, as well as additional information regarding the policies for valuing portfolios, calculating performance, and preparing compliant presentations. GIPS Compliance: AQR claims compliance with the Global Investment Performance Standards (GIPS®) and has prepared and presented this report in compliance with the GIPS standards. AQR has been independently verified for the period August 1, 1998 through December 31, 2017. The verification reports are available upon request. Verification assesses whether (1) the Firm has complied with all composite construction requirements of the GIPS standards on a firm-wide basis and (2) the Firm’s policies and procedures are designed to calculate and present performance in compliance with the GIPS standards. Verification does not ensure the accuracy of any specific composite presentation. Composite Characteristics: New accounts that fit a composite definition are added at the start of the first full calendar month after the assets come under management, or after it is deemed that the investment decisions made by the investment advisor fully reflect the intended investment strategy of the portfolio. A composite will exclude terminated accounts after the last full calendar month performance measurement period that the assets were under management. The composite will continue to include the performance results for all periods prior to termination. For periods beginning July 1, 2010 through February 28, 2015, AQR defined a significant cash flow as an external cash flow within a portfolio of 50%. Additional information is available upon request. Calculation Methodology: All portfolios are valued daily, weekly, intra-monthly or monthly as defined by Firm policy. The Modified Dietz calculation methodology is used when calculating monthly and intra-month returns. Mutual funds and UCITS are valued daily and performance is calculated on a daily basis. Gross of fees returns are calculated gross of management and performance fees, administrative and custodial costs, and net of transaction costs beginning January 1, 2010. Prior to January 1, 2010, gross of fees returns are gross of management and performance fees, and net of administrative, custodial, and transaction costs. Additional information regarding fees and the calculation of gross and net performance is available upon request. The dispersion measure is the equal-weighted standard deviation of accounts in a composite for the entire year. Dispersion is not considered meaningful for periods shorter than one year or for periods during which a composite contains five or fewer accounts for the full period. The three-year annualized ex-post standard deviation measure is inapplicable when 36 monthly returns are not available. Returns are calculated net of all withholding taxes on foreign dividends. Accruals for fixed income and equity securities are included in calculations. AQR’s management or advisory fees are described in Part 2A of its Form ADV. In addition, AQR funds may have a redemption charge up to 2.00% based on gross redemption proceeds that may be charged upon early withdrawals. Consultants supplied with gross results are to use this data in accordance with SEC, CFTC and NFA guidelines. Other Disclosures: AQR may engage in leveraged, derivative, and short positions in order to meet its performance objectives. The use of these positions may have a material impact on performance results. Additionally, there may be subjective unobservable inputs used in the valuation of certain financial instruments utilized by certain AQR managed investment vehicles. The risks inherent to the strategies employed by accounts included are set forth in the applicable offering documents and other information provided to potential subscribers, from where more detailed information regarding the extent to which leverage, derivatives, and short positions can be obtained. These are available upon request, if not provided along with this presentation itself. Past performance is not an indication of future performance.

96 Performance Disclosures AQR Capital Management, LLC Style Premia Composite 9/1/2012 ‒ 12/31/2017

Year Gross Return Net Return 1 Net Return 2 Benchmark * Number of Composite Benchmark * Composite Total Firm % % % Return % Portfolios 3-Yr StDev % 3-Yr StDev % Assets ($M) Assets ($M) 2012 -1.20 -1.69 -1.44 0.05 1 N/A N/A 6.76 71,122.42 2013 29.84 27.95 26.15 0.07 1 N/A N/A 1,041.75 98,302.69 2014 13.36 11.69 11.27 0.03 1 N/A N/A 3,038.15 122,655.99 2015 8.37 6.77 6.81 0.05 1 9.93 0.02 6,626.91 142,173.39 2016 0.00 -1.49 -0.74 0.33 1 8.06 0.05 11,540.40 175,089.36 2017 14.88 13.19 12.70 0.86 1 7.66 0.11 14,520.44 223,432.52

*Bank of America ML US 3-Month Treasury Bill Index Net Return 1 calculated based on 1.50% management fee per annum Net Return 2 calculated based on 0.75% management fee and 10.00% performance fee per annum

AQR claims compliance with the Global Investment Performance Standards (GIPS®) and has prepared and presented this report in compliance with the GIPS standards. AQR has been independently verified for the period August 1, 1998 through December 31, 2017. Verification assesses whether (1) the firm has complied with all the composite construction requirements of the GIPS standards on a firm-wide basis and (2) the firm’s policies and procedures are designed to calculate and present performance in compliance with the GIPS standards. The Style Premia Composite has been examined for the periods from its inception through December 31, 2017. The verification and performance examination reports are available upon request. Composite Description: The Style Premia Composite (The “Composite”) was created September 2012. The Composite's strategy seeks to deliver efficient exposure to a well-diversified portfolio of long- short style strategies across six asset group contexts including Stock and Industry Selection, Equity Indices, Bonds, Interest Rates, Currencies, and Commodities. AQR pursues these goals by investing in instruments not limited to stocks, futures, swaps, and currency forwards. The Composite's strategy targets the highest ex-ante volatility relative to all of the Firm's Style Premia Composites. The Composite is denominated in USD. Benchmark: The Composite benchmark is the Bank of America ML US 3-Month Treasury Bill Index (the “Benchmark”). The index measures the rate of return an investor would realize when purchasing a single U.S. 3-month treasury bill, holding it for one month, selling it, and rolling it into a newly selected issue at the beginning of the next month. The investments in the Composite vary substantially from those in the Benchmark. The index has not been selected to represent an appropriate benchmark to compare an investor’s performance, but rather is disclosed to allow for comparison of the investor’s performance to that of a certain well-known and widely recognized index. Benchmark returns are not covered by the report of independent verifiers. Fees: Composite net of fees returns are calculated by deducting the maximum model management or advisory fee AQR could charge from the composite monthly gross returns. AQR’s asset-based fees for portfolios within the Composite may range up to 1.50% of assets under management and are generally billed monthly or quarterly at the commencement of the calendar month or quarter during which AQR will perform the services to which the fees relate. Composite assets may have been exposed to the impact of performance fees. Past performance is not an indication of future performance.