Opportunities Factor Investing: Challenges and

” Factor Investing: Challenges and Opportunities ^”

Antti Ilmanen, AQR

Swedish House of Finance Conference on Financial Markets and Corporate Decisions

August 19-20, 2019

1 2019-08-19

For Institutional Investor Use Only

Factor Investing:

Challenges and Opportunities

Presented by Antti Ilmanen

August 2019

Principal, Global Head of Portfolio Solutions, AQR

Prepared exclusively for Swedish House

of Finance conference in Stockholm

Disclosures

2 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The information contained herein is only as current as of the date indicated, and may be superseded by subsequent market events or for other reasons.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.

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

Challenges of the Current

Market Environment

5

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 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.

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

The Challenge: A World of Low Expected Returns Prospective real returns are low for all long-only assets

-0.1%

3.3%

1.9%

-4%

0%

4%

8%

12%

16%

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

U.S. Equity Real Yield U.S. 10Y Treasury Real Yield

0%

2%

4%

6%

8%

10%

12%

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

U.S. 60/40 Real Yield 5% Real Return

6

2. Add Illiquid / Private Assets

Illiquidity; contain much equity exposure; also historically rich;

overrated illiquidity premia

‘Endowment Model’ Beliefs:

High returns historically;

perceived illiquidity premium

1. More Equities Concentration: already dominant

risk to many; not cheap Belief in Equity Premium:

Highest conviction potential long-term return source

3. Add Factor Tilts and Alternative Risk Premia

Leverage and other tools are required to meet return targets Multi-Factor Beliefs:

Evidence on multiple rewarded factors, potential diversification benefits

Possible Solution Motivation

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

What Can Investors Do?

Three broad paths to address the low expected return challenge

Challenges

What Then Are the Well-Rewarded Factors?

Almost a century of evidence

Hypothetical Gross Sharpe Ratios

January 1920 – June 2018

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. 7

Asset Class Premia Alternative Risk Premia

0.54

0.43

0.27

0.50

0.62 0.67

0.83

0.77

1.14

0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40

Global Equities

Global Bonds

Commodities Credit Excess

Value Momentum Carry Defensive Trend

our focus today

(Re-)Introduction to Factors

For Investor Professional Use Only

Factors, Styles and ARP The semantic debates

9

Factors are investment strategies that go long stocks (or other assets) with pre-defined, rule-based characteristics and short stocks with

opposite characteristics

Many historically rewarded factors have been identified in research, giving rise to a pejorative term ‘factor zoo’

• We believe only a handful of factors that combine pervasive empirical evidence with economic intuition deserve strategic allocations in investor portfolios

Source: AQR. For illustrative purposes only. Past performance is not a guarantee of future performance. Please refer to the Appendix for further information.

Alternative risk premia

‘Smart beta’ ≈ long-only factor

investing in stock selection

Style premia investing, or

‘Smart beta’ refers to factor-tilted long-only equity strategies

• Investors are moving toward multi-factor strategies

Factors are also known as styles, and can be accessed in long-only and long/short (L/S) strategies. Style premia strategies can be applied to other asset classes too. L/S factors or styles are part of a broader group of strategies called alternative risk premia (ARP).

Well-known L/S factor premia or ARP differ from manager-specific alpha, though the boundary is fuzzy.

• In academic jargon, ARP may be one-factor alpha but not multi-factor alpha

• Factors may also be useful for demystifying active managers, hedge funds, and even PE

Birth of a Factor: Value

An investment philosophy becomes a key early factor

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. 10

Value effect first published in Rosenberg, Reid, and Lanstein (1985) and Fama and French (1992) using the book-to-market (BE/ME) indicator

“The value of any investment is, and always must be, a function of the price you pay for it.”

Benjamin Graham,

The Intelligent Investor (1949)

Old School

New

School

Birth of a Factor: Momentum

A centuries-old maxim describes another important factor

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. 11

Jegadeesh and Titman (1993) Asness (1995)

“Cut short your losses, let your profits run on.”

David Ricardo (1772-1823) (reputed)

Old School

New

School

Introduction to Major Style Premia Focusing on four intuitive* styles

* Even for fundamental investors, the main systematic factors make intuitive sense. A fundamental analyst often looks for a cheap company with a catalyst for improvement, subject to quality filters. This is not so different from a systematic stock selection strategy based on value, momentum, and defensive styles. A bigger difference is systematic portfolio’s breadth.

Source: AQR. Past performance is not a guarantee of future performance. Please read important disclosures in the Appendix. 12

Momentum An asset’s recent relative performance tends to continue in the near future

Value Cheaper assets tend to outperform more expensive ones

Carry Higher-yielding assets tend to provide higher returns than lower-yielding assets

Defensive Lower-risk and higher-quality assets tend to generate higher risk-adjusted returns

Characteristics of Style Premia:

• Persistent

Long-term evidence supported by economic intuition

• Pervasive

Exist broadly across regions and asset groups

• Liquid

Can be captured by trading liquid instruments

• Dynamic

Limited static exposure to any

asset or market

Source: AQR. Past performance is not a guarantee of future performance. Please read important disclosures in the Appendix. 13

Momentum

• Initial underreaction

• Subsequent overreaction

• Disposition effect

Value ^• Over-extrapolated growth prospects

• Compensation for greater default risk

Carry

• Capital supply/demand imbalance

• Central bank actions

• Risk compensation

Defensive

• Leverage aversion

• Investors overpaying for “lottery”

characteristics

Description Possible explanations

An asset’s recent relative performance tends to continue in the near future

Cheaper assets tend to outperform more expensive ones

Higher-yielding assets tend to provide higher returns than lower-yielding assets

Lower-risk and higher-quality assets tend to generate higher risk-adjusted returns

Style Premia May Have Risk-Based and Behavioral Origins

Some candidate explanations (much debated by academics)

Long-Run Empirical

Evidence

Single-Style Long-Only Premia (“Smart Beta”)

Academic evidence of hypothetical excess returns among U.S. stocks

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.

Momentum (excess of market): Price Momentum 1951-2017

Defensive (excess of risk-free): Low Beta 1951-2017

Value (excess of market): Book-to-Market 1951-2017

Defensive (excess of market): Gross Profits-to-Assets 1951-2017

15

-6%

-4%

-2%

0%

2%

4%

6%

Losers Winners

-6%

-4%

-2%

0%

2%

4%

6%

Expensive Cheap

Value Quintiles

0.00 0.20 0.40 0.60 0.80

-15%

0%

15%

30%

45%

Highest Mid Lowest

Sharpe Ratio

Annualized Return Annualized Risk -6%

-4%

-2%

0%

2%

4%

6%

Unprofitable Profitable

Commodities

Growth

(Expensive)

How to Access Factors or Style Premia?

Style investing exists along a spectrum

Long/short may capture styles more efficiently - and give better diversification

Source: AQR. For illustrative purposes only. Diversification does not eliminate the risk of experiencing investment losses. 16

Increasing Efficiency/Diversification

Value

(Cheap)

Long S hort

Value

(Cheap)

Value

(Cheap)

Value

(Cheap)

Ch e a p E x pe ns iv e

Value

Out - pe rforme rs Un de r- pe rforme rs

Momentum

High Y ie lding Low Y ie lding

Carry

High Ris k

Defensive

Low Ris k

Go Long/Short Go Multi-Style

• More active, less constrained exposure to alternative premia

• Uncorrelated to traditional markets

• More diversified than single premia tilt

Go Multi-Asset

• Even more diversified

• Higher expected risk-adjusted returns

• Even greater improvement from implementation choices

• Seeks to improve portfolio by adding more favorable characteristics

• Returns largely driven by market beta

Market Add

Style Tilt

Stocks &

Industries Equity

Indices Fixed Currencies Income

Hypothetical Gross Sharpe Ratios of Long/Short Style Components Across Asset Groups

January 1990 – December 2017

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Sharpe Ratio

Value Momentum Carry Defensive

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

Fixed Income

Stocks & Industries EQ Indices Currencies Commodities

Evidence Across Many Asset Groups and Styles Single long/short style-asset portfolios and composites

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 Mo. T-bill. 17

Stocks &

Industries

Equity Indices Fixed Income Currencies Commodities Style Composite Asset Group Composite

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

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

Hypothetical Correlations Between Long/Short Style-Asset Portfolios

January 1990 – December 2017

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. 18

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

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

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

} See Ilmanen-Israel-Moskowitz-Thapar-Wang (2019): Factor Premia and Factor Timing: A Century of Evidence.

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. 19

Value Momentum Carry Defensive Multistyle

0.3

0.5

0.2 0.3 0.3

0.6 0.6

0.5

0.7

0.4

0.1

0.5

0.2

0.7

0.3

0.7

0.5 0.4

0.8 0.7

1.1

0.5

0.1

0.8 1.2

1.5

0.6

0.5

0.8

0.6

1.6

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8

U.S. Stocks International Stocks Equity Indices Fixed Income Commodities Currencies Multi-Asset

Is Performance Also Consistent Across Time?

Multi-asset styles performed well in every decade

Sharpe Ratios of Multi-Asset Styles by Decade

*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. 20

Value Momentum Carry Defensive Multistyle

-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5

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

Is Performance Consistent Across Macro Environments?

L/S factors have much less macro sensitivity than market risk premia

Long-Only Market Risk Premia 1972 – 2018

Hypothetical Long/Short Style Premia 1972 – 2018

Hypothetical Simple Portfolios 1972 – 2018

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. 21

For Investor Professional Use Only

All

Growth Up + Inflation Up Growth Up + Inflation Down Growth Down + Inflation Up Growth Down + Inflation Down

0.29 0.39 0.31

0.60

-0.03

0.83 1.10

0.73

-0.09

-0.21 -0.07

0.51 0.25

1.11

-0.18 -0.5

0.0 0.5 1.0 1.5 2.0

Global Equities Global Bonds Commodities

Sharpe Ratio

0.63

1.07

0.77 1.01

0.82 0.47

1.38

0.78

1.24 0.63 0.84

1.20

0.61

1.29 1.09

0.50

0.94 0.88

0.39 0.55

1.13

0.88 0.82

1.18 1.32

-0.5 0.0 0.5 1.0 1.5 2.0

Value Momentum Carry Defensive Trend

Sharpe Ratio

0.34

1.77

0.58

1.80 1.30

1.79

-0.23

1.49

0.39

1.98

-0.5 0.0 0.5 1.0 1.5 2.0

Global 60/40 Simple Style 5

Sharpe Ratio

Defining Fed Hiking Cycles January 1972 – June 2018

Hypothetical Gross Sharpe Ratios in Hiking and Non-Hiking Periods January 1972 – June 2018

-0.5 0.0 0.5 1.0 1.5

Global Equities

Global Bonds

Real Estate

Commod- ities

Equity Value

Equity Momentum

Equity Low Risk

Multi-Asset Value

Multi-Asset Momentum

Multi-Asset Carry

Multi-Asset Defensive

Multi-Asset Trend

Global 60/40

4 Equity Styles

5 Multi-Asset Styles

Sharpe Ratio

Not Hiking Hiking

Likewise, Little Apparent Interest Rate Sensitivity

Long/short styles exhibit limited sensitivity to U.S. hiking cycles

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. 22 Multi-Asset Styles

Equity Styles 0%

5%

10%

15%

20%

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

Hiking Cycle Indicator Fed Funds Rate

Current hiking cycle triggered in March 2017

Asset Classes Simple Portfolios

What Are Realistic Expectations for the Future?

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

Skepticism is partly warranted

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. 23

New Challenges – and Lure of New Opportunities Why worry? Why not do even better?

24

The growing popularity of factor investing has made skeptics worry about overcrowding (with well-known factors) or about overfitting (with less well-known strategies)

Another concern is that the world has changed. These worries intensify when factors experience an extended period of underperformance. Such periods are inevitable for all factors, including the equity premium. It is harder to be patient with less conventional factors.

The latest challenge to factor investing come from the poor performance of value-based stock selection in 2010s, especially in 2018-9

• This pattern seems to reflect the market liking disruptive growth companies, echoing 1999

• Crowding clearly is not the root cause, as the losses are led by underperforming Value-based stock selection and not by the 2010s star style Defensive stock selection (next slide)

Related, many investors hope that tactical factor timing can help improve the performance of strategically diversified factor tilts. We have studied extensively factor timing based on valuations, factor momentum, and macro signals – and find their help limited.

• I will discuss the timing evidence further during the panel

• I may also highlight results from our new “century” paper and from “Who is on the other side?”

-4 -3 -2 -1 0 1 2 3 4

1990 1994 1998 2002 2006 2010 2014 2018

All Value Momentum Defensive

-4 -3 -2 -1 0 1 2 3 4

1990 1994 1998 2002 2006 2010 2014 2018

All Value Momentum Carry Defensive

Hypothetical Stock Selection Style Premia Value Spreads

January 1990 – May 2019

Hypothetical Asset Allocation Style Premia Value Spreads

January 1990 – May 2019

Cheap

Expensive

Style Premia Valuations May Be Useful Crowding Indicators But so far no red light: more oscillations than a broad downtrend

Source: AQR. Value spreads are calculated for long/short style portfolios using several different valuation measures as described in AQRWhitepaper “Are Defensive Stocks Expensive? A Closer Look at Value Spreads”, Chandra, Ilmanen and Nielsen (2015). Please read important disclosures in the Appendix for a description of the investment universe and the methodology. Chart is for illustrative purposes only and not based on an actual portfolio AQR manages. Hypothetical data has inherent limitations some of which are disclosed

in the Appendix. 25

Cheap

Expensive

Another Use of Factors (if time…)

- Demystifying Active Managers, Great

and Small

Using Factors to Demystify Active Managers

Evolving return attribution => Better understanding and fairer fees

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. 27

Alpha

Alpha Alternative Risk Premia

Market Risk Premia Alpha

Market Risk Premia

Prior to Cap-Weighted Equity Indices

− Returns thought of as “Alpha”

Market Risk Premia Introduced

Examples

− S&P 500 Index

− MSCI World

− Bloomberg Barclays Aggregate

Alternative Risk Premia Introduced

Examples

− Classic factors / styles (style premia)

− Classic hedge fund strategies (hedge fund risk premia)

Time

C ap aci ty C o n str ai n ed C o st

We Can Use Regressions to Demystify Superstars…

Berkshire Hathaway 1977-2016 record reflects quality even more than value

28

*Warren Buffett, Berkshire Hathaway Inc., Annual Report, 2008.

Sources: AQR, 4Q2016 Alternative Thinking, CRSP, HRF, Morningstar, Barclays. Date ranges for each investor: Berkshire Hathaway Jan 1977 – May 2016. Regression-based Return Attribution are the product of the regression coefficients and average premium for each factor. Regression alpha not statistically significant . Factor returns are all gross of fees and transactions costs. For consistency, U.S. Equities throughout this presentation are the CRSP capitalization-weighted equity market factor from Kenneth French’s website. See appendix for details on factor construction. Past performance is not a guarantee of future performance; please read important disclosures at the end of this presentation. Please refer to the Appendix for image sources. For illustrative purposes only.

“Whether we’re talking about socks or stocks, I like buying quality merchandise when it is marked down.”

– Warren Buffett*

We find exposure to systematic:

• quality

• low beta + leverage

• value

Regression-based Return Attribution

6.8%

1.2%

2.6%

3.4%

3.6%

-5%

0%

5%

10%

15%

20%

25%

Berkshire Hathaway PIMCO Total Return Fund Quantum Magellan

Regression Alpha Momentum - Stocks Size Trend - Asset Classes

Currencies - Value Currencies - Momentum+ Short Volatility Credit

Quality Low-Risk Value Market

Note: Short-volatility also explains some of Berkshires’ returns, but options data is available only since 1987

Hedge Funds’ Typical Long Run Factor Exposures

Hedge fund index returns since 1994 are regressed on various market risk premia (MRP) and

alternative risk premia (ARP)

The 4.3% average excess return of the HF combo is attributed to: 1.9% MRP, 1.5% ARP, 0.9% alpha Very significant equity market loadings and

significant loadings on many l/s factors – especially procyclic ones

Negative loading on defensive stock selection, incidentally the most successful factor of 2010s

HF industry returns reflect market and alternative risk premia

HF Index Composite 14-Factor Regression

January 1994 – December 2017

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. 29

RSQ = 77%

Return Contribution

Intercept 0.001 1.3 0.9%

Equities 0.276

5.5% 1.5%

Equities Lag 0.030

5.6% 0.2%

Bonds 0.088 1.6 2.7% 0.2%

Commodities 0.019

0.6% 0.0%

Val-SS

6.8%

Mom-SS 0.059

4.6% 0.3%

Def-SS

8.6%

Val-AA 0.003 0.2 2.6% 0.0%

Mom-AA 0.045

4.2% 0.2%

Car-AA 0.017 1.0 9.1% 0.2%

Def-AA 0.027 1.6 3.9% 0.1%

VRP 0.052

7.1% 0.4%

Trend 0.092

8.9% 0.8%

Small Cap 0.194

0.9%

Alpha

1.9%

MRP

1.5%

ARP

Appendix

Performance Disclosures

31 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/.

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Index Definitions

32 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.

Historical Long Run Risk Premia

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.

Data Sources and Definitions

33 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 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).

Historical Long Run Risk Premia

Dates of pre-, original, and post-sample periods

Source: AQR. 34

Asset Class Style Pre-Sample Period Original Sample Period Post-Sample Period Asset Class Style Original Sample Period Post-Sample Period

Multi-Asset Value Mar-1926 - Dec-1963 Jan-1963 - Dec-1990 Jan-1991 - Jun-2018 International Stocks Value Jul-1984 - Dec-1990 Jan-1991 - Mar-2018 Multi-Asset Momentum Feb-1926 - Dec-1964 Jan-1964 - Dec-1989 Jan-1990 - Jun-2018 International Stocks Momentum Jan-1985 - Dec-1989 Jan-1990 - Mar-2018 Multi-Asset Carry Feb-1926 - Dec-1973 Jan-1973 - Dec-1981 Jan-1982 - Jun-2018 International Stocks Defensive Feb-1987 - Dec-2009 Jan-2010 - Mar-2018 Multi-Asset Defensive Feb-1926 - Dec-1960 Jan-1960 - Dec-2009 Jan-2010 - Jun-2018 International Stocks Multistyle Jul-1984 - Dec-1990 Jan-1991 - Mar-2018 Multi-Asset Multistyle Feb-1926 - Dec-1960 Jan-1960 - Dec-1990 Jan-1991 - Jun-2018 Currencies Value Jan-1974 - Dec-1990 Jan-1991 - Jun-2018 U.S. Stocks Value Jul-1927 - Dec-1963 Jan-1963 - Dec-1990 Jan-1991 - Mar-2018 Currencies Momentum Jan-1974 - Dec-1989 Jan-1990 - Jun-2018 U.S. Stocks Momentum Feb-1928 - Dec-1964 Jan-1964 - Dec-1989 Jan-1990 - Mar-2018 Currencies Carry Jan-1974 - Dec-1981 Jan-1982 - Jun-2018 U.S. Stocks Defensive Mar-1928 - Dec-1960 Jan-1960 - Dec-2009 Jan-2010 - Mar-2018 Currencies Multistyle Jan-1974 - Dec-1990 Jan-1991 - Jun-2018 U.S. Stocks Multistyle Jul-1927 - Dec-1960 Jan-1960 - Dec-1990 Jan-1991 - Mar-2018

Fixed Income Value Mar-1923 - Dec-1963 Jan-1963 - Dec-1990 Jan-1991 - Jun-2018 Fixed Income Momentum Feb-1923 - Dec-1964 Jan-1964 - Dec-1989 Jan-1990 - Jun-2018 Fixed Income Carry Feb-1923 - Dec-1973 Jan-1973 - Dec-1981 Jan-1982 - Jun-2018 Fixed Income Defensive Feb-1923 - Dec-1960 Jan-1960 - Dec-2009 Jan-2010 - Jun-2018 Fixed Income Multistyle Feb-1923 - Dec-1960 Jan-1960 - Dec-1990 Jan-1991 - Jun-2018 Commodities Value Jan-1920 - Dec-1963 Jan-1963 - Dec-1990 Jan-1991 - Jun-2018 Commodities Momentum Jan-1920 - Dec-1964 Jan-1964 - Dec-1989 Jan-1990 - Jun-2018 Commodities Carry Jan-1920 - Dec-1973 Jan-1973 - Dec-1981 Jan-1982 - Jun-2018 Commodities Multistyle Jan-1920 - Dec-1960 Jan-1960 - Dec-1990 Jan-1991 - Jun-2018 Equity Indices Value Feb-1925 - Dec-1963 Jan-1963 - Dec-1990 Jan-1991 - Jun-2018 Equity Indices Momentum Feb-1923 - Dec-1964 Jan-1964 - Dec-1989 Jan-1990 - Jun-2018 Equity Indices Carry Feb-1923 - Dec-1973 Jan-1973 - Dec-1981 Jan-1982 - Jun-2018 Equity Indices Defensive Feb-1923 - Dec-1960 Jan-1960 - Dec-2009 Jan-2010 - Jun-2018 Equity Indices Multistyle Feb-1923 - Dec-1960 Jan-1960 - Dec-1990 Jan-1991 - Jun-2018

Additional Details on Macro Sensitivity Analysis

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). The three other macro indicators combine the level and change aspects of real yield, volatility and liquidity conditions. (This is a design decision; someone else could have chosen indicators based on changes only.) For example, both high and rising real yields can imply adverse conditions for many investors. We study real long-term bond yields (subtracting a survey-based forecast of long-term inflation from the 10-year Treasury yield) and real short yields (subtracting a survey-based forecast of next-year inflation from the three-month Treasury bill rate). We normalize both their levels and one-year changes, and then average these to give us a composite Real Yield Indicator. Likewise, we estimate the volatility of the S&P500 and 10-year Treasuries using a one-year window. We normalize both the level of volatility and its change from a year ago, and average these to give a composite Volatility Indicator. Finally, we proxy market illiquidity using the “TED” spread of funding liquidity and Amihud’s “ILLIQ” price impact measure in equity markets. We normalize both the levels and one-year changes, and average these to give a composite Illiquidity Indicator. We use median forecasts from the Survey of Professional Forecasters data published by the Philadelphia Fed. While data surprises a priori have a zero mean, this series has exhibited a downward trend in recent decades, reflecting the (partly unexpected) relative decline of the U.S. manufacturing sector. The TED spread is the yield difference between Eurodollar and Treasury bill rates (we use the 3-month maturity). This spread tends to widen when market concerns on banking sector credit risk rise or funding liquidity conditions deteriorate. The ILLIQ measure of a stock’s market impact costs, developed by Amihud (2002) and often used in empirical studies, is the ratio of absolute return over volume. Intuitively, the price change induced by a given dollar volume is higher for less liquid stocks. The aggregate measure widens when overall market liquidity worsens.

Constructing macro indicators

35

Interest Rate Sensitivity Analysis

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

Data sources and methodology 1

36

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

US IG Credit Excess Barclays U.S. IG Credit Excess Return Index (Barclays U.S. IG Corporate Bond Index minus duration-matched

Treasuries) Barclays

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

Simple Risk Parity Allocates equal volatility to 3 asset classes: developed equities (GDP-weighted), government bonds (GDP-weighted)

and commodities (equal-weighted). Allocations are based on rolling 12-month volatility. AQR