Alice in Factorland - Research Affiliates - 1 - Rob Arnott... · Trend Chasing and Contrarian Strategies Average Alpha (Ann.) Sharpe Ratio 1.5% 1.2% 2.0% 0.34 0.25 0.33 Equally Weighted

Alice in Factorland

Rob ArnottFounder and CEO

Research Affiliates, LLC

2

Our Adventure in Factorland

» Factor timing is difficult but possible

» Relative valuation of the strategy or the factor (i.e., relative to its own

history) is a powerful predictor of future return

» Most investors already practice a form of market “timing”—

unfortunately in the wrong direction by chasing past performance

which can erode the benefits of factor investing

» They fund the success of contrarian investors

» Emphasizing factors or strategies that are trading cheap relative to

their own historical norms and deemphasizing the more expensive

factors or strategies can improve performance

3

Continue Our Journey in Factorland

» Do mutual funds capture their factor returns?

» Simulated factor returns are believed to describe investor opportunities

» Instead, we observe the incredible shrinking factor returns in live assets

» Smart beta is becoming synonymous with factor investing

» In our view, this is not correct: “Smart beta” originally covered strategies

that break the link between price and portfolio weight

» Factor tilts don’t do this! Factor tilts are not true “smart beta”

» Manager evaluation: factor tilts can help predict fund returns

» Past performance is worse than useless

» Funds with factor tilts that are trading cheap will tend to outperform

» Troubles with momentum

» Harvesting momentum premia appears to be “mission impossible”

» Can we save “momentum”? Maybe.

4

PART I.

Our Factor Timing

Research

5

Trend Chasing Everywhere – Survivorship Bias

Harvey, Liu, Zhu (2015); Beck, Hsu, Kalesnik, Kostka (2016).

Fama, French (2002); Arnott, Bernstein (2002); Campbell, Shiller (1988); Cochrane (2008).

Practitioners look for best historical performance.

Academics look for best historical performance.

Asset Owners look for best historical performance.

» Problem: Not all factors are robust.

» Selection bias and data mining are mistaken for persistent alpha1

» Rising valuations are mistaken for persistent alpha2

6

Alpha Decomposition

» Alpha due to change in relative valuation

» is mean reverting and averaging roughly zero in the long run

» contributes significantly to strategy performance in the “short run”

» “Short run” can mean decades!

» Alpha adjusted for change in relative valuation is a good measure of

unconditional expected return of a strategy

Portfolio

Alpha≈ +

Return Due to Change

in Relative Valuation

“Revaluation Alpha” “Structural Alpha”

Valuation-

Adjusted Alpha

7

0.13

0.25

0.50

1.00

0.50

1.00

2.00

4.00

1968 1976 1984 1992 2000 2008 2016

Relative

Valuation,*

Value

vs.

Growth

Relative

Performance,

Value

vs.

Growth

Value vs. Growth, United States (July 1968–December 2016)

Value Performance Relative Valuation

A

B

C

A

Nifty Fifty

Biotech BubbleGlobal Financial

CrisisC

D

E

D

E

B

Tech Bubble

Valuation Cycle for Value Factor

*Based on a blend of four valuation metrics: Price/Book, Price/5yrSales, Price/5yrEarnings, Price/5yrDividends.

Source: Research Affiliates, LLC, using data from CRSP and Compustat.

8

Factor Valuations Are Predictive of Future Returns:

Example: The Value Factor

Source: Research Affiliates, LLC, using data from CRSP, Compustat, Worldscope, and Datastream.

-15%

0%

15%

30%

0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45

Subsequent

Five-Year

Return

Relative Valuation (Aggregate)

Value vs. Growth (July 1968–December 2016)

US Developed EM Median Valuation

9

Factor Valuations Are Predictive of Future Returns:

Source: Research Affiliates, LLC, using data from CRSP and Compustat. As of December 2016.

Two-Tail statistical significance: * = 10% threshold; ** = 5% threshold; *** = 1% threshold.

-15%

-10%

-5%

0%

5%

10%

15%

1 1.5 2 2.5 3 3.5

Su

bse

qu

en

t 5-Y

r R

etu

rn

Relative Valuation (aggregate)

Gross Profitability Factor

Correlation: -0.41

t-stat: -2.06**

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

0.2 0.7 1.2

Su

bse

qu

en

t 5-Y

r R

etu

rn


Size Factor

Correlation: -0.78

t-stat: -7.53***

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

0 2 4 6

Su

bse

qu

en

t 5-Y

r R

etu

rn


Momentum Factor

Long-Term Forecast Near-Term Forecast

Correlation: -0.27

t-stat: -1.79*

-20%

-10%

0%

10%

20%

0 1 2 3

Su

bse

qu

en

t 5-Y

r R

etu

rn


Low Beta Factor

Correlation: -0.11

t-stat: -0.79

10

0.0625

0.125

0.25

0.5

1

2

4

8

Rela

tive V

alu

ati

on

of

Fact

ors

Gross

ProfitabilityMomentum

Low

BetaIlliquidity

Small

CapInvestments

Value

(B/P)

Value

(Blend)

U.S.

(1967 – Mar 2016)

Aggregate

Dev ex U.S.

(1983 – Mar 2016)

Aggregate

Emerging Markets

(1996 – Mar 2016)

Aggregate

Legend

Factor is Expensive

Current Valuation

Median Valuation

Factor is Cheap

What We Saw in June 2016

Source: Research Affiliates, LLC, using data from CRSP and Compustat, 1967–Mar 2016. The chart was originally published in

“To Win With “Smart Beta” Ask if the Price is Right,” June 2016, Arnott, Beck, and Kalesnik.

11

What Happened Afterwards in 2016

Source: Research Affiliates, LLC, using Bloomberg data.

Value Won, Quality & Momentum Lost, and Low-Vol Cratered Everywhere, Jul–Dec 2016

Region Index

Performance Relative to the Benchmark Absolute Index Performance

Jul-Dec

2016

Prior

3 Years

Prior

5 Years

Jul-Dec

2016

Prior

3 Years

Prior

5 Years

Un

ited

Sta

tes

S&P 500 7.8% 39.7% 76.8%

FTSE RAFI US 1000 3.9% -5.3% -2.8% 11.7% 34.4% 74.0%

Russell 1000 Value 2.6% -7.0% -5.6% 10.4% 32.6% 71.2%

S&P 500 Low Volatility -9.6% 9.4% 21.6% -1.7% 49.1% 98.4%

MSCI USA Quality -0.8% 0.8% 1.6% 7.0% 40.5% 78.5%

S&P 500 Momentum -4.2% -3.0% -7.9% 3.6% 36.6% 69.0%

Develo

ped

Mark

et MSCI World 6.8% 22.3% 37.8%

FTSE RAFI Developed 4.1% -5.3% -8.4% 10.9% 17.0% 29.5%

MSCI World Value 3.9% -6.5% -7.2% 10.7% 15.9% 30.7%

S&P Developed Low Volatility -8.7% 9.2% 18.1% -1.9% 31.5% 56.0%

MSCI World Quality -3.3% 10.8% 20.4% 3.5% 33.2% 58.3%

S&P Momentum Developed -5.7% -0.5% 8.5% 1.1% 21.9% 46.3%

Em

erg

ing

Mark

et

MSCI Emerging Markets 4.5% -4.6% -17.5%

FTSE RAFI Emerging 10.6% -3.1% -7.7% 15.1% -7.7% -25.3%

MSCI Emerging Markets Value 2.5% -4.9% -7.2% 7.0% -9.6% -24.8%

S&P Emerging Markets Low Volatility -7.0% -6.2% 10.7% -2.5% -10.8% -6.8%

MSCI Emerging Markets Quality -4.1% 7.5% 11.5% 0.4% 2.9% -6.0%

S&P Emerging Markets Momentum -3.7% 12.7% 26.2% 0.8% 8.1% 8.7%

12

Most Academics Are Trend Chasers!

Source: Research Affiliates, LLC, using CRSP/Compustat and Worldscope/Datastream data.

Return Degradation Before and After Factor PublicationUnited States (Jan 1967–Aug 2016)

Annualized Results Value

(Blend)

Value

(B/P)Momentum Size Illiquidity

Low

BetaProfitability Investment Average

Year Published 1977 1977 1993 1981 2002 1975 2013 2004

Before Publication 9.8% 9.1% 5.4% 7.0% 2.5% 7.4% 1.2% 3.5% 5.8%

After Publication 2.3% 1.4% 3.7% 0.8% 5.0% 2.1% 5.0% -1.0% 2.4%

Difference -7.5% -7.8% -1.8% -6.2% 2.5% -5.4% 3.8% -4.5% -3.3%

» After-Publication Alpha is Not Large!

» 2.4% is for long-short portfolio … 1.2% per side

» That’s before trading costs, implementation shortfall, and fees

» Residual alpha for end customers could easily be zero!

13

Most Product Providers Are Trend Chasers!


Return Degradation Before and After Smart Beta Index LaunchUnited States (Jan 1967–Aug 2016)

Annualized Results Fundamental

Index

Equal

Weight

Low-Vol

Index

FTSE RAFI

Low Vol

Quality

Index

Dividend

Index

Risk

EfficientMaximum

DiversificationAverage

Year Launched Nov-05 Jan-03 Feb-11 Apr-13 Dec-12 Nov-03 Jan-10 Nov-11

Before Launch 2.0% 1.3% 1.2% 2.2% 0.4% 2.9% 2.7% 1.6% 1.8%

After Launch 0.5% 2.3% 2.1% 0.1% 0.1% 1.3% 0.9% 4.1% 1.4%

Difference -1.5% 1.0% 0.9% -2.1% -0.4% -1.6% -1.9% 2.5% -0.4%

» Here, at least, there’s some hope …

» 1.4% after launch is not bad, not far below prior simulated results

» Again, this is before trading costs, implementation shortfall, and fees

» But, many of these have low turnover, and most have delivered live

results ahead of benchmark since launch, net of all fees and costs

14

6.87%

5.22%

8.05% 8.23%

6.76%

8.81%8.38%

9.36%9.78%

8.66%

S&P 500Index

8.97%

All Funds Growth Funds Value Funds Small-Cap Funds Large-Cap Funds

Dollar-Weighted Return Buy-&-Hold Return S&P 500 Index

Most Investors Are Trend Chasers!

Source: Hsu, Myers, and Whitby, “Timing Poorly: A Guide to Generating Poor Returns While Investing in Successful Strategies,”

Journal of Portfolio Management (Winter 2016).

15

2.4%

1.2%

6.1%

0.52

0.14

0.66

Equally

Weighted Factor

Allocation

Three Best

Performing Factors

(1,3,5,10 yr

Performance)

Three Cheapest

Factors

FactorsTrend Chasing and Contrarian Strategies

Average Alpha (Ann.) Sharpe Ratio

1.5%1.2%

2.0%

0.34

0.25

0.33

Equally Weighted

Smart Beta

Allocation

Three Best

Performing Smart

Beta Strategies

(1,3,5,10 yr

Performance)

Three Cheapest

Smart Beta

Strategies

Smart Beta StrategiesTrend Chasing and Contrarian Strategies

Value Add (Ann.) Information Ratio

Trend Chasing Is Costly


Performance Characteristics of Trend Chasing and Contrarian Allocations, United States(Jan 1977–Aug 2016)

16

1977 1986 1995 2004 2013

Presence of Smart Betas

Dividend Index

Fundamental

Index

FTSE RAFI

Low Vol

Low-Vol Index

Risk Efficient

Equal Weight

Maximum

Diversification

Quality Index

1977 1986 1995 2004 2013

Presence of Factors

Portfolio of Least Expensive Portfolio of Most Expensive

Value (Agg)

Value (B/M)

Investments

Low Beta

Size

Illiquidity

Momentum

Profitability

Note: Cheapest and most expensive valuations are computed as a blended average of Five-Year Average Earnings-to-Price, Five-Year

Average Sales-to-Price, Five-Year Average Dividends-to-Price, and most recent Book-to-Price ratios. For each factor/valuation metric

we compare their current valuation relative to long run average valuation.

Source: Research Affiliates, LLC, using CRSP/Compustat and Worldscope/Datastream data .

Allocation of Strategies and Factors Used in the Most and Least Expensive Series, Relative to Own History, United States (Jan 1977–Aug 2016)

Timing with Valuations Is Not Always a Value Tilt

17

Research Affiliates Smart Beta Interactive Site


US EM

0.125

0.25

0.5

1

2

4

Rela

tive

Valu

ati

on

Relative Valuation,

Select Smart Beta Strategies

(as of 12/31/2016)

Value

Income

Low Volatility

Quality

MomentumSmall Cap

US, RAFI Fundamental Index

EM, RAFI Fundamental Index

-4%

0%

4%

8%

12%

0% 5% 10% 15% 20% 25%

Exp

ect

ed

5-Y

ear

Retu

rns

(An

n.)

Volatility

Real Long-Term Expected Return,

Net of Transaction Costs, US Smart

Beta Strategies (as of 12/31/2016)

18



US EM

0.125

0.25

0.5

1

2

4

Rela

tive

Valu

ati

on

Relative Valuation, Select Factors(as of 12/31/2016)

Profitability

Momentum

Low Beta

IlliquiditySize

Investment

US Value (Aggregate)

US Value (P/B)

EM Value (Aggregate)EM Value (P/B)

-4%

0%

4%

8%

12%

0% 5% 10% 15% 20% 25%

Exp

ect

ed

5-Y

ear

Retu

rns

(An

n.)

Volatility

Real Long-Term Expected Return, US Factors (as of 12/31/2016)

19

Profitability

Momentum

Low Beta

Illiquidity

Size

Investment

Value (Aggregate)

Value (P/B)

-4%

0%

4%

8%

12%

0% 5% 10% 15% 20% 25%

Exp

ect

ed

5-Y

ear

Retu

rns

(An

n.)

Volatility

Real Long-Term Expected Return, Developed Factors (as of 12/31/2016)

0.125

0.25

0.5

1

2

4

Rela

tive

Valu

ati

on

Relative Valuation, Select Factors(as of 12/31/2016)


Developed

Source: Research Affiliates, LLC, using Worldscope/Datastream data.

20

ValueIncome

Low Volatility

Quality Momentum

Small Cap

RAFI Fundamental Index

-4%

0%

4%

8%

0% 5% 10% 15% 20% 25%

Exp

ect

ed

5-Y

ear

Retu

rns

(An

n.)

Volatility

Real Long-Term Expected Return,

Net of Transaction Costs,

Developed Smart Beta Strategies

(as of 12/31/2016)

0.125

0.25

0.5

1

2

4

Rela

tive

Valu

ati

on

Relative Valuation,

Select Smart Beta Strategies

(as of 12/31/2016)


Developed

Source: Research Affiliates, LLC, using Worldscope/Datastream data.

21

PART II a.

The Incredible Shrinking

Factor Returns

22

Our Methodology

» Our database

» All funds from Morningstar Direct survivorship bias-free fund universe

» US open-ended long-only active equity funds, with at least two year

return history from January 1990 to December 2016

» Share-class inclusion: A-share, No-load-share, and Institutional-share

» We can “reverse-engineer” the factor returns earned by managers

» We first measure mutual fund’s average factor loadings over time by

regressing fund returns against conventional constructed factor returns

» We then run a cross-sectional regression, of fund returns on fund factor

loadings, to estimate monthly factor returns, as realized by live funds

» The slippage in factor returns

» While the conventional factor returns and the factor returns realized by

the managers show ~0.9 correlation, there is often a huge shortfall

23

-8%

-4%

0%

4%

8%

-8% -4% 0% 4% 8%

Valu

e F

act

or

Retu

rn C

ap

ture

d

by M

an

ag

ers

Observed Value (HML) Factor Return

Value

Correlation = 0.89

Slope = 0.95

-8%

-4%

0%

4%

8%

-8% -4% 0% 4% 8%

Siz

e F

act

or

Retu

rn C

ap

ture

d

by M

an

ag

ers

Observed Size (SMB) Factor Return

Size

Correlation = 0.96

Slope = 1.00

-12%

-4%

4%

12%

-12% -8% -4% 0% 4% 8% 12%

Mark

et

Fact

or

Retu

rn C

ap

ture

d

by M

an

ag

ers

Observed Market (Mkt - RFR) Factor Return

Market

Correlation = 0.92

Slope = 1.01

-16%

-8%

0%

8%

16%

-16% -8% 0% 8% 16%Mo

men

tum

Fact

or

Retu

rn C

ap

ture

d

by M

an

ag

ers

Observed Momentum (UMD) Factor Return

Momentum

Correlation = 0.90

Slope = 0.98

Long−Short Factor Returns vs. Realized Factor

Returns Captured by Managers

Source: Research Affiliates, LLC, using data from CRSP, Compustat, Morningstar Direct, and the website of Kenneth French.

Jannuary 1991–December 2016. Monthly Correlations are shown in all four graphs.

24

$111

$247

$50

$100

$200

$400

1990 1996 2001 2006 2011 2016

Gro

wth

of

$100 (

log

sca

le)

Cumulative Returns

Return Captured by Manager

Theoretical L/S Factor Return

$15

$30

$60

$120

1990 1996 2001 2006 2011 2016

Gro

wth

of

$100 (

log

sca

le)

Cumulative Difference, Realized Versus Theoretical

Shortfall (per ann):- 5.2%

t-stat = -3.43


Jannuary 1991–December 2016. Note: Cumulative returns are compounded using arithmetic returns.

Factor Slippage Momentum Factor

25

$205

$314

$50

$100

$200

$400

1990 1996 2001 2006 2011 2016

Gro

wth

of

$100 (

log

sca

le)

Cumulative Returns



$25

$50

$100

$200

1990 1996 2001 2006 2011 2016

Gro

wth

of

$100 (

log

sca

le)


Shortfall (per ann): -4.2%

t-stat = -3.54

Factor Slippage Market FactorMarket-Risk Free



26

$157

$194

$16

$32

$64

$128

$256

1990 1996 2001 2006 2011 2016

Gro

wth

of

$100 (

log

sca

le)

Cumulative Returns



$0

$25

$50

$75

$100

$125

1990 1996 2001 2006 2011 2016

Gro

wth

of

$100


Shortfall (per ann): -1.4%

t-stat = -1.38



Factor Slippage Value Factor

27

$185

$167

$50

$100

$200

1990 1996 2001 2006 2011 2016

Gro

wth

of

$100 (

log

sca

le)

Cumulative Returns



$75

$100

$125

1990 1996 2001 2006 2011 2016

Gro

wth

of

$100


Excess (per ann): 0.7%

t-stat = 1.13



Factor Slippage Size FactorSmall Cap–Large Cap

28

8.2%

2.6%

3.6%

5.7%

4.1%3.3%

2.2%

0.4%

Mkt (Mkt-Rf) Size Value Momentum

Annualized Factor Returns, US Equity Funds (Jan 1991–Dec 2016)

Theoretical L/S Portfolio Realized Return in Live Portfolio

Do Mutual Funds Capture Their Factor Returns? No!


» The returns realized by mutual fund managers can fall short due to:

» Transaction costs, fees, bid−ask spreads, and trades that “get away”

» Hard to replicate features of theoretical factor portfolios (usually

constructed ex post, bringing in data-mining and selection bias)

29Source: Research Affiliates, LLC, using data from Worldscope, Datastream, and Morningstar Direct.

Similar Slippage in International Equity Funds

6.3%

1.6%

4.9%

6.6%

1.6%2.3% 2.1%

-0.6%Mkt (Mkt-Rf) Size Value Momentum

Annualized Factor Returns, International Equity Funds (Jan 1991–Dec 2016)

Theoretical L/S Portfolio Realized Return in Live Portfolio

30

PART II b.

Why Factor Tilts Are Not

Smart “Smart Beta”

31

Factor Tilts = “Smart Beta”?

*These theoretical factors are Market, SMB, HML, MOM, and BAB.

» The definition of “smart beta” has been vastly extended

» Now, almost anything formulaic, other than a full-market cap-weighted

index, seems to qualify for the “smart beta” label

» Factor tilt and multi-factor strategies are sold as “smart beta” even

though most of these strategies begin with, and anchor on, cap weighting

» Let’s replicate a few “generation one” smart beta strategies with

factors:*

» Fundamental Index™

» Equally Weighted Index

» Minimum Volatility Index

» How well do the factor replicated “smart beta” strategies fare?

» … much is lost in translation.

32

Early Criticism of the Fundamental Index™

– It Is Repackaged Value

*** Significance at the 1% level, **Significance at the 5% level, * Significance at the 10% level.

Source: Research Affiliates, LLC, based on data from CRSP and Compustat.

Return Performance and Factor Loadings for Fundamental Index, Equal Weight, and Minimum Variance Strategies (January 1974−June 2016)

Panel A: Fama−French Three−Factor Model Plus Momentum

Alpha (Ann.) Market Value Size Momentum R-Sq.

Fundamental Index0.97% *** 0.98 *** 0.35 *** -0.08 *** -0.07 *** 97.92%

(2.74) (147.47) (33.46) (-8.07) (-10.23)

Equal Weight0.67% 1.03 *** 0.18 *** 0.24 *** -0.02 *** 97.23%

(1.48) (120.96) (13.78) (19.59) (-2.73)

Low Volatility US1.59% ** 0.83 *** 0.16 *** -0.16 *** 0.05 *** 87.38%

(2.1) (58.05) (6.97) (-7.61) (3.1)

Panel B: Fama−French Three−Factor Model Plus Momentum and BAB Factor

Alpha (Ann.) Market Value Size Momentum BAB R-Sq.

Fundamental Index0.67% * 0.98 *** 0.33 *** -0.08 *** -0.08 *** 0.04 *** 98.01%

(1.9) (150.06) (29.2) (-8.31) (-11.47) (4.83)

Equal Weight0.38% 1.02 *** 0.16 *** 0.24 *** -0.04 *** 0.04 *** 97.30%

(0.83) (121.87) (11.25) (19.77) (-3.8) (3.57)

Minimum Variance0.51% 0.82 *** 0.08 *** -0.16 *** 0.00 0.16 *** 88.94%

(0.71) (61.28) (3.31) (-8.24) (0.26) (8.45)

» What about those alphas?

33

Factor Replications Miss the Mark…

*These theoretical factors are Market, SMB, HML, MOM, and BAB.


Relative to Cap US 1000

Investment Allocation Returns Volatility Sharpe Ratio Value Add Tracking ErrorInformation

Ratio

Fundamental Index original 12.9% 15.3% 0.53 1.8% 4.3% 0.42

Full Long/Short Factor

Replicated12.0% 15.2% 0.47 0.9% 3.4% 0.28

Factor Replicated Long Only 12.0% 15.7% 0.46 0.9% 3.2% 0.29

Equal Weight US 1000 original 13.1% 16.9% 0.49 2.0% 4.8% 0.41


Replicated12.8% 17.4% 0.46 1.7% 5.3% 0.32


Minimum Variance original 12.4% 13.3% 0.57 1.3% 5.7% 0.23


Replicated12.2% 13.7% 0.54 1.1% 3.0% 0.38


Cap Weight US 1000 11.1% 15.4% 0.41

Return Performance of Various Strategies (January 1974−June 2016)

34

When It Comes to Implementation They Miss the

Mark Big Time!


Annual

Return

Ann.

Return

Net of

Costs

Long Leg

Turnover

Short Leg

Turnover

Trading Cost

(bps)

Capacity

($Bn)

Average

Long Leg

Leverage

Average

Short Leg

Leverage

Fundamental Index original 12.9% 12.9% 11% 1 615 100% 0%

Factor Replication 12.0% 11.7% 57% 15% 32 16 113% 13%

Factor Replication Long Only 12.0% 11.8% 50% 20 25 100% 0%

Equal Weight original 13.1% 13.0% 18% 4 116 100% 0%



Minimum Variance original 12.4% 12.2% 25% 19 26 100% 0%



Portfolio Trading Costs, Capacity, Turnover, and Leverage (January 1974–June 2016)

35

Why Factor Tilts Are Not True “Smart Beta”

» Fundamental Index, as one example, is not a repackaged value

» Smart beta strategies have factor tilts, but they are much more than this

» Smart beta strategies deliver alpha net of their Fama–French four- or

five−factor regressions; factor-replicated strategies do not

» Factor loadings show us some of the systematic drivers of return

» But, simple factor tilt strategies based on theoretical factors are not the

best way to capture return premiums

» Replication portfolios have lower performance, higher turnover, and

smaller capacity

» Construction details matter!

» Efficient implementation taking into account transaction costs can help

better capture the premia

36

PART II c.

Manager Evaluation

37

The Big Picture

* The first metric is drawn from the methodology of Arnott, Beck, Kalesnik (2016a, b, and c).

» Trend−chasing is tempting and damaging

» Investors tend to focus overwhelmingly on past performance in making

investment decisions, especially for fund selection

» But, past performance is worse than useless

» To win, ask if the price is right

» Valuation is a powerful tool in gauging future performance

» Fees and turnover can eat away take-home returns

» Implication for manager evaluation

» We find reversal, not persistence, in mutual fund manager performance

» We think there are reliable quantitative metrics that can help

» Fund style return expectation*

» Fees and turnover

38

Mutual Fund Data Sample

» Fund inclusion criteria

» We used Morningstar Direct survivorship bias-free fund universe

» US open-ended long-only active equity funds

» We included A-share, No-load-share, and Institutional-share

» For funds with multiple subclasses (share-classes), we select the

subclass with the oldest inception date

» Funds with at least two-year return history from January 1990 to

December 2016

» Our sample data

» A total of 3,331 funds, classified into 9 Morningstar categories:

» Large Blend/Growth/Value

» Mid-Cap Blend/Growth/Value

» Small Blend/Growth/Value

» Fund returns at monthly frequency and are net-of-fees

39

Does Past Skill Predict Future Skill?

*We looked at an array of return measurement which included: simple return, excess return relative to benchmark or peer group,

CAPM risk-adjusted return, and Fama−French alpha, net of factor returns.

» The belief that manager outperformance* is persistent is false

» Manager performance is mean-reverting. Among many possible predictors

for return,* one of the most “powerful and reliable” predictor is the simple

past return … with the wrong sign!

» Pooled correlation between past three-year simple return and subsequent

three-year simple return is -26%, with an adjusted t-stat of -6.75

» “Positive” reasons to hire a manager (i.e., good things, like past alpha) are

never even half as powerful

40

A Naïve Contrarian Strategy Can Work

Source: Research Affiliates, LLC, based on data from Morningstar Direct.

11.7%

11.0%

10.6%

10.3%10.2% 10.1%

9.9% 10.0%9.8%

9.7%

1 2 3 4 5 6 7 8 9 10

Subsequent

3-Year

Average

Annualized

Return

Decile Portfolios, Based On Prior 3-Year Returns

Average Mutual Fund Subsequent Three-Year Performance, Sorted by Prior Three-Year Returns, US Long-Only Equity Funds (Jan 1990–Dec 2016)

= Lowest

Decile= Top

Decile

Average 10.3%

This result is arguably created by our industry’s favorite decision rule: Three bad years and out!

41

Factor Tilts Can Help Predict Future

Relative Performance

* The factor return forecasts (valuation-ratio based model plus structural alpha) are done following the methodology introduced in

Arnott, Beck, Kalesnik, West (2016), and Arnott, Beck, Kalesnik (2016a and b).

» Managers with exposure to out-of-favor factors outperform

the market

» We can measure fund factor loadings

» We can estimate factor expected returns based on valuations*

» Therefore, we can predict fund returns

» Pooled correlation between fund style expectation (i.e., fund return

expectation based on manager factor tilt) and subsequent one-year

excess return relative to market is 29%, with an adjusted t-stat of 10.30

» This is also the strongest relationship we found

42

Fees and Turnover

» Managers with higher fees tend to underperform their peers

» In the median-run (future three-year) and long-run (future five-year

and above), 10 bps more expense delivers more than 10 bps lower

relative net-of-fee performance

» The relationship between turnover and future performance is

inconclusive

» Managers with higher turnover seem to underperform their peers

» But, high turnover seems to be associated with higher return relative

to market

43

PART II d.

Momentum:Crowded Space and the Problem

of Stale Momentum Crashes

44

Troubles with Momentum:Factor Premia Hard to Capture and Funds Underperform

Source: Research Affiliates, LLC, based on data from Morningstar Direct.

*Data sample includes all US equity mutual funds, including non-survivors, with "A" or No-load or institutional share-classes. The oldest

share class is kept for funds with multiple share classes. The reported performance stats are averaged within each month, and

compounded over the full span. ** Funds with "RAFI" or "RAE" or "Fundammental Index" in the name ***Funds (ETFs and Open-ended

Funds) labeled as "multi-factor" in their "Strategic Beta Attribute" by Morningstar Direct, with at least 12 month return history.

Average Annualized Relative Performance Across Funds (Jan 1991−Dec 2016*)

Keyword No. of Funds Average Value-add

Relative to Market

Average

FF4 Alpha

RAFI** Funds 9 2.0% 0.8%

Small 684 1.8% -1.0%

Multi-Factor*** 62 1.3% -1.0%

Value 732 0.8% -0.3%

Opportunity 50 0.6% -0.7%

Growth 966 0.1% -1.0%

Research 37 -0.1% -1.1%

Advantage 39 -0.1% -1.0%

Dynamic 22 -0.2% -2.1%

Fundamental 30 -0.3% 0.2%

Dividend 103 -0.4% 0.0%

Contrarian 12 -0.5% -0.2%

Volatility 34 -0.8% -2.0%

Large 454 -0.9% -1.1%

Momentum 10 -1.1% -2.5%

Income 142 -1.1% -0.4%

Quality 18 -1.7% -0.3%

45

Source: Research Affiliates, LLC, based on data from Morningstar.

*Data sample includes all US equity mutual funds, including non-survivors, with "A" or No-load or institutional share-classes. The oldest

share class is kept for funds with multiple share classes. Decile funds are sorted based on fund full-sample FF4 factor loading estimates.

Troubles with Momentum:Factor Premia Hard to Capture and Funds Underperform

Average Annualized Relative Performance by Style, Jan 1991−Dec 2016*

Panel A. Average Value-Add Relative to Market

Sorting Variable Top Decile Funds Bottom Decile FundsTop Decile minus

Bottom Decile

t-stats

(Top - Bottom)

Market Beta 1.3% -0.8% 2.0% 0.72

Size Beta 2.3% -1.0% 3.3% 1.21

Value Beta 1.4% 0.5% 0.9% 0.34

Momentum Beta 1.2% 0.1% 1.1% 0.53

Panel B. Average FF4 Alpha

Sorting Variable Top Decile Funds Bottom Decile FundsTop Decile minus

Bottom Decile

t-stats

(Top - Bottom)

Market Beta -1.9% 0.5% -2.4% -2.09

Size Beta -1.2% -0.4% -0.8% -0.96

Value Beta -0.2% -1.0% 0.8% 0.65

Momentum Beta -1.7% -0.4% -1.3% -1.43

46

-8%

-4%

0%

4%

8%

0 6 12 18 24 30 36

Cumulative

Returns

Months

Cumulative Returns Over Subsequent Months, United States(Jan 1963−Dec 2016)

Standard Momentum Stale Momentum

Does Momentum Trade Get More Crowded in

Bubble Stocks?

Source: Research Affiliates, LLC, based on data from Compustat and CRSP.

47

0.1

1.0

10.0

100.0

1000.0

1963 1973 1983 1993 2003 2013

Dollar

Growth

(Log)

Year

Dollar Growth of Standard and Stale Momentum, United States (Jan 1963 − Dec 2016)

Standard Momentum Stale Momentum

Stale Momentum – Poor Performance and Crashes

Source: Research Affiliates, LLC, based on data from Compustat and CRSP.

48

Saving Momentum

» Harvesting momentum premium seems to be mission impossible

» First, there may be no momentum effect left; it’s basically flat-lined since 1999

» And that’s for long/short paper portfolio investors, before fees!

» And before trading costs!

» Is momentum trade crowded?

» What’s the Achilles’ Heel for momentum? Well, there are two:

» How about shunning stale momentum?

» Once momentum has been running for two years or more, stocks are very

expensive, running out of gas.

» It’s dreadful!! Shun stale momentum.

» How about momentum conscious trading of other strategies?

» If a strategy wants to buy a stock in free fall, why not wait a month?

» If a strategy wants to sell a stock that’s soaring, why not wait a month?

» Trading costs? Zero-to-negative!

49

@ra_insights

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www.ResearchAffiliates.com

Thank You

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