factor investing with etfs

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© 2012 Morningstar, Inc. All rights reserved.

Factor Investing with ETFs

× Samuel Lee, ETF Strategist

2

Outline

× Model-based investing 101× Factor-based view of the world× Practical factor investing

× Presenter: Alex Bryan× Parting thoughts

× Shameless product pitch

3

Why go quant?

Human foibles

× Overconfidence× Most people think they’re above-average

drivers, lovers, socializers, workers, etc.× Privilege vivid, resonant ideas and thoughts over

statistical facts× Over-reliant on narrative thinking

4

Models usually beat intuition

× Paul Meehl, “Clinical vs. Statistical Prediction: A Theoretical Analysis and a Review of the Evidence”, 1954

× Found that in 20 studies, overall simple models beat trained experts

× William Grove, et. al., “Clinical Versus Mechanical Prediction: A Meta-Analysis”, 2000

× Of 136 studies, 46% favored models, 48% tied, 6% favored humans

× Philip Tetlock, “Expert Political Judgment”, 2006× 20-year forecasting study on 300 experts;

simple prediction models won.

5

Imitating the best

× Top investors are model-driven.× Warren Buffett: buy when price < intrinsic

value with margin of safety× Jeremy Grantham: buy when current

valuation < historical averages× Ray Dalio: Economy is a transaction-based

machine driven by short-term debt cycle, long-term debt cycle, productivity growth

× Jim Simons: ??? Black box models

6

7

On data-mining/data-snooping

Data-snooping

× Definition: Testing many different models to obtain a desired outcome.

8

The problem with science as practiced

× Many studies turn out to be false.× John P.A. Ioannidis, “Why Most Published

Research Findings are False”, 2005× Argues current study methodologies don’t

have enough statistical power and are biased to false positives.

× Pharma giant Bayer couldn’t replicate 2/3rds of 67 studies it tried to replicate (2011)

× Amgen couldn’t replicate more 90+% of 53 “landmark” papers in cancer research (2012)

9

Skewed incentives

× Publication bias: Statistically significant positive findings more likely to be published.

× Encourages data-snooping by researchers to get published.

10

Quantitative strategies: witchcraft?

× Even worse incentives to data-snoop.× Joel M. Dickson, et. al., “Joined at the Hip: ETF

and Index Development”, 2012× Back-tested equity indexes beat U.S.

market 12.25% annualized in back-tests, returned -0.26% annualized live.

11

Here’s a back-test from 1997-2006 showing 10% ann. alpha

12

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 20060%

50%

100%

150%

200%

250%

300%

350%

400%

DWA Technical Leaders Index S&P 500

Here’s subsequent live performance for the index

13

Apr-07Jun-07Aug-07Oct-07Dec-07Feb-08Apr-08Jun-08Aug-08Oct-08Dec-08Feb-09Apr-09Jun-09Aug-09Oct-09Dec-09Feb-10Apr-10Jun-10Aug-10Oct-10Dec-10Feb-11Apr-11Jun-11Aug-11Oct-11Dec-11Feb-12Apr-12Jun-12Aug-12Oct-12Dec-12Feb-13Apr-13Jun-13Aug-13

-60%

-40%

-20%

0%

20%

40%

60%

PowerShares DWA Technical Leaders S&P 500

Hallmarks of a good back-test

× From most to least important:× Strong economic intuition× Intellectually honest source. Credibility!× Simple and transparent methodology.× Large sample size that spans many

decades and many countries.× Economically and statistically significant

results.× Then find multiple independent researchers

coming to the same conclusion!

14

Academia has a decent record in producing models

× R. David McLean and Jeffrey Pontiff, "Does Academic Research Destroy Stock Return Predictability“, 2013.

× Independently replicated 82 characteristics purported to predict stock returns.

× Of 72 that could be replicated, returns after study on average decayed 35%--10% from statistical bias, 25% from arbitrage.

15

The big two, value and momentum

× Value – Tendency for stocks cheap by fundamental measures to outperform stocks expensive by such measures.

× Usually defined as having low price/book. Low price/earnings, price/cashflow, price/sales and the like also work.

× Momentum – Tendency for performance to persist.

× Relative momentum assets are ones with highest relative 12-month returns.

× Time-series momentum assets have positive 12-month returns.

16

How a value strategy usually works

× Define a universe of stocks.× Calculate a valuation ratio, most commonly

price/book, for each stock.× Sort stocks.× Select a basket of the “cheapest”.× Hold for one year and repeat.

17

18

Source: French Data Library

1926

1929

1932

1935

1938

1941

1944

1947

1950

1953

1956

1959

1962

1965

1968

1971

1974

1977

1980

1983

1986

1989

1992

1995

1998

2001

2004

2007

2010

0.1

1

10

100

1000

10000

100000

Hi 30 Med 40 Lo 30

Lo 30Med 40 Hi 30

Ann. Return 9.19%

10.20%

12.44%

Std. Dev.18.81

%20.24

%25.49

%Sharpe 0.30 0.33 0.35

High book/price (value) stock portfolios have outperformed

How a stock-based momentum strategy usually works

× Define a universe of stocks.× Calculate past return, usually 12 months,

excluding the last month.× Sort stocks.× Select a basket of the highest-return.× Hold for one month and repeat.

19

20

1927

1931

1935

1939

1943

1947

1951

1955

1959

1963

1967

1971

1975

1979

1983

1987

1991

1995

1999

2003

2007

2011

0.01

0.1

1

10

100

1000

10000

100000

High Med Low

Low Med HighAnn. Return 2.44% 8.35%

13.13%

Std. Dev.29.74

%20.10

%20.41

%Sharpe -0.04 0.24 0.47

High momentum stock portfolios have outperformed


Why believe these back-tests?

× Strong economic intuition.× Value effect: Investors overextrapolate

recent trends, leading to stocks overshooting to up or downside.

× Momentum effect: Investors underreact to new information, and herd into same stocks/positions.

× Identified by numerous credible and independent researchers.

× Simple and transparent methodology.× Found in almost every country studied (except for

Japan).× Statistically and economically significant.

21

22

The factor-model view of the world

What is a factor?

× A characteristic that explains an asset's returns.

× Risk factors:× Market/economic growth× Inflation× Duration× Illiquidity

× Behavioral “factors”:× Value?× Momentum

23

Factors : assets :: nutrients : foods

× In factor theory, an asset’s expected returns are derived wholly from its exposure to various risk factors.

× Assets are bundles of factors.× Risk factors represent unique and different kinds

of “bad times.”

24

Junk bonds stripped of interest-rate risk behave like stocks

25

Dec-99May

-00Oct-

00Mar-

01Au

g-01Jan

-02Jun

-02Nov

-02Ap

r-03Sep

-03Feb

-04Jul-04Dec-

04May

-05Oct-

05Mar-

06Au

g-06Jan

-07Jun

-07Nov

-07Ap

r-08Sep

-08Feb

-09Jul-09Dec-

09May

-10Oct-

10Mar-

11Au

g-11Jan

-12Jun

-12Nov

-12

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

High yield minus Treasury, volatility-scaled S&P 500 minus risk-free

What is a (linear) factor model?

× A theory about what explains an asset’s returns× Usually takes the form of a linear relationship

26

r=𝛼+𝛽1 𝑓 1+…+𝛽𝑘 𝑓 𝑘+𝜀

Original factor model: CAPM

× Asset’s expected returns determined by its covariance with market.

where is the expected return of the asset, is the risk-free (cash) rate, is the asset’s beta to the market, and is the market return

27

Lots of strict assumptions

× Everyone rational× Risk averse× No one can influence prices× No transaction costs× Everything tradable× Investors only care about return and standard

deviation× Everyone agrees on correlations, expected

returns of all assets× One period× Investors can lend and borrow at risk-free rate

28

Implications

× All investors would own market portfolio.× Only differences are portion of cash + market

portfolio + leverage.× Market portfolio is “mean-variance efficient”—no

other combination can produce a superior risk-adjusted return

29

Linear regression

Date Mkt-RFFMAGX-RF

May-03 6.05 4.33Jun-03 1.42 1.01Jul-03 2.34 1.25

Aug-03 2.34 1.59Sep-03 -1.23 -1.56Oct-03 6.08 4.81Nov-03 1.35 0.37Dec-03 4.3 5.15Jan-04 2.15 1.21Feb-04 1.4 1.36Mar-04 -1.33 -1.35Apr-04 -1.83 -1.78

May-04 1.18 0.99Jun-04 1.86 1.38Jul-04 -4.07 -4.01

… … …

30

× Statistical procedure to estimate linear relationship between two sets of data.

× Often interpreted as cause-effect relationship: x causes y.

× CAPM is based on simple linear regression!

× Example: How well does the market’s monthly excess return predict Fidelity Magellan’s FMAGX excess return?

31

-20 -15 -10 -5 0 5 10 15

-25

-20

-15

-10

-5

0

5

10

15

20

Market-RF Return

FMA

GX

-RF

Mon

th R

etur

n

Fidelity Magellan versus market, April 2003-March 2013

CAPM regression

× Model parameters (betas) cannot be directly observed. They must be estimated.

× CAPM regression:

Where is a variable representing the excess return of the asset at time , is the intercept, is the asset’s estimated beta to the market, is the market’s excess return, and is a noise term.

32

Abbreviated Excel Output

33

SUMMARY OUTPUT

Regression StatisticsMultiple R 0.96R Square 0.92Adjusted R Square 0.92Standard Error 1.50Observations 119

Coefficie

ntsStandard Error t Stat

P-value

Intercept -0.35 0.14 -2.55 0.01Mkt-RF 1.16 0.03 36.54 0.00

CAPM doesn’t work!

× Black, Jensen, Scholes, “The Capital Asset Pricing Model: Some Empirical Tests”, 1972.

× Created portfolios of stocks sorted by CAPM beta.

× Found that CAPM beta did not predict excess returns linearly, i.e., securities market line was too flat.

× Low-volatility anomaly: Higher beta != higher returns

34

Multi-factor models

× Fama-French model: Added “size” and “value” to CAPM.

× Fama and French argued size and value predicted and explained stock returns.

× Carhart model: Added “momentum” to Fama-French model.

× Jegadeesh and Titman “discovered” momentum effect. Carhart added it to FF.

35

Carhart model

× CAPM plus three new “factors”: value, size and momentum:

Where (or “high minus low”) is the return of the value-factor-mimicking portfolio, (or “small minus big”) is the return of the size-factor-mimicking portfolio, and (“up minus down”) is the return of the momentum-factor-mimicking portfolio.

36

Splitting the equity universe by size and value

37

Small value

Small neutral

Big growthSmall growth

Big neutral

Big valueMedian market cap

70th B/P percentile

30th B/P percentile

× Portfolios are intersections of size and value breakpoints, formed yearly at June end.

× Market-cap breakpoint determined at June end.× B/P breakpoint determined previous fiscal year book/end of last

year market-cap—can be up to 1.5 years stale!

Size factor construction

× SMB, or small minus big, is the average return of three small portfolios minus average return of three big portfolios.

38

Value factor construction

× HML, or high minus low, is the average return of two value portfolios minus the average return of two growth portfolios.

39

Splitting the universe by size and momentum

40

Small up

Small medium

Big downSmall down

Big medium

Big upMedian market cap

70th prior (2-12) percentile

30th prior (2-12) percentile

× Portfolios are intersections of size and momentum breakpoints, formed monthly.

× Market-cap breakpoint determined at June end.× Prior 2-12 returns are the 12-month returns of stocks, excluding

most recent month.

Momentum factor construction

× UMD, or up minus down, is the average return of two winner portfolios minus the average return of two loser portfolios.

41

Momentum is the strongest; size is the weakest

42

Dec-26Au

g-29Ap

r-32Dec-

34Au

g-37Ap

r-40Dec-

42Au

g-45Ap

r-48Dec-

50Au

g-53Ap

r-56Dec-

58Au

g-61Ap

r-64Dec-

66Au

g-69Ap

r-72

Dec-74Au

g-77Ap

r-80Dec-

82Au

g-85Ap

r-88Dec-

90Au

g-93Ap

r-96Dec-

98Au

g-01Ap

r-04Dec-

06Au

g-09Ap

r-12

0.1

1

10

100

1000

SMB HML UMDSMB HML UMDAnn. Ret. 2.28% 4.13% 6.85%Std. Dev. 11.33%12.30%16.52%Sharpe 0.20 0.34 0.41


Carhart Regression Results for FMAGX

SUMMARY OUTPUT


Coeffici

entsStandard Error t Stat

P-value

Intercept -0.32 0.14 -2.40 0.02Mkt-RF 1.18 0.04 31.38 0.00SMB -0.03 0.07 -0.43 0.67HML -0.20 0.06 -3.28 0.00UMD -0.04 0.03 -1.28 0.20

43

Factor models raise the benchmark for active managers

× A manager’s excess returns attributable to a factor is no longer “alpha”

× Studies demonstrating active managers can’t outperform use factor models

× Factor models have a hard time detecting statistically sig. alpha

× Factor research is ongoing

44

Quality/profitability

× Firms with high profits, low leverage, low earnings variability, high payouts persistently outperform

× “Gross profitability” defined as (revenues – cost of goods sold)/assets

× Robert Novy-Marx, “The Other Side of value: The Gross Profitability Premium”, 2013

× Gross profitability is strong as value factor, but negatively related.

45

Splitting the equity universe by size and gross profitability

46

Small profitable

Small neutral

Big unprofitableSmall unprofitable

Big neutral

Big profitableMedian market cap

70th GP/A percentile

30th GP/A percentile

× Portfolios are intersections of size and profitability breakpoints, formed yearly at June end.

× Market-cap breakpoint determined at June end.× GP/A breakpoint determined previous fiscal year book/end of last

year market-cap—can be up to 1.5 years stale!

Profitability factor construction

× PMU, or profitable minus unprofitable, is the average return of two profitable portfolios minus the average return of two unprofitable portfolios.

47

Quality stocks took a beating after the Nifty Fifty craze

48

Jun-63Nov

-64Ap

r-66Sep

-67Feb

-69Jul-70Dec-

71May

-73Oct-

74Mar-

76Au

g-77Jan

-79Jun

-80Nov

-81Ap

r-83Sep

-84Feb

-86Jul-

87Dec-

88May

-90Oct-

91Mar-

93Au

g-94Jan

-96Jun

-97Nov

-98Ap

r-00Sep

-01Feb

-03Jul-04Dec-

05May

-07Oct-

08Mar-

10Au

g-11

0.1

1

10

PMUPMU

Ann. Ret. 3.87%Std. Dev. 7.92%Sharpe 0.49

Source: Robert Novy-Marx Data Library

At what price quality

× Jeremy Siegel, “Valuing Growth Stocks: Revisiting the Nifty Fifty”, 1998

× Argues high-quality “Nifty Fifty” stocks of early 1970s warranted high valuations

49

Ann. Return.

1972 Actual P/E

Warranted P/E

EPS Growth

Rebalanced 12.5% 41.9 40.6 11.0%Non-Rebalanced 12.2% 41.9 38.4 11.0%S&P 500 12.7% 18.9 18.9 8.0%

GMO Quality III GQETX Regression Results, 3/03-12/12

SUMMARY OUTPUT


Coefficien

tsStandard

Error t Stat P-valueIntercept -0.01 0.11 -0.11 0.91Mkt-RF 0.85 0.03 25.63 0.00SMB -0.32 0.06 -5.54 0.00HML -0.09 0.05 -1.84 0.07UMD 0.05 0.02 2.04 0.04PMU 0.24 0.06 3.86 0.00 50

Jensen Quality Growth JENSX Regression Results, 9/93-12/12

51

SUMMARY OUTPUT


Coefficie

ntsStandard

Error t Stat P-valueIntercept -0.01 0.13 -0.05 0.96Mkt-RF 0.81 0.03 27.10 0.00SMB -0.14 0.04 -3.58 0.00HML 0.12 0.04 3.03 0.00UMD 0.01 0.02 0.29 0.77PMU 0.18 0.05 3.35 0.00

Low-volatility factor, or betting against beta

× Low-volatility stocks outperform high-volatility stocks on a risk-adjusted basis.

× Andrea Frazzini, Lasse Pedersen, “Betting Against Beta”, 2011

× Low-beta stocks outperform high-beta stocks on risk-adjusted basis

× Same found in Treasury bonds, corporate bonds, international markets

52

Betting against beta factor construction

× A little more complicated!× Estimate rolling volatilities of stock and market w/

1-year daily returns× Estimate rolling correlation of stock and market

w/ 5-year daily returns× Estimate beta, and shrink beta est. to 1× Rank stocks and sort stocks into two high and low

beta (above and below median)× Weight by rank and rescale long/short legs to be

beta neutral× Recalculate monthly

53

Betting against beta has been eerily profitable

54

3/1/19

29

8/1/19

31

1/1/19

34

6/1/19

36

11/1/1

938

4/1/19

41

9/1/19

43

2/1/19

46

7/1/19

48

12/1/1

950

5/1/19

53

10/1/1

955

3/1/19

58

8/1/19

60

1/1/19

63

6/1/19

65

11/1/1

967

4/1/19

70

9/1/19

72

2/1/19

75

7/1/19

77

12/1/1

979

5/1/19

82

10/1/1

984

3/1/19

87

8/1/19

89

1/1/19

92

6/1/19

94

11/1/1

996

4/1/19

99

9/1/20

01

2/1/20

04

7/1/20

06

12/1/2

008

5/1/20

110.1

1

10

100

1000

BABBAB

Ann. Ret. 8.10%Std. Dev. 10.75%Sharpe 0.75

Source: Lasse H. Pedersen Data Library

Berkshire Hathaway’s 13F portfolio, 1980-2011

Ann. Alpha 5.30% 3.50% 0.30%

-2.53 -1.65 -0.12MKT 0.86 0.86 0.98

-21.55 -21.91 -20.99SMB -0.18 -0.18 0.00

-3.14 -3.22 -0.02HML 0.39 0.24 0.31

-6.12 -3.26 -4.24UMD -0.02 -0.08 -0.10

-0.55 -1.98 -2.66BAB 0.22 0.15

-4.05 -2.58QMJ 0.44

-4.55R2 0.57 0.58 0.60

55

Source: Andrea Frazzini, David Kabiller, Lasse H. Pedersen, “Buffett’s Alpha”, working paper, Aug. 18, 2013

Caveats

× Obtaining full factor exposures is impossible× Transaction costs will eat away much of excess

returns× Once discovered, factor strategies often not as

profitable× A factor can go for many years without paying off

56

<#>

© 2012 Morningstar, Inc. All rights reserved.

Practical Factor Investing

× Alex Bryan, Fund Analyst

Agenda

× How to use factor models × Demonstration

× Factor investing × Investment ideas

× ETF momentum strategies

58

How to Use Factor Models

59

× Step 1: Download monthly (total) return data for the fund of interest

× At least five years of history × Step 2: Go to the French Data Library

http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

× Step 3: If this analysis is for a U.S. fund, click on the first Fama/French Factors link (under U.S. Research Returns Data) and open the text file.

× Click on the first Momentum Factor link and open the text file.

× Step 4: Copy the data from both files into Excel. For each of the two data series:

× Step 4a: Highlight the data, click on the Data tab and click on “Text to Columns”

× Step 4b: Select the “Fixed width” radio button in the dialog window. Click next. Then click Finish.

× Step 5: Delete the data you don’t need × Step 6: Subtract the risk free returns from the fund’s total returns




60

× Step 7: Organize the data so that the following columns are next to each other:

× Step 8: Under the Data tab, click on Data Analysis and select regression from the menu. Click ok.

× Step 8a: Select the data in the Fund-Rf column for the Input Y range (including the label). This is the dependent variable

× Step 8b: Select the data in the Mkt-Rf, SMB, HML, and Mom columns for the Input X Range (including the labels). These are the independent variables

× Step 8c: Check the Labels box × Step 8d: Click ok


61

× Step 9: Select the cell where you want the results to appear in the Output Range


62

× Step 10: InterpretOAKLX Factor Regression (12/96-07/13)


ANOVA

df SS MS FSignificanc

e F

Regression 4 4466.94721116.73

68174.34

08 0.0000Residual 195 1249.0689 6.4055Total 199 5716.0161

Coefficie

ntsStandard

Error t Stat P-value Lower 95%Upper 95%

Intercept 0.38 0.18 2.07 0.04 0.02 0.74Beta 0.98 0.04 23.42 0.00 0.90 1.06Size -0.03 0.05 -0.50 0.62 -0.13 0.08Value 0.41 0.06 7.24 0.00 0.30 0.52Mom -0.07 0.03 -2.05 0.04 -0.14 0.00


63

× Live example

Practical Limitations

64

× It may not be possible to replicate the fund’s factor loadings with passive alternatives

× An actively managed fund may offer the cheapest way to achieve a certain factor exposure

× Even when there is no alpha, an active manager may be skilled in timing exposure to each of the factors

× Factor loadings aren’t static × The Fama/French factors are calculated using long-short portfolios.

Most mutual funds and ETFs are long-only

Factors that work

65

× Value× Dividend Income × Fundamental Indexing

× Momentum × Quality

× Profitability × Dividend Growth × Low volatility

× Illiquidity/Size

Factor Investing: Value

66

× Invest in a market-cap weighted fund that owns the cheapest X% of the market

× Most passive funds with “Value” in their names do this:Vanguard Value ETF VTViShares Russell 1000 Value Index IWD iShares S&P 500 Value Index IVE

× Advantages: Cheap and lots of options × Disadvantages

× Can water down a fund’s exposure to the value factor

Factor Investing: Value

67

× Fundamental Indexing× Idea: Break the link between market prices and portfolio weights× Rather than targeting a segment of the market, fundamental

indices simply weight their holdings based on characteristics such as dividends, sales, and book value

× Value tilt× Contra-trading: Buy low and sell high

× Advantages× More closely resembles how active managers think about

value × Allows investors to maintain core exposure to the market

× Disadvantages × More expensive than traditional passive value funds × Ignores potentially useful information contained in market

prices

Large-Cap Value Options

68

× WisdomTree LargeCap Dividend DLN, 0.28% E.R.× Dividend weighting × Lowest volatility

× PowerShares FTSE RAFI US 1000 PRF, 0.39% E.R.× iShares Morningstar Large-Cap Value JKF, 0.25% E.R.× DFA US Large Cap Value DFLVX, 0.27% E.R.× Vanguard Value ETF VTV, 0.10% E.R.× iShares Russell 1000 Value Index IWD, 0.21% E.R.

Large-Cap Value: Factor Loadings

(7/2006-07/2013) DLN PRF JKF DFLVX VTV IWDBeta 0.88 1.00 0.93 1.14 0.96 0.98Small -0.36 -0.07 -0.47 0.03 -0.25 -0.18Value 0.30 0.31 0.34 0.31 0.31 0.31Mom -0.03 -0.15 0.03 -0.07 -0.03 -0.01

Value and Size

69

× Historically, the value premium has been the largest among small-cap stocks

× Less analyst coverage, greater chance for mispricing

Annualized Returns (07/1926-08/2013)

Deep Value Value Blend Growth

High Growth

Deep Value - High

GrowthLarge 11.2% 9.6% 9.8% 9.5% 9.2% 2.0%Mid 12.0% 12.8% 11.9% 10.6% 9.9% 2.1%Small-Mid 13.7% 13.8% 13.1% 11.9% 8.9% 4.8%Small 15.3% 13.9% 13.4% 12.0% 7.0% 8.3%Micro 16.5% 14.4% 11.8% 7.3% 2.0% 14.6% *Calculated using data from the French Data Library

Small-Cap Value Options

× WisdomTree SmallCap Dividend DES, 0.38% E.R.× iShares Morningstar Small-Cap Value JKL, 0.30% E.R.× iShares Russell 2000 Value Index IWN, 0.36% E.R.× DFA US Small Cap Value DFSVX, 0.52% E.R.× Vanguard Small Cap Value VBR, 0.10% E.R.× PowerShares FTSE RAFI US 1500, Small-Mid PRFZ, 0.39% E.R.

70

Small-Cap Value: Factor Loadings (10/2006-

07/2013)

DES JKL IWN DFSVX VBR PRFZBeta 0.80 0.95 0.94 1.06 0.98 1.02Small 0.70 0.58 0.79 0.91 0.63 0.90Value 0.64 0.53 0.45 0.41 0.36 0.25Mom -0.19 -0.15 -0.01 -0.03 -0.09 -0.19

Factor Investing: Size

× Size is a relatively weak factor × Small cap growth stocks have historically underperformed

their large-cap counterparts× Small cap stocks have historically outperformed large-caps, but

differences in market risk can partially explain the performance gap

× The small-cap premium may simply be compensation for illiquidity× But most small-cap ETFs and index funds invest in

relatively liquid stocks × Using ETFs to invest in micro-cap stocks is a bad idea

× Alternative explanation: Neglected firm effect

71

Factor Investing: Size

× Vanguard Small Cap ETF VB, 0.10% E.R.× Invests in bottom 80%-98% of the U.S. stock market

× DFA US Micro Cap DFSCX, 0.52% E.R.× Invests in the smallest 5% of the U.S. stock market (but

market cap > $10 million)× Flexibility and DFA’s trading expertise help

72

Factor Regression (02/2004-07/2013)

VB DFSCXBeta 1.05 1.05Small 0.72 0.94Value 0.11 0.41Mom -0.04 -0.03

Factor Investing: Quality

× iShares MSCI USA Quality Factor QUAL, 0.15% E.R.× High ROE× Low Debt/Equity × Low earnings variability (standard deviation of year over

year EPS growth over the past 5 years) × Weights holdings using a combination of relative quality

and market capitalization× Vanguard Dividend Appreciation VIG, 0.10% E.R.

× Targets stocks that have raised their dividends in 10 consecutive years

× Market cap weighting

73

Factor Regressions (05/2006-12/2012)

VIGMSCI Qual

Beta 0.92 0.99Small -0.13 -0.12Value 0.15 -0.14Mom 0.00 -0.01Quality 0.26 0.23 *Data for the first 4 factors from the French Data Library **Data for the quality factor from the Frazzini Data Library

Factor Investing: Low Volatility

× PowerShares S&P 500 Low Volatility SPLV, 0.25% E.R.× Selects the 100 least volatile members of the S&P 500

and weights them by the inverse of their volatility× Big sector bets

× iShares MSCI USA Minimum Volatility USMV, 0.15% E.R.× Anchors sector weights

× iShares MSCI EAFE Minimum Volatility EFAV, 0.20% E.R.× iShares MSCI Emerging Markets Minimum Volatility EEMV, 0.25%

E.R.

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MSCI US Min Vol

Beta 0.79 Small -0.12 Value 0.22 Mom 0.00 Quality 0.16 *Data for the first 4 factors from the French Data Library **Data for the quality factor from the Frazzini Data Library


EAFE Min Vol

EM Min Vol

Alpha -0.19 0.68Beta 0.94 1.16Small 0.33 0.58Value 0.30 -0.25Mom -0.06 0.06Quality 0.70 0.32 *Data calculated using global factors from the French and Frazzini Data Libraries

Factor Investing: Momentum

× iShares MSCI USA Momentum Factor MTUM, 0.15% E.R.× Targets stocks with the strongest risk-adjusted 6- and 12-

month momentum, excluding most recent month × Stock weights = momentum score * market cap× Semi-annual rebalancing

× AQR Momentum AMOMX, 0.49% E.R.× Targets the third of the U.S. large cap market with the

strongest total returns over the past 12 months, excluding the most recent month

× Rebalances at least quarterly, but the managers may do so more often

× Weights each stock based on both relative momentum and market cap

× AQR’s trading expertise can help reduce transaction costs

75


AQR Mom Index

MSCI USA Mom

Beta 1.09 1.06Size 0.00 -0.18Value -0.05 0.01Mom 0.35 0.29

Momentum with ETFs

× Momentum works both at the stock level and at the broader asset class and index level

× Momentum may be harder to arbitrage away at the index level

× Easier to implement × Common Strategies

× Sector rotation × Country momentum × Asset Class momentum

× Caveat: High turnover, best suited for tax sheltered accounts

76

Sector Rotation Momentum

× The Strategy: Order each of the 9 Sector Select SPDRs by 12 month total return, excluding the most recent month. Hold the 3 with the highest ranks in equal weight. Rebalance monthly.

× Buffer: Same as before, only now don’t sell a holding in the portfolio as long as it remains in the top 4.

× Based on data from 01/1999-08/2013

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Results StrategyEqual

Weight S&P 500 Annualized

Return 3.95% 5.33% 2.62% Standard Deviation 16.83% 15.32% 15.71%

Sharpe Ratio 0.06 0.15 -0.02 Turnover 143% - - Avg # of Trades/Yr 8 - -


Weight S&P 500 Annualized


Sharpe Ratio 0.14 0.15 -0.02 Turnover 282% - - Avg # of Trades/Yr 15 - -

Sector Rotation Momentum (with buffer)

78

12/31

/1999

7/2/20

00

1/2/20

01

7/2/20

01

1/2/20

02

7/2/20

02

1/2/20

03

7/2/20

03

1/2/20

04

7/2/20

04

1/2/20

05

7/2/20

05

1/2/20

06

7/2/20

06

1/2/20

07

7/2/20

07

1/2/20

08

7/2/20

08

1/2/20

09

7/2/20

09

1/2/20

10

7/2/20

10

1/2/20

11

7/2/20

11

1/2/20

12

7/2/20

12

1/2/20

13

7/2/20

135

50

Strategy Return Equal Weight Return S&P 500 Return

Log

Scal

e ($

tho

usan

ds)

Growth of $10,000

Country Momentum

× The Strategy: Order each country index by 12 month total return, excluding the most recent month. Hold the 3 with the highest ranks in equal weight. Rebalance monthly.



79

ResultsStrateg

yEqual

WeightMSCI World

Annualized Return 10.65% 10.60% 8.99%

Standard Deviation 18.17% 16.28% 14.95%

Sharpe Ratio 0.42 0.47 0.40 Turnover 162% - -Avg # of Trades/Yr 9 - -

ResultsStrateg

yEqual

WeightMSCI World

Annualized Return 11.75% 10.60% 8.99%

Standard Deviation 17.97% 16.28% 14.95%

Sharpe Ratio 0.49 0.47 0.40 Turnover 259% - - Avg # of Trades/Yr 14 - -

MSCI Switzerland MSCI Japan

MSCI Italy MSCI Canada

MSCI Netherlands MSCI France

MSCI UKMSCI Australia

MSCI USAMSCI Germany

Country Momentum (with buffer)

80

12/31

/1970

5/3/19

72

9/3/19

73

1/3/19

75

5/3/19

76

9/3/19

77

1/3/19

79

5/3/19

80

9/3/19

81

1/3/19

83

5/3/19

84

9/3/19

85

1/3/19

87

5/3/19

88

9/3/19

89

1/3/19

91

5/3/19

92

9/3/19

93

1/3/19

95

5/3/19

96

9/3/19

97

1/3/19

99

5/3/20

00

9/3/20

01

1/3/20

03

5/3/20

04

9/3/20

05

1/3/20

07

5/3/20

08

9/3/20

09

1/3/20

11

5/3/20

125

50

500

5000

Strategy Return Equal Weight Return Benchmark Return

Log

Scal

e ($

tho

usan

ds)

Growth of $10,000

Asset Class Momentum

× The Strategy: Order each of the following indices and funds by 12 month total return, excluding the most recent month. Hold the 3 with the highest ranks in equal weight. Rebalance monthly.



81


Weight

Vanguard Wellingto

n Annualized




Weight

Vanguard Wellingto

n Annualized



MSCI USA IndexS&P Developed REIT Index

MSCI EAFE IndexVanguard High-Yield Corporate

DJ UBS Commodity TR

Franklin Templeton Hard Currency

Spot Gold MSCI Emerging Markets Barclays US Agg Bond

Asset Class Momentum (with buffer)

82

12/31

/1991

10/2/1

992

7/2/19

93

4/2/19

94

1/2/19

95

10/2/1

995

7/2/19

96

4/2/19

97

1/2/19

98

10/2/1

998

7/2/19

99

4/2/20

00

1/2/20

01

10/2/2

001

7/2/20

02

4/2/20

03

1/2/20

04

10/2/2

004

7/2/20

05

4/2/20

06

1/2/20

07

10/2/2

007

7/2/20

08

4/2/20

09

1/2/20

10

10/2/2

010

7/2/20

11

4/2/20

12

1/2/20

135

50

500

Growth of $10,000

Strategy Return Equal Weight Return Benchmark Return

Log

Scal

e ($

tho

usan

ds)

Summary

× Model-based investing is sensible× Factors explain asset returns× Major equity factors: market, value, momentum,

quality, low-vol, size/liquidity× After controlling for factor exposures, it’s hard to

find evidence of manager skill× Factor research is raising the hurdle for active

managers

83

ETFInvestor: A rational approach to asset allocation

× Factor-based approach + valuation + common sense

× Two portfolios× Income—conservative, absolute return× Asset Allocation—more aggressive,

benchmark sensitive× Go to etf.morningstar.com× Email me at [email protected]

84

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×Visit: etf.morningstar.com×Call: 1-866-910-1145

Morningstar ETFInvestor

Editor Picture

A global approach to investing in discounted markets with improving fundamentalsMorningstar ETFInvestor scans the globe for value and improving fundamentals across virtually all asset classes. Editor Samuel Lee draws upon academic and practitioner research—including Morningstar's sizeable bench of stock, bond and fund analysts—to find reliable drivers of outperformance.

Samuel Lee ETF Analyst Editor, Morningstar ETFInvestor

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