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Low Volatility Investing from a Fundamental Perspective
Mark Ingham31 May 2012
1GMOMI_DiscMatls_CFAconf_5-12
Proprietary information – not for distribution. Copyright © 2012 by GMO LLC. All rights reserved.
OutlineIntroduction
Risk seems to be backwards
– behavioural factors are big drivers of this
– but not the only drivers...
Fundamental risk and quality characteristics also work backwards
On some dimensions low fundamental risk is more attractive than low trailing return risk:
– payoff structure
– liquidity
– valuation
2GMOMI_DiscMatls_CFAconf_5-12
Proprietary information – not for distribution. Copyright © 2012 by GMO LLC. All rights reserved.
Low Volatility – What a Backtest!
Source: GMO As of 5/15/12Note: High Beta = Top 25% of Beta by Market Cap in Top 1000 U.S. Equities and Low Beta = Bottom 25% of Beta by Market Cap in Top 1000 U.S. Equities.
0.5
1
2
4
8
16
32
64
128
1964 1969 1974 1979 1984 1989 1994 1999 2004 2009
Cum
ulat
ive
Ret
urn
High Beta
Low Beta
3GMOMI_DiscMatls_CFAconf_5-12
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This Isn’t a New Phenomenon
Source: GMO As of 5/15/12Note: High Beta = Top 25% of Beta by Market Cap in Top 1000 U.S. Equities and Low Beta = Bottom 25% of Beta by Market Cap in Top 1000 U.S. Equities.
0.1250.25
0.51248
163264
128256512
102420484096
1927 1934 1941 1948 1955 1962 1969 1976 1983 1990 1997 2004 2011
High Beta
Low Beta
Cum
ulat
ive
Ret
urn
4GMOMI_DiscMatls_CFAconf_5-12
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This Isn’t a U.S. Phenomenon
Source: GMO As of 7/31/11Note: Global High Beta = Top 25% of Beta by Market Cap in Top 2000 Global Equities and Global Low Beta = Bottom 25% of Beta by Market Cap in Top 2000Global Equities.
0.5
1
2
4
8
16
32
84 86 88 90 92 94 96 98 00 02 04 06 08 10
Global High Beta
Global Low Beta
Dec-
Cum
ulat
ive
Ret
urn
5GMOMI_DiscMatls_CFAconf_5-12
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This Isn’t an Equity Phenomenon
Source: Frazzini and Pederson “Betting against Beta
Note: Results here are taken from Frazzini and Pederson “Betting against Beta.” To construct the BAB factor, all instruments are assigned to one of two portfolios: low beta and high beta. Instruments are weighted by the ranked betas and the portfolios are rebalanced every calendar month. Both portfolios are rescaled to have a beta of 1 at portfolio formation. The BAB factor is a zero-cost portfolio that is long the low-beta portfolio and shorts the high-beta portfolio.
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
U.S
. Sto
cks
Aust
ralia
Aust
ria Belg
ium
Can
ada
Switz
erla
ndG
erm
any
Den
mar
kSp
ain
Finl
and
Fran
ceU
nite
d K
ingd
omH
ong
Kong
Italy
Japa
nN
ethe
rland
sN
orw
ayN
ew Z
eala
ndSi
ngap
ore
Swed
enG
loba
l Sto
cks
(all)
Cre
dit I
ndic
esC
orpo
rate
Bon
dsC
redi
t Hed
ged
(CD
S)Tr
easu
ries
Equi
ty In
dice
sC
ount
ry B
onds
Fore
ign
Exc
hang
eC
omm
oditi
esAnnu
alis
ed S
harp
e Rat
io o
f BAB
Fac
tor
6GMOMI_DiscMatls_CFAconf_5-12
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And Pretty Much the Same Phenomenon as MVP
Source: GMO As of 7/31/11Note: MVP = Minimum volatility portfolio, High Beta = Top 25% of Beta by Market Cap in Top 1000, Low Beta = Bottom 25% of Beta by Market Cap in Top 1000.
0.5
1
2
4
8
16
32
64
128
69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 01 03 05 07 09
High Beta
Low Beta
Dec-
MVP
Cum
ulat
ive
Ret
urn
7GMOMI_DiscMatls_CFAconf_5-12
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Behavioural or People are weak and stupid!Why Risk Is Backwards
Career Risk
– Benchmark huggers run the risk of picking high volatility, low return stocks in order to keep tracking error down.
Glamour Stocks
– High volatility tends to be associated with exciting growth stories. The chance of this growth not materialising is generally underappreciated.
Lottery Love
– Investors like positive skew, just as they overpay for lottery tickets.
8GMOMI_DiscMatls_CFAconf_5-12
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Lottery LoveThe favourite long-shot bias
Source: Snowberg and Wolfers (2010)
BreakEven
-20
-40
-60
-80
Rat
e of
Ret
urn
per D
olla
r Bet
(%)
1/3 1/2 Evens 2/1 5/1 10/1 20/1 50/1 100/1 200/1Odds (Log Scale)
Sample: U.S. Horse Races, 1992-2001Raw Data: Aggregated into PercentilesAll RacesSubsample: Exotic Betting Data AvailableSubsample: Last Race of the Day
9GMOMI_DiscMatls_CFAconf_5-12
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Behavioural or People are weak and stupid!Why Risk Is Backwards
Career Risk
– Benchmark huggers run the risk of picking high volatility, low return stocks in order to keep tracking error down.
Glamour Stocks
– High volatility tends to be associated with exciting growth stories. The chance of this growth not materialising is generally underappreciated.
Lottery Love
– Investors like positive skew, just as they overpay for lottery tickets.
Implicit leverage High beta can give additional exposure to investors who want
to go extra-long but can’t (or won’t) take on leverage
10GMOMI_DiscMatls_CFAconf_5-12
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Not All Leverage Is Created EqualBeta is leverage, but beta’s leverage is “zero recourse”
Source: GMO
-200
-150
-100
-50
0
50
100
150
200
250
-100 -50 0 50 100
Theo
retic
al R
etur
n
Market Return
MarketLevered MarketBeta = 2
11GMOMI_DiscMatls_CFAconf_5-12
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-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
-40%-30%-20%-10% 0% 10% 20% 30%
High
Bet
a Re
turn
Market Return
High Beta
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
-40%-30%-20%-10% 0% 10% 20% 30%
Low
Bet
a Re
turn
Market Return
Low Beta
Not All Leverage Is Created EqualIt’s not just about leverage, it’s about convexity
Beta ~ 2
Beta Converging to 1
Beta ~ 0.5
Note: Return = 20-Day Rolling Compound Return; Market = Top 1000 U.S. Equity Stocks by Market Capitalization; High Beta = Top 25%of Beta by Market Cap in Top 1000 U.S. Equities and Low Beta = Bottom 25% of Beta by Market Cap in Top 1000 U.S. Equities.
12GMOMI_DiscMatls_CFAconf_5-12
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Not All Leverage Is Created EqualIn down markets, correlations go to 1
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6
Market Down5% or More
Market Between-5% and 5%
Market Up5% or More
Note: Return = 20-Day Rolling Compound Return; Market = Top 1000 U.S. Equity Stocks by Market Capitalization; High Beta = Top 25%of Beta by Market Cap in Top 1000 U.S. Equities and Low Beta = Bottom 25% of Beta by Market Cap in Top 1000 U.S. Equities.
Low Beta Portfolio
High Beta Portfolio
Realized Betas
13GMOMI_DiscMatls_CFAconf_5-12
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Leverage Convexity / Concavity
High beta has a convex payoff.
– The bigger the market return, the higher the “beta.”
– Looks like a call option (or, at least, a stock plus a call).
Conversely, low beta has a concave payoff with a greater sensitivity to down markets than up
Convex payoffs are good, and good things are rarely free
Concave payoffs are bad, and you should be paid to take them
14GMOMI_DiscMatls_CFAconf_5-12
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Mitigating Concavity
Can one maximise the behavioural aspects of low volatility investing whilst minimising concavity (or have your cake and eat it?)
YES!
– Reassess risk
15GMOMI_DiscMatls_CFAconf_5-12
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Three routes to the permanent impairment of capital:
Real Risk Is Losing Your Money!
Fundamental risk
– “Real risk is measured not by the percent that a stock may decline in price in relation to the general market in a given period, but by the danger of a loss of quality and earnings power through economic change or deterioration in management.” — Ben Graham
Financing risk
– Leverage – “An investor who proposes to ignore near-term market fluctuations needs greater resources for safety and must not operate on so large a scale, if at all, with borrowed money.” — Maynard Keynes
Price risk
– Buying overvalued assets dooms you to low long run returns
16GMOMI_DiscMatls_CFAconf_5-12
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Fundamental Risk…also seems backwards – high quality seems to pay!
2.4%
-2.8%
2.2%
-2.4%
1.8%
-1.7%
1.2%
-1.1%
0.7%
-1.6%
CombinedQuality* BetaLeverage Profitability
ProfitVolatility
MSCI EAFE
Low RiskStocks
High RiskStocks
LowHighLow
High Low
HighLowHigh Low High
EAFE
Source: GMO annualized data from 1/79 – 12/11
1.7%
-4.3%
2.9%
-5.5%
1.8%
-3.7%
2.1%
-4.7%
1.6%
-3.7%
Low RiskStocks
High RiskStocks
LowHighLow High Low
HighLowHigh Low High
Small Combined Quality* Small Beta
SmallLeverage
SmallProfitability
Small ProfitVolatility
U.S. Small Cap
Small Cap
* Leverage, profitability and profit volatility Note: GMO defines quality companies as those with high profitability, low profit volatility, and minimal use of leverage. Simulated relative returns of a hypothetical portfolio.
Source: GMO annualized data from 1/85 – 12/11
17GMOMI_DiscMatls_CFAconf_5-12
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-6%
-4%
-2%
0%
2%
4%
6%
Rela
tive
Prof
itabi
lity
(RO
E)
Years
U.S. Quality U.S. Low Quality Realized U.S. Quality Realized U.S. Low Quality
After 30 years the high quality quartile is almost twice as profitable.
1975Highest Quality Quartile
IBM Eastman Kodak Co. Procter & Gamble Co. 3M Co.Wyeth Merck & Co., Inc. Johnson & Johnson Coca-Cola Co.Xerox Corp. Eli Lilly & Co. J.C. Penney Co., Inc. Warner-Lambert Co.Schering-Plough Corp. Hewlett-Packard Co. Federated Dept. Stores Bristol-Myers Squibb Co.Avon Products Inc. Emerson Electric Co. Pfizer Inc. PepsiCo, Inc.Kellogg Co. General Foods Corp. CBS Inc. Carnation Co.
Note: Profitability = Return On Equity Source: GMO, U.S. 1965-2010; Realized GMO 2004-31/10/11The securities identified above represent a selection of securities identified by the GMO quantitative model. These specific securities are selected for presentation by GMO based ontheir underlying characteristics and are not selected on the basis of their investment performance. These securities are not necessarily representative of the securities purchased, sold orrecommended for advisory clients, and it should not be assumed that the investment in the securities identified will be profitable.
Quality Characteristics PersistContrary to economic theory, quality exhibits a sustainable competitive advantage
1975Lowest Quality Quartile
0 3 6 9 12 15 18 21 24 30
United States Steel Corp. Atlantic Richfield Co. Standard Oil Co. Tenneco Inc.CitiCorp International Paper Co. American Electric Power Unicom Corp.Pharmacia Corp. Union Pacific Corp. Amax Inc. Bethlehem Steel Corp.Morgan (JP) & Co. Southern Co. Alcoa Inc. Aetna Inc.BCE Inc. Unocal Corp. Ford Motor Co. RCA Corp.ConocoPhillips BankAmerica Corp.
18GMOMI_DiscMatls_CFAconf_5-12
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Source: GMO
Quality, the Market and Low Volatility during Historic Downturns
Market Quality Min vol Dates Name Duration (months)-71% N/A -68% Jan-29 toJan-32 Great Depression 36-48% N/A -45% Feb-37 to Mar-38 Echo of above 13-22% N/A -20% May-46 to Nov-46 Post-WWII 6-23% N/A -20% Dec-61 to Jun-62 Kennedy Break 6-29% -19% -30% Dec-68 to Jun-70 GGM meltdown* 18-44% -46% -37% Jan-73 to Sep-74 First Oil Shock 20-19% 11% 13% Nov-80 to Jul-82 Second Oil Shock 20-28% -27% -22% Sep-87 to Nov-87 Black Monday 2-15% -10% -13% May-90 to Oct-90 S & L crisis 5-16% -12% -11% Jun-98 to Aug-98 LTCM 2-46% -39% 7% Mar-00 to Sep-02 TMT meltdown 30-50% -37% -34% Oct-07 to Feb-09 Financial crisis 16
*Great Garbage Market
19GMOMI_DiscMatls_CFAconf_5-12
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-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
-40%-30%-20%-10% 0% 10% 20% 30%
Low
Bet
a Re
turn
Market Return
Low Beta
Concavity / Convexity of Low Beta and QualityFlight to Quality NOT flight to Low Beta!
Note: Return = 20-Day Rolling Compound Return; Market = Top 1000 U.S. Equity Stocks by Market Capitalization; High Beta = Top 25%of Beta by Market Cap in Top 1000 U.S. Equities and Low Beta = Bottom 25% of Beta by Market Cap in Top 1000 U.S. Equities.
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
-40% -30% -20% -10% 0% 10% 20% 30%
Hig
h Q
ualit
y R
etur
n
Market Return
High Quality
20GMOMI_DiscMatls_CFAconf_5-12
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Concavity / Convexity of Low Beta, Quality and MVP…and, on average, low beta
Note: Return = 20-Day Rolling Compound Return; Market = Top 1000 U.S. Equity Stocks by Market Capitalization; High Beta = Top 25%of Beta by Market Cap in Top 1000 U.S. Equities and Low Beta = Bottom 25% of Beta by Market Cap in Top 1000 U.S. Equities.
21GMOMI_DiscMatls_CFAconf_5-12
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Mitigating Concavity
Can one maximise the behavioural aspects of low volatility investing whilst minimising concavity (or have your cake and eat it?)
YES!
– Reassess risk
– Extend investment horizon
22GMOMI_DiscMatls_CFAconf_5-12
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Convexity Is a Shorter-Term PhenomenonConvexity over different return horizons
-1.0
-0.5
0.0
0.5
1.0
1.5
1 day 1 week 1 month 1 quarter 2 quarters 3 quarters 4 quarters
Con
vexi
ty
high betalow beta
Notes: High Beta = Top 25% of Beta by Market Cap in Top 1000 U.S. Equities and Low Beta = Bottom 25% of Beta by Market Cap in Top 1000 U.S. Equities. Convexity is the coefficient of the quadratic term from a quadratic fit of high beta and low beta returns against the market for different return horizons. Data is from 01-Jan-1966 to 30-Apr-2012
23GMOMI_DiscMatls_CFAconf_5-12
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Deleveraging can drive correlations to 1
Real Risk Is Losing Your Money!
Fundamental risk
– “Real risk is measured not by the percent that a stock may decline in price in relation to the general market in a given period, but by the danger of a loss of quality and earnings power through economic change or deterioration in management.” — Ben Graham
Financing risk
– Leverage – “An investor who proposes to ignore near-term market fluctuations needs greater resources for safety and must not operate on so large a scale, if at all, with borrowed money.” — Maynard Keynes
Price risk
– Buying overvalued assets dooms you to low long run returns
24GMOMI_DiscMatls_CFAconf_5-12
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$0
$10
$20
$30
$40
$50
$60
$70
1/04 5/04 9/04 1/05 5/05 9/05 1/06 5/06 9/06 1/07 5/07 9/07
Source: GMO As of 30/9/07
Tota
l Ass
ets
($ B
illio
ns) i
n H
igh
Ow
ners
hip
Gro
up
24x Growth
$61 Billion
$2.5 Billion
August 2007 – Correlations Going to 1USD value of coincident holdings of 8 prominent quants
25GMOMI_DiscMatls_CFAconf_5-12
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Qua
nt L
ong/
Shor
t Per
form
ance
-6%
-4%
-2%
0%
2%
4%
6%
8%
10%
12%
1/04 5/04 9/04 1/05 5/05 9/05 1/06 5/06 9/06 1/07 5/07
Source: GMO, LionShare As of 31/8/07
3 Years of Returns Were Wiped Out in 3 DaysQuant became the risk factor
The above illustrate a hypothetical strategy that GMO does not manage. Hypothetical returns are not predictive of future allocations. The results reflect returns an investor would have obtained had they investedin this hypothetical strategy during the time periods shown and do not represent actual returns. Hypothetical returns are calculated by the retroactive application of a model constructed on the basis of historicaldata and based on assumptions integral to the model which may or may not be testable. In the above, we aggregated reported holdings from eight quantitative managers identified by GMO to come up with a long/short portfolio.Changes in these assumptions may have a material impact on the hypothetical returns presented. Certain assumptions have been made for modeling purposes and are unlikely to be realized. No representations and warranties are made as to the reasonableness of the assumptions. Hypothetical returns are developed with the benefit of hindsight and have inherent limitations. Specifically, hypothetical returns do not reflect actual trading or the effect of material economic and market factors on the decision-making process. Because trades have not actually been executed, results may have under- or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity, and may not reflect the impact that certain economic or market factors may have had on the decision-making process. Actual returns may differ significantly from the hypothetical returns.
26GMOMI_DiscMatls_CFAconf_5-12
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Source: GMO As of 30/9/07
0
30
60
90
120
150
1 2 3 4 5 6 7 8 9 10
Num
ber o
f Nam
es in
H
igh
Ow
ners
hip
Gro
up
Days to Liquidate
2004August 2007
Ret
urn
on A
ug 7
th-9
th
Days to Liquidate
-12%-10%
-8%-6%-4%-2%0%2%
1 2 3 4 5 6 7 8 9 10
Illiquidity Was the Real RiskPerformance was determined by where the crowding took place
Estimated number of days to liquidate securities that were held by six or more of the eight prominent quantitative managers as determined by GMO while seeking to minimize market impacts.
27GMOMI_DiscMatls_CFAconf_5-12
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Low Volatility Looking Forward
Source: GMO As of 30/4/12Note: Mega Quality represents Quality companies with the largest 100 companies by market cap.
0%
5%
10%
15%
20%
25%
30%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 >30
Low Volatility - $10 billion
Perc
enta
ge o
f Hol
ding
s
Days to Liquidate
0%
10%
20%
30%
40%
50%
60%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 >30
Mega Quality- $10 billion
Perc
enta
ge o
f Hol
ding
s
Days to Liquidate
Liquidity of Low Volatility and Quality
28GMOMI_DiscMatls_CFAconf_5-12
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Three routes to the permanent impairment of capital:
Real Risk Is Losing Your Money!
Fundamental risk
– “Real risk is measured not by the percent that a stock may decline in price in relation to the general market in a given period, but by the danger of a loss of quality and earnings power through economic change or deterioration in management.” — Ben Graham
Financing risk
– Leverage – “An investor who proposes to ignore near-term market fluctuations needs greater resources for safety and must not operate on so large a scale, if at all, with borrowed money.” — Maynard Keynes
Price risk
– Buying overvalued assets dooms you to low long run returns
29GMOMI_DiscMatls_CFAconf_5-12
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Note: Valuations = Price to Normalized EarningsSource: GMO As of 29/2/12
Low Volatility Looking ForwardValuation of trailing low volatility
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Valu
atio
n of
Low
Vol
atili
ty R
elat
ive
to th
e M
arke
t
30GMOMI_DiscMatls_CFAconf_5-12
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-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
-10%
0%
10%
20%
30%
40%
50%
60%
65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 01 03 05 07 09 11
Cumulative Return of M
EGA Cap Q
uality Stocks Relative to the Market
Valu
atio
n of
MEG
A Ca
p Q
ualit
y St
ocks
Rel
ativ
e to
the
Mar
ket
Dec-
ReturnValuation
Price Also MattersQuality MEGA cap stocks have won over the long term
Source: GMO As of 29/2/12* Stocks’ historical premium valuation since 1980.
Note: GMO defines quality companies as those with high profitability, low profit volatility, and minimal use of leverage. The historical valuation is determined by our proprietary intrinsic valuation measure. MEGA Cap Quality represents Quality companies within the largest 100 companies by market cap.
Stocks’ Historical Premium
Market AverageGMO LaunchesQuality Strategy
*
31GMOMI_DiscMatls_CFAconf_5-12
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Conclusion
Low volatility investing has done well because of:
– Behavioural drivers
– Marketplace structure
– Concave payoffs
Considering fundamental risk can reduce concavity and is a good indicator of future fundamental risk
It can also reduce liquidity risk
Low trailing return volatility is expensive today and therefore at risk of disappointing
Appendix
33GMOMI_DiscMatls_CFAconf_5-12
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-3
-2
-1
0
1
2
3
4
1 2 3 4 5
Implicit LeverageAverage bet on beta quintiles by U.S. mutual funds
Note: Data is taken from Karceski 2002 “Returns-Chasing Behaviour,Mutual Funds and Beta’s Death” and is based on U.S. domestic mutual fundholdings in Morningstar’s “Mutual Funds ondisc” reported from 1984-1996
Source: Karceksi 2002 “Returns-Chasing Behaviour, Mutual Funds and Beta’s death”
Bet
ver
sus
S&P
500
34GMOMI_DiscMatls_CFAconf_5-12
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Not All Leverage Is Created EqualIt’s not just about leverage, it’s about convexity
Note: Return = 20-Day Rolling Compound Return; Market = Top 1000 U.S. Equity Stocks by Market Capitalization; High Beta = Top 25%of Beta by Market Cap in Top 1000 U.S. Equities and Low Beta = Bottom 25% of Beta by Market Cap in Top 1000 U.S. Equities.
Stra
tegy
Ret
urn
Market Return
35GMOMI_DiscMatls_CFAconf_5-12
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Getting Convexity Is EasySystematically buying call options gives a nice convex result
Note: Market = S&P 500. No commissions, options priced at mid-point. Source: Ivy DB, GMO.
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
-30% -20% -10% 0% 10% 20% 30%
Ret
urn
Market Return
Buy 1 ATM Call with 1 Year Expiry
36GMOMI_DiscMatls_CFAconf_5-12
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0.5
1.0
2.0
4.0
8.0
1996 1999 2002 2005 2008 2011
Cum
ulat
ive
Ret
urn
High Beta
Monetizing the Option Brings Back the ReturnBeta + Call option = Market
High Beta Covered Call
Market
Source: Ivy DB, GMO As of 9/1/11
Note: High Beta = Top 25% of Beta by Market Cap within Top 200 U.S. Equities; Low Beta Quartile = Bottom 25% of Beta by Market Cap within Top200 U.S. Equities; High Beta Covered Call = Top 25% of Beta by Market Cap with Top 200 U.S. Equities Paired with a Written 1 Month at the MoneyCall Option; Low Beta Covered Call = Bottom 25% of Beta by Market Cap with Top 200 U.S. Equities Paired with a Written 1 Month at the Money CallOption; Market = Top 200 U.S. Equities. All option trades assume crossing the spread and paying commission.
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GMO NetherlandsGustav Mahlerplein 109 – 115, 26th floor
1082 MS Amsterdam Tel: +31 (0)20- 7085789
GMO Netherlands is a branch of GMO UK Limited. GMO UK Limited is registered with the AFM and the branch office is subject to, and regulated by, the AFM’s conduct of business rules.
Performance data quoted represents past performance and is not predictive of future performance. The foregoing does notconstitute an offer of any securities for sale. Returns are presented gross of management fees, net of transaction costs andinclude the reinvestment of dividends and other income. If these expenses were deducted performance would be lower. Forexample, if the strategy were to achieve a 10% annual rate of return each year for ten years and an annual advisory fee of0.75% were charged during that period, the resulting average annual net return (after the deduction of advisory fees) would be9.25%. A GIPS compliant presentation of composite performance has preceded this report in the past 12 months oraccompanies this presentation, or is also available at www.gmo.com. Actual fees are disclosed in the Prospectus for eachfund and are also available in each strategies compliant presentation.
Performance is shown compared to broad-based securities market indices. Broad-based indices are unmanaged and are notsubject to fees and expenses typically associated with managed accounts or investment funds. Investments cannot be madedirectly into an index.