Low-volatility Equity in Asset Allocation Bill Hoyt Head of Quantitative Equities and Portfolio Manager DC Investment Forum September 27-28, 2012 and Portfolio Manager P) 625853.1.0 F) 625857.1.0 Overview 1. Introduction 2. Traditional Asset Allocation 3. Absolute Return / Risk Parity Asset Allocation 2 For Institutional Use Only See "Important Information" for a discussion of performance data, some of the principal risks related to any of the investment strategies referred to in this presentation and other information related to this presentation.
Transcript
Low-volatility Equity in Asset Allocation
Bill HoytHead of Quantitative Equities and Portfolio Manager
DC Investment Forum
September 27-28, 2012
and Portfolio Manager
P) 625853.1.0 F) 625857.1.0
Overview
1. Introduction
2. Traditional Asset Allocation
3. Absolute Return / Risk Parity Asset Allocation
2 For Institutional Use Only
See "Important Information" for a discussion of performance data, some of the principal risks related to any of the investment strategies referred to in this presentation and other information related to this presentation.
Introduction
Slide divider subtitle placeholder
For Institutional Use Only
Introduction to Low-volatility InvestingFinance Theory Versus Empirical Reality
Finance theory postulates that investors in high risk stocks are rewarded for bearing additional risk through higher returns
In reality, low risk stocks have generally outperformed high risk stocks over multiple decades and across the globe
• “Risk and the Rate of Return on Financial Assets: Some Old Wine in New Bottles” – Haugen & Hines [1975] – question positive risk-return relationship
• “The Cross-Section of Volatility and Expected Returns” – Ang, Hodrick, Xing, Zhang [2006] – find stocks with high idiosyncratic volatility have low future returns in the US from 1963 to 2000
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• “High Idiosyncratic Volatility and Low Returns: International and Further US Evidence” – Ang, Hodrick, Xing, Zhang [2009] – find stocks with high idiosyncratic volatility have low future returns across 23 developed markets from 1980 to 2003
Introduction to Low-volatility InvestingFinance Theory Versus Empirical Reality (Canadian Evidence: 1990-2012)
Unlike traditional equity strategies, low-volatility strategies…
• Do not focus on tracking error relative to a cap-weighted benchmarkI t d f b l t i k ith id l f i k hil f ll i t d• Instead focus on absolute risk with ideal of zero risk while fully invested
• Do not incorporate many benchmark-relative risk controls – sectors, styles, etc.• Instead allow biases to flow through if they reduce expected volatility
• Do not optimize against an equity benchmark• Instead use the benchmark to define an investable universe
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• Do not focus on stocks with near unit (1) betas• Instead focus on low beta – low-volatility stocks
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12
Low Volatility Portfolio
Market
Low-volatility Portfolios in CanadaVersus Cap-Weighted Benchmark Relative Strategies
Model portfolio data has inherent limitations due to the retroactive application of a model designed with the benefit of hindsight and may not reflect the effect that any material market or economic factors may have had on Pyramis' use of the model during the time periods shown.Thus, Model Performance is speculative and of extremely limited use to any investor and should not be relied upon.Hypothetical performance of the model is no guarantee of future results
Low-volatility Portfolios Over 80 YearsVersus Cap-Weighted Benchmark Relative StrategiesReturn potential of low-volatility equity investing spans more than eight decades, according to our research
• Our model portfolio accounts for market liquidity and investability
U S Largest 500 by Market Capitalization 1929 2012 Q1
Low-volatilityModel Portfolio
Cap-WeightedEquities
Annualized Geometric Return 10.4% 7.3%
Annualized Volatility 10.1% 17.4%
Return / Risk Ratio 1.03 0.42
Beginning Value (1929/1/1) $1.00 $1.00
Ending Value (2012/3/31) $3602.77 $312.33
U.S. Largest 500 by Market Capitalization, 1929 – 2012 Q1
For Institutional Use Only12
Source: CRSP, Pyramis Global Advisors
Model portfolio data has inherent limitations due to the retroactive application of a model designed with the benefit of hindsight and may not reflect the effect that any material market or economic factors may have had on Pyramis' use of the model during the time periods shown. Thus, Model Performance is speculative and of extremely limited use to any investor and should not be relied upon.Hypothetical performance of the model is no guarantee of future results
• Low-volatility investing is associated with a 3.1% annualized U.S. equity outpeformance, by reducing risk to 60% of cap-weighted equities
• The turtle outpaces the hare (by reducing “volatility drag”)
Low-volatility Portfolios Over 80 Years Managing Volatility with Equities: Low-volatility Investing vs. Low-beta Investing
Low-volatility investing should not be confused with naïve low-beta investing
U.S. Largest 500 by Market Capitalization, 1929 – 2012 Q1
Model portfolio data has inherent limitations due to the retroactive application of a model designed with the benefit of hindsight and may not reflect the effect that any material market or economic factors may have had on Pyramis' use of the model during the time periods shown. Thus, Model Performance is speculative and of extremely limited use to any investor and should not be relied upon.Hypothetical performance of the model is no guarantee of future results
For Institutional Use Only
Low-volatility Portfolios Over 80 YearsDownside Protection In Market Stress Periods, 1929 – 2012 Q1
Low-volatilityModel Portfolio
Cap-WeightedEquities
Great Depression (1929/10/01 – 1939/12/31)Period Return -36.2% -64.8%
Period Annualized Volatility 16.0% 30.3%
1987 Black Monday (1987/10/14 – 1987/10/19)Period Return -16.7% -26.4%
Global Financial Crisis (2008/03/01 – 2009/02/27)Period Return -26.6% -42.8%
Period Annualized Volatility 26.3% 40.8%
Model portfolio data has inherent limitations due to the retroactive application of a model designed with the benefit of hindsight and may not reflect the effect that any material market or economic factors may have had on Pyramis' use of the model during the time periods shown. Thus, Model Performance is speculative and of extremely limited use to any investor and should not be relied upon.Hypothetical performance of the model is no guarantee of future results
Traditional Asset Allocation
Slide divider subtitle placeholder
For Institutional Use Only
Traditional Asset Allocation (Stocks and Bonds)Managing Volatility – Balancing Risk and Returns
Given the past few years of volatile equity markets, investors have found the diversification of bond investing to dampen portfolio risk
Yet, given historically low interest rates and the potential of low bond returns, g ybonds may not be the best asset class to manage volatility.
Ideally, investors would want returns that correlate with bonds yet achieve equity-like levels of returns.
25%
30%
35%
Correlation with DEX Universe Bonds
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10%
15%
20%
EM Equity Intl-Ex US Equity
US Equity Canadian Equity
Canadian Low Vol
High Yield
Traditional Asset Allocation Managing Volatility – Balancing Risk and Returns
Stocks
10%
11%
12%
20%
25%
Volatility
ReturnBonds
4%
5%
6%
7%
8%
9%
0%
5%
10%
15%
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From Most to Least Volatile
Traditional Asset Allocation Example Use
Strategy Return (Gross) Volatility Sharpe
Canada
Traditional Allocation (60/40) 8.6% 9.6% 0.46
Traditional with Low-vol Equity 10.3% 7.1% 0.86
Difference 1.7% -2.5% 0.40US
Traditional Allocation (60/40) 9.6% 10.0% 0.43
Traditional with Low-vol Equity 11.9% 8.5% 0.78
Difference 2.3% -1.6% 0.35
Global
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Global
Traditional Allocation (60/40) 4.5% 11.3% 0.15
Traditional with Low-vol Equity 8.0% 7.2% 0.72
Difference 3.4% -4.2% 0.57Model portfolio data has inherent limitations due to the retroactive application of a model designed with the benefit of hindsight and may not reflect the effect that any material market or economic factors may have had on Pyramis' use of the model during the time periods shown. Thus, Model Performance is speculative and of extremely limited use to any investor and should not be relied upon.Hypothetical performance of the model is no guarantee of future results
For Institutional Use Only
Key Takeaways of Low-Volatility Equity
Main Benefits
It may be possible to simultaneously reduce risk and
Actionable Items
Discuss with your consultant and get confirmation on the value
enhance returns
Effective asset allocation diversifiers in comparison with cap-weighted equities
Risk parity provides a powerful
of low-volatility equity
Explore how low-volatilityequity would interact with other investment options in the plan line-up
For Institutional Use Only19
Risk parity provides a powerful analytical framework for investing in low-volatility equities
Work with your plan to educate your plan members
Absolute Return / Risk Parity Asset Allocation
Slide divider subtitle placeholder
For Institutional Use Only
BackgroundEquity Risk Dominance
-10%
0%
10%
-10%
0%
10%Period 1928 - 2011
-60%
-50%
-40%
-30%
-20%
-60%
-50%
-40%
-30%
-20%
S&P 500 Drawdown
60/40 (S&P 500 / US Treasuries) Drawdown
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-80%
-70%
-80%
-70%
1928
0119
3001
1932
0119
3401
1936
0119
3801
1940
0119
4201
1944
0119
4601
1948
0119
5001
1952
0119
5401
1956
0119
5801
1960
0119
6201
1964
0119
6601
1968
0119
7001
1972
0119
7401
1976
0119
7801
1980
0119
8201
1984
0119
8601
1988
0119
9001
1992
0119
9401
1996
0119
9801
2000
0120
0201
2004
0120
0601
2008
0120
1001
BackgroundMotivation
Absolute returns requires an asset allocation framework that produces positive returns in all environments, not just in a rising stock marketstoc a et
Risk parity is an allocation framework that balances the risks in a portfolio
Need to reduce dominance of equity risk in traditional asset allocation
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An example of achieving risk parity is through equalizing economic risk factors in a portfolio
BackgroundBasic Economic Risk Factor Framework
In order to reduce / balance out the risk of equities and high risk assets, typically investors bucket asset classes in economic scenarios of growth and inflation. The goal is to balance out the risk for all scenarios.
GDP Growth
Rising
Inflation
CommoditiesTIPS
EquityHigh Yield
CommoditiesREITS
Eq it
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FallingGovernment Bonds
TIPS
EquityHigh Yield
REITSGovernment Bonds
BackgroundBasic Economic Risk Factor Framework
In a risk model formRisk Factor Covariance Matrix (V)
Growth Risk Inflation
Asset Exposure Matrix (H)
Growth Risk Rgrowth 0
Inflation Risk 0 Rinflation
Growth Risk RiskEquity + -Bonds - -
Commodities + +
H x V x HT
Examples
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If Rgrowth = Rinflation cor(Equity,Bonds) = 0
If Rinflation > Rgrowth cor(Equity,Bonds) = +
If Rgrowth > Rinflation cor(Equity,Bonds) = -
BackgroundBasic Economic Risk Factor Framework
Inflation Falling Inflation Rising
GDPGrowth 0.80
1.20
1.60
tio 0.80
1.20
1.60
tio
Rising
-0.40
0.00
0.40
S&P500 BarCap US Agg
Commodities 60/40 RiskParity
Shar
pe R
at
-0.40
0.00
0.40
S&P500 BarCap US Agg
Commodities 60/40 RiskParity
Shar
pe R
at
1.20
1.60
o
1.20
1.60
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GDPGrowthFalling
‐0.40
0.00
0.40
0.80
S&P500 BarCap US Agg
Commodities 60/40 RiskParity
Shar
pe R
atio
-0.40
0.00
0.40
0.80
S&P500 BarCap US Agg
Commodities 60/40 RiskParity
Shar
pe R
atio
Absolute Return / Risk Parity in Canada1990-2012
-15%
-10%
-5%
0%
-45%
-40%
-35%
-30%
-25%
-20%
1990
06
1992
06
1994
06
1996
06
1998
06
2000
06
2002
06
2004
06
2006
06
2008
06
2010
06
TSX Drawdown60/40 DrawdownRiskParity with Low Vol
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Ann.Return Volatility Ratio Ann. Rel.
ReturnAnn. Rel.Volatility Sharpe
Risk Parity with Low-vol 9.63% 5.25% 1.83 5.43% 5.26% 1.03
60/40 8.62% 9.58% 0.90 4.42% 9.60% 0.46
Absolute Return / Risk Parity in Canada1990-2012
4
8
Risk Parity with Low Vol60/40
1
2
199…
199…
199…
199…
199…
199…
199…
199…
199…
199…
200…
200…
200…
200…
200…
200…
200…
200…
200…
200…
201…
201…
29%30%35%
% of Time Drawdown below 5%
19%25%
% of Time Drawdown below 10%
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8%
0%5%
10%15%20%25%30%
Risk Parity with Low Vol 60/40
1%
19%
0%
5%
10%
15%
20%
Risk Parity with Low Vol 60/40
Absolute Return / Risk Parity Summary for Different Regions
Strategy Return (Gross) Volatility Sharpe
Canada
Traditional Allocation (60/40) 8.6% 9.6% 0.46
T diti l ith L l E it 10 3% 7 1% 0 86Traditional with Low-vol Equity 10.3% 7.1% 0.86
Risk Parity with Low-vol Equity 9.6% 5.2% 1.03
US
Traditional Allocation (60/40) 9.6% 10.0% 0.43
Traditional with Low-vol Equity 11.9% 8.5% 0.78
Risk Parity with Low-vol Equity 9.9% 5.1% 0.90
Global
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Global
Traditional Allocation (60/40) 4.5% 11.3% 0.15
Traditional with Low-vol Equity 8.0% 7.2% 0.72
Risk Parity with Low-vol Equity 8.1% 6.5% 0.82Model portfolio data has inherent limitations due to the retroactive application of a model designed with the benefit of hindsight and may not reflect the effect that any material market or economic factors may have had on Pyramis' use of the model during the time periods shown. Thus, Model Performance is speculative and of extremely limited use to any investor and should not be relied upon.Hypothetical performance of the model is no guarantee of future results.
Key Takeaways of Low-volatility Equity
Main Benefits
It may be possible to simultaneously reduce risk and
Actionable Items
Discuss with your consultant and get confirmation on the value
enhance returns
Effective asset allocation diversifiers in comparison with cap-weighted equities
Risk parity provides a powerful
of low-volatility equity
Explore how low-volatilityequity would interact with other investment options in the plan line-up
For Institutional Use Only29
Risk parity provides a powerful analytical framework for investing in low-volatility equities
Work with your plan to educate your plan members
Questions?
For Institutional Use Only
Appendix
Slide divider subtitle placeholder
For Institutional Use Only
Methodology and Notes
The quintiles (five groups) are defined by sorting on predicted beta for each stock as defined by Barra’s CNE4L model within the TSX Composite universe. The quintiles are formed every month and the returns associated with the quintiles are calculated on a monthly basis with equal weighting for each stock. The monthly returns are then used to calculated the data and results. The time period (June 1990 to March 2012) for the analysis was chosen because of data availability and the ability to match all time periods associated with Canadian portfolio simulations in the
Slide 5
y y p ppresentation.
Slide 6The quintiles (five groups) are defined by sorting on historical beta for each stock as defined by trailing 3 year daily return data at each point in time and a regression to the market using the Center for Research in Security Prices (CRSP) data set and entire universe. The quintiles are formed every month using the above methodology and the returns associated with the quintiles are calculated on a monthly basis with equal weighting for each stock. The monthly returns are then used to calculated the data and results. The time period (January 1929 to March 2012) for the analysis was chosen because of data availability and the ability to match all time periods associated with U.S. 80 year portfolio simulations in the presentation.
Slide 11
For Institutional Use Only32
The Canadian low-volatility portfolio is created by using Barra’s CNE4L risk model and executing a minimum variance optimization every month using a proprietary optimizer. The optimization process uses TSX Composite stocks as its universe with various realistic portfolio constraints and transaction costs settings, such as maximum position size, liquidity availability, asset under management capacity and turnover limits. The weights from the resulting optimization are then used to calculate the return of the portfolio every month with appropriate transaction costs taken out. The monthly returns are then used to calculated the data and results. The time period (June 1990 to March 2012) for the analysis was chosen because of data availability and the ability to match all time periods associated with Canadian portfolio simulations in the presentation.
Slide 11
Methodology and Notes
The U.S. low-volatility portfolio is created by using a proprietary risk model and executing a minimum variance optimization every month using a proprietary optimizer. The risk model combines both fundamental and quantitative factors in determining the variance and covariance of stocks in the optimization universe. The optimization process uses the largest 500 stocks by market capitalization in the CRSP data set on a monthly basis as its universe with various realistic portfolio constraints and transaction costs settings, such as maximum position size, liquidity
Slide 12-14
availability, asset under management capacity, and turnover limits. The weights from the resulting optimization are then used to calculate the return of the portfolio every month. The monthly returns are then used to calculated the data and results. The time for the analysis (January 1929 to March 2012) was chosen because of data availability and the ability to match all time periods associated with U.S. 80 year portfolio simulations in the presentation.
Slide 16-17
The date range used in the study is June 1990 to March 2012. The EM equity results use MSCI EM total monthly returns. The Intl-exUS equity results use MSCI EAFE total monthly returns. The U.S. equity results use the S&P 500 total monthly returns. The Canadian equity results use the TSX Composite monthly returns. The high yield results use Bank of America/Merrill Lynch U.S. High-Yield Master II Constrained Index total monthly returns. The bond results were taken from the family of bond indexes for DEX as shown on the slides.
For Institutional Use Only33
Methodology and Notes
For each region, different date ranges were used based on availability of data. Canadian results: the date range used in the study is June 1990 to March 2012. U.S. results: date range is January 1973 to March 2012 . Global results: date range is January 1999 to March 2012. Each region also has different corresponding components. The Canadian results use the TSX Composite, the Canadian low-volatility portfolio described in slide 32, and the DEX Universe Bond Index. The U.S. results use the S&P 500, the U.S. low-volatility portfolio described in slide 33, and Barclays U.S.
Slide 18
, y p , yAggregate Bond index when data available and then Ibbotson U.S. Immediate Treasuries as a substitute. The global results use the MSCI ACWI Index, a global low-volatility portfolio, and Barclays Global Aggregate Bond Index. The global low-volatility portfolio is created by using Barra’s GEM2L risk model and executing a minimum variance optimization every month using a proprietary optimizer. The optimization process uses MSCI ACWI stocks as its universe with various realistic portfolio constraints and transaction costs settings, such as maximum position size, liquidity availability, asset under management capacity and turnover limits. The weights from the resulting optimization are then used to calculated the return of the portfolio every month with appropriate transaction costs taken out. The monthly returns are then used to calculated the data and results.
Slide 25The date range used in the study is January 1971 to March 2012. The commodity results uses DJUBS Commodity Index total monthly returns. The U.S. risk parity results combine six assets (Bank of America/Merrill Lynch U.S. High-
For Institutional Use Only34
y p y ( y gYield Master II Constrained Index, DJUBS Commodity Index, U.S. Treasury Inflation Protected Securities and its proxy sourced from Fidelity, Barclays Government Bonds Index, U.S. low-volatility portfolio, and the FTSE NAREIT Index) in a proprietary framework that weights the six assets each month based on balancing economic risk. The resulting weights and the returns for each asset combine to determine the monthly returns of the risk parity portfolio used for the results of the slide.
Methodology and Notes
The date range used in the study is June 1990 to March 2012. The Canadian risk parity results combine five assets (Bank of America/Merrill Lynch U.S. High-Yield Master II Constrained Index, Barclays Canadian Treasury Inflation Protected Securities, DEX Government Bonds Index, Canadian low-volatility portfolio, and the FTSE NAREIT Index) in a proprietary framework that weights the five assets each month based on balancing economic risk. The DJ-UBS Commodity Index was not used due to the Canadian stock market’s already significant exposure to commodities. The
Slide 26-27
y y g presulting weights and the returns for each asset combine to determine the monthly returns of the risk parity portfolio used for the results of the slide.
Slide 28For each region, different date ranges were used based on availability of data. Canadian results: the date range used in the study is June 1990 to March 2012. U.S. results: date range is January 1973 to March 2012 . Global results: date range is January 1999 to March 2012. Each region also has different corresponding components. The Canadian results use the TSX Composite, the Canadian low-volatility portfolio described in slide 32, the DEX Universe Bond Index, and the Canadian risk parity portfolio described in slide 36. The U.S. results use the S&P 500, the US low-volatility portfolio described in slide 33, the Barclays U.S. Aggregate Bond Index when data available and then Ibbotson U.S. Immediate Treasuries as a substitute, and the U.S. risk parity portfolio described in slide 34. The global
lt bi t t (B l U S Hi h Yi ld V Li id I d DJUBS d GSCI dit i di U S
For Institutional Use Only35
results combine ten assets (Barclays U.S. High Yield Very Liquid Index, DJUBS and GSCI commodity indices, U.S. Treasury Inflation Protected Securities (TIPS) and Barclays Global TIPS indices, Barclays U.S. Government Bonds Index, Citi Non U.S. World Government Bond Index, JP Morgan EMBI Global Core Index, Barclays Emerging Market Local Currency Government Index, and the global low-volatility portfolio) in a proprietary framework that weights the six assets each month based on balancing economic risk. The resulting weights and the returns for each asset combine to determine the monthly returns of the risk parity portfolio used for the results of the slide.
Important InformationThe business unit of Pyramis Global Advisors (Pyramis) consists of: Pyramis Global Advisors Holdings Corp., a Delaware corporation; Pyramis Global Advisors Trust Company, a non-depository limited purpose trust company (PGATC); Pyramis Global Advisors, LLC, a U.S. registered investment adviser (PGA LLC); Pyramis Global Advisors (Canada) ULC, an Ontario registered investment adviser; Pyramis Global Advisors (UK) Limited, a U.K. registered investment manager (Pyramis-UK); Pyramis Global Advisors (Hong Kong) Limited, a Hong Kong registered investment adviser (Pyramis-HK); Pyramis Distributors Corporation LLC, a U.S. registered broker-dealer; and Fidelity Investments Canada ULC, an Alberta corporation (FIC). Investment services are provided by y ( ) yPGATC, PGA LLC, Pyramis Global Advisors (Canada) ULC, Pyramis-UK and/or Pyramis-HK.
"Fidelity Investments" refers collectively to FMR LLC, a US company, and its subsidiaries, including but not limited to Fidelity Management & Research Company (FMR Co.) and Pyramis. “Fidelity International” refers collectively to FIL Limited, a non-US company, and its subsidiaries. “Fidelity” refers collectively to Pyramis and Fidelity Investments.
Certain data and other information in this presentation have been supplied by outside sources and are believed to be reliable as of the date of this document. Data and information from third-party databases, such as those sponsored by eVestment Alliance and Callan, are self-reported by investment management firms that generally pay a subscription fee to use such databases, and the database sponsors do not guarantee or audit the accuracy, timeliness or completeness of the data and information provided including any rankings. Pyramis has not verified and cannot verify the accuracy of information from outside sources, and potential investors should be aware that such information is subject to change without notice Information is current as of the date noted
36 For Institutional Use Only
such information is subject to change without notice. Information is current as of the date noted.
Pyramis has prepared this presentation for, and only intends to provide it to, institutional, sophisticated and/or qualified investors in one-on-one or comparable presentations. Do not distribute or reproduce this report.
All trademarks and service marks included herein belong to FMR LLC or an affiliate, except third-party trademarks and service marks, which belong to their respective owners. Pyramis does not provide legal or tax advice and we encourage you to consult your own lawyer, accountant or other advisor before making an investment.
