1. This presentation is intended for investment professionals
PRMIA - Being smarter than your beta
A case for neither passive indexing nor traditional active
portfolio construction
Emmanuel Matte CFA, FSA,FICA
Senior Vice-President, Investment Solutions
514-499-2538, emmanuel.matte@standardlife.ca
April 2013

2. Back to basics…
Typical current process:
• Selecting asset classes (i.e. which «beta» to invest in)
• Allocation (strategic mix) to these asset classes
• Active management
 Tactical allocation Active management
(alpha)
 Security selection
 Portfolio construction
Asset Allocation
(Beta)
Sources of return
2

3. Some Observations
Current process can hide some risks:
• Modeling risks
 The ultimate benchmark of a pension plan is the liabilities
 True investors’ objectives (i.e. absolute return, pension liabilities) often not reflected in the
decision model (relatives return vs benchmark)
• The selection of asset classes based on benchmark that are sub-optimal; thus the
resulting strategic portfolio will also be sub-optimal
• Some assets classes serve to hedge a liability (i.e. bonds within a pension portfolio)
and not as a return seeking asset nor to «diversified» returns volatility
• Tactical considerations often considered in setting the strategic or selecting the
market investment policy (i.e. level of rates)
“Insanity is doing the same thing, over and over again, but expecting different
results.”
3
- Albert Einstein

4. To keep things simple…
Bonds Equities
Alternatives
• Bonds: Traditional indices (i.e. DEX, DEX Long) hide significant embedded
uncompensated risks when not aligned with the desired liability structure
• Equities: Market Cap based indices forced investors into risky exposure and significant
«alpha» is in fact «beta management»
• Alternatives: Typical indices are almost always not representative of the actual investment
made 4

5. Conclusion: Market indices may be simple to
use but are not meeting investors’ objectives
The fact that an opinion has been widely held is no evidence whatever that
it is not utterly absurd.
- Bertrand Russell
5

6. A customized bonds portfolio

7. Your fixed income (FI) is not like any other
asset class
Key Messages:
Typical expected cash flow
• FI act as an offsetting position to your
liabilities
Cash Flow
• Mismatches between FI and liabilities
are typically uncompensated risks
• If FI is highly correlated with liabilities,
then it should not be seen as an asset
2019
2024
2029
2034
2039
2044
2049
2054
2059
2104
2013
2014
2113
2114
class providing diversification but as a
hedging strategy
You start with a debt, not cash (it is like being “short” a portfolio of bonds) 7

8. Do you have the right bond benchmark?
Typical Pension Plan Liability vs DEX Universe Bond Index Typical Pension Plan Liability vs DEX Long Term Bond Index
Cash Flow
Cash Flow
Maturity Maturity
Liabilities DEX Universe Bond Index Liabilities DEX Long Term Bond Index
Universe bonds are not aligned with most client liabilities 8

9. Hidden Risks of Actuarial Valuations
9

10. Not worth your while?
Asset Mix Liability Hedging Assets Benchmark
Return Liability SLI Customized Combination of
DEX Universe DEX LTB
Seeking Assets Hedging Assets Benchmark DEX Indices*
0% 100% 0.2% 15.3% 5.5% 5.5%
10% 90% 1.7% 15.7% 6.7% 6.7%
20% 80% 3.3% 16.2% 8.2% 8.1%
30% 70% 5.0% 16.8% 9.8% 9.7%
40% 60% 6.9% 17.4% 11.4% 11.3%
50% 50% 8.9% 18.1% 13.1% 13.1%
60% 40% 11.1% 18.8% 14.9% 14.8%
70% 30% 13.5% 19.5% 16.7% 16.6%
80% 20% 16.1% 20.3% 18.4% 18.4%
90% 10% 19.0% 21.2% 20.2% 20.2%
100% 0% 22.2% 22.0% 22.0% 22.0%
* Combination of DEX indices that matches pension plan total duration
Source: PC-Bond and Standard Life Invetsments
10

11. Moving Away from Market Cap Weighted
Benchmarks for Equities

12. The risk of market-cap based benchmark
• Concentration risks (sector, region, securities)
• Momentum driven strategy (weights driven by herd mentality)
• Implicit risk positions uncontrolled over time
• Counterintuitive strategy (« Buy high, sell low »)
TSX
23%
46%
31%
Other
Financials
Commodities
& Energy
12

13. Is it true portfolio construction?
• “Macro themes” often dominate added value and/or risk profile
• Portfolio construction skill or “beta management”; example :
 Value vs Growth
 Long term commodities views
 « Low Vol » S&P 500 Sector Weights (%)
 High dividends 35
Information Technology Financials
 Etc… Health Care Consumer Discretionary
30 Consumer Staples Energy
Industrials Utilities
Materials Telecommunication Services
25
20
15
10
5
0
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Source: Bloomberg. 13

14. Revisiting portfolio construction
What if we were ignoring published indices?
• Concept : When you prepare dinner, do you make use of everything that you
have in your pantry? Are you weighing them equally?
• Then, why not…
 1) Pick the desired meal (investment/risk objectives)
 2) Find the right ingredients (stock selection)
 3) Follow the recipe (weight the securities to best meet the objectives)
Currently, the majority of investors are following a “recipe” proportional to the
offering in the grocery store 14

15. Smart Beta
• Heuristic-based weighting methodologies
 Equally weighted (dollar)
 Equally weighted (risk)
 Fundamental (value, growth, multiples, profits, dividends, etc.)
 Technique factors (low volatility, momentum, etc.)
 Macro-economic, thematic based
 RAFI index
 Etc.
1Source: Financial Analysts Journal, A survey of Alternative Equity Index Strategies, September/October, 2011. 15

16. Smart Beta
• Optimization-based weighting methodologies
 Maximize certain risk measures subject to constraints
 Max. Sharpe ratio
 Min. variance / Min-VaR
 Max. diversification index
 EDHEC-Risk Efficient Equity Indices
 Etc.
1Source: Financial Analysts Journal, A survey of Alternative Equity Index Strategies, September/October, 2011. 16

17. Illustration
Just like with the asset allocation…
…building an efficient frontier with “N” securties
Return
Market Cap Index
Risk
But is this only schoolbook theory? 17

18. Smart Beta
• Smart Beta strategies suffer from two main issues:
1. Highly reliant on models and parameters
2. Ignore market knowledge
• Potential consequences/risks:
 High turnover
 High concentration in small caps/low liquidity stocks
 Heavy sector or style bias
“Any investor who strays from a weighting scheme such as capitalisation
weighting, for which the assumptions that determine the construction are
largely open to criticism and not proven, will probably take a good risk, in the
sense that there is a strong probability of doing better in the long term.”
- Smart Beta 2.0, EDHEC-Risk Institute, March 2013. 18

19. Issue – Models and Parameters
• Some heuristics models may sound simpler, but are often good only a
specific time period
 i.e.: Equally vs market-cap weighting
Rolling 48-month Sharpe Ratio
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
Market Weight Equal Weight
S&P 500
19

20. Issue – Models and Parameters
• Optimization-based weighting methodologies
 Theory: Low volatility anomaly = “less risk is more return!”
 Reality: High model risk
Sharpe Ratio by Volatility Quintiles
1.00
0.80
0.60
Sharpe Ratio
0.40
In Sample
0.20
Out Sample
0.00
-0.20
-0.40
1 2 3 4 5
Quintile
S&P 500, (rolling 48 months data from 1999 to 2012)
20

21. Issue – Models and Parameters
• Minimum volatility optimization can lead to high concentration issue
Security Allocation Security Allocation
Minimum Volatility S&P500
Risk management or risk transfer ?
21

22. Smart Beta : From theory… to reality
• Problem # 1: highly reliant on models and parameters
 Robustness: Model remains valid under different parameters and market conditions
 Risk: Model and parameters are not representative of the future reality
 In-sample results/choices may not be reproducible out-of-sample
Theory (ex-ante) Reality (ex-post)
22

23. Solutions
• Solution #1: Pick THE right model… and be right (or lucky)
 Will require to change model frequently
• Solution #2: Combined models (static)
 i.e.: Value + Growth; High Div + Low Vol, etc…
 Risk of having offsetting models (« closet indexer ») ou that amplify the risk
• Solution #3: Multi-model approach with statistical credibility (“smart portfolio”)
 Recognize that each models have a (changing) probability of being the right one and
building the most robust portfolio in any of the scenarios
Equally Market Cap.
Low Vol High Div. Mean/Variance … …
Weighted based
y% z% w% s%
X% …%
Optimal Portfolio
(the most robust “beta”) 23

24. Problems…
• Problem # 2: Ignore market knowledge (qualitative)
 M & A, IPO, Profit warning, Company transformation, Liquidity, etc…
24

25. Solution…
• Solution: Apply active management (stock selection and top-down strategies)
on the optimal portfolio
Active
Optimal management
Universe
(top-down /
Portfolio
multi-models
(quantitatif) bottom-up)
Acticve
Management « Alpha » from active manager
(rechearch and/or skill)
Index optimisation
« Alpha » from beta optimisation
(process, methodology)
Market « Beta »
Sources of return
“Opportunity is missed by most people because it is dressed in overalls and
looks like work” 25
- Thomas Edison

26. Customize Approach for « Alternatives »

27. Indices for alternatives
• Indices are typically « non investable » (i.e. real estate)
• Indices are non representative of the actual product used (i.e. hedge funds)
• Modeling process for allocation :
 Breakdown asset class (or even better the actual product) into risk factor and then
assess risks diversification
• Process for manager/product selection (and monitoring) :
 Absolute return / outcome approcah (i.e. benchmark or peer agnostic)
 cash+x% with vol of y% on z years
“There are risks and costs to a program of action, but they are far less than
the long-range risks and costs of comfortable in action."
- J.F. Kennedy 27

28. The information shown relates to the past. Past performance is not a guide to the future. The value of investment can go down as well as up.
Any data contained herein which is attributed to a third party ("Third Party Data") is the property of (a) third party supplier(s) (the “Owner”) and is licensed for use by
Standard Life**. Third Party Data may not be copied or distributed. Third Party Data is provided “as is” and is not warranted to be accurate, complete or timely. To
the extent permitted by applicable law, none of the Owner, Standard Life** or any other third party (including any third party involved in providing and/or compiling
Third Party Data) shall have any liability for Third Party Data or for any use made of Third Party Data. Past performance is no guarantee of future results. Neither
the Owner nor any other third party sponsors, endorses or promotes the fund or product to which Third Party Data relates.
**Standard Life means the relevant member of the Standard Life group, being Standard Life plc together with its subsidiaries, subsidiary undertakings and
associated companies (whether direct or indirect) from time to time."
Montréal Toronto Calgary
Investissements Standard Life inc. Standard Life Investments Inc. Standard Life Investments Inc.
1001, de Maisonneuve Blvd. West 121 King Street West 639 5th Avenue S.W.
Suite 1000 Suite 810 Suite 1700
Montréal, Québec Toronto, Ontario Calgary, Alberta
H3A 3C8 M5H 3T9 T2P 0M9
Standard Life Investments Inc., with offices in Calgary, Montréal and Toronto, is a wholly owned subsidiary of Standard Life Investments Limited.
Standard Life Investments Limited is registered in Scotland (SC123321) at 1 George Street, Edinburgh EH2 2LL.
Standard Life Investments Limited is authorised and regulated in the UK by the Financial Services Authority.
Calls may be monitored and/or recorded to protect both you and us and help with our training.
© 2013 Standard Life, images reproduced under licence