PRMIA - Being smarter than your beta

This presentation is intended for investment professionals

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

Back to basics…

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Typical current process:

• Selecting asset classes (i.e. which «beta» to invest in)

• Allocation (strategic mix) to these asset classes

• Active management

Tactical allocation

Security selection

Portfolio construction Asset Allocation

(Beta)

Active management

(alpha)

Sources of return

Some Observations

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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.” - Albert Einstein

To keep things simple…

Equities

Alternatives

Bonds

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• 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

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The fact that an opinion has been widely held is no evidence whatever that

it is not utterly absurd.

- Bertrand Russell

Conclusion: Market indices may be simple to use but are not meeting investors’ objectives

A customized bonds portfolio

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Typical expected cash flow

2013

2014

2019

2024

2029

2034

2039

2044

2049

2054

2059

2104

2113

2114

Ca

sh

Flo

w

Your fixed income (FI) is not like any other asset class

You start with a debt, not cash (it is like being “short” a portfolio of bonds)

Key Messages:

• FI act as an offsetting position to your

liabilities

• 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

class providing diversification but as a

hedging strategy

Do you have the right bond benchmark?

8 Universe bonds are not aligned with most client liabilities

Typical Pension Plan Liability vs DEX Universe Bond Index

Maturity

Cas

h F

low

Liabilities DEX Universe Bond Index

Typical Pension Plan Liability vs DEX Long Term Bond Index

Maturity

Cas

h F

low

Liabilities DEX Long Term Bond Index

Hidden Risks of Actuarial Valuations

9

Not worth your while?

10

Return

Seeking Assets

Liability

Hedging Assets

SLI Customized

BenchmarkDEX Universe DEX LTB

Combination of

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%

Asset Mix Liability Hedging Assets Benchmark

* Combination of DEX indices that matches pension plan total durationSource: PC-Bond and Standard Life Invetsments

Moving Away from Market Cap Weighted Benchmarks for Equities

The risk of market-cap based benchmark

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• 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 »)

31%

46%

23%

Commodities

& Energy

Financials

Other

TSX

• “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 »

High dividends

Etc…

Is it true portfolio construction?

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0

5

10

15

20

25

30

35

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

S&P 500 Sector Weights (%)

Information Technology Financials

Health Care Consumer Discretionary

Consumer Staples Energy

Industrials Utilities

Materials Telecommunication Services

Source: Bloomberg.

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)

Revisiting portfolio construction

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Currently, the majority of investors are following a “recipe” proportional to the

offering in the grocery store

Smart Beta

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• 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.

Smart Beta

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• 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.

Illustration

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Just like with the asset allocation…

…building an efficient frontier with “N” securties

Risk

Retu

rn

Market Cap Index

But is this only schoolbook theory?

Smart Beta

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• 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.

Issue – Models and Parameters

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• Some heuristics models may sound simpler, but are often good only a

specific time period

i.e.: Equally vs market-cap weighting

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

Rolling 48-month Sharpe Ratio

Market Weight Equal Weight

S&P 500

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• Optimization-based weighting methodologies

Theory: Low volatility anomaly = “less risk is more return!”

Reality: High model risk

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

1 2 3 4 5

Sh

arp

e R

ati

o

Quintile

Sharpe Ratio by Volatility Quintiles

In Sample

Out Sample

S&P 500, (rolling 48 months data from 1999 to 2012)

Issue – Models and Parameters

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• Minimum volatility optimization can lead to high concentration issue

Security Allocation Minimum Volatility

Security AllocationS&P500

Risk management or risk transfer ?

Issue – Models and Parameters

Smart Beta : From theory… to reality

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• 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)

Solutions

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• 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

Low Vol High Div. Equally

Weighted

Market Cap.

based Mean/Variance … …

Optimal Portfolio

(the most robust “beta”)

X% y% z% w% s% …%

Problems…

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• Problem # 2: Ignore market knowledge (qualitative)

M & A, IPO, Profit warning, Company transformation, Liquidity, etc…

Solution…

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• Solution: Apply active management (stock selection and top-down strategies)

on the optimal portfolio

Universe Portfolio Optimal

multi-models

(quantitatif)

Active

management

(top-down /

bottom-up)

Market

Index optimisation

Sources of return

Acticve

Management « Alpha » from active manager

(rechearch and/or skill)

« Beta »

« Alpha » from beta optimisation

(process, methodology)

“Opportunity is missed by most people because it is dressed in overalls and

looks like work” - Thomas Edison

Customize Approach for « Alternatives »

Indices for alternatives

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• 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

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