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Quants: Beyond multi-factor models

Nomura Global Quantitative Equity Conference

May 21st 2010

Generating alpha from quant models…

It is like the old story of the man who was going to fight a duel the next day.

His second asked him, "Are you a good shot?“

"Well," said the duelist, "I can snap the stem of a wine glass at twenty paces"

…and he looked modest.

"That's all very well," said the unimpressed second.

"But can you snap the stem of the wineglass while the wineglass is pointing a loaded

pistol straight at your heart?"

(Jesse Livermore Quote)

The story so far… or how we arrived here

Quant has been a long-term value creator

US Quantitative managers versus the S&P 500. Average relative

return and Range around it (1995 through first-half 2009)

Source: eVestment Alliance, Empirical Research Partners Analysis.

1 Fifteen large core funds including products managed by Acadian Asset Management, Analytic Investors, Aronson + Johnson + Ortiz, Barclays Global Investors (select poducts),

Battertmarch Financial Management, Chicago Equity Partners, INTECH Investment Management, Jacobs Levy Equity Management, LSV Asset Management, Nicholas-Applegate

Capital Management, Numeric Investors, PanAgora Asset Management, Quantitative Management Associates and State Street Global Advisors.

(25)

(20)

(15)

(10)

(5)

0

5

10

15

20

25

95 96 97 98 99 00 01 02 03 04 05 06 07 08 09

%

Average

Performance since 2006 has not been stellar, but positive nonetheless.

2009 deserves an explanation

US Quantitative managers: Share of Actively-Managed Equities Held

in Defined-Benefit Plans (LH) and average performance (RS)

Source: Federal Reserve Board, Pensions & Investments, Greenwich Associates, eVestment Alliance, Empirical Research Partners Analysis

0

2

4

6

8

10

12

14

16

18

20

00 01 02 03 04 05 06 07 08 09

(3.0)

(2.0)

(1.0)

0.0

1.0

2.0

3.0

4.0

5.0

6.0%

Average = 2.9%

Average = 0%

A tragedy in four acts: how quant moved from value-based models

to momentum and „dynamic‟ factor allocation

Hedge Fund Market Neutral Index (LHS)

Correlation with the performance of momentum factors (RHS)

850

900

950

1000

1050

1100

1150

Mar-

03

Sep-

03

Mar-

04

Sep-

04

Mar-

05

Sep-

05

Mar-

06

Sep-

06

Mar-

07

Sep-

07

Mar-

08

Sep-

08

Mar-

09

Sep-

09

Mar-

10

(0.40)

(0.20)

-

0.20

0.40

0.60

0.80

Average = 0.0

Average = 60%

Value starts to fail

1

Correlation to

momentum

increases

2

Aug 07: models

become more

‘dynamic’

3

Momentum breaks

down

4

Source: HFRX Equity Market Neutral Index; Aviva Investors

Our story so far…

● Multi-factor models were initially value driven models

● Gradually, as value became out of favour, models became more momentum driven

● Increasing quant research and new entrants caused them to converge

● As we all realised this in August 2007, we enhanced the models

● Part of this enhancement meant more ‘dynamic’ models: which lead to more momentum

● These dynamic approaches broke down in May 2009

Evolution… and the fallacy of optimisation

Multi-factor models evolved in different ways

Evolution of multi-factor models

Dynamic models

New alpha models

Static models

1

2

3

● Models based on either momentum or dispersion of factors

● Momentum based rotation creates significant exposure to reversals

● Dispersion based rotation can lead to periods of poor performance

● Efforts to identify new alpha factors lead to a lot of ‘data mining’

● No compelling evidence that new factors were identified after Aug 07

● Better and more diversified models were a tangible output

● Do not fall into the ‘momentum’ and ‘dispersion’ traps

● Replicable by passive factor strategies

● Consultants and other buyers believe it lacks innovation / research

Source: Aviva Investors

Multi-factor models are created from a combination of market

anomalies

Building up multi-factor models

Value

Momentum

Capital Allocation

Earnings Quality

Growth

2%

3%

4%

5%

6%

7%

8%

9%

10%

11%

12%

5% 7% 9% 11% 13% 15%

Quant

Model

Tracking error

Relative

Return

(Q1-Q5)

In building multi-factor

models, we aim to…

● Maximise our

information ratio (i.e.

return per unit of

volatility)

● Maximise our hit-rate

(i.e. number of months

we outperform the

market)

● Minimise our turnover

● Minimise our

drawdowns


The fallacy of optimisation

● Our desire for monthly/quarterly leveraged out-performance lead us to all of this

● We forgot that these inefficiencies exist because they do not work all the time

● In optimising our models for higher hit-rates, we were undermining the alpha of the

underlying strategies

● Value investors did not stop being value investors in 1998-1999, but we stopped

believing in what we were doing because it did not work in 2007-2008

● …But the alpha from these strategies is still there

Breaking the paradigm: decomposing our alpha

A number of sub-strategies have emerged

Quant sub-strategies

Enhanced Multi-Factor

Models

Quant +

FundamentalAlpha Decomposition

MathematicalHigh Frequency trading

Quant

Universe

Static and dynamic models

Quant model + analyst input

Algorithmic trading

Selling Implied alpha in segregated strategies

Relative volatility and correlation

1

2

3 4

5

!


Quants can do more by acting at SAA level

Strategic Asset Allocation Process

● ALM Study

● Competitor’s Review

● Broader Constraints

● Long term economic drivers

● Sustainable asset returns

● Portfolio optimisation

● Philosophy

● Process

● People

● Performance

Strategic Asset

Allocation

Constraints

EstablishedManager Selection

Quant

manager

1 2 3

Performanc

e versus

benchmark

‘Equities’ as

cap-

weighted

benchmark


Asset allocators see “equities” as a single asset class represented

by a cap-weighted index

Mean-variance comparison: S&P500 versus non-cap weighted alternatives


JP equities

JP Bonds

UK equities

EU equities

NA equities

AP equities

EM equities

Global equities

UK Gilts

UK IG Credits

UK index-linked

EU Bonds

US Bonds

EM Inflation-Linked Bonds

Global ConvertsUK Property

UK Cash

Global IG Credits

Global Developed Bonds

EM USD Bonds

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

0% 5% 10% 15% 20% 25% 30% 35% 40%

Expected Volatility

Su

sta

ina

ble

re

turn

„Equities‟ should not be synonymous with to cap-weighted indexation

Mean-variance comparison: S&P500 versus non-cap weighted alternatives

EDHEC Risk and Asset Management Research Centre

10%

11%

12%

13%

14%

15%

16%

17%

18%

10% 11% 12% 13% 14% 15% 16% 17% 18%

S&P 500

WisdomTree

Dividend

Intellidex

DJ Select Dividend

GWA

Mergent

RAFI 1000

VTL

Realised

Return

Alpha decomposition: Illustrative example (1)

Fundamental strategies, as a proxy for the value premium

Fundamental Strategy (Value Driven): UK Market 1990-2010

80

90

100

110

120

130

140

150

160

90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Potential for significant periods of

underperformance



There are ways to measure the potential from this strategy

United Kingdom

Composite valuation dispersion

1988-2010

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Very High

High

Normal

Low

Very Low

United Kingdom

Composite Valuation Dispersion

As Of 04/23/10

Source: Bernstein Quant Research


Low vol strategies present much higher risk-adjusted returns

Global portfolio: relative returns and Sharpe ratios of low vs high volatility strategies

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

D1 D2 D3 D4 D5 D6 D7 D8 D9 D10

0.00

0.10

0.20

0.30

0.40

0.50

0.60

Alpha (LHS) %

Sharpe Ratio (RHS) %



There are also ways to measure the potential from this strategy

Volatility Strategies: Hit Rates versus direction of the market

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

D1 D2 D3 D4 D5 D6 D7 D8 D9 D10

Decile

Hit

ra

te (

%)

Up

Down



Index funds continue to grow… and so arbitrage opportunities

US: Index Fund and ETF Assets as a share of all long-term

mutual fund and ETF Assets (1993 Through July 2009)

Source: Investment Company Institute, Greenwich Associates, Empirical Research Partners Analysis and Estimate.

0

2

4

6

8

10

12

14

16

18

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09

Index Funds ETFs

%

Memo:

Institutions

Indexed

Share 08

5.0%3.9%

-2.8%

-8.5%

-5.9%

14.7%

-10%

-5%

0%

5%

10%

15%

20%

Announcement Date Announcement date to Event Date Post-Event Date

Additions

Deletion


Index arbitrage: an anomaly that has been increasing in size

S&P 500: Relative performance of additions versus deletions

between announcement and effective dates

Source: The price response to S&P 500 index additions and deletions: Evidence of asymmetry and a new explanation. Chen,

Noronha and Singal, The Journal of Finance, 2004 (data analysis from July 1962 to December 2000)

Alpha decomposition can fight cap-weighted indexation

● Why are we allowing such huge pools of assets to be moved to passive strategies when

we can offer better risk-adjusted solutions?

● Cap-weighted indexation is a poor solution: it has the known problem of overweighting

overpriced securities – and even equal-weighted portfolios can generate better risk-

adjusted returns.

● In order to be able to pitch our proposition to asset allocators and fight indexation, we

should simplify and decompose our strategies – and therefore be much more

transparent about the alpha opportunity we are presenting.

This is not about a “new passive” approach

● Implementing any of the above-mentioned strategies is far from trivial. It requires

extensive modelling to understand risk-adjusted returns, hit-rates, drawdowns and

turnover associated with each of them.

● They can often be implemented in a number of different ways (sector neutral, beta

neutral, maximum weights for single stocks).

● Only Quants can perform the necessary modelling to successfully implement these

strategies. And all of them should represent a better solution than classical indexation.

Breaking the paradigm: decomposing our alpha

Alpha decomposition, the story so far…

Delivering Alpha Decomposition

Multi-factor Quant

Models

Multi-factor Quant

Models

Global Income

Funds

Multi-factor Quant

Models

Global Income

Funds

Fundamental

Strategies

Multi-factor Quant

Models

Global Income

Funds

Fundamental

Strategies

Index Arbitrage

Minimum Variance

Increasing depth

of research

Quant will continue to develop into a multitude of decomposed alpha

strategies

Decomposing multi-factor models into new products

Multi-factor Quant

Models

Quantitative Research

Fundam.

Funds

Quality

Strategies?

Structured

Solutions

Index

Arbitrage

Minimum

Variance

TrendFollowing?

Income

funds

High Quality Structured Solutions

Long-Only and Long-Short Funds

Trend Following

Recap on the main points discussed today

● Multi-factor models will continue to be the core of our proposition as quants, and each of

us will continue to try to differentiate ourselves in this space

● But there is more to our proposition than multi-factor models. We should fight cap-

weighted indexation

● We are the only ones that can provide a credible alternative to this trend

● For doing that, we should be prepared to decompose our alpha and work together with

the asset allocation process

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